esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 3 Outdoor Activity Recognition System
### Semantic Location Extraction  
Once the POI identification module explained in the previous section has identified the potential POIs (those clusters or places where the user has spent 5 min or more), the reverse geocoding process starts in order to obtain from the coordinates the name and category of the POI.  
We have opted to use OpenStreetMaps (OSM) as our main data source for the reverse geocoding process. There have been two main reasons to select OSM as the main data source for the semantic location extraction. First, unlike other Geographical Data Providers such as Google Maps or Foursquare, all the data in OSM is publicly available in different forms such as database dumps of precise locations or various public APIs (Nominatim, Overpass, etc.). Second, OSM provides the geometry of the buildings, a data that we have used in order to enhance the reverse geocoding process, as it will be explained in Section 3.2.2. In addition to these two main reasons, OSM provides:  
* A high granularity place categorization taxonomy.
* It does not only provide a large database of coordinates related to places with semantic meaning, but also provides other kinds of information such as the area of the buildings, if a facility is adapted for disabled people, if a restaurant provides the takeaway option and so on.
* Anyone can contribute to OSM adding new places or updating old ones.
* Allows the retrieval of the nearest places to the coordinates providing the radius of the circumference.
* Accurate transformation from coordinates to a street address.  
With regards to the access to OSM data, we have used two different public endpoints:  
* Nominatim ([[https://nominatim.org/](https://nominatim.org/)]([https://nominatim.org/](https://nominatim.org/)) (accessed on 19 January 2022)): is the geocoding and reverse geocoding endpoint of OSM. Providing the coordinates of a location, it returns the characteristics (name, address, category, etc.) of a place if there is one in those exact coordinates (with a very small search radius); otherwise, it returns the street address associated with those coordinates.
* Overpass ([[https://wiki.openstreetmap.org/wiki/Overpass_API](https://wiki.openstreetmap.org/wiki/Overpass_API)]([https://wiki.openstreetmap.org/wiki/Overpass_API](https://wiki.openstreetmap.org/wiki/Overpass_API)) (accessed on 19 January 2022)): allows the retrieval of all the information that OSM has in a specific location delimited by a bounding box. Unlike Nominatim where the logic is on the server side, with Overpass we retrieve all the data that we consider that is necessary for the semantic location extraction (nodes, buildings' polygons, roads, etc.) and we process it locally.  
Figure 11: Multi-clustering. Raw (**left**), first interaction (**center**), second interaction (**right**).
## 3 Design Methodology  
In this section, the proposed antenna dimensions are studied parametrically to realize the effects of varying them with respect to the antenna performance. The parametric study is conducted using a numerical analysis based on CST MWS [3]. The proposed antenna is structured from four unit cells with CPW feed as seen in Figure 1. The proposal patch is printed on the substrate without a ground plane. The cell geometry is considered to increase the effective length within a limited area. The proposed TR has the advantage to increase the bandwidth and support the antenna to reduce the patch losses [15]. The transmission line is used to transfer the power from the RF source to the antenna part. The matching load is used to match the antenna radiation to the free space impedance [7]. The proposed CPW feed is considered to match the power over a wide range of frequencies with low dispersion effects and phase retardation [9].
# Combining NMR Spectroscopy and Chemometrics to Monitor Structural Features of Crude Hep-arin
###### Abstract  
Because of the complexity and global nature of the heparin supply chain, the control of heparin quality during manufacturing steps is essential to ensure the safety of the final active pharmaceutical ingredient (API). For this reason, there is a need to develop consistent analytical methods able to assess the quality of heparin early in production (i.e., as the crude heparin before it is purified to API under cGMP conditions). Although a number of analytical techniques have been applied to characterize heparin APIs, few of them have been applied for crude heparin structure and composition analyses. Here, to address this issue, NMR spectroscopy and chemometrics were applied to characterize 88 crude heparin samples. The samples were also analyzed by strong anion exchange HPLC (SAX-HPLC) as an orthogonal check of the purity levels of the crudes analyzed by NMR. The HPLC data showed that the chemometric analysis of the NMR data differentiated the samples based on their purity. These orthogonal approaches differentiated samples according their glycosaminoglycan (GAG) composition and their mono and disaccharide composition and structure for each GAG family (e.g., heparin/heparan, dermatan sulfate, and chondro sulfate A). Moreover, quantitative HSQC and multivariate analysis (PCA) were used to distinguish between crude heparin of different animal and tissue sources.  
**Keywords:** heparin; crude heparin; NMR; quantitative NMR; PCA; chemometric; HSQC
## 2 Analysis
### Validation and Mesh Stability  
The validations are down under the following conditions: (\(\mathrm{q_{w}}=1000\) w/m\({}^{2}\), \(\mathrm{T_{0}}=20\ ^{\circ}\mathrm{C}\), \(\mathrm{T_{nf}}=20\ ^{\circ}\mathrm{C}\), \(\mathrm{d}=0.015\) m, \(\mathrm{H}=0.2\) m, \(\mathrm{u_{0}}=0.5\) m/s, relative humidity equal to 50% and \(\mathrm{m_{0L}}=0.01\) kg\(\cdot\)m\({}^{-1}\)s\({}^{-1}\)). A comparison is made with [PERSON] et al. [17]. Figure 2 shows that the relative difference between our results and those obtained by [PERSON] [17] is lower than 8% for the interfacial temperature. These discrepancies cold be explained by the difference between the physical properties used in the two works and the effect of convection terms neglected by [PERSON] [17] in the liquid phase. For the mesh stability as mention in Table 1, a maximum variation observed is less than 1% for the interfacial temperature. To reduce the cost of computation, we choose the grid \(71\times 51\times 21\) in this study.  
Figure 2: Validation of the code simulation. Interfacial temperature – comparison with [PERSON] et al. [17].
## 3 Development of Reliability Assessment Program
### DB and Formula for Reliability Assessment of Pulley  
For the pulley, which is the main mechanical equipment of the secondary energy conversion system, an efficiency DB and formula were built. First, the efficiency of the pulley system is determined by the performance of the belt. Therefore, the transmission efficiency DB of the pulley system is built on the basis of commercial products and is shown in Table 3. The items are company, belt type, and model. The reliability assessment program was configured to use DB by selecting the type of classical V-belts, cogged V-belts, timing belts, etc., from the pulley list. In addition, the formula for evaluating the performance to apply the efficiency of the pulley system is given Equation (2). As shown below, the pulley system uses the same performance formulas as the gear, and only the expression of the ratio is different.  
\[V_{2}=V_{1}\frac{D_{1}}{D_{2}}\eta\ (\text{rpm}) \tag{2}\]  
where, \(V_{1}\) is the RPM of the rotor (rpm); \(V_{2}\) is the RPM of the output shaft (rpm); \(D_{1}\) is the diameter of the rotor (m); \(D_{2}\) is the diameter of the output shaft (m); \(\eta\) is efficiency of the pulley (%).  
Since the circumferential speeds of the rotor and shaft are the same, the ratio of the diameter between the pulleys is the ratio of the rotational speed. Accordingly, the performance formula by secondary energy conversion using pulleys without loss is derived as in Equation (2). Finally, in this study, the efficiency DB and formula of the gear and pulley were constructed, and a program was developed to assess the reliability of the secondary energy conversion mechanical system using these.
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 2 System Model and Problem Definition
### Problem Definition
The utilization rate of access satellite coverage time is defined as \(P_{u}\):  
\[P_{u}=\frac{t_{u}}{\underset{L,n,m}{\overset{\mathbf{Q}}{\underset{L,n,m}{\overset {\mathbf{Q}}{\underset{L,n,m}{\overset{\mathbf{Q}}{\underset{L,n,m}{\overset{\mathbf{Q}}{ \underset{L,n,m}{\overset{\mathbf{Q}}{\underset{L,n,m}{\overset{\mathbf{Q}}{\underset{L,n,m }{\overset{\mathbf{Q}}{\underset{L,n,m}{\overset{\mathbf{Q}}{\underset{L,n,m}{ \overset{\mathbf{Q}}{\underset{\underset{L,n,m}{\overset{\mathbf{Q}}{\underset{ \underset{\overset{\overset{\overset{\overset{\leftleftleftleftleftleft({\leftleftleftleft({\\((t_{4}-t_{0})<t_{u}<(t_{5}-t_{0})\), ES preferentially chooses \(v_{1}\) as the access satellite. The access satellites selected above can theoretically complete data transmission without switching ESLs. However, when \(t_{u}\) is larger than the coverage time of any satellite, it is necessary to switch the access satellite. In this case, how to choose the access satellite set \(\mathbf{Q}\) and the access sequence to maximize the \(P_{u}\) value. We will give a strategy to solve this problem in the next section.
## 7 Conclusions and Future Work  
The absorber tube is an essential part of Parabolic Trough CSPs and is very likely to get damaged due to its hostile operating conditions. Hence, NDT techniques are required for their monitoring and/or inspection. A promising technique for this application is the use of high-temperature EMAT transducers for the excitation of guided waves and more particularly of the T(0,1) wave mode. A theoretical study about the GWT of absorber tubes, their dispersion curves, and the wave mode that should be applied for their inspection were calculated. The ultrasonic response of a PPM EMAT and its wave purity characteristics were also presented. A pair of PPM EMATs was experimentally evaluated regarding its wave purity, its sensitivity to noise, its lift-off limitations, its power requirements, and its performance at high temperatures while it was exciting/receiving guided waves. It was found that a PPM EMAT receiver can mainly detect SH\({}_{0}\) and thus it can be used for the GWT of the absorber tubes in terms of wave purity characteristics, as the theoretical study also showed. However, laser interferometry tests are also required for the transmitter to be validated. Also, a common ground connection between the EMAT and the specimen can significantly enhance the SNR of the signal received. The current EMAT design is not efficient enough to inspect a stainless steel pipe with a lift-off larger than 1 mm and therefore it cannot be employed for the inspection of absorber tubes. A room-temperature EMAT cannot be applied at high temperatures (\(<\)200 \({}^{\circ}\)C) and thus a high-temperature EMAT is still required for the guided wave monitoring of high-temperature structures.  
The design and manufacturing of a high-temperature PPM EMAT with racetrack coil operating efficiently at temperatures higher than 300 \({}^{\circ}\)C for long-term inspection or monitoring is our next step. Several thermal, electromagnetic, and mechanical simulations have already been carried out and have given encouraging results. Moreover, a further experimental investigation regarding the impedance of the EMAT and its relationship with the lift-off and the material properties of the specimen being tested is also another part of our future research.  
Figure 12: Amplitude of the signal transmitted against temperature.
## 3 Experimental Methods and Results
### Experimental Method and Results of the Neuro-Fuzzy Transformation with and without MEP
Considering the performance of the model in each station, the NFT-MEP model had much better performance than the NT model in the CHM-Yup and NAN-Hos stations. On the other hand, this model slightly improved efficiency on the other two stations. Next, the NFT-MEP model was used to predict hourly PM\({}_{2.5}\) with three classes of output data. The results found that the NFT-MEP model had higher statistical indicators than the NT model in every station similar to the PM\({}_{10}\) model. The results of the NFT-MEP model were 81.45% and 85.29% for average accuracy and 0.7851 and 0.7824 for average F-score for CHM-Yup and NAN-Hos stations, respectively. The NFT-MEP model had a much-improved efficiency of the NT model, especially in the NAN-Hos station. Finally, the results in this section showed that the NFT-MEP model had a higher model performance to predict hourly classes for both types of PM in every selected station than the NT model. Therefore, applying FMF with MEP to the NT model could improve the efficiency of the model. The average accuracy of the prediction model was more than 80% of both types of PM. In addition, the average F-scores of the prediction model was mostly greater than 0.7 for both types of PM, except the NAN-Hos station.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 6 Performance Evaluation
### TT Flow Routing Experiment Based on GATTRP
The convergence curves are shown in Figure 14. The results showed that GATTRP converged faster and the final result after convergence was better.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Symbolic Representation of Transition Systems
#### 2.12 Symbolic Invariant Verification
: _Given a transition system \(\mathcal{M}\) and a monotonic trace \(\mathbf{F_{k}}=(F_{0},\ldots,F_{k})\) with respect to \(\mathcal{M}\), each timeframe \(F_{i}\) of \(\mathbf{F_{k}}\), with \(0\leq i<k\), is an overapproximation of the set of states reachable in within \(i\) steps in \(\mathcal{M}\), i.e., \(\mathcal{R}_{i}(\mathcal{M})\to F_{i}\)._  
**Lemma 5** (Safety up to Trace Bound). : _Given a transition system \(\mathcal{M}\), a property \(P\) over \(V\) and a safe and monotonic trace \(\mathbf{F_{k}}=(F_{0},\ldots,F_{k})\) with respect to \(P\) and \(\mathcal{M}\), then there does not exist any counterexample to \(P\) of length at most \(k-1\) in \(\mathcal{M}\)._  
**Lemma 6** (Inductive Invariants/Strengthening in Traces). : _Given a transition system \(\mathcal{M}\) and a trace \(\mathbf{F_{k}}=(F_{0},\ldots,F_{k})\) with respect to \(\mathcal{M}\), let us define \(F_{0,i}\stackrel{{\text{\tiny def}}}{{=}}\overset{i}{\underset{j} {\overset{\tiny f}{\vee}}}F_{j}\) for each \(0\leq i<k\). If there is \(F_{0,i}\) so that \(F_{i+1}\to F_{0,i}\) then \(F_{0,i}\) is an inductive invariant of \(\mathcal{M}\). If \(\mathbf{F_{k}}\) is also safe with respect to a property \(P\), then \(F_{0,i}\) is also an inductive strengthening for \(P\)._  
**Lemma 7** (Inductive Invariants/Strengthening in Monotonic Traces). : _Given a transition system \(\mathcal{M}\) and a monotonic trace \(\mathbf{F_{k}}=(F_{0},\ldots,F_{k})\) with respect to \(\mathcal{M}\), if \(F_{i+1}\to F_{i}\) for some \(0\leq i<k\) then \(F_{i}=F_{i+1}\) is an inductive invariant of \(\mathcal{M}\). If \(\mathbf{F_{k}}\) is also safe with respect to a property \(P\), then \(F_{i}=F_{i+1}\) is also an inductive strengthening for \(P\)._  
These properties makes traces a very useful data structure to aid the search of an inductive strengthening in \(\mathcal{M}\).
## 5 Results and Discussion
### Average End-to-End Delay (Avg. Eed)  
Figure 5 illustrates the end-to-end delay comparison of the MRLAM, MP-OLSRv2, and MP-OLSR routing schemes with a variation of node speed between 10 m/s and 60 m/s. It can be seen from the figure that all the schemes show almost similar results when the node speed is till 30 m/s. As the node speed increases from 40 m/s, the end-to-end delay of the conventional MP-OLSR and MP-OLSRv2 routing schemes substantially increases. It is due to the fact that both routing schemes do not consider mobility awareness factor of intermediate nodes in the selection of an optimal route, which leads to increased delay at high-speed node scenarios. The MP-OLSR and MP-OLSRv2 routing schemes select the intermediate nodes based on the multiple node-disjoint and link disjoint metric cost functions, respectively. Therefore, these routing schemes forward packets through a longer route from source to destination, that induces propagation and transmission delays in the network. Moreover, MRLAM scheme maintains and controls end-to-end delay by exploiting EAX metric that minimizes the number of intermediate nodes and number of retransmissions required for reliable packet's delivery on optimal route. The MRLAM utilizes pause time and moving time factor for mobility estimation of nodes from the RWP model. Therefore, it stabilizes the dynamic network to effectively deliver the packet with less time. In addition, using the Q-learning algorithm, the MRLAM selects the intermediate nodes which  
Figure 4: Network throughput with different speed of nodes.  
have low mobility and better link quality when determining the best path from available alternate paths for forwarding data packets toward the destination that leads to minimized end-to-end delay. Overall, it can be observed that MRLAM reduces the end to end delay by approximately 15% and 10% compared to MP-OLSR and MP-OLSRv2 routing schemes, respectively, at 60 m/s node speed.
## 2 Triterpenoids
### 8,9-ene-triterpenoids
#### 2.2.3 C27 Triterpenoids  
Since Nishitoba et al. [286] first isolated C27 triterpenoids lucidenic acids A, B and C (**367-369**) from _G. lucidum_ in 1984, a series of C27 triterpenoids were found one after another. Until now, several C27 triterpenoids have been isolated mainly from _G. lucidum_ and _G. sintense_. Their structures also contain 8,9-ene or 7,9(11)-dien fractions, which are the same as those of C30 triterpenoids, except that C-25, C-26, and C-27 in the side chain were degraded as C27 triterpenoids [274,287,288]. Because the carboxyl and hydroxyl groups were present in C-24 and C-20, respectively, three C27 triterpenoids with a \(\gamma\)  
Figure 12: Chemical structures of several C29 triterpenoids (**357–363**). lactone, named as ganolactone B (**392**), ganolactone A (**393**) and lucidenic lactone (**394**), were isolated [274; 287; 288]. According to Wei et al. [64], systemic investigation into the triterpenoids of \(G\). _lucidum_ isolated and identified three 27-nor Ganoderlactones structures with the C27 skeleton and were named as 7-oxo-ganoderlactone D (**403**), 21-hydroxyganoderlactone D (**404**), and ganoderlactone F (**405**). These three compounds displayed a moderate inhibitory effect on AChE. Ethyl lucidenate A (**368**) is a C27 triterpenoid separated from \(G\). _lucidum_ and displayed cytotoxic activity as previously reported [289]. In addition, ethyl lucidenate A has potent effects in reversing P-gp-mediated multidrug resistance. It may be a potential agent for reversing drug resistance in cancer chemotherapy [289]. The strange accumulation of melanin generates skin pigmentation and tyrosinase regulates melanin synthesis. Methyl lucidenate F (**365**), a C27 triterpenoid compound extracted from \(G\). _lucidum_ displayed a dose-dependent tyrosinase inhibitory activity, with an IC\({}_{50}\) of 32.23 uM [65]. Chemical structures of different C27 triterpenoids are shown in Figure 13 and their bioactivities are tabulated in Table 3.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 4 Experimental Results
### Correctness Analysis of Selected Voxels  
In this paper, the accuracy rate (AR) and determination rate (DR) are designed to measure the accuracy of the VS-RS. They are defined as follows:  
\[\text{AR}= \frac{N(A\cap P)}{N(P)}, \tag{10}\] \[\text{DR}= \frac{N(A\cap P)}{N(A)} \tag{11}\]  
where \(A\) is the voxel set that changes the projection plane after refining segmentation in the process of using TMC2, and \(P\) is the set of voxels selected using VS-RS. \(N(\cdot)\) denotes the number of voxels. AR denotes the proportion of \(A\) in \(P\) to \(P\). DR denotes the proportion of \(A\) in \(P\) to \(A\). As shown in Table 5, the value of DR is 85.32-95.45%, and on average is 93.22%. This indicates that most of the voxels selected using the VS-RS algorithm are related to the voxels whose projection plane index changes during the refining segmentation process in TMC2. The accuracy of VS-RS is high in predicting voxels that are associated with the refined projection plane index. The average value of AR is 85.19%. Most of the voxels that should be refining segmentation are correctly selected. In addition, the distributions of \(A\) and \(P\) can be visually compared. The color region in Figure 9(a) represents the distribution of \(P\). The green voxels are the initial selection voxels, while the red voxels are the final selection of voxels. The red region in Figure 9(b) represents the distribution of \(A\). The two distributions are essentially identical. Therefore, it has been proven that the VS-RS is highly accurate.  
The statistical results of the improved BD-rate performance and running time of the proposed algorithm, compared with TMC2, are shown in Table 6. It can be seen that the proposed algorithm can reduce the encoding time consumption by up to 43.50%. On average, it achieves \(-0.22\%\) D1 BD-rate gains and \(-0.28\%\) D2 BD-rate gains on average. In terms of attributes, obtains \(-0.15\%\) Luma BD-rate gains on average and \(-0.65\%\) Cr BD-rate gains on average. Compared to most methods that can only trade off performance for efficiency, our proposed method can enhance coding performance while saving coding time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant** & **Geometry** & **Texture Video** & **Occupancy Precision** \\ \hline R01 & 32 & 42 & 4 \\ R02 & 28 & 37 & 4 \\ R03 & 24 & 32 & 4 \\ R04 & 20 & 27 & 4 \\ R05 & 16 & 22 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantization parameters of the common test conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Point Cloud** & **AR (\%)** & **DR (\%)** \\ \hline Loot & 90.28\% & 95.12\% \\ Redandblack & 85.41\% & 95.06\% \\ Soldier & 85.09\% & 95.45\% \\ Longdress & 84.35\% & 94.09\% \\ Basketball\_player & 85.66\% & 94.28\% \\ Dancer & 80.35\% & 85.32\% \\ \hline average & 85.19\% & 93.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tables of AR and DR.
###### Abstract  
The aim of this study was to evaluate the transport phenomena of smoke flow and vertical temperature distribution in a 21-story stairwell with multiple fire locations and openings. A large eddy simulation (LES) method was used to model the smoke flow in a stairwell model with a set of simulation parameters, wherein the fire heat release rate (HRR) and fire location were varied. Based on the results, a wall attachment effect was found in three-dimensional figures. Moreover, with an increase in the fire HRR, the effects were more pronounced. The simulation results verified that the vertical temperature distribution is an index model with a natural logarithm, where the pre-finger factor and attenuation coefficient increase considerably in accordance with an increase in the fire HRR. Moreover, there was a decrease in the maximum temperature (\(T_{m}\)) with an increase in the fire location factor (\(h^{*}\)) due to the upward thermal smoke. Moreover, heat mainly accumulates in the area above a fire source. However, \(h^{*}\) has a slight influence on the time required to reach \(T_{m}\) within the range of 53-64 s. Furthermore, the direction of the airflow at each side opening in the stairwell varied in accordance with the variation in the fire location changes, and a regular calculation was carried out.  
Keywords:stairwell; smoke flow; numerical simulation; temperature; building fire +
Footnote †: journal: Applied Sciences