esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Trend of NPP  
Figure 4 details the temporal trends in annual NPP across China. Over a 14-year period (2001-2014), 17.69% of total pixels displayed either significantly increased or decreased trends (Figure 4b; \(p\) < 0.05) in the whole study region. Pixels with a positive (i.e., increased NPP) trend in NPP accounted for 78.94% of the total pixels, and 15.18% of the total pixels exhibited a significantly positive trend (Figure 4b,c; \(p\) < 0.05). The increasing rate of NPP in China mainly occurred between 0 and 0.4 \(\times\) 10\({}^{-2}\) kg C m\({}^{-2}\) a\({}^{-1}\) (Figure 4c). At a regional scale, 79.15% of the total pixels showed a positive trend in Northern China, and the increasing rate was mostly distributed in 0-0.6 \(\times\) 10\({}^{-2}\) kg C m\({}^{-2}\) a\({}^{-1}\) (Figure 4d). Similarly, 88.12% of the total pixels displayed an increasing trend in the Tibetan Plateau, where an increasing magnitude mostly occurred at 0-0.4 \(\times\) 10\({}^{-2}\) kg C m\({}^{-2}\) a\({}^{-1}\) (Figure 4f). In contrast, negative trends occurred in 57.64% of the total pixels in Southern China, with an absolute magnitude mainly ranging from 0.2 \(\times\) 10\({}^{-2}\) kg C m\({}^{-2}\) a\({}^{-1}\) to 0.8 \(\times\) 10\({}^{-2}\) kg C m\({}^{-2}\) a\({}^{-1}\) (Figure 4e).  
Figure 3: Mean annual Net Primary Productivity (NPP) for each vegetation type over 2001–2014. The error bar in each column indicates the standard deviation. Different lowercase letters indicate significant differences in NPP among vegetation types (one-way ANOVA and \(t\)-test, \(p\) < 0.001).  
Figure 4: Trends in annual Net Primary Productivity (NPP) within China between 2001 and 2014 (**a**); and pixels with significant (\(p<0.05\)) and very significant (\(p<0.01\)) trends are shown in **(b)**. The count distributions of NPP trends in China **(c)**; Northern China **(d)**; Southern China **(e)** and the Tibetan Plateau **(f)** are also shown. A positive trend indicated an increase in NPP and vice versa; DS and DVS represent decreased significantly and very significantly, respectively; and IS and IVS represent increased significantly and very significantly, respectively. The red lines in figures **(c)**, **(d)**, **(e)** and **(f)** represent boundary for negative slop and positive slop.
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 4 Results and Evaluation
### Microcontroller-Based Evaluation  
This section assesses the performance and simplicity achieved by the encryption process for LAES and its counterparts in terms of CPU cycles, consumed power, and the amount of charge required. This experiment used an 8-bit microcontroller Arduino Uno board (MCU). The popularity of the Arduino board is due to its ease of use, its extendibility for managing a large number of sensors, and the fact that installation and maintenance of its components are inexpensive [48]. The Arduino Uno board has been used in many lightweight IoT applications, including smart cities [49], wearable sensor-based IoT sensors [50], temperature measurement devices [51], and heart rate monitoring systems [52].  
Figure 8: Average encryption time for a single block.  
Figure 9: Average encryption time for a single round.  
The Arduino Uno used in this experiment was powered and connected to computers through a USB port. It can also be powered via an AC/DC adapter. The experimental setup and specifications are similar to those used by [14], whereby sketch programming was used to implement the proposed LAES and its counterparts on the Arduino board. When the board was turned on, the cyclic encryption mode started immediately, and a built-in timer was used to measure the time required for each cycle. A fixed-size block of random data was encrypted 10,000 times, and several measures were obtained using a multimeter to compute the average power consumption for block encryption. Table 8 shows the results of the evaluation for LAES and its counterparts using the measures below, which aimed to evaluate the proposed LAES from a different perspective: the average required encryption time for (1) block, (2) round, and (3) entire process; the average CPU cycle required for (4) encrypting block and (5) round; (6) the average power and battery charge consumption required for encrypting a block.  
The evaluation results show that LAES outperforms its counterparts in the specified measures. LAES required fewer CPU cycles, less power, and less charge consumption for block encryption. As indicated in Table 8, the number of CPU cycles per encryption round required by Present is less than LAES, AES, and Clefia; however, the fact that Present requires 31 rounds for block encryption causes the cipher to consume additional CPU cycles and energy. Clefia requires more CPU cycles and energy than Present and LAES, due to some complex operations during the encryption round and the key generation strategy. LAES shows promising performance in terms of the number of CPU cycles required, as well as efficient energy consumption and battery charge consumption for block encryption, which are important measures for resource-constrained devices in IoT.
# Robust Static Structural System Identification Using Rotations
###### Abstract  
Deflections are commonly measured in the static structural system identification of structures. Comparatively less attention has been paid to the possibility of measuring rotations for structural system identification purposes, despite the many advantages of using inclinometers, such as a high resolution and being reference free. Although some work using rotations can be found in the literature, this paper, for the very first time, proposes a statistical analysis that justifies the theoretical advantage of measuring rotations. The analytical expressions for the target parameters are obtained via static structural system identification using the constrained observability method first. Combined with the inverse distribution theory, the probability density function of the estimations of the target parameters can be obtained. Comparative studies on a simply supported bridge and a frame structure demonstrate the advantage of measuring rotations regarding the unbiasedness and the extent of variation in the estimations. To achieve robust parameter estimations, four strategies to use redundant rotations are proposed and compared. Numerical verifications on a bridge structure and a high-rise building have shown promising results.  
rotations; static; observability; structural system identification; inverse distribution Article  
Robust Static Structural System Identification Using Rotations
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 2 Basic Principles
### Convolutional Neural Network
#### 2.1.2 Activation Layer  
In the forward propagation of a convolutional neural network, the activation layer nonlinearly transforms the output of the convolutional layer using an activation function. This maps low-dimensional, linearly indistinguishable features to higher dimensions and enhances the linear separability of the features. As a result, it enhances the nonlinear approximation and feature extraction capability of the neural network.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Co-Location of TLS and GB-RAR Instruments and Co-Registration of Their Measurements  
The co-location of TLS and GB-RAR is carried out by a TS topographic survey providing the coordinates of the TLS and GB-RAR, as well as of the base of the wind tower in the same local reference system. Figure 2 shows the layout of the TS survey (a) and a sketch of the local reference system with the positions of the TS, TLS, GB-RAR and wind tower (b). The slant ranges \(\mathrm{R_{TS,TLS}}\) and \(\mathrm{R_{TS,GB-RAR}}\) of TLS and GB-RAR with respect to the TS are indicated, along with the slant distances \(\mathrm{R_{TS,WT-B}}\) and \(\mathrm{R_{TS,WT-T}}\) of the base and top of the wind tower. The TS survey makes it possible to compute the coordinates of each point P of the tower, in the same local reference system, and hence their slant ranges \(\mathrm{R_{PTLS}}\) and \(\mathrm{R_{PGB-RAR}}\) with respect to the TLS and GB-RAR. The topographic survey is not necessary if the entire structure can be surveyed by the TLS, which in this case allows us to obtain, along with the mutual positioning of the structure and GB-RAR, a 3D model of the structure and of the surrounding environment.  
Figure 3 displays a high-resolution 3D digital model of the tower provided by the TLS with the slant ranges \(\mathrm{R_{PGB-RAR}}\) and \(\mathrm{R_{PTLS}}\). As a result, the spatial co-registration of TLS and GB-RAR measurements is obtained in a seamless way, taking into consideration the different spatial resolutions of TLS and GB-RAR measurements. Figure 4 shows the TLS measurements points mapping the shape  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Range Resolution** & **0.75 m** \\ \hline Central frequency & 17.2 GHz \\ Frequency bandwidth B & 200 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical Specifications of IBIS-F/L GB-RAR.  
of the wind tower and the points corresponding to the center of each of 0.75 m-resolution cell of the GB-RAR. For each range cell of GB-RAR data, the corresponding TLS measurements are easily identified and compared or merged with GB-RAR data. The co-registration of the data in time is provided by a synchronization of TLS and GB-RAR acquisitions.  
Figure 3: 3D digital surface model of the wind tower (**a**); The distances RP, GB-RAR and RP, TLS of point P on the tower with respect to TLS and GB-RAR (**b**).  
Figure 2: The TS survey layout with slant distances of TLS, GB-RAR, top and bottom of wind tower (**a**); The local reference system (TLS centered) used to co-locate TLS and GB-RAR measurements (**b**).
## 2 Related Work
### The Construction of Residual Block  
The basic structure of the residual module is shown in Figure 1.  
In this figure, \(x\) denotes the input and \(H(x)\) denotes the desired underlying mapping. The stacked nonlinear layers is expected to fit another mapping of \(F(x)=H(x)-x\). The original mapping is recast into \(F(x)+x\). It is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. In the extreme, if an identity mapping is optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. The formulation of \(F(x)\) + \(x\) can be realized by feedforward neural networks with \"shortcut connections\". Shortcut connections are those skipping one or more layers. The simple addition can greatly increase the training speed of the model and improve the training results without adding extra parameters or computational complexity.  
Figure 1: A basic block of ResNet (adapted from a previous study [9]).  
The residual learning is adopted to each stacked layer. The module is defined as follows.  
\[y=F(x,\{W_{i}\})+x \tag{1}\]  
where \(x\) and \(y\) denote the input and output of the module respectively. The function \(F(x,\{W_{i}\})\) represents the residual mapping to be learned. The operation \(F+x\) is performed by a shortcut connection and element-wise addition. The dimensions of \(x\) and \(F\) must be equal in Equation (1). If the dimensions are inconsistent, a linear projection \(W\)s shall be performed by the shortcut connections to match the dimensions.  
\[y=F(x,\{W_{i}\})+W_{s}x \tag{2}\]  
The square matrix \(W_{s}\) is only used when matching dimensions.  
This residual hopping structure breaks the convention that the output of the traditional neural network n - 1 layer can only give the n layer as an input, so that the output of a certain layer can directly cross several layers as the input of a later layer. In this way, the error rate of the whole learning model will not be improved after superimposing the multi-layer network, which provides feasibility for the extraction and classification of high-level semantic features.
## 3 Results and Discussion
### Results
#### 3.4.1 Packet Delivery Ratio  
We compared the Hybrid Wireless Mesh Protocol (HWMP) algorithm with our proposed extension Multi-Path Multi-Channel Hybrid Wireless Mesh Protocol (MPC-HWMP) in terms of packet delivery ratio (and its 95% confidence interval) for different grid sizes (from 9-36 nodes). The PDR defines the relationship between the number of successfully received packets and the total number of transmitted packets. Figure 7 shows the results for the four traffic types considered (Figure 7a-d, respectively). Besides, for every traffic type, the graph on the left side shows the results under the load conditions NL1, while the one on the right shows them for NL2. The results confirmed that, as the size of the network increased, the PDR decreased for the four traffic types and for the two network load conditions considered. As can be seen, under the NL1 conditions, the network started to be very loaded for a number of nodes greater than 16, while the NL2 conditions led to a total saturation and a PDR value equal to zero when the basic HWMP was used. However, the PDR decrement was much lower with MCP-HWMP. In addition, this figure highlights that, when using MPC-HWMP, the traffic with higher priorities, which uses the best available paths, received a better service from the network than that with lower priorities.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 7 Conclusions  
The primary objective of this project was to develop a user-friendly mobile augmented reality application to visualise buildings in the city of Coimbra affected by flooding due to the water level of the Mondego river. A second objective was to predict the water level based on precipitation. The unavailability of accessible data from government agencies led to the use of open-access datasets, which are less precise than direct measurements from the Mondego river, but still allowed us to create realistic models.  
The methodology followed consisted of analysing the correlation between meteorological variables and water levels, employing a regression model for its efficacy with datasets obtained from the Historical Weather API and the Copernicus Land Monitoring Service portfolio. Optimisation of the 3D real-sized model was used to reduce memory and CPU usage, and the final mobile application provided users with a reliable approximation of impacted buildings by colour-coding them and displaying the user's real-time position within the 3D model using a GPS indicator in an AR environment facilitated by Vuforia.  
This work shows that MAR is a solution that adds value to develop visualisation applications for FRM by using low-price-entry devices, existing data, software libraries and plugins. In future work, the area under analysis may be expanded and other prediction models can be explored. Real-time weather information could also be incorporated in order to add to the system the ability to predict the probability of flood in near real-time.  
Conceptualisation, J.A., R.G. and M.M.; methodology, J.A., M.M. and R.G.; software, M.L.; validation, J.A., M.M. and R.G.; resources, J.A.; writing--original draft preparation, M.L.; writing--review and editing, J.A., R.G. and M.M.; supervision, J.A. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Using Coherent Hemodynamic Spectroscopy Model to Investigate Cardiac Arrest
## 1 Introduction  
The Coherent Hemodynamic Spectroscopy (CHS) model, first introduced by [PERSON] et al. (2014), provides a framework for interpreting changes in cerebral hemodynamics and metabolism based on time-varying hemoglobin concentrations and oxygen saturation [1]. Initially developed for small perturbations in physiological parameters, the linear CHS model is limited to scenarios with 10-20% changes in cerebral blood flow (\(CBF\)) and other physiological parameters. These assumptions are appropriate for controlled conditions,such as neuroimaging studies involving mild physiological changes. However, larger perturbations, such as those observed during cardiac arrest (CA) and cardiopulmonary resuscitation (CPR), fall outside the linear model range of validity.  
To address this limitation, [PERSON] et al. (2016) introduced a non-linear extension to the CHS model [2], capable of handling large perturbations by solving partial differential equations for capillary and venous oxygen saturation without approximation. This non-linear CHS model enables accurate characterization of physiological changes under extreme conditions, where deviations from baseline exceed 100%. Such advancements are particularly relevant in the study of cardiac arrest, a condition marked by complete cessation of cerebral blood flow and oxygen delivery, followed by rapid resuscitation efforts during CPR.  
Although the CHS model can be applied to any tissue with blood circulation, our work so far has been focused on cerebral hemodynamics during cardiac arrest and CPR. In our previous study [3], we applied the non-linear CHS model to analyze cerebral microvascular and metabolic responses during CA and CPR in 27 pigs. In addition, in [4], we employed our algorithm to analyze the relationship between the cerebral metabolic rate of oxygen and redox cytochrome C oxidase during cardiac arrest and cardiopulmonary resuscitation using the data from 10 pigs. This work demonstrated the model's ability to reconstruct time traces of oxyhemoglobin [\(Hb\)O\({}_{2}\)] and deoxyhemoglobin [\(HHb\)] concentrations, providing valuable insights into the dynamics of cerebral oxygenation. However, while the study highlighted the utility of the non-linear CHS model, it did not delve into the algorithmic details of its implementation, leaving a critical gap for researchers aiming to replicate or extend this approach. This paper addresses this gap by presenting a detailed description of the non-linear CHS algorithm, with a focus on its application to large perturbations during CA and CPR. Specifically, we describe the algorithm workflow, including the solution of the partial differential equations for the capillary and venule oxygen saturations, the use of non-linear multivariate optimization for inverse parameter fitting, and the recovery of hemodynamic and metabolic parameters. By highlighting the computational aspects of the non-linear CHS model, this study bridges the gap between the theoretical modeling and practical application, laying the groundwork for future research and clinical innovation.
## 3 Macro-Element Application
### Linear Range
#### 3.1.1 Elasticity Problems  
The conducted test addresses a squared plate subjected to different loading cases and with two possible boundary conditions. The squared plate benchmark has a side length, L, and belongs to the set of patch tests proposed by [PERSON] and [PERSON] [99]. Aiming to especially serve as a reference test for plate bending elements, a thickness equal to \(L/100\) is assumed, that approximates well thin-plate solutions. Two squared plates, one with pinned (SSSS) and the other with clamped supports (CCCC) at its edges, are subjected to a point and a surface (normal) load.  
The convergence of the discrete macro-model has been assessed by comparing the maximum elastic displacement, \(w\), obtained with the exact solution, \(w_{exact}\), for different mesh refinement levels, N. The maximum deflection obtained at the centre of the plate is normalised with the exact theoretical solution and presented in Figure 5. Although the discrete element behaves better in the presence of simply supported edges, which is to be expected given the lower gradients of curvature, the load type seems to have a further extent on the solution accuracy. The model can better reproduce the point load behaviour due to its localised effect, which goes in favour with the data from the Rigid-Body-Spring model (RBSM) developed by [PERSON] [79]. Globally, a refinement of \(N=15\) (\(H=L/15\)) is a proper choice allowing estimations within 10% of a maximum deflection error. Hence, a higher refinement is recommended for clamped edges (\(N=L/16\)) than simply supported edges (\(N=L/12\)).  
The plate benchmark test is inexpensive to carry out and a time-cost analysis has been disregarded. The value of the errors found are admissible and in agreement with typical simplified mixed-FE strategies, as the RBSM by [PERSON] [79] and the Hermann-Hellan constant-stress-triangular elements [101]. Besides the simplification adopted for the decoupled flexural-torsional behaviour, it may be emphasised that the deviations found, especially when clamped supported edges exist, stem from the macro-element assumptions. Quadrilateral elements are adopted which restrains the model adaptability and deformability near cornered regions. Therefore, a mesh bias occurs near the supports that explains the stiffer responses found for the clamped edges cases, i.e., no deformation exists within a distance from the border given by the mesh size H. This also supports the fact that the response is different than classical continuous FE-based models since it gives lower bound estimations of the displacement (for different mesh refinements).
## 3 FRA Open Circuit Measurement
### Measured and Simulated FRA Signature for Normal vs. Coking Taps  
Figure 8 shows the measured FRA signature for 50% and 100% tap contacts coking along with the normal condition FRA signature. It can be seen that there was a dramatic reduction in the FRA signature due to the tap changer coking contact at the low-frequency region, 20 Hz to 2 kHz. This effect was attributed to the increase in the tap resistance due to coking, as explained above. This effect could be simulated by changing the equivalent circuit parameters, as shown in Table 3. The parameters were obtained through finite element analysis simulation to the investigated transformer using its physical dimensions, insulation properties, and winding topology as detailed in [23; 24; 25]. The measured and simulated FRA signatures are plotted as shown in Figures 8 and 9, which revealed a similar trend for both. Thus, the RLC model could better understand the effect of tap changer faults on the FRA signature. It can be seen that, while the series resistance was significantly increasing due to coking, the capacitive and inductive components were almost constant, as shown in Table 3. This explains the signature magnitude changes, particularly in the low-frequency range without an observable change in the resonance frequencies. A slight variation can be observed by comparing coking at different levels of degradation severity (50% and 100% coking) (Figure 8). This variation at the low-frequency range was due to increased resistance of the contacts with more deposited oil film layers. However, at the high frequencies range, the winding structure influenced the frequency response and made a rising trend in the signature due to the winding structure's high capacitance.