esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Reduction of Energy Losses for 24 h
#### 4.2.3 Comparative Analysis for Reducing Energy Losses with GAMS  
To validate the reduction of energy losses proposed in scenario (3) and the proposed algorithm, a comparative analysis using the GAMS software and its BONMIN solver was carried out to solve the exact mathematical model given by Equations (1)-(6) [37]. According to the above, for the systems of 33 and 69 nodes, Tables 8 and 9 present the solution vectors of the PV systems results obtained for scenario (3) with the GAMS-BONMIN method and the DCVSA method proposed in this article.  
\begin{table}
\begin{tabular}{c c} \hline
**Scenario** & \(\mathbf{E_{\text{loss}}}\) **(kWh/day)** \\ \hline (1) & 2664.7952 \\ (2) & 1202.1918 \\ (3) & 2014.9508 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Power losses for 69-node system scenarios.  
Figure 5: The behavior of power losses for each hour of the study period in the 69-node system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Nodes Localization** & **Sizing (MW)** & \(\mathbf{E_{\text{loss}}}\) **(kWh/day)** \\ \hline GAMS-BONMIN & \{08, 24, 25\} & \{1.9082, 0.8805, 0.4963\} & 2034.9850 \\ \hline DCVSA & \{14, 24, 30\} & \{1.1332, 1.5824, 1.5531\} & 1922.5098 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of energy losses for the 33-node system.  
From Table 8, it is evident that, for the 33-node system, the GAMS-BONMIN method presents a percentage of reduction of energy losses of 18.8808% concerning scenario (1), which shows it is inferior to the proposed method. The DCVSA represents an efficiency increase of 4.4835% since the solution vector considers \(\{14,\ 24,\ 30\}\) as the nodes to install the PV systems and \(\{1.1332,\ 1.5824,\ 1.5531\}\) MW as their installed capacity.  
On the other hand, in Table 9, the comparison of the two methods can be observed again, but this time for the system of 69 nodes, where the GAMS-BONMIN method with the proposed solution vector achieves a reduction in energy losses of 23.0151%. For this system, DCVSA obtains a reduction in energy losses 1.3712% greater than the GAMS-BONMIN method. The difference in the decrease of energy losses for the 69-node system is not that wide since the location of the PV systems is very similar; only node 5 for GAMS-BONMIN changes to node 17 for DCVSA, and the difference in installed power is 0.286 MW higher for the DCVSA method.
Remote Sens._**2014**, \(6\), 9145-9169; doi:10.3390/rs6109145  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Discussion_  
Earth Resources Observation and Science (EROS) Center, U.S. Geological Survey, Sioux Falls, SD 57198, USA; E-Mail: [EMAIL_ADDRESS]  
ASRC Federal InuTeq, Sioux Falls, SD 57198, USA; E-Mails: [EMAIL_ADDRESS] (M.R.); [EMAIL_ADDRESS] (J.L.)  
Canada Centre for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester Street, Ottawa, ON, K1S 4M2, Canada; E-Mails: [EMAIL_ADDRESS] (B.B.); [EMAIL_ADDRESS] (K.M.)  
Author to whom correspondence should be addressed; E-Mail: [PERSON]L_ADDRESS]; Tel.: +1-605-594-6078; Fax: +1-605-594-6529.  
External editor: [PERSON]  
_Received: 31 March 2014; in revised form: 19 September 2014 / Accepted: 19 September 2014 / Published: 26 September 2014_  
A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000-2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30-40 years following fire, and deciduous stands generally have higher EPA than confierous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.  
keywords: boreal; climate change; fire; succession; forest composition; future +
Footnote †: journal: Journal of the American Statistical Association
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 3 Dimensionality Reduction (PCA/NLPCA)  
Following the preprocessing and optional decimation, the data underwent dimensionality reduction using either Principal Component Analysis (PCA) or Nonlinear PCA (NLPCA). These techniques were used to reduce the feature space while retaining the most significant information, which improved the generalization and reduced the overfitting in the machine learning models.
## 5 Ride Performance Analysis
### Result Analysis
#### 5.2.2 Effect of the Damping Pipe Length  
The EF, BA and AA value when traversing the trapezoidal barrier and step barrier with different damping pipe length are presented in Figure 14 in which the pipe diameter is 6 mm and the pneumatic chamber length is 85 mm. The length of the damping pipe will not exceed 2 m due to the limitations of the interior space. As seen in Figure 14a, the increase of pipe length causes slight growth in EF value and weaken ride performance. Nevertheless, the change in EF value is less than 1 in both road conditions, indicating the length of the damping pipe has a limited influence on vehicle ride performance.  
The bounce acceleration and angular acceleration in Figure 14b,c also show that the acceleration of the vehicle body does not change much when the pipe length is 0.1 m, 1 m, and 2 m in both road conditions. Actually, the length of pipe between the cylinders is less than 1 m, so it is not the main factor affecting the ride performance.
# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
###### Abstract  
Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.  
spectral traits (ST); spectral trait variations (STV); in-situ; remote sensing (RS) approaches; plant phenomics facilities; wireless sensor networks (WSN); RADAR; optical; LiDAR; RS models 2017
## 2 Methodology of SSI-COV
### System Model  
The stochastic state-space system model is characterized by being rational, discrete, linear, and time-invariant [30], and is denoted as follows:  
\[x_{k+1}=Ax_{k}+Bu_{k}+w_{k} \tag{1}\]  
\[y_{k}=Cx_{k}+Du_{k}+e_{k} \tag{2}\]  
where \(x_{k}\in\mathbb{R}^{n\times 1}\) is the discrete state vector of the system, \(u_{k}\) is the input vector, and \(y_{k}\in\mathbb{R}^{m\times 1}\) is the output vector of the system, both sampled in time. \(A\) and \(B\in\mathbb{R}^{n\times n}\) are discrete state matrices, and \(C\) and \(D\in\mathbb{R}^{m\times n}\) are output matrices. \(w_{k}\) represents the noise due to system inaccuracies, and \(e_{k}\) represents the noise resulting from measurement inaccuracies in the sensors used.  
If the measurement is performed with known test forces, \(u_{k}\) and \(e_{k}\) would have values close to zero; in other words, they would disappear from Equations (1) and (2). However, \(u_{k}\) and \(e_{k}\) are not known yet necessary vectors; therefore, their small but influential values in the system are assumed to be white noise, and the equations are modified as follows:  
\[x_{k+1}=Ax_{k}+w_{k} \tag{3}\]  
\[y_{k}=Cx_{k}+\theta_{k} \tag{4}\]  
where \(w_{k}=Bu_{k}\) and \(\theta_{k}=Du_{k}+e_{k}\), representing process noise and measurement noise, respectively. The covariance matrix of \(w_{k}\) and \(\theta_{k}\) is  
\[E\Bigg{\{}\begin{pmatrix}\omega_{k}\\ \theta_{k}\end{pmatrix}\quad\begin{pmatrix}\omega_{s}^{\text{T}}&\theta_{s}^{ \text{T}}\end{pmatrix}\Bigg{\}}=\begin{bmatrix}Q&S\\ S^{\text{T}}&R\end{bmatrix}\delta_{ks} \tag{5}\]  
where \(E\) is the expected value operator and \(\delta_{ks}\) is the Kronecker delta function, indicating that \(\omega_{k}\) and \(\theta_{k}\) are uncorrelated for \(k\
eq s\), modeling white noise.  
In this matrix, \(Q\in\mathbb{R}^{n\times n}\) is a non-negative matrix that describes the intensity of process noise, \(R\in\mathbb{R}^{n\times n}\) is a positive matrix representing measurement noise, and \(S\in\mathbb{R}^{n\times n}\) describes the cross-correlation between the process noise and measurement noise. These properties ensure the stability and consistency of the model, allowing the separation of noise contributions from the system's dynamic modes.  
The use of the covariance matrix is critical for capturing the stochastic dynamics of the system, as it distinguishes noise components from structural responses, even under operational conditions where the excitation forces are unknown. This is especially important for applications such as an operational modal analysis, where the objective is to identify the system's dynamic modes in the presence of noise.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### New Program Structure Based on Information Transmission  
To enhance the performance of GP programs in classification tasks, this study proposes a program structure based on information transmission. This structure incorporates \(11\) distinct functional layers, of which \(7\) are designated as fixed and \(4\) as flexible. The fixed layers ensure the consistent inclusion of predefined functions within these layers in the GP program. In contrast, the flexible layers allow for the functions from these layers to appear in the generated GP program zero times, once, or multiple times.  
By introducing the concept of information transmission, the GP program selects specific functional layers and selectively passes the information obtained from these layers to multiple subsequent layers.  
Figure 2 displays this program structure and provides an example of a potential GP program. In this figure, layers connected by solid lines signify that information transmission necessarily occurs at these layers. Conversely, layers connected by dashed lines indicate that information transmission happens only in specific scenarios. Each node in the GP program example represents a function included in various layers.  
The image preprocessing layer preliminarily processes the raw image data, including noise reduction and smoothing. The region extraction layer extracts key information-containing image regions from the original or filtered image data. The image fusion layer performs pixel-level fusion of different image regions to obtain a new region rich in discriminative content.  
The dimension reduction layer performs downsampling operations on regions to reduce the dimensionality of features and obtain new images after downsampling.  
Notably, based on the concept of information transmission, this layer preserves the original image region and passes it to subsequent layers as needed.  
The feature extraction layer extracts features from the image region. The feature construction layer constructs features based on multiple types of features. The transfer layer decides whether to pass the effective information generated by the feature construction layer to subsequent layers, based on the type of input. The feature concatenation layer combines the input data into a feature vector. The classification layer utilizes a fixed classification algorithm to conduct predictive classification based on the input features, ultimately outputting the predicted class labels.  
Figure 2: The proposed ITACIE-GP program structure (IPF: Image Preprocessing Layer Function; REF: Region Extraction Layer Function; IFF: Image Fusion Layer Function; DF: Downsampling Layer Function; FEF: Feature Extraction Layer Function; FCF: Feature Construction Layer Function; TF: Transmission Layer Function; FCF: Feature Concatenation Layer Function).
## 3 Diagnosis Using Simulation Data
### Envelope Analysis (Lines 50-64)  
After two pre-processing techniques--discrete signal separation and band-pass filtering--one obtains the AM signal that includes only components of resonance (carrier) and fault (modulation) frequency. Then, the envelope analysis is used as the last step for the diagnosis of bearing faults, which consists of three sub-steps: First, Hilbert transform is applied to the AM signal to obtain the analytic signal [14; 15; 17] (line 51). Next, its magnitude is calculated to obtain the envelope signal, which is the absolute value of the analytic signal (line 52). It is further translated to have zero mean by removing the DC offset (line 53). Last, FFT is taken to the envelope signal to retrieve the fault information from the amplitudes at the fault frequencies (lines 54-55).  
The Hilbert transform is referred to as a \(90^{\circ}\) phase shifter and results in the analytic signals, expressed as follows [18]:  
\[a_{x}(t)\ =\ x(t)+j\hat{x}(t)\ =\ \sqrt{x^{2}(t)+\hat{x}^{2}(t)}e^{j\phi_{x}(t)}\ =\ A_{x}(t)e^{j\phi_{x}(t)} \tag{14}\]  
where \(a_{x}\) indicates the analytic signal, in which the real part \(x(t)\) is the original AM signal, imaginary part \(\hat{x}(t)\) is its Hilbert transform, \(\phi_{x}(t)\) is the instantaneous phase, and \(A_{x}\) is the instantaneous amplitude, i.e., the envelope signal. The resulting envelope signal with the DC offset is shown in Figure 10(a). The envelope spectrum after taking FFT is given in Figure 10(b), in which the fault is identified by examining the amplitude at the given fault frequencies (lines 56-64). In the figure, the peaks near the fault frequency 10 Hz and its harmonics are apparent, which indicates that the fault has occurred indeed. Compare the Figure 10(a),b with the original ones in Figure 3(a),d to find out that result of the  
Figure 10: Demodulation band selection: (**a**) spectral kurtosis for different window lengths, (**b**) frequency band selection input in MATLAB, and (**c**) vibration signal after band-pass filtering.  
envelope spectrum is different, which we call smearing. The reason is because the sampling rate is low and the length of residual signal is changed from the original one after the AR filtering. Though not addressed here, it is left to the reader to implement and figure out what the result will be when the fault is small or nonexistent by assigning smaller value to \(A\). For comparison, envelop analysis results of healthy bearing are also given in Figure 11c,d, which can be easily made by assigning A = 0 in the MATLAB code (_Bearing fault simulation_). Comparing Figure 11a,b with the fault, the normal bearing does not show periodic impulse representing the fault in the time domain and the peak at the fault frequency in the frequency domain either. Furthermore, the kurtosis value is about 3 in the normal bearing, which is much smaller than that of fault and the typical of the Gaussian distribution.
## 3 Results
### Sinusoidal Fitting for Comparison between Heading and Pointing Tasks
#### 3.5.2 Head versus Trunk  
The amplitude of sinusoidal waves describing the orientation of the head with respect to the trunk varied significantly with movement task (\(t\)-test, \(p<0.001\)) and was larger for heading than pointing. For heading, no significant differences were found between the amplitude measured in space or with respect to the trunk. In contrast, for pointing, the amplitude of the periodic modulation of the head rotation was significantly smaller when head posture was measured by reference to the trunk (AP and LA, Student \(t\)-test; \(p<0.001\)). The offset was different for the heading and pointing tasks (\(p<0.001\) for AP and RO). For heading, there was a consistent bias in leftward axial rotation (\(4.11\pm 1.46^{\circ}\)). For pointing, there was a consistent anterior bias (\(-4.36\pm 0.62^{\circ}\)) associated with a consistent rightward rotation (\(-5.75\pm 0.43^{\circ}\)). We found significant differences between offset computed in space or head vs. trunk (for heading: AP and RO, \(p<0.001\); LA, \(p<0.05\); and pointing, AP, LA and RO \(p<0.001\)). In brief, the analysis of the offset suggests that the involvement of the trunk in pointing induced complex bias on the orientation of the head relative to the trunk: it exaggerated the bias in anterior tilt and right axial rotation and reduced that in lateral tilt. The period and phase were similar to those computed for the posture of the head in space, except for a shorter period for RO during pointing (\(154^{\circ}\)).
## 3 Results
### Comparison of Satellite and In Situ AOT
The spectral variability of the sea brightness coefficient showed identical spectral behavior for the cases of both the western and eastern parts of the Black Sea, with a clear underestimation of the Rrs(\(\lambda\)) values in the shortwave region. Another day on which dust transport from the Sahara occurred in the Black Sea region, namely 27 September 2020, was analyzed in a similar way. The return trajectories of AERONET also confirmed the presence of dust transport (Figure 5).
## 2 Materials and Methods
### Ecosystem Service Assessment Methods
#### 2.3.3 Carbon Storage  
This study utilizes the carbon sequestration services of carbon stocks in Liaoning Province for the year 2020. Carbon stocks are represented by four fundamental carbon pools. The InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model is instrumental in assessing the energetic and economic value of ecosystem services across terrestrial, freshwater, and marine ecosystems. It quantifies carbon storage based on four critical carbon reservoirs: aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter. These reservoirs are fundamental to ecosystem carbon cycling and play a significant role in global climate dynamics. Carbon density classification is predicated on field measurements, scholarly literature, or pre-existing datasets, with a preference for locally sourced data to enhance accuracy. Adjustments for temperature and precipitation are incorporated to account for environmental variability. The model estimates current carbon stocks by integrating land use and land cover data with carbon value tables for the four carbon pools. It categorizes carbon storage into aboveground biomass, belowground biomass, soil carbon, and dead organic carbon, evaluating their respective contributions to ecosystem services. By integrating land use data with corresponding carbon density classifications, we calculated the carbon stocks for these four pools and subsequently determined the overall carbon stock for Liaoning Province. The carbon density data for various land types were adjusted based on existing research in the study area and the specific conditions of the region. The formula for calculating carbon stocks is as follows:  
\[C_{t}=C_{a}+C_{b}+C_{s}+C_{d} \tag{4}\]  
In the equation: \(C_{t}\) represents the total carbon stock; \(C_{a}\) denotes the aboveground biomass carbon pool; \(C_{b}\) indicates the belowground biomass carbon pool; \(C_{s}\) refers to the soil carbon pool; and \(C_{d}\) stands for the dead organic matter carbon pool [44].
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Ip Satellite Network Scenario
#### 5.4.1 Experimental Scenario
First, LEO1 continuously sends UDP packets to GS at a rate of 100 Mbps during this period. Figures 17, 18a and 19, respectively, give the receive throughput at GS, end-to-end link delay, and connection variation between LEO1 and GS in different routing strategies which show backup routing has a better link connectivity. When the transmission path has no burst interruption, the performance of OSPF and backup routing has no significant difference. When the link is interrupted, OSPF triggers the process of discovering the interruption and routing reconvergence as presented in Figure 18a. While the backup routing directly switches to the sub-optimal path, it switches back to the original optimal path when the interrupted link is restored. Figure 19: Receive throughput at GS. Figure 17: Topology of the IP routing scenario. Then, the effect of bandwidth on the routing convergence time, which could be reflected by packet loss ratio, is taken into consideration. We configure the maximum bandwidth to 10 Mbps, 100 Mbps, and 1000 Mbps, respectively, while other link relationships and parameters remain unchanged. The end-to-end packet loss ratio in different maximum bandwidth is shown in Figure 20. The bandwidth variation has little effect on backup routing. However, it has a great impact on OSPF, and the impact increases with the decrease in bandwidth. Because OSPF monitors the link status through the Hello packet, less bandwidth increases the transmission time of the Hello packet. Backup routing selects the sub-optimal path directly, thus avoiding this problem.
# Numerical Study on Track-Bridge Interaction of Integral Railway Rigid-Frame Bridge
## 2 Track-Bridge Interaction of Integral Rigid-Frame Bridge  
### Basic Principle  
The mechanism of track-bridge interaction between rails and an integral rigid-frame bridge is similar to simply supported bridges. Taking the temperature rising condition as an example, the basic principle of track-bridge interaction is shown in Figure 1. The point x = 0 is the fixed point where the displacement of the bridge under temperature rising is 0, depending on the stiffness of piers.  
Under the load of temperature rising, the beam ends tend to extend and produce longitudinal displacements, causing the rails to move towards the beam ends. Due to the resistance of fasteners, sleepers, the track bed and other constraints, the rails' displacement is restricted. Therefore, the rail and the bridge are subjected to additional longitudinal forces and the magnitude and direction of the additional forces acting on the bridge and the rail are determined by the relative displacement.