esa-sceva/satcom-chunk-collection huggingface dataset:
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 4 Leakage Localization Based on BP Neural Network  
As discussed above, the FBG hoop strain sensors can be installed along the pipeline as the sensing element to measure the hoop strain variations. A series of hoop strain values composes eigenvectors to predict leakage locations. Compared with the traditional leak monitoring method based on the pressure sensor, this hoop strain-based method can provide more characteristic information since the FBG sensors can be nondestructively installed on the pipeline and arranged flexibly. In this part, a leakage localization based on the BP neural network is proposed by using multiple hoop strain characteristics.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 2 Related Work
### Hybrid Indexing Structure  
Although the single index methods mentioned above are used widely and some improved versions are put forward, there are certain defects and limitations insoluble, such as the tree is too deep or unbalanced and so on. To overcome the shortcoming of single index methods, more and more scholars try to design hybrid index technology. Some proposed strategies are combining with different index structures organically and achieve a better performance in query operation [9; 11; 16; 17; 18; 19].  
KDB-tree [16], combining KD-tree and B-tree, builds a balanced tree by dynamically adjusting. Thereby, it improves the query speed. KD-octree [17], combining KD-tree and octree, constructs a relatively balanced tree using KD-tree, and then constructs octree at each leaf node of KD-tree. In this way, it overcomes the disadvantage that the tree is too deep and cannot query quickly. Octree forest [20] intends to organize Point Cloud data to obtain a better performance of query. Meanwhile, it is just a truncated octree which cuts the octree off at a certain level and then obtains the octree forest. Paper [21] proposed a spatial index method for 3D Point Cloud data. The method consists of two levels; the top-level is a octree and the bottom-level is a set of R-tree corresponding to the leaf nodes of octree. Meanwhile, the leaf nodes are encoded and sorted with Morton-code. There are also other similar methods; while these methods' performances are almost on a par, each of them has strengths and weaknesses. Therefore, the hybrid index methods need to be further researched.
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 3 Results
### Daily LAI Calculation of Small and Medium-Sized Regional Scale  
When calculating the daily LAI, the LAI normalized growth curve was required. In order to make the LAI normalized curve more representative of the average LAI growth condition of the forest stands in the study area, the vector map of the small group distribution in the study area was overlaid with the smoothed MODIS LAI dataset, and the pure forest region of interest (ROI) points of coniferous forests were selected evenly in the study area. In order to avoid the heterogeneity of the MODIS pixels covering the surface and including the spatial change information of the heterogeneous surface, pure pixels of the stand type should be selected as much as possible to minimize the estimation error of the Sentinel-2 LAI temporal and spatial distribution caused by MODIS mixed pixels, and the extracted normalized LAI should be averaged, as shown in Figure 7. Based on Equation (6), the daily LAI calculation was realized.  
Figure 6: Comparison of observed and extracted LAI and normalized LAI for two sample plots based on MODIS. (**a**) LAI of sample plot I; (**b**) LAI of sample plot II; (**c**) Normalized LAI of sample plot I; (**d**) Normalized LAI of sample plot II.  
The time-series LAI data of two measured points were extracted from the daily LAI data set of the simulated study area and compared with the measured values (Figure 8). It can be seen that compared with the LAI growth curve directly extracted from MODIS in Figure 6, the LAI growth curve obtained using this method matched better with the measured LAI of the sample plot, with \(RMSE\) = 0.266 for sample plot III and \(RMSE\) = 0.478 for sample plot IV.
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
###### Abstract  
The growing demand for extensive and reliable structural health monitoring resulted in the development of advanced optical sensing systems (OSS) that in conjunction with wireless optical networks (WON) are capable of extending the reach of optical sensing to places where fibre provision is not feasible. To support this effort, the paper proposes a new type of a variable weight code called multiweight zero cross-correlation (MW-ZCC) code for its application in wireless optical networks based optical code division multiple access (WON-OCDMA). The code provides improved quality of service (QoS) and better support for simultaneous transmission of video surveillance, comms and sensor data by reducing the impact of multiple access interference (MAI). The MW-ZCC code's power of two code-weight properties provide enhanced support for the needed service differentiation provisioning. The performance of this novel code has been studied by simulations. This investigation revealed that for a minimum allowable bit error rate of \(10^{-3}\), \(10^{-9}\) and \(10^{-12}\) when supporting triple-play services (sensing, datacomms and video surveillance, respectively), the proposed WON-OCDMA using MW-ZCC codes could support up to 32 simultaneous services over transmission distances up to 32 km in the presence of moderate atmospheric turbulence.  
Keywords:optical sensors; vibration sensing; quality of service differentiation; wireless optical networks; free space optics; multiwavelength laser; optical code division multiple access (OCDMA) +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Mathematical Model of Acquisition  
The mathematical model of a system using an NI 9237 module and a Flintec Q50-0.5T load cell can be described in terms of the relationships between the applied force, the sensor output (voltage), and the measurement corrections made by the NI 9237.  
The pancake-type load cell converts the applied force \(F\) into an output voltage \(V\_out\), according to Equation (2):  
\[V_{out}=C_{f}\cdot F+V_{0} \tag{2}\]  
where \(C_{f}\) is the load cell conversion factor (mV/N) and \(V_{0}\) is the offset voltage when no force is applied.  
The NI 9237 module uses remote sensing to correct gain errors caused by the resistance of the excitation wires. Because of the excitation wire resistance, if we consider the excitation voltage \(V\) and the associated voltage drop \(V_{f}\), the effective excitation voltage \(V_{eff}\) applied is  
\[V_{eff}=V-V_{f} \tag{3}\]  
The module compensates for voltage drop by adjusting the output reading to reflect the correct excitation voltage.  
\[V_{out\ eff}=V_{out}\times\frac{V}{V_{eff}} \tag{4}\]  
Incorporating the load cell sensitivity and measurement corrections, the final relationship between the applied force and the system output is  
\[F=\frac{V_{out\ eff}-V_{0}}{C_{f}} \tag{5}\]  
where \(V_{out\ eff}\) is the output voltage corrected by the NI 9237 module, \(C_{f}\) is the conversion factor, and \(V_{0}\) is the offset voltage. Table 1 specifies the numerical values for the conversion factor and the offset voltage the load cell manufacturer provided.
## 4 Discussion
LAI is the most influential parameter in our models, as in SCOPE and STEPS, it determines photosynthetic foliage and influences the interception of rainfall and the subsequent evaporation from the vegetation canopy. In TSEB, it determines the fraction of incoming solar radiation intercepted by the vegetation canopy. Other models like GLEAM emphasize the role of vegetation dynamics and precipitation in driving ET by relying on VOD and precipitation, their comparison was challenging due to differences in input data and estimation algorithms; however, this revealed that accuracy can be increased over the region by improvinginputs and modifying parameters for dry ecosystems. The area-specific parameterization can improve ET models' performance in a given area or basin [60; 61]. The \"one-size-fits-all\" approach for model parameters might not be optimal for the entire Mediterranean with its diverse climate clustering. Future research should prioritize area/climate-specific parameterization. This involves customizing model parameters based on unique regional conditions of soil types, vegetation characteristics, and prevailing climatic patterns. Such fine-tuning can significantly enhance model performance in specific regions within the Mediterranean basin considering the extremely arid and humid conditions. Future research should focus on parameterizing and training models to handle these extremes effectively. This is particularly critical given the increasing atmospheric demand leading to flash droughts and floods across the region. The assimilation of high-resolution remote sensing data with advanced modeling techniques holds immense potential for generating robust ET estimates. By addressing these areas, we can ensure a more robust scientific foundation for ET estimation in the Mediterranean. This will not only improve the accuracy of water resource management but also enhance our understanding of the complex hydrological processes within this diverse and ecologically sensitive region. This inter-comparison somehow facilitated comprehending the shortcomings of each model, and how they can be modified for their future adaptation to the heterogeneous Mediterranean region.
# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
## 3 Data Description  
### Selection of Test Routes  
The European Commission (EC) has suggested test routes in accordance with RDE requirements, and these routes can be divided into three types: urban, rural, and motorway routes. The distance share is distributed as follows: 29-44% in urban sections, 23-43% in rural sections, and 23-43% in motorway sections. The driving speed is 60 km/h or less in urban sections, between 60 and 90 km/h in rural sections, and more than 90 km/h in motorway sections [35].  
In compliance with the RDE light-duty vehicle (LDV) regulations suggested by the EC, the Ministry of Environment in South Korea developed RDE LDV routes (KOR-NIER Route) with PEMS [36; 37]. Figure 2 presents the KOR-NIER routes for cold and hot tests, which were set in Seoul city and its surroundings, as summarized in Table 1. The total length of the KOR-NIER route for the cold test is 90.6 km: 32.7 km in urban sections, 29.0 km in rural sections, and 28.9 km in motorway sections. The total length of the KOR-NIERroute for the hot test is 73.6 km: 26.5 km in urban sections, 19.7 km in rural sections, and 27.4 km in motorway sections.
## 3 Proposed System  
As COVID-19 continues to threaten global populations with its rapid transmission, the prototype of a specifically-designed assistive technology is proposed. This technology could record and transmit human body temperatures in a contactless manner, collect the facial images of the same human subjects and identify the global positioning satellite (GPS) location of the subjects. The fog-based record could be stored on a local server for immediate analysis and simultaneously, the design of the proposed system has the capability to transmit and store the data in the cloud database through fog computing architecture for future monitoring and analysis. The conceptual diagram and workflow of the proposed system is presented in Figure 2. To meet the need of the hour, the proposed system could be installed at various locations through fog-based multi-node architecture and further, the monitoring and analysis could be performed by health-workers at a distance through the cloud storage.  
The design architecture of the proposed system is divided into three stages: the Fog Computing-based Single Node (FCSN) architecture, the hardware design of FCSN, and the Fog Computing-based Multi-Node (FCMN) architecture. The detailed discussion of the platform aims to understand the work flow of a single node and the interface circuitry with other components. Further, the hardware design and interface requirements for the FCSN architecture of the proposed system are crucial to understanding its proper implementation. Finally, to reduce the technical or human errors in recording and tracking the health conditions of the patient/subjects, the FCMN architecture is proposed.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### PV Cell Models
#### 2.1.2 Double-Diode Model (DDM)  
This model takes into account the effects of recombination current loss in the depletion region by incorporating a second diode in the PV cell. Although this model is more accurate than the SDM, it is also difficult to put into practice. In contrast to the SDM, the DDM contains two parallel-connected diodes in its equivalent circuit, as seen in Figure 2. Equation (2) describes the mathematical formulas required to calculate the output current [37].  
\[I=I_{ph}-I_{sd1}\left[exp\bigg{(}\frac{V+IR_{s}}{n_{1}V_{t}}\bigg{)}-1\right]- I_{sd_{2}}\left[exp\bigg{(}\frac{V+IR_{s}}{n_{2}V_{t}}\bigg{)}-1\right]- \frac{V+IR_{s}}{R_{sh}}, \tag{2}\]  
In this model, the PV cell is characterized by the seven parameters: \(I_{ph}\), \(I_{sd1}\), \(I_{sd_{2}}\), \(n_{1}\), \(n_{2}\), \(R_{sh}\), and \(R_{s}\). The reverse saturation current and ideality factor of the diffusion mechanism are indicated by \(I_{sd1}\) and \(n_{1}\), whereas the parameters that represent the generation-recombination process are denoted by \(I_{sd_{2}}\) and \(n_{2}\).  
Figure 1: Equivalent circuit of single-diode model.  
Figure 2: Equivalent circuit of double-diode model.
## 3 Results
### Full Antarctic Data Set  
Here we present the 9 year time series of SWH for the southern ocean and marginal Antarctic seas. This data has been processed with same methodology as for the Arctic up to \(-50^{\circ}\)S, but due to lack of Southern Ocean wave buoys, the detailed in situ comparisons of Figure 6 are not repeatable. The LRM mode data has been bias corrected in accordance to the methodology in Section 3.6 with an emergent LRM correction of SWH\({}_{L}c=0.30+s_{L}/1.07\) (see Supplemental Figure S1).  
Figure 12 shows the Southern Ocean between \(-50^{\circ}\)and \(-60^{\circ}\)S has a strong season cycle in SWH peaking during July (see Supplemental Figure S2). The Southern Ocean and Bellinghausen and Admunsen sectors have a seasonal cycle similar to the North Atlantic sectors in the Artic, wave heights following the seasonal cycle in wind speeds. The Weddell Sea sector has the seasonal ice covered sea characteristics, wave heights increasing with decreasing sea ice cover, with a peak in March. The Ross Sea, West Pacific and Indian Ocean sectors have cycles influenced by both sea ice cover and atmospheric conditions.  
A notable characteristic for the data shown in Figure 12, is the emergence of wave height data within the limits of 15% ice concentration. These data have been masked by  
Figure 11: The 50 th, 95 th and 99 th percentile of SWH for the entire period 2011–2019.  
Figure 10: Seasonal cycle (**left**) and interannual variability (**right**) of SWH (m) and ice concentration for the regions indicated in Figure 9a. The interannual variability is for the days 01-01 to 03-10 (solid lines) and days 06-10 to 08-30 (dashed lines) periods shown by the grey bands on the seasonal cycle.  
the NSIDC daily ice concentrations, so show the retrieval of wave height information from within the large polynynas that form with Antarctic sea ice.
## 4 Discussion  
### Electrical Conductivity in the Long Wavelength Limit  
Let us first assume the case of a graphene layer in the long wavelength limit \(\beta=0\), for which Equation (13) reduces to:  
\[\tilde{\mathbf{\sigma}}(\omega,\mathbf{0})=i\frac{2e^{2}}{\pi^{2}}v_{x}^{2}\frac{1}{ \omega-i\
u}\int_{c_{r}}\hat{\mathbf{k}}\hat{\mathbf{k}}^{\prime}\Delta[\bar{x}(\mathbf{k} ^{\prime})]d^{\prime}\mathbf{k}^{\prime} \tag{20}\]  
In the reference system indicated in Figure 1, the four components, \(\sigma_{xx}\)(\(\omega\),0), \(\sigma_{yy}\)(\(\omega\),0), \(\sigma_{xy}\)(\(\omega\),0) and \(\sigma_{yx}\)(\(\omega\),0) of the dyad, \(\sigma\)(\(\omega\),0), reduce to the following expressions:  
\[\sigma_{xx}\left(\omega,0\right)=\sigma_{yy}\left(\omega,0\right)=\sigma( \omega),\ \sigma_{xy}\left(\omega,0\right)=\sigma_{yx}\left(\omega,0\right)=0 \tag{21}\]  
where:  
\[\sigma(\omega)=\frac{\sigma_{c}}{1+i\omega/\
u} \tag{22}\]  
In the same condition, the generalized [PERSON]'s law for a graphene nanoribbon Equation (16) would reduce to:  
\[\hat{J}_{x}\left(\beta,\omega\right)=\frac{\sigma_{0}}{1+i\omega/\
u}\hat{E}_ {x}\left(\beta,\omega\right). \tag{23}\]  
Figure 6: Equivalent number of conducting channels for metallic and semiconducting armchair graphene nanoribbons (GNRs) at 300 K.  
The conductivity, \(\sigma_{c}\), in Expression (22) is consistent with \(\sigma_{0}\) in Equation (23): \(\sigma_{c}\) may be obtained from \(\sigma_{0}\) by taking the limit \(W\rightarrow\infty\), _i.e._, when the transverse size of the GNR tends to infinity. To explain this behavior, Figure 6 shows the computed value of the number of conducting channels, \(M\), in Equation (18), for a metallic and semiconducting armchair GNR _vs._ the GNR width. For smaller values of \(W\), the contribution to the conductivity given from the semiconducting GNRs is negligible, but it becomes non-negligible as \(W\) increases. Figure 7, instead, shows the behavior of the ratio, \(\sigma_{0}/\sigma_{c}\), as a function of \(W\), which proves the consistency stated above.  
As a conclusion, in the case of a long wavelength limit, the conductivity of graphene does not show spatial dispersion, but only frequency dispersion, according to Equation (22). For a GNR (as in Figure 3), the conductivity may be assumed to have only the longitudinal component \(\sigma_{xx}(\omega,0)=\sigma(\omega)\).
## 4 Simulation and Result Analysis
### System Parameters Analysis of UWAOSDM/PN  
Figure 7 shows the performance comparison of the UWAOSDM/PN system with different vector lengths under different UWA channels. According to the channel model adopted in this paper, the maximum delay time of the channel model 1 is 6.9 ms and that of the model 2 is 12.5 ms. The system sampling rate is 96 kHz and the up-sample coefficient is 16. With these parameters, we can calculate the vector duration of different length vectors, such that the vector duration is 5.34 ms when the vector length is 32, the vector duration is 10.67 ms when the vector length is 64, and the vector duration is 21.33 ms when the length is 128. The simulation results in Figure 7a,b show that the system bit error rate (BER) of OSDM is lower than that of OFDM when the vector length M of OSDM system is larger than the maximum channel delay spread. With the increase of the vector length, the system can obtain more diversity gain. Simultaneously, the simulation results in Figure 7a,b show that the performance of a single-carrier system is also lower than that of the OSDM system. Therefore, the increase of vector length is limited in a scope. The vector length can be expressed as:  
\[M=2^{\lceil\log_{2}\lceil\frac{\beta\epsilon}{\lambda}\rceil\rceil} \tag{17}\]  
where, \(D\) represents channel maximum delay spread, \(S\) is system sampling frequency, \(\lambda\) is up-sample coefficient and \(\lceil\rceil\) means ceil the number.  
According to Equation (17), the vector length of the OSDM system should be larger than the maximum channel delay and keep as many subcarriers as possible to ensure the system carrier flexibility.  
Figure 6: Time domain impulse response of the underwater acoustic channel: (**a**) the channel model 1 (**b**) the channel model 2.
## 4 Improved PSO-Optimized Fuzzy-BPNN Control Algorithm
### BP Neural Network Weight Coefficient Update  
According to the BP learning algorithm, first define the objective cost function \(J\):  
\[J=\tfrac{1}{2}e^{2}(k)=\tfrac{1}{2}[r(k)-y(k)]^{2} \tag{6}\]  
After determining the target cost function \(J\), perform a negative gradient direction search with the objective of minimizing the target function \(J\), with an additional inertia term that accelerates the convergence of the search to a global minimum as follows:  
\[\Delta\omega^{(3)}_{li}(k+1)=-\eta\tfrac{\partial u(k)}{\partial\omega^{(3)}_ {li}}+\alpha\Delta\omega^{(3)}_{li}(k) \tag{7}\]  
where \(\eta\) is the learning rate; \(\alpha\) is the inertia coefficient.  
The correction formula for the weighting coefficient of the output layer of the BP neural network is further obtained as:  
\[\left\{\begin{aligned} &\Delta\omega^{(3)}_{li}(k+1)=\eta \delta^{(3)}_{l}O^{(2)}_{i}(k)+\alpha\Delta\omega^{(3)}_{li}(k)\\ &\delta^{(3)}_{l}=e(k+1)sgn\Big{(}\tfrac{\partial y(k+1)}{ \partial u}\Big{)}\times\tfrac{\partial u(k)}{\partial O^{(3)}_{l}(k)}\quad \times g^{\prime}\Big{[}net^{(3)}_{l}(k)\Big{]}\\ &(l=1,2,3)\end{aligned}\right. \tag{8}\]Based on the above derivation, the correction formula for the implied layer weighting factor can be obtained as follows:  
\[\left\{\begin{aligned} &\Delta\omega_{ij}^{(2)}(k+1)=\eta\delta_{i}^{(2)} \mathcal{O}_{j}^{(1)}(k)+\alpha\Delta\omega_{ij}^{(2)}(k)\\ &\delta_{i}^{(2)}=f^{\prime}\Big{[}net_{i}^{(2)}(k)\Big{]}\sum \limits_{l=1}^{3}\delta_{l}^{(3)}\omega_{li}^{(3)}(k)(i=1,2,\cdots 9)\end{aligned}\right. \tag{9}\]  
which sets up: \(g^{\prime}(x)=g(x)[1-g(x)]\); \(f^{\prime}(x)=\big{[}1-f^{2}(x)\big{]}/2\).  
Therefore, the workflow of the Fuzzy-BPNN-PID controller can be summarized as follows: Set the initial values of the network parameters of each layer of the BPNN, as well as the learning rate and inertia coefficient of the network; use the fuzzy controller to fuzz the acquired \(e\) and \(ec\); input the fuzzified output \(v(k)\) to the BPNN; then, calculate the input and output of each layer of neurons through the BPNN; and finally, calculate the three adjustable parameters \(k_{p}\), \(k_{i}\) and \(k_{d}\) of the PID controller, which are calculated by Equation (5). The output control signal is \(u(k)\). The system continuously updates \(u(k)\) and the network weight parameters according to the updates of \(e\) and \(ec\). The control schematic is shown in Figure 9.