esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 3 The Energy Harvesting Network System  
Let a network topology with \(n\) nodes and \(\ell\) links be represented by a Graph \(G\) where \(V(G)\) and \(E(G)\) are the set of nodes and the set of links, respectively. Consequently, \(n=|V(G)|\) and \(\ell=|E(G)|\). In addition, let \(S_{u}\) denote node's \(u\) neighbors. It is common practice in wireless sensor network environments that an operational node periodically transmits beacon frames to inform the neighbor nodes about its presence. A node is operational if the amount of the remaining energy in its battery is above a particular threshold. When the remaining energy falls below this threshold, the node becomes non-operational and thus is not participating in the network. Moreover, in energy harvesting environments, when the amount of energy collected is larger than the consumed energy, the node is _charging_; otherwise, it is _discharging_. Consequently, a node may be in four different states presented in Table 1 depending on its operational state (i.e., ON, OFF) and the amount of the collected energy from the ambient environment (i.e., C, D).  
Thus, ON-C corresponds to the state of a node which is operational and its battery is charging (i.e., State 1), ON-D corresponds to the state of a node that is operational and its battery is discharging (i.e., State 2), OFF-C corresponds to the state of a node that is non-operational and its battery is charging (i.e., State 3) and OFF-D corresponds to the state of a node that is non-operational and its battery is discharging (i.e., State 4).  
Figure 1 depicts the four states and the corresponding _transition probabilities_ as a Markov chain [62]. This chain is further studied and simplified in Section 5 and along with the realistic dataset obtained from the NASA Langley Research Center (LaRC) POWER Project [6] is used to compare the proposed information dissemination mechanism against PF in Section 6.
## 3 System Model and Basic Assumptions  
In this section, the implemented two-hop MIMO RNCS will be presented and described, which consists of a source (emulating base station, eNodeB), a Relay Node (A&F/D&F), and UE. The eNodeB is composed of two blocks, as can be seen in Figure 1: the baseband 4G signal processing block (\(\mathbf{T}_{\text{BB}}\)) and RF transmit chains (\(\mathbf{M}_{\text{ST}}\)) to up-convert the signal to a 4G frequency band, which from the point of view the implementation has been developed through the USRP, as will be presented in the following section. From Figure 1, it can be noted that a transmit diversity mode in LTE based on Alamouti codes in the frequency domain (Space-Frequency Block Coding, SFBC) has been considered. The implemented scheme exploits diversity and therefore shows the best performance when the spatial correlation between antennas is low. In our architecture, the most interesting block of the network is the Relay Node, which its function is to help the communication between the source and destination nodes. It comprises an RF receiver (\(\mathbf{M}_{\text{RR}}\)), implemented by USRP, a baseband signal processing algorithms associated with the implemented relayingstrategies (\(\mathbf{R}_{\text{BB}}\)), and RF transmitter (\(\mathbf{M}_{\text{RT}}\)) emulated through USRP. On the other hand, the UE node consists of a RF receiver block (\(\mathbf{M}_{\text{DR}}\)) and the processing baseband block (\(\mathbf{G}_{\text{BB}}\)).  
From Figure 1, we show a general MIMO eNodeB corresponding to the 3 GPP LTE downlink system model [40; 41]. The RNCS works in half-duplex mode and Orthogonal Space Frequency Block Code (OSFBC) matrices are used for the transmission of data symbols in the multiple antenna environment. The transmission of the OSFBC data from the eNB to the UE is performed in a two-hop manner, where we assume that at the source node, the number of transmit antennas is \(\mathbf{N}_{\text{T}}^{\text{S}}\) and the number of data symbols is \(\mathbf{N}_{\text{s}}\). In first-hop, the source transmits \(\mathbf{S}\in\mathbb{C}^{\mathbf{N}_{\text{s}}\times\mathbf{T}}\) data symbols through the baseband precoding matrix \(\mathbf{T}_{\text{BB}}\in\mathbb{C}^{\mathbf{N}_{\text{T}}^{\text{S}}\times \mathbf{N}_{\text{s}}}\) and RF transmitter \(\mathbf{M}_{\text{ST}}\in\mathbb{C}^{\mathbf{N}_{\text{T}}^{\text{S}}\times \mathbf{N}_{\text{t}}^{\text{S}}}\). Thus, the transmitted signal from the source can be expressed as  
\[\mathbf{X}=\mathbf{M}_{\text{ST}}\mathbf{T}_{\text{BB}}\mathbf{S}, \tag{1}\]  
where power normalization is satisfied \(||\mathbf{M}_{\text{ST}}\mathbf{T}_{\text{BB}}||_{\text{F}}^{2}=\mathbf{T}_{ \text{T}}^{\text{S}}\). It should be noticed that the generated and configuration of the signal and channel in the eNodeB fulfil the 4G standardization of the 3 GPP [13], which is one of the main novelty of our proposal. In the following subsections, we present the A&F and D&F MIMO Signal Models, techniques, and algorithms, which are included for the study of physical layer capacity.
## 2 Materials and Methods
### Data  
#### 2.2.1 Remote Sensing Data  
Remote sensing data, including the annual evapotranspiration (ET) (MOD16A3 GF) and annual gross primary productivity (GPP) (MOD17A3 HGF) for 2001-2022, were obtained along with 8 d ET data (MOD16A2) and 8 d GPP data (MOD17A2 HGF) for the period 2001-2021 from the MODIS data products ([[https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)]([https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)), from 1 January 2001 to 31 December 2021). All MODIS data had a spatial resolution of 500 m and were subjected to data format conversion, image mosaicking, cropping, projection transformation, and outlier processing. Data processing was completed in ArcGIS 10.7.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
###### Abstract  
Truncated singular value decomposition (TSVD) is a popular recovery algorithm for multimode fiber single-pixel imaging (MMF-SPI), and it uses truncation thresholds to suppress noise influences. However, due to the sensitivity of MMF relative to stochastic disturbances, the threshold requires frequent re-determination as noise levels dynamically fluctuate. In response, we design an adaptive truncation threshold determination (ATTD) method for TSVD-based MMF-SPI in disturbed environments. Simulations and experiments reveal that ATTD approaches the performance of ideal clairvoyant benchmarks, and it corresponds to the best possible image recovery under certain noise levels and surpasses both traditional truncation threshold determination methods with less computation--fixed threshold and [PERSON]'s unbiased risk estimator (SURE)--specifically under high noise levels. Moreover, target insensitivity is demonstrated via numerical simulations, and the robustness of the self-contained parameters is explored. Finally, we also compare and discuss the performance of TSVD-based MMF-SPI, which uses ATTD, and machine learning-based MMF-SPI, which uses diffusion models, to provide a comprehensive understanding of ATTD.  
fiber imaging; multimode fiber imaging; single-pixel imaging; truncated singular value decomposition; truncation threshold determination Article  
Adaptive Truncation Threshold Determination for Multiple Fiber Single-Pixel Imaging  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (2020)Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging. Appl. Sci.2024 14 6875. [[https://doi.org/10.3390/app14166875](https://doi.org/10.3390/app14166875)]([https://doi.org/10.3390/app14166875](https://doi.org/10.3390/app14166875))
## 4 Evaluation and Discussion
### Time Analysis
#### 4.5.2 Keystream Shuffling Time Analysis  
This analysis only assesses the time taken to shuffle a keystream for each secret image and cover image pair. As with the keystream generation time analysis, this keystream shuffling time analysis has seven iterations, and the average time of the seven yields aggregate results. The parameters for the chaotic function and precision level are not changed and remain constant throughout all iterations.  
Similar to Table 13, Table 14 also shows an increase in the keystream generation time when going from embedding rainbow to monarch. While the reason for this is the same as the previous analysis, one might question why the time to shuffle the keystream takes longer than to generate it. One of the reasons why this process takes a long time is because of the deletion operation on a dequeue data structure. This can be seen in Algorithm 1, where the said use of deque is performed.
# Polynomial Expressions for the Thermal Efficiency of the Parabolic Trough Solar Collector
## 4 Conclusions
### Nomenclature  
\begin{tabular}{l l} \(A\) & Area, m\({}^{2}\) \\ \(a_{i}\) & Approximation coefficient (i = 0, 1, 2, 3, 4) \\ \(b\) & Approximation coefficient, K\({}^{-1}\) \\ \(c_{i}\) & Approximation coefficient (i = 0, 1, 2, 3, 4) \\ \(C\) & Concentration ratio \\  & Specific heat capacity under constant pressure, J kg\({}^{-1}\) \\ \(c_{7}\) & K\({}^{-1}\) \\ \(D\) & Diameter, m \\ \(E_{L}\) & Longwave irradiance, W m\({}^{-2}\) \\ \(F\) & Focal distance, m \\ \(C_{6}\) & Solar direct beam irradiation, W m\({}^{-2}\) \\ \(h\) & Heat transfer coefficient of the flow, W m\({}^{-2}\) K\({}^{-1}\) \\  & Convection coefficient between cover and ambient, W \\ \(h_{\rm ent}\) & m\({}^{-2}\) K\({}^{-1}\) \\ i & Counter \\ \(k\) & Thermal conductivity, W m\({}^{-1}\) K\({}^{-1}\) \\ \(L\) & Tube length, m \\ \(m\) & Power of the approximation term \\ \(m_{\rm gal}\) & Thermal oil mass flow rate, kg s\({}^{-1}\) \\ \(n\) & Number of the examined scenarios \\ \(Nu\) & Nusselt number \\ \(Pr\) & Prandtl number \\ \(Q\) & Heat rate, W \\ \(Re\) & Reynolds number \\ R\({}^{2}\) & Coefficient of determination \\ \(T\) & Temperature, \({}^{\circ}\)C \\ \(T_{\rm day}\) & Sky temperature, K \\ \(V\) & Volumetric flow rate, L min\({}^{-1}\) \\ \(V_{\rm w}\) & Wind speed, m s\({}^{-1}\) \\ \(W_{a}\) & Width, m \\ \multicolumn{2}{l}{**Greek symbols**} \\ \(a\) & Absorbance \\ \(\gamma\) & Intercept factor \\ \(\Delta T\) & Temperature difference [= T\({}_{\rm in-T}\), \({}_{\rm am}\)], \({}^{\circ}\)C \\ \(\varepsilon\) & Emittance \\ \(\eta_{\rm ent}\) & Collector thermal efficiency \\ \(\eta_{\rm ent}\) & Optical efficiency \\ \(\theta\) & Solar incident angle, \({}^{\circ}\) \\ \(\mu\) & Dynamic Viscosity, Pa s \\ \(\rho\) & Equivalent reflectance \\ \(\sigma\) & Stefan-Boltzmann constant [= \(5.67\cdot 10^{-8}\) W m\({}^{-2}\) K\({}^{-4}\)] \\ \(\tau\) & Transmittance \\ \multicolumn{2}{l}{**Subscripts and superscripts**} \\ \(\rm a\) & aperture \\ \(\rm am\) & ambient \\ \(\rm aprx\) & approximation \\ \(\rm b\) & beam \\ \(\rm c\) & cover \\ \(\rm ci\) & cover inner \\ \(\rm co\) & outer cover \\ \(\rm d\) & diffuse \\ \(\rm fm\) & mean fluid \\ \(\rm in\) & inlet \\ \(\rm m\) & power of the approximation term \\ \(\rm max\) & maximum \\ \(\rm out\) & outlet \\ \(\rm r\) & receiver \\ \(\rm ri\) & inner receiver \\ \(\rm ro\) & receiver outer \\ \(\rm s\) & solar \\ \(\rm u\) & useful \\ \end{tabular}  
_Appl. Sci._**2020**, _10_, 6901
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 2 Algorithms
### Construction of 3D Structure Model  
We propose a new meshing method to calculate the travel time in CCS. Firstly, a CCS is divided into several uniform blocks with unique identifiers. Then, each block is divided into small regular cuboid cells that each cell only belongs to one block. Each vertex of a cell represents a node. By doing this, the block boundaries, ray paths, detector positions, and source positions are determined by the nodes. The nodes on block boundaries can belong to multiple blocks because they are shared by the adjacent cells.  
Two nodes are referred to as adjacent nodes if there is a direct path between them. AE rays can directly propagate between the adjacent nodes. Two nodes in the same block are treated as adjacent nodes if the spacing in all directions of \(x\), \(y\) and \(z\) between the two nodesis less than or equal to the adjacency radius. Here, the adjacency radius must be preset. The calculation accuracy as well as the amount of computation will increase with increasing adjacency radius.  
The node adjacency relationship is schematically shown in Figure 1. The CCS is composed of two blocks in pink and blue. The red node in Figure 1a is the internal node of the pink block. According to the definition of the adjacent nodes, the adjacent nodes of the red node, are all internal nodes of the pink block. In addition, the spacing in the direction of \(x\), \(y\) and \(z\) between the red node and the black solid nodes is less than or equal to the adjacency radius, Ra. However, the red node in Figure 1b is the boundary node between the pink and blue blocks. In such situation, the adjacent nodes of the red node, denoted by the black solid nodes in Figure 1b, could belong to different blocks as long as the spacing in the direction of \(x\), \(y\) and \(z\) between the red node and the black solid nodes is less than or equal to \(R_{a}\). The travel time between a node and its adjacent node is the linear distance between the two nodes divided by the wave velocity of the block, to which the two nodes belong. When the two nodes belong to multiple blocks, the highest wave velocity among the blocks is selected.  
The attributes of a node are stored in 8 bytes (64 bits) in our program, whereas a block can be represented by 1 bit. This means that the number of blocks can be up to 64 in one model. As such, more bytes are required to store node attributes if larger complex structures with more than 64 blocks are used. In our program, two nodes are determined as adjacent nodes if the spacing in all directions of \(x\), \(y\) and \(z\) between the two nodes is less than or equal to the adjacency radius and non-zero is obtained after \"and\" operation is applied to corresponding elements of the attributes between the two nodes. By doing this, all rays passing through the block boundary will pass through one of the boundary nodes.
## 5 Discussion
The correlations between the MOD17 f(\(W\)) and the GWC were not significant at the Guide station (r = 0.02, \(p>0.1\), n = 25) and not significant at the Tuotuo River station (r = 0.11, \(p>0.1\), n = 27). The correlation between the MOD17 f(\(W\)) and the GWC was low because the threshold of \(\mathrm{VPD_{dmin}}\) is too large for the TRH region. The \(\mathrm{VPD_{dmin}}\) was 650 Pa and the \(\mathrm{VPD_{dmax}}\) was 4300 Pa, while the \(\mathrm{VPD_{dav}}\) at the Guide and Tuotuo River stations was generally lower than that of the \(\mathrm{VPD_{dmin}}\) in the MOD17 around the year. When calculating the normalized \(\mathrm{VPD_{dav}}\), the estimated f(\(W\)) was nearly 1.0 for the majority of the time (Figure 5e,f). This means that the vegetation growth in the TRH region was not limited by water stress; however, this was contrary to many recent related studies [5; 67; 80; 81; 82; 83; 84]. In the future, the model needs to improve the resolution of the derived RS data due to spatial heterogeneity. Two approaches are feasible to improve the RS resolution, which are as follows: (1) using an RS dataset with higher spatial resolution, such as Sentinel and Landsat; unfortunately, they need complex preprocessing when used for a time series because of the wedge-shaped gaps of Landsat 7 [85; 86; 87]; (2) conducting data fusion of different sensors, such as MODIS and Landsat or MODIS and Sentinel, to improve the spatial resolution of time-series RS datasets and reduce the impact of mixed pixels [88; 89; 90; 91].
## 3 Results  
### Spatiotemporal Distribution of Vegetation Growth in the Mining Area  
#### 3.1.1 Comparison of kNDVI and NDVI  
In this study, we utilized vegetation histograms and spatial distribution maps from different years to analyze the advantages of kNDVI over NDVI. Specifically, we conducted comparative analyses of kNDVI and NDVI at 8-year intervals from 1999 to 2023, as illustrated in Table 4 and Figures 7 and 8. The results indicate that the overall vegetation growth condition in the study area has been predominantly poor over the past 25 years, with most NDVI values falling below 0.5 (Figure 7). The proportion of NDVI values between 0 and 0.1 is minimal, which does not adequately reflect the true extent of poor vegetation growth in the study area. In contrast, the distribution of kNDVI values effectively captures areas characterized by poor vegetation growth (Figure 7). Furthermore, the spatial heterogeneity of kNDVI is more pronounced (Table 4 and Figure 8), with the spatial variation coefficients for kNDVI being 51.48%, 47.82%, 48.58%, and 58.18% for the selected years, compared to 24.39%, 26.01%, 28.19%, and 32.28% for NDVI. This indicates that kNDVI can more sensitively monitor vegetation growth conditions in the study area.  
that kNDVI captures more detailed variations in vegetation growth, aligning better with the observed distribution in the measured image data. Therefore, this study opted to use kNDVI for analysis.
# Measurement of Relaxation Modulus of Viscoelastic Materials and Design of Testing Device
## 1 Introduction
Evaluating the viscoelastic properties of the material is essential in industries to ensure effective functioning of conveyor belts. The aim of this study was to test the linear viscoelastic relaxation modulus of a 75 HA conveyor belt. The conveyor belt exhibits nonlinear viscoelasticity because when strain is applied to the viscoelastic body, the resulting effect is nonlinear. This is a property often observed in large deformations. Analysis of nonlinear viscoelasticity is more complex than the analysis of linear viscoelasticity. Linear viscoelasticity occurs when the strain applied to a viscoelastic material can be expressed linearly. The Maxwell model is usually used to represent this attribute. The nonlinear viscoelasticity of a viscoelastic material can be approximated as linear viscoelasticity if its deformation is negligible [19]. Several methods have been developed to determine the linear viscoelastic properties of materials, including the time domain and frequency domain methods [20]. The most commonly used method in the frequency domain is the dynamic mechanical analysis (DMA) method [21]. The DMA method can determine the change in the function of relaxation modulus with frequency, but it is challenging to control the experimental conditions for this method. Several time domain methods have been reported, including stretching, twisting and pressing. The time-based methods can measure the change in the function of the relaxation modulus with time. In this study, the linear viscoelastic relaxation modulus in the time domain of the rubber sheet was determined by applying a small strain to the rubber sheet. The experimental device designed in this study causes the sliding table to move downwards by controlling the rotation of the stepping motor. Subsequently, it presses the spherical indenter into the rubber sheet using the lever principle. The displacement and load of the spherical indenter are then measured using the displacement sensor and the force sensor, respectively. The relaxation modulus parameters of the rubber sheet are obtained by fitting the experimental results.
## 3 Experiments and Results
### Experiments
#### 3.2.1 Orientation  
First of all, the orientation representation is studied. For this, two custom callbacks are created that compute the position and orientation error. The position error callback takes a batch of validation data, predicts the output from the given input, de-normalizes the ground truth and predicted values, and solves the Forward Kinematics (FK) for the predicted output. With the ground truth and predicted positions, the mean error is stored as the batch mean of the Euclidean distance between the ground truth and predicted values. The orientation error callback proceeds in the same way but solves the FK for both the predicted and ground truth outputs, obtaining the quaternion orientation for both, independently of the input. The error is then computed following Equation (5).  
The results can be seen in Table 2 and Figure 10. As expected, when the orientation is not considered, the MSE loss rises. The quaternion representation performs significantly better than its counterparts in every aspect except the position error, where they are all on par. The option with no orientation achieves slightly better results for position. This one, as anticipated, performs worse on the orientation error. The difference in position error is not significant between quaternion and rotation matrix representations; however, the gap in loss and orientation error is noticeable. This is ascribed to the DNNs performing better with fewer input parameters (7 for quaternions, 12 for rotation matrices).  
In light of all the above, the quaternion representation is preferred from this moment on. The sudden spikes that can be seen in the first plot are due to no normalization being applied to the dataset, which will be fixed in the next subsection.