esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Existing Standards
### IEEE 802.15.4 and ZigBee  
ZigBee [19] is a standard designed for low power, minimal cost and extended battery life applications. The standard incorporates an application layer to define an application framework to support a number of application profiles and a network layer to implement networking and security. ZigBee has adopted 802.15.4 as its physical and MAC layers to make a complete protocol stack implementation. A simplified ZigBee protocol stack is shown in Figure 4. The application profiles include a number of standards including Building Automation, Smart Energy, Home Automation and Healthcare. The Healthcare profile enables specific health monitoring applications to use the ZigBee protocol stack. 802.15.4 operates in three frequency bands: 868 MHz (Europe), 915 MHz (ISM USA) and 2.4 GHz (ISM worldwide). The 2.4 GHz ISM band has the maximum data rate of 250 kb/s operating across 16 channels. Modulation at 2.4 GHz uses Orthogonal Quadrature Phase Shift Keying (O-QPSK) where the digital data resides in the phase of the signal. 802.15.4 uses a spread spectrum method--Direct Sequence Spread Spectrum (DSSS) to improve receiver sensitivity and reduce effects of multipath. 802.15.4 uses the concept of a Fully Function Device (FFD) and a Reduced Function Device (RFD) as the node roles.  
A FFD is normally connected to a wired power supply and is capable of performing all the tasks in the 802.15.4 standard and can communicate with any device in the network. A RFD is battery powered node that is deliberately designed with limited functionality to extend battery life. Channel access in 802.15.4 can operate in beacon enabled mode via the use of superframes (slotted CSMA/CA) or in non-beacon mode (unsloResearch has shown [20; 21; 22], that although 802.15.4 has been proven as a suitable standard for multihop WSNs providing QoS and adequate data rates, it is not the best solution for low power communications in WBANs. The technology was not designed to support WBANs and any WBAN implementation using 802.15.4 will ultimately have to adapt to the compromises necessary to deliver an efficient implementation.
## 3 Mathematical Formulation
### Array Far-Field Calculation  
The array far field is then analyzed by computing the array factor as [35]  
\[\underline{A}E(\theta,\varphi)=\sum_{n=1}^{N}w_{n}e^{jk\{x^{n}\hat{n}+y^{n}\hat{ \varphi}+z^{n}\hat{\varphi}\}}\underline{E}_{n}(\theta,\varphi), \tag{11}\]  
where \(w_{n}=\alpha_{n}e^{i\beta_{n}}\) is the complex excitation for the \(n\)th element located in \((x,y,z)^{n}\), \(k=2\pi/\lambda\) is the wavenumber defined at wavelength \(\lambda\) and \((\hat{n}=\sin\theta\cos\varphi,\hat{\varphi}=\sin\theta\sin\varphi,\hat{ \varphi}=\cos\theta)\) is the direction cosine.  
As previously mentioned, since the two antenna connectors radiate similarly, the LHCP case was chosen as an example for array calculations. Similar results can be achieved by considering the RHCP case. In this scenario, the LHCPs correspond to the co-polarized and the RHCP to the cross-polarized fields.
## 4 Three-Phase Inverters
### Modes of Operation
#### 4.2.3 Grid Supporting Inverters  
Grid supporting inverters operate somewhat similarly to synchronous generators: they deliver power to the grid, while contributing to the stability and reliability of the system [34; 35; 51; 52]. The frequency and voltage magnitude are controlled by a droop mechanism, such that there is a linear relationship between \(\omega\) and \(P\), and between \(|E|\) and \(Q\).  
This type of control promotes fair sharing of the active and reactive powers among generators, while regulating the frequency and voltage. Grid supporting inverters may be connected in parallel to other generators, and can operate in isolation. In power flow studies, such an inverter cannot be represented as a standard bus. However, if the frequency \(\omega\) is known, and the voltage droop mechanism can be ignored, then the inverter is represented as a P-V bus with constant active power \(P\) and voltage amplitude \(|E|\), similarly to a synchronous machine. Typical applications are energy storage systems, online UPS systems, and distributed generators that operate in isolated or weak grids.  
Several properties of the different operation modes are summarized in Table 2.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 2 Experimental Investigation
### Experimental Tests of SCC
#### 2.6.1 Compressive Strength (CS) of SCC  
CS, which holds significant importance in structural design, is a critical concrete parameter. Test specimens measuring 150 mm \(\times\) 150 mm \(\times\) 150 mm were fabricated specifically for the CS test. The test was carried out utilizing a Compression Testing Machine (CTM) following the prescribed curing process, with a load rate of 14 MPa/minute following the guidelines outlined in IS 516 (2004) [30]. The outcomes of the compressive test on the concrete are illustrated in Figure 4.  
Figure 3: (a) View of the unheated specimens and heated specimens. (b) Time-temperature curve.  
The compressive strength of the reference mix, which did not include EPA as a fine aggregate, was higher than the mix containing 2.5% EPA. The decrease in strength can be attributed to several factors, including the inadequate bond between the lightweight mineral aggregate (EPA) and the cement paste. This insufficient bond is mainly influenced by the shape and size of the aggregate, which creates a weak interfacial transition zone (ITZ). Moreover, the presence of EPA in the mixture may also impact the overall consistency of the concrete. Furthermore, including mineral and chemical admixtures in the mix can prolong the initial strength development by delaying the hydration process of cement.  
The SCC mix incorporating GGBFS demonstrates a faster rate of strength development and achieves higher strength values due to the hydraulic cementitious properties of the mineral pozzolan. However, varying SP content decreases workability and strength parameters by reducing the demand for high water content. The SCC mix with GGBFS shows the highest strength, with approximately 24.03 MPa at seven days and 38.89 MPa at 90 days. The SCC mix containing GGBFS and 2.5% EPA exhibits a slight reduction in strength, about 11.53% lower than the mix without EPA. Similarly, the reference SCC mix with FA shows similar strength gains as the GGBFS-based mix, measuring approximately 17.15 MPa at seven days and 35.36 MPa at 90 days. Therefore, the GGBFS-based SCC mix demonstrates superior early-age strength development compared to the FA-based SCC mix.
## 3 Results and Discussion
### Mechanical Fracture Parameters
The value at the age of 530 days about 43 MPa is almost twofold in comparison with the value at the age of 40 days. The compressive strength of the investigated AAS composite at the age of 90 days is comparable with the OPC materials with a similar w/b ratio and the same s/b ratio stored at lower RH [39] and slightly lower than for the AAS composite with the similar w/b and s/b ratio and the same activator dose stored at a lower RH of about 65% [24]. It is necessary to emphasize that the 28 days' compressive strength of AAS with sodium hydroxide is about 70% lower than in the case of OPC materials of similar composition cured under RH \(\geq\) 95%. While OPC materials show a significant increase in strength during the first 28 days, which makes it possible to use the 28 days' compressive strength for reliable structural design, in the case of AAS materials, the increase in strength is much slower and takes a longer time. It is evident from Figure 8e that in the case of the investigated AAS composite, the steep increase in compressive strength is observed up to the age of about a half-year. It is necessary to respect this fact during structural design where the long-term characteristics should be considered for effective and reliable design. The flexural strength also gradually increases during the whole period of hardening (see Figure 8f); however, the increase is not so significant as in the case of compressive strength. The value at the age of 530 days about 4.7 MPa is about 20% higher in comparison with the value at the age of 40 days. A similar trend is also observed for tensile strength obtained by identification, but the values are equal to about 60% of the flexural strength. The flexural strength of the investigated AAS composite at the age of 90 days is slightly lower in comparison with the AAS composite with a similar w/b and s/b ratio and same activator dose stored at a lower RH of about 65% [24]. The same ratio of flexural to compressive strength of about 0.13 was observed [24].
## 4 Results and Discussion
### Time-Aware Consumer Classification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cost**} & \multicolumn{3}{c}{**KNN Filter + Deep Learning**} & \multicolumn{3}{c}{**J48 Filter + Deep Learning**} \\ \cline{2-7}  & **Recall** & **Precision** & **F-Measure** & **Recall** & **Precision** & **F-Measure** \\ \hline
1 & 0.930 & 0.936 & 0.927 & 0.945 & 0.947 & 0.944 \\
5 & 0.968 & 0.968 & 0.968 & 0.963 & 0.963 & 0.962 \\
10 & 0.969 & 0.969 & 0.969 & 0.967 & 0.967 & 0.967 \\
15 & 0.972 & 0.971 & 0.971 & 0.967 & 0.967 & 0.967 \\
20 & 0.973 & 0.973 & 0.973 & 0.968 & 0.968 & 0.968 \\
25 & 0.974 & 0.974 & 0.974 & 0.968 & 0.969 & 0.968 \\
30 & 0.976 & 0.976 & 0.976 & 0.968 & 0.969 & 0.968 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall classification accuracy of the framework according to cost change. Figure 6: Changes in accuracy and time according to changes in the cost. number of instances grew enormously, the time required for filtering was considerable. However, the time difference was far more conspicuous than that if we had classified the entire dataset using plain deep learning. When analyzing hundreds of thousands or millions of units of data, the proposed framework is confirmed to significantly reduce the time requirements.
## 3 AI-Based Identification and Authentication
### Biometric Recognition  
Biometric recognition systems have gained significant traction in industrial environments due to their high accuracy and reliability. Biometric recognition uses unique physical or behavioral characteristics, such as fingerprints, facial features, or iris patterns, to authenticate users [27]. AI-driven biometric systems leverage deep learning algorithms to enhance the accuracy and robustness of biometric matching, even in challenging industrial conditions [28; 29].  
AI-powered facial recognition systems analyze facial features and match them against a database of known individuals, offering high accuracy and speed. These systems are ideal for industrial IAM applications such as access control, attendance tracking, and security monitoring [30].  
These advancements extend beyond facial recognition to other biometric modalities such as fingerprint, iris, and voice recognition, making AI-driven systems versatile and highly reliable [31].  
Voice recognition systems use AI algorithms to analyze voice patterns and authenticate users based on their unique vocal characteristics. Voice recognition provides a hands-free and secure authentication method, ideal for industrial environments where users may have limited mobility or need to access systems while performing other tasks [32; 33].
## 4 Experimental Verification
### Case Study I: Depending on Different Fault Components
#### 4.1.2 Parameter Selection
The results show that with the increase in output dimension, the diagnostic accuracy increases and the corresponding standard deviation decreases. Figure 10: SF parameter selection. Figure 9: SAE parameter selection. The results show that with the increase of the output dimension, the diagnostic accuracy increases and the corresponding standard deviation decreases, while the average training time increases. Therefore, a trade-off was made between classification accuracy and time spent. Considering that the increase in accuracy was no longer significant after the output dimension reached 200, 200 was selected as the number of neurons in the output layer. The diagnostic results with different proportions of training samples in the SF part are shown in Figure 10. It can be seen that with the increase in sample percentage, the diagnostic accuracy was constantly improved and the standard deviation was reduced. When the sample percentage was 10%, the classification accuracy reached 99.13% \(\pm\) 0.15%. However, as the percentage of training samples increased further, the diagnostic accuracy hardly improved, and the diagnostic time fluctuated only within a small range. Therefore, 10% was chosen as the proportion of training sample.
## 5 Discussion of Trends from Section 4 against Models Delivering Analytical Results
### Proposed Optimization Concept  
Figure 16 reveals that higher actuation voltages are required if cantilever thickness is increased. Thus, instead of reducing the actuation time, the opposite could also happen in practice due to non-idealities. Switching higher voltages results in longer electrical switching times, which is known from circuit design. That could reveal in practice that thicker cantilevers (requiring higher voltages) move slower in combination with real driving circuits, although simulations predict the opposite. Mobile sensors require in most cases that voltage sources from battery packs are used; therefore, the question of adequate voltage converters arises. The converters are only available in defined voltage steps and there are threshold values, making the converters considerably more bulky and more expensive. Thus, increasing the cantilever thickness could be beneficial, but it could also be contraproductive, since electrical switching becomes slower, more expensive and bulkier in case larger voltages have to be modulated. Note that in many cases, size is superior to speed and the main goal is to reduce the actuation voltage in order to substantially miniaturize the driver circuit for mobile sensor applications in smart personal environments.  
A possible approach for an optimization procedure is given here. Step 1: Identifying which degree of miniaturization (\(l\), \(b\)) is still acceptable for the application, since the smaller the structure, the lower the required actuation voltage and the shorter the actuation times. Step 2: The thickness \(h\) is optimized. Step 3: For the chosen geometry (strongest acceptable miniaturization), the actuation time as a function of the pull-in voltage is calculated (Figure 16) for the envisaged metal heterostructure stack or metal stack with metal oxides on the top. Step 4: Finetuning of \(h\) and of the individual metal layer thicknesses is performed. Step 5: If no restrictions concerning the metals are existing, it is advisable to choose Al. Step 6: Several iterations involving steps 1-5 are performed to obtain planarization if it is required for the device performance. Step 7: Identifying which maximum voltage can be tolerated that still allows for envisaged economic and unbulky solutions for the drive circuits. This is related to the choice of adequate voltage converters. Choosing the maximum voltage is a trade-off between size (miniaturization), price and power consumption, and it has an influence on the smallest achievable switching time of the MEMS device. Step 8: Finally, some iterations involving all seven steps have to be performed.  
Concerning the dynamical behavior, the performance of real MEMS devices is often deviating from theoretical model calculations due to strong non-linearities, non-idealities in the system, or by damping related to the geometry and the working environment.
## 4 Conclusions  
In this paper, we have proposed a mixed time partitioning method. We have tested several variants with a different combination of time integration schemes, but also various choices of the size of the time step. In particular, we performed a series of tests and made several important observations:  
* We were the first to show that it is possible to use a mixed time partitioning method with various combination of schemes during the numerical simulations of solidification. Moreover, we have shown that different time steps can be chosen for different sub-domains demonstrating that the stability condition for one sub-domain does not enforce the same time step for all domains.
* We have demonstrated that the method presented here offers significant improvements in computational studies of solidification. We have shown that an increasing efficiency depends on the choice of time integration schemes and multiplication \(m\) factor,
* We have rigorously shown that the accuracy of the numerical solution is not too affected, as long as stability condition is satisfied.  
Figure 16: Erroneous results. Errors appearing in computational results for (**A**) temperature and (**B**) solid phase fraction are caused by too large the time step (e.g., in the explicit scheme or when multiplication factor \(m>15\)). There were occurred at the interface between the mold and the cast. Contrasting colors (blue in **A**, and red in **B**) denote incorrect values of solid phase fraction.  
* Finally, we note that the method presented here does not require specialized hardware or sophisticated algorithms. It can be relatively easy explored on desktop stations commonly available in the foundry and metallurgical manufacture.  
This research received no external funding.  
The author declares no conflict of interest.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 3 Results
### Geographical Detection Model for NDVI Drivers
#### 3.4.2 Appropriate Range of Different Driving Factors  
The most suitable range or type of vegetation growth in Inner Mongolia for each factor was analyzed using the risk detector method, and the results of each factor passed the statistical test at 95% confidence (Figure 10). Among the findings, total annual precipitation, which was the most influential driver of the NDVI in Inner Mongolia, showed a positive correlation with the NDVI, and the mean value of the NDVI reached a maximum when the total annual precipitation was 452-535 mm. The areas with the highest precipitation are mainly located in Hulunbuir and the Xing'an League in northeastern Inner Mongolia, where forests and typical steppe are present; these areas had the highest vegetation cover in Inner Mongolia. This indicates that arid and semi-arid areas are more sensitive to changes in precipitation conditions, and that increased precipitation has a more pronounced effect on the growth of forests and typical steppe landscapes [73; 74]. As grassland forest areas with high vegetation cover in Inner Mongolia are widely distributed with luvisols, which have high soil fertility and are suitable for forestry, the mean NDVI values were the highest in areas with luvisols. Moreover, an increase in temperature stimulates the growth of vegetation, but also leads to a decrease in soil moisture, negatively impacting vegetation  
Figure 9: Factor detector results for each factor of the NDVI in Inner Mongolia during 2000–2019.  
growth. Therefore, the annual mean temperature in Inner Mongolia is negatively correlated with NDVI, and the overall annual mean temperature in Inner Mongolia ranges from \(-3.7\) to \(11.2~{}^{\circ}\)C, while the most suitable mean temperature range for vegetation growth is within this range. The mean NDVI value is the highest in Inner Mongolia. The mean NDVI of vegetation fluctuates with DEM and slope, wherein the highest mean NDVI values occur at a DEM of 505-840 m and a slope of 15-21\({}^{\circ}\). Among social drivers, the implementation of ecological restoration projects in recent years played an important role in the recovery of vegetation in Inner Mongolia, while population, GDP, and livestock are unevenly distributed in a few areas, occupying a small proportion of the area, thereby having a low overall impact on vegetation growth.
## 4 Analysis and Results
### Dynamic Analysis  
Numerous virtual experiments were carried out on the digital model of the index finger and the hand to simulate dynamic impact with small-, medium-, and heavy-payload-capacity robots at different velocities and loading conditions. The purpose of the virtual experiments was validation, reinforcement, and extension of the technical standard. The maximum shear elastic strain and shear stress in the soft tissue, the maximum principal stresses developed in the bones, the contact forces at the point of impact, and the energy transfer between the robot and the human body were important outputs of the simulations. The mechanical strength of human cortical bone has been widely reported in the literature. In our analysis, principal stresses higher than 135 MPa were considered unsafe for the cortical bone [39], whereas the benchmark shear elastic stress and strain values were used for soft tissue.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 4 Experimental Environment and Results
### Experimental Results
#### 4.3.3 Compression Results  
An experiment was conducted to measure the performance of the deduplication algorithm using the point-cloud that was pre-processed using the proposed method (i.e., point transformation). The results of the compression were rendered, checked, and analyzed using the recorded data in this experiment. The results of compression include voxel sizes of 1.26, 1.55, 2.0, which are 30, 50, and 70%, respectively: the same as the parameter values of the conventional method. The result of each voxel size was visualized.  
Figure 5: Comparing the quality of the results of voxel size k = 1.55 of the proposed method: (**a**) whole body, (**b**) skirt, (**c**) detailed skirt, (**d**) head, (**e**) face, (**f**) whole surface and (**g**) hand.  
Figure 6: Comparison of the quality of the results of voxel size k = 1.26 of the proposed method: (**a**) whole body, (**b**) skirt, (**c**) detailed skirt, (**d**) head, (**e**) face, (**f**) whole surface and (**g**) hand.  
Figure 7 is a visual representation of the duplicate extraction result. The first picture on the left of each row shows the total data for each reference voxel size and the result of comparing more detailed rendering quality in the remaining pictures.  
The extracted superposition information was rendered to obtain the results of extracting data similar to the clear outline of the 3D model. The amount of duplicate data extracted gradually increased. However, it proved difficult to determine a distinction with the naked eye. Therefore, the graph in the following figure is the result of an experiment based on voxel size.  
Figure 8 measures the amount of change in the size of the rearranged file according to the voxel size, the number of vertices, RMSE, and RC (rearrangement cost). The vertice number and RMSE correspond to RC and file size, respectively, in Figure 8a. Figure 8b measured the results of the proposed method obtained using the overlapping vertex extraction size between the two point-clouds. Data were recorded in units of 1% of the range of the experiment.  
The suggested method uses two point-clouds. Therefore, there is another graph like Figure 8a. In this graph, Figure 8a and the overall data show almost the same level, with a difference of about 0.05%. The vertice number decreases continuously as the voxel size increases, and the RMSE and RC generally increase significantly over a period of time (such as when the voxel size is 1.00, 1.5, or 2.0). File size tends to decrease with repeated slight increases or decreases. This result is reduced to 32% when the voxel size is 2.0.  
Figure 8b shows the compression result. The pattern on this graph tends to be linear. The existing 6.63% will increase up to 16.85%.  
Figure 7: Rendering overlap extraction data of the divider algorithm according to voxel size change: (**a**) k = 1.25, (**b**) k = 1.55, and (**c**) k = 2.0.