esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 15500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R\({}^{2}\) = 0.56, RMSE = 3.07 g C m\({}^{-2}\) d\({}^{-1}\)) than at field scale (R\({}^{2}\) = 0.51, RMSE = 3.34 g C m\({}^{-2}\) d\({}^{-1}\)), and the same situation occurred in the validation of high-resolution downscaled GPP (R\({}^{2}\) = 0.85, RMSE = 1.34 g C m\({}^{-2}\) d\({}^{-1}\) when validated at footprint scale; R\({}^{2}\) = 0.82, RMSE = 1.47 g C m\({}^{-2}\) d\({}^{-1}\) when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.  
G 2021 13 5016. [[https://doi.org/10.3390/rs13245016](https://doi.org/10.3390/rs13245016)]([https://doi.org/10.3390/rs13245016](https://doi.org/10.3390/rs13245016))  
10 November 2021  
1
## 7 Results  
To assess the reliability of the proposed solution, we analyzed the real data in a month of observation and in particular the amount of data packets lost. We know that each sensor node, connected to the BUS, cyclically queries each FRER and acquires 16 values from 45 available. This is because not all the available measures are of interest for this application. Each measure is acquired every 2 seconds and the middleware aggregates one record when it receives all 16 measures. Theoretically 32 seconds are required to complete one record and in a day we expect 2700 records. The real amount of records stored in the database is less than the theoretical value, due to some loss in the WSN.  
The graph of Figure 10 shows the percentages of data loss for each day of one BUS and it is interesting to verify that in the worst case such a value is less than 4%. On March 12, 2011 we have measured a 9% of packets lost. This particular situation occurred when several metal cranes were operating in the industrial site, thus causing transmission interferences.  
Such results confirm that the WSN approach allows to realize a reliable monitoring architecture. This is also due to the characteristic of the adopted routing protocol that is able to balance the load among all the possible redundant routes toward the sink. The solution is also scalable. In fact, if the number of substation to be monitored increases it is easily to install, in a plug-and-play way, new sensors in order to manage the corresponding load growth and/or to increase the coverage area.  
Figure 9: Historical data chart screenshot.
## 4 Discussion
### Transition from Meteorological Drought to Ecological Drought  
Meteorological drought is one of the most basic types of droughs. At present, there are many studies on the transition of meteorological droughs to agricultural and hydrological droughs. Numerous scholars have discovered that agricultural droughs typically lag behind meteorological droughs spatially, with varying lag times across different regions. However, there are relatively few studies on the transition from meteorological drought to ecological drought, and this paper suggests that climate is not the only factor affecting the transition from meteorological drought to ecological drought. The correlation between meteorological drought and ecological drought transitions in the study area is low, only about 55%. While the transition frequency in certain regions reached 90%, this indicates a high transition frequency in a confined area and a pronounced drought transition effect; however, it does not demonstrate that climatic drought is the primary factor influencing ecological drought. The transition frequency was high in the north and low in the south, meaning that the study area is in an ecologically fragile agropastoral ecotone. In the north of this fragile zone, the climate is drier, and the imbalance of ecological water is more likely to occur, which increases the transition frequency. The water and heat conditions in the south of the fragile zone are better than those in the north, the ecological water is more balanced, and the climate has little influence on the ecological drought. Different land use types can form different vegetation coverages, which may also have a certain impact on this transition [69]. Regions with scant vegetation display a deficient or nonexistent vegetation ecosystem; in contrast, locations with abundant vegetation coverage reveal a comparatively excellent vegetation ecosystem. Therefore, the transition from meteorological drought to ecological drought is a more complicated problem.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 3 Multichannel SA-CNN-BiLSTM Network Model  
### Remaining Life Prediction Model Training Prediction Process  
Existing studies on the RUL prediction of aero-engines have used RNNs and CNNs as data-driven prediction methods, but CNNs cannot handle time-series signals efficiently and RNNs suffer from vanishing and exploding gradients. Here, a multichannel SA-CNN-BILSTM network model is proposed to predict the RUL of an aero-engine on a related basis. A separate channel is created for each sensor, with each channel containing two 1D-CNN layers and one BILSTM layer. The spatial features of the sensor data are extracted using the two 1D-CNN layers, and the extracted features are processed using the BILSTM layer with a time-demand process. Then, the extracted features are processed using the self-service network model. Time-demand processing is carried out, and then the self-attention mechanism is used to assign weights to the features and focus on the key information. The flow chart is shown in Figure 5.  
Figure 5: Prediction flow of multichannel SA-CNN-BiLSTM.
# Study on Seismic Performance of a Mold Transformer through Shaking Table Tests
## 3 Test Results and Discussion
### Acceleration Response and Dynamic Amplification of Test Specim
In addition, the dynamic amplification was evaluated, which is the key parameter in seismic engineering of non-structural components. In this study, the dynamic amplification of the mold transformer can be evaluated by means of the acceleration amplification factor, \(a_{P}\), which was defined as the ratio between the peak response acceleration of the test specimen (PRA), and the peak floor acceleration (PFA) [4; 8]:  
\[a_{P}=\frac{\text{PRA}}{\text{PFA}}. \tag{7}\]  
In Equation (7), the values of PRA were obtained from the accelerometers mounted on the specimens and the values of PFA were obtained from the accelerometers mounted on top of the RC slab, as shown in Figures 2 and 3. Figure 15 illustrates the values of the amplification factor according to the peak ground acceleration calculated at the top beam, center coil, bottom beam, and left coil of the test specimen with respect to the X, Y, and Z-directions. According to the recommendations of  
Figure 14: Peak acceleration response versus PGA at different locations of accelerometers. (**a**) At top beam; (**b**) At center coil; (**c**) At bottom beam; (**d**) At left coil. FEMA E-74 [1] and American Society of Civil Engineers (ASCE) ASCE 7-16 [24], the design component amplification factor varies from 1.0 (for rigid components) to 2.5 (for flexible components), which is also presented in Figure 15 for comparison. Overall, the amplification factors in the X and Y-directions were in the range of those for non-structural elements specified in FEMA E-74 [1]. Meanwhile, in the Z-direction, the acceleration amplification factors were smaller than the lower limit specified in FEMA E-74 [1]. From the study by [PERSON] et al. [12; 13] on the seismic performance of electrical components, the amplification factors in the horizontal and vertical directions were almost the same, which showed a discrepant trend to the present results. This could be attributed to the discrepancy of anchoring details connecting the transformer to the concrete slab, resulting in the different response acceleration of the test specimen in horizontal and vertical directions.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
###### Abstract  
The 6 TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In this study, we propose an innovative cell allocation strategy based on node position clustering using the K-means algorithm, specifically designed to address congestion and optimize resource distribution in the 6 TiSCH network. Our mechanism effectively groups nodes into clusters, allowing for dynamic adjustment of cell capacities in congested areas by analyzing traffic patterns and the spatial distribution of nodes. This clustering approach enhances the efficiency of slot frame utilization and minimizes communication delays by reducing interference and improving routing stability. The proposed strategy leverages the clustering results to improve cell usage efficiency and reduce communication latency between nodes. By tailoring cell allocation to the specific traffic needs of each cluster, we significantly reduce packet loss, manage congestion more effectively, and enhance data transmission reliability. We evaluated the clustering method using the K-means algorithm through experiments with the 6 TiSCH simulator. Additionally, we considered using objective functions in Routing Protocol for Low-Power and Lossy Networks (RPL), such as OFO and MRHOF, to assess clustering results and their impact on throughput and packet delivery. Our method resulted in significantly improved average performance metrics. Under the OFO routing protocol, we achieved a 30.01% latency reduction, a 15.95% faster joining time, an 8% higher packet delivery ratio, and a 13.82% throughput increase. Similarly, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed with the MRHOF routing protocol. These findings highlight the effectiveness of the improved cell allocation strategy in congested 6 TiSCH environments, offering a better solution for enhancing network performance in IIoT applications.  
6 TSCH; cell allocation; K-means; node density; wireless sensor network +
Footnote †: journal: Sensors  
18 July 2024  
Revised: 22 August 2024  
Accepted: 26 August 2024  
Published: 29 August 2024
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 2 Increasing the Surface Area of Graphene
### Surface Enhancement by Creating Ripples
[40], these defect-guided ripples in graphene were further simulated and examined. Using a line defect, which was defined as a long one-dimensional periodic Stone-Wales defect, the researchers found that the amplitude (the height difference between successive peaks) and the wavelength (the distance between periodic ripple peaks) decreased with an increasing angle between the line defect and the direction of strain [41; 42]. When graphene is characterized by flaws or exposed to thermal fluctuations, which change the C-C bonds inside the lattice both temporally and spatially, these ripples may arise. Scholars at Radboud University conducted a simulation aimed at elucidating the characteristics of the ripples by examining graphene's behavior at ambient (300 K) and elevated (1000 K, 2000 K, and 3500 K) temperatures, ultimately concluding that intrinsic ripples could emerge as a result of thermal fluctuations. This discovery is exceptionally promising as it suggests that the ripples can be manipulated artificially through the modulation of temperature [34]. These modulations, such as variations in temperature or the presence of defects, result in the carbon atoms occupying additional spatial dimensions, thereby facilitating the formation of ripples. This alteration in the carbon configuration prompts the electrons within the p-cloud to reallocate, leading to variations in the symmetries of the bond lengths within the structure [43]. The disruption of symmetry engenders a non-planar configuration, which subsequently reduces the free energy associated with the graphene. Consequently, ripples serve to preserve the structural stability of graphene in the face of architectural changes [44]. Figure 4 shows a schematic that depicts the formation of ripples on a graphene surface to achieve an enhanced surface area. Starting with a smooth graphene sheet, a stimulus, such as ion beam irradiation, is applied. This stimulus induces surface instability, leading to the self-organization of the graphene into a periodic ripple pattern. The resulting structure exhibits a significantly increased surface area compared to the initial smooth graphene.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Separating Families
#### 4.3.2 Minimal Separating Families  
In the elementary examples presented above, the size of the separating family is \(r\), which means that we must append at least \(r\) tags to any message to protect against impersonation by internal adversaries. This places a constraint on the lengths of the tags in light of the overall upper bound on the number of bits we can use for sender authentication. In order to minimize this overhead, we want to use separating families that comprise the least number of subsets of \(\mathcal{R}\). Therefore, we are faced with the following problem:  
For a given number \(r\), find a separating family for \(\{1,\ldots,r\}\) consisting of the minimal number of sets. This is referred to as a minimal separating family for \(\mathcal{R}\), and we denote its size by \(\Psi(r)\).  
As seen from the above examples, \(\Psi(r)\leq r\). We aim to show that, except for \(r\leq 4\), \(\Psi(r)\) is much lower than \(r\), and consequently we can improve the construction of tags.  
Before proceeding, we observe that \(\Psi(r)\) is weakly monotonic, i.e., \(\Psi(r)\leq\Psi(r+1)\); indeed, if we have a separating family for \(r+1\) elements, we may remove the element \(r+1\) from all sets and be left with a separating family for the remaining \(r\) elements.  
In the next step, it can be observed that a logarithmic number of sets is sufficient to create a separating family.  
Let us represent each number \(i\leq r\) by a binary string of length \(\lceil\log_{2}(r)\rceil\). For \(a\leq\lceil\log_{2}(r)\rceil\) and \(b\in\{0,1\}\), we set the following:  
\[F_{a,b}=\{i\leq r\mid b\text{ is the bit on position $a$ of $r$}\}\]  
Let \(\mathcal{SF}=\{F_{a,b}\mid a\leq\lceil\log_{2}(r)\rceil,b\in\{0,1\}\}\).  
Obviously, family \(\mathcal{SF}\) from Definition 4 is a separating family. If \(i\
eq j\), there is a position \(a\) in their binary representations where they differ. Let \(i\) have bit \(b\) in this position; then, \(i\in F_{a,b}\), but \(j\
otin F_{a,b}\).  
\(\Psi(r)\leq 2\cdot\lceil\log_{2}(r)\rceil\).  
Asymptotically, Definition 4 yields a significant reduction of the upper bound on \(\Psi(r)\), but is not of much use for small values of \(r\), which we are most interested in for the purposes of this work. For \(r=4\), we have \(2\cdot\lceil\log_{2}(r)\rceil=r\). For \(r=5\) it is even worse, with \(2\cdot\lceil\log_{2}(r)\rceil=6>r\).
## 1 Introduction
However, the size of the former AMC is relatively large [26], and it is challenging to realize the FSS layer on the ferrite substrate in the latter AMC because it may cause manufacturing intolerance and losses [27]. In addition, the fabrication of the hole on the ferrite surface is complex and expensive, as commercially available ferrites with holes are not common. In this paper, we propose a compact and thin ferrite-loaded AMC structure with a novel design for complete magnetic shielding and PTE improvement of WPTSs, which was obtained by utilizing an inverted microstrip line (IML) design. Furthermore, the additional manufacturing cost of the via hole for the AMC can be eliminated with proper placement selection of the ferrite tiles. The performance of the proposed design of the IML-based AMC was verified with a WPTS, which showed very good performance in terms of magnetic shielding while improving the PTE. The overall profile, weight, and complexity of the IML-based AMC structure were greatly improved compared with those of AMC-based magnetic shielding structures. The possible applications of the proposed AMC in the WPTS may include smart home appliances utilizing wireless charging features and wireless  
Figure 1: Representation of magnetic field leakage in a WPTS. charging stations for mobile devices in public areas, as well as wireless electric vehicle charging stations, where the electromagnetic exposure should be regulated.
## 2 Study Area and Data
### Ground Synchronous Measurement
#### 2.3.2 Azimuth and Elevation Angle of CR Calculation  
The peak RCS of the trihedral CR varied with the azimuth and elevation angles [14]. Therefore, the angles of CR were required to be consistent with the direction of the radar incident beam during the SAR satellite transit to ensure that the radar antenna received the peak RCS signal from the ground point target [15], thus maintaining the accuracy of the SAR satellite radiation calibration.  
The orbital parameters of the C-SAR/01 were used to predict its position coordinates at transit [16]. Thereafter, the azimuth angle \(\varphi_{S}\) (orange arc line in Figure 5a) and incidence  
Figure 4: Distribution of automatic CRs of FSI mode images in the experimental area on 15 May.  
Figure 3: Distribution of automatic CRs of UFS mode images in the experimental area on 12 May.  
angle \(\theta_{S}\) (green arc line in Figure 5b) of the satellite were calculated with the help of spatial geometry between the satellite and the ground, where the satellite azimuth angle \(\varphi_{S}\) is the azimuth of the ground equipment--\(O\) pointing towards the satellite--\(S\). Based on the relationship between the radar beam and satellite vector \(\overrightarrow{V}\), the azimuth angle of CR \(\varphi_{\text{CR}}\) was calculated by combining the satellite orbit and look directions, using a feature where the normal line \(OE\) (orange straight line in Figure 5b) of the triangular trihedral CR \(O-ABC\) was perpendicular to the bottom edge \(BC\). By simulating various combinations of the satellite ascending-descending orbit and left-right look, the formula for calculating the CR azimuth angle \(\varphi_{\text{CR}}\) was derived as Equation (1), and the geometric relationship between the satellite azimuth angle \(\varphi_{S}\) and the CR azimuth angle \(\varphi_{\text{CR}}\) is shown in Figure 5a for the left look of the ascending orbit. Finally, the CR elevation angle \(\theta_{\text{CR}}\) (purple arc line in Figure 5b) was obtained from the geometric structure of the triangular trihedral CR \(O-ABC\) as Equation (2).  
\[\varphi_{\text{CR}}=\varphi_{S}+90^{\circ} \tag{1}\]  
\[\theta_{\text{CR}}=\theta_{S}-35.264^{\circ} \tag{2}\]
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 7 Discussion  
The objective of this study was to quantitatively analyze the impact of energy consumption based dynamic selfish nodes, comparing with that of the static and altruistic ones, in MANETs. The computer simulation based experimental results in this work demonstrate that the impact of the selfish nodes is strictly influenced by their mobilities, densities, proportions, and combinations in terms of average packet loss rates, round-trip delay, and throughput. The results suggest that node mobility models and the proportion of selfish nodes are the most influential factors that impact selfish nodes. The node densities and selfish node combinations are not negligible factors when investigating node selfishness in the network. Especially, in some subclasses of MANETs, e.g., VANETs, where nodes behave in a more organized fashion, these two factors should not be underrated. Generally, the more selfish nodes involved in the network, the worse the network performance is in MANETs. Nevertheless, in a dense network, the communication performance is slightly worse because of the link breakages caused by the high node mobility. These results should be taken into account when considering how to detect or incentivize selfish nodes in MANETs. Furthermore, the dynamic selfish nodes are less harmful to the performance of the network, but they are more realistic than the static ones.  
The reliability of these data is partially limited by the simple analog model type, unit-disk model. In theory, more complex analog models are more accurate but computationally intensive. The unit-disk analog model is suitable for wireless simulations in which the details of the physical layer are not important. Therefore, the data produced by the simulation based on the unit-disk model are plausible and adequate to this work at the present stage.  
There are some threats to the validity of our experimental results. Internally, the parameter setting in our experiments is an influential factor of the validity of results. Especially, the energy consumption parameters could affect the number of dynamic selfish nodes in the network. Externally, the physical environment (e.g., buildings, mountains, etc.) around mobile nodes in MANETs has a great impact on the link quality of wireless communications. It becomes more realistic that environmental obstacles are considered in the simulations. Intrinsically, the randomness produced by the finite number of random seeds is hard to cover all of probabilities. In our experiments, each simulation was executed 50 times with different random seeds, and the average values are plotted in the figures. It ensures the validity of the results as much as possible.  
Further studies should take into account the integrated consideration of mobility, density, proportion, and combination of selfish nodes with different weights in MANETs. It is a solid base for the detection and incentive mechanisms of selfish nodes, which can improve network performance by fully utilizing all possible resources in the networks.