esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Frequencies and Mode Shapes of the Double-Beam System  
### Mathematical Formulation  
The double-beam system considered in this study is consisted of a primary beam (\(w_{1}\)) and an equal-length secondary beam (\(w_{2}\)) as shown in Figure 1. Both beams are taken to be Euler-Bernoulli beams. The two beams are interconnected with uniformly distributed springs of stiffness \(k_{\mathrm{e}}\). The boundary conditions for the primary beam are the same as for the secondary beam (symmetric boundary conditions). Five general kinds of symmetric boundary conditions are considered in this section: fixed-fixed, pinned-pinned, fixed-pinned, pinned-fixed and free-free. The equations of motion of the secondary beam and the primary beam can be expressed as:  
\[E_{2}I_{2}w_{2}^{\prime\prime\prime\prime}+\rho_{2}A_{2}\ddot{w}_{2}-k_{ \mathrm{e}}(w_{1}-w_{2})=0 \tag{1}\]  
\[s_{\mathrm{k}}E_{2}I_{2}w_{1}^{\prime\prime\prime}+s_{\mathrm{m}}\rho_{2}A_{2} \ddot{w}_{1}+k_{\mathrm{e}}(w_{1}-w_{2})=0 \tag{2}\]  
where \(s_{\mathrm{k}}=\frac{E_{1}I_{1}}{E_{2}I_{2}}\) and \(s_{\mathrm{m}}=\frac{\rho_{1}A_{1}}{\rho_{2}A_{2}}\) are the beam stiffness ratio and mass ratio of the primary beam to the secondary beam, respectively. Using the Bernoulli-Fourier method [41], the displacement of the two beams can be expressed as  
\[\left\{ \begin{array}{rl}w_{2}(x,t)=\varphi_{2}(x)e^{i\omega t}\\ w_{1}(x,t)=\varphi_{1}(x)e^{i\omega t}\end{array} \right. \tag{3}\]  
Figure 1: Double-beam system with symmetric boundary conditions. where \(\varphi_{1}\) and \(\varphi_{2}\) are mode shapes of the primary beam and the secondary beam, respectively and \(\omega\) is the natural frequency of the double-beam system. Substituting Equation (3) into Equation (1), one can obtain the relationship between \(\varphi_{1}\) and \(\varphi_{2}\) as  
\[\varphi_{1}=\frac{E_{2}I_{2}}{k_{\mathrm{e}}}\varphi_{2}^{\prime\prime\prime \prime}+\left(1-\frac{\omega^{2}\rho_{2}A_{2}}{k_{\mathrm{e}}}\right)\varphi_ {2} \tag{4}\]  
Substituting Equation (4) into Equation (2) and removing the term \(e^{i\omega t}\) result in  
\[a\varphi_{2}^{\mathrm{VIII}}+b\varphi_{2}^{\mathrm{IV}}+c\varphi_{2}=0 \tag{5}\]  
where  
\[ \begin{array}{c}a=\frac{S_{\mathrm{b}}(E_{2}I_{2})^{2}}{k_{\mathrm{e}}}\\ b=\left(s_{\mathrm{k}}E_{2}I_{2}-\frac{s_{\mathrm{k}}E_{2}I_{2}\omega^{2}\rho_ {2}A_{2}}{k_{\mathrm{e}}}\right)+\left(k_{\mathrm{e}}-\omega^{2}S_{\mathrm{m }}\rho_{2}A_{2}\right)\frac{E_{2}I_{2}}{k_{\mathrm{e}}}\\ c=\left(-\omega^{2}\rho_{2}A_{2}+\frac{s_{\mathrm{m}}\left(\omega^{2}\rho_{2} A_{2}\right)^{2}}{k_{\mathrm{e}}}-\omega^{2}s_{\mathrm{m}}\rho_{2}A_{2}\right) \end{array}  \tag{6}\]  
The general solution of Equation (5) is  
\[\varphi_{2}(x)=C_{1}e^{r_{1}x}+C_{2}e^{r_{2}x}+C_{3}e^{r_{3}x}+C_{4}e^{r_{4}x} +C_{5}e^{r_{3}x}+C_{6}e^{r_{6}ex}+C_{7}e^{r_{7}x}+C_{8}e^{r_{8}x} \tag{7}\]  
where \(C_{i}\) (\(i=1,\ 2,\ \ldots 8\)) are unknown coefficients and \(r_{i}\) (\(i=1,\ 2,\ \ldots 8\)) are the roots of the characteristic equation of Equation (5)  
\[ar^{8}+br^{4}+c=0 \tag{8}\]  
Substituting Equation (6) into Equation (8) and taking \(\omega\) as the unknown result in  
\[
# Fault Coupling Analysis and Reliability Assessment of Actuation System Based on Bond Graph Model
###### Abstract  
In this paper, a new degradation model of the more electrical aircraft (MEA) actuation system is proposed. The bond graph model is used to build the dissimilar redundancy actuation system model, taking into account the degradation behavior (wear, fatigue, aging) of components in the system operation and the fault coupling phenomenon caused by the accumulation of dissipated energy in space and time, and a fault coupling degradation model of the actuation system is established with clear mechanism support. Through the fault mechanism analysis, various fault types are injected into the bond graph model of the actuation system, and the simulation results are compared. The influence of fault propagation on system output is analyzed, the system's reliability is assessed, and the performance evaluation of the actuation system is realized while taking the fault coupling mechanism into account.  
fault analysis; dissimilar redundancy actuation system; bond graph model; fault coupling; physics of failure model; reliability assessment +
Footnote †: journal: Applied sciences
## 5 Experimental Verification
### Controllable Mode: AVC at (1,4) Modal Frequency  
The AVC results in the (1,4) mode are shown in Figure 11. When AVC is carried out, the voltage level captured by the PZT sensor is attenuated by 16 dB. Meanwhile, the acceleration level at the same location is reduced by 15 dB. Thus, the active vibration attenuation at the position of the PZT sensor is achieved.  
Furthermore, the global vibration response of the plate is captured and shown in Figure 12. The ratio of the closed-loop energy to the open-loop energy is calculated and shown in Equation (20). The overall vibration response of the plate is obviously decreased because the ratio is smaller than 1. Therefore, the (1,4) mode is controllable for AVC using PZT actuators and sensors. Similarly, the results are consistent with corresponding simulation results.  
\[\gamma=\frac{\sum 10^{0.1L_{q;\epsilon}}}{\sum 10^{0.1L_{q;\epsilon}}}=\frac{2.4 }{17.4}=0.1 \tag{20}\]  
Figure 11: Control effect in the (1,4) mode. (**a**) Description of the sensing voltage of PZT; (**b**) description of the monitoring acceleration.  
Figure 10: Vibration response of the smart plate in the (3,3) mode. (**a**) Description of the uncontrolled acceleration level; (**b**) description of the controlled acceleration level.  
The experimental results show that AVC is achieved when the specified controllable mode is excited, and fails in the predicated uncontrollable mode.
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 4 Experimental Results
### The Breast Cancer Wisconsin Dataset  
Figures 3 and 4 show the AUC rates and the type I errors of different approaches, respectively. Different from the previous results, the approach that performed the best for the AUC was SMOTE (i.e., 0.962), whereas the second one was the baseline (i.e., 0.960). On the other hand, the approach that performed the best for the type I error is SMOTE+IG (i.e., 0.032), whereas the second-best ones are the baseline and SMOTE (i.e., 0.037). The other approaches producing similar AUC results were IG (i.e., 0.959), IG+SMOTE (i.e., 0.957) and SMOTE+IG (i.e., 0.955), whereas IG+SMOTE and IG produced similar type I errors, which were0.038 and 0.044. These approaches do not have a significant level of performance difference. In particular, for the feature reduction result, using IG and GA produce 8 and 1 selected features, respectively.  
The experimental results based on two different breast cancer datasets indicate that when the collected dataset is highly class imbalanced and contains a certain number of features, it is better to consider the combination of feature selection and over-sampling. Particularly, performing feature selection first and over-sampling second is likely to cause the classifier to provide higher accuracy than performing over-sampling first and feature selection second.  
On the other hand, if the imbalance ratio of the collected dataset is not very high and it does not contain a large number of features, there is no need to consider the combination of feature selection and over-sampling. On the contrary, performing over-sampling to re-balance the dataset is enough to allow the classifier to provide relatively good performance.  
Figure 2: The type I errors of different approaches over the KDD Cup 2008 breast cancer dataset.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 6 Performance Evaluation
### Resilience to Report Disruption Attacks  
The authors compare the performance of PDF, DSEDA, and PKAEF in terms of resilience to report disruption attacks. During each experiment, \(cn\) cluster nodes in the two event areas were compromised randomly. When a compromised cluster node was selected as a participating node, it would launch a report disruption attack; otherwise it would not launch a report disruption attack. Concerning PDF and PKAEF, a compromised non-CH node participating in report generation could refuse to provide \(k_{j}P\) or a partial signature or provide an incorrect partial signature. Considering DSEDA, a compromised non-CH node participating in report generation could contribute a secret share with an incorrect MAC or refuse to participate in report generation (DSEDA is denoted as DSEDA_MAC in this case), or it could forge a false secret share \(S_{u}\) and generate a correct MAC for it (DSEDA is denoted as DSEDA_Su in this case). When a CH node was compromised in PDF, DSEDA, and PKAEF, it could refuse to send out the signed report or send out a report with an incorrect signature. The authors evaluate the resilience to report disruption attacks according to the proportion of correct signed reports sent out by the CH.  
Figure 5a,b shows the results of the proportion of correct signed reports when \(cn\) varied from 1 to 20 in Scenario 1 and Scenario 2, respectively. It can be observed that, when arranged according to the proportion of correct signed reports, the sequence is PKAEF, DSEDA_MAC, PDF, and DSEDA_Su from the highest to the lowest in order. PKAEF has the highest proportion of correct signed reports, which benefits from the verification of partial signatures, the detection of malicious behavior, and the substitution mechanism. DSEDA_Su has the lowest proportion of correct signed reports because it does not provide any method to detect false \(S_{u}\). Furthermore, in both scenarios, all the proportions of correct signed reports gradually decrease to 0% with the increase of \(cn\). This is because, in DSEDA, if the number of compromised cluster nodes in an event area is greater than \(Nc-T\), then there are not enough normal cluster nodes to generate correct signed reports; while in PDF and PKAEF, if the number of compromised cluster nodes in an event area is greater than \(Nc-t\), then there are not enough normal cluster nodes to generate correct signed reports.  
Figure 4: Filtering energy expenditure as a function of the number of compromised cluster nodes. (a) in Scenario 1; (b) in Scenario 2.
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 4 Overview of Companding  
Companding is a technique that is commonly used to implement nonuniform quantization in speech communications to achieve high signal-noise-ratio (SNR). The technique is based on compression (\(C\)) and expansion (\(E\)) function [32]. Commonly, the technique first compresses a signal and then expands it [4]. Here, compression does not mean to say data compression. Instead, compression here represents the change in a dynamic range of the original signal to a narrower range and after the expansion of the compressed signal; the expanded signal would be close to the original signal. Now, for a signal \(x\), \(C\) and \(E\) correlate as follows:  
\[E(C(x))=x \tag{3}\]  
If Equation (3) is satisfied, this technique can be effectively used in reversible data hiding [4]. However, in the case of the digital signal, \(C_{Q}\) and \(E_{Q}\), respectively represent the quantized versions of \(C\) and \(E\), where \(Q\) denotes quantization function [14]. The quantization function (\(C_{Q}\)) is represented as:  
\[X_{Q}=C_{Q}(X)\]  
Figure 3: (a) The lifting based DWT structure; (b) 2-level lifting based DWT (haar) of the 8-bit Lena image. Source ([PERSON] and [PERSON] [30]).  
\[C_{Q}(x)=sign(x)\times\left(\frac{|x|}{2}\right) \tag{5}\]  
where sign(\(\cdot\)) is the sign function. The expansion function (\(E_{Q}\)) is represented as:  
\[E_{Q}(x)=sign(x)\times 2x \tag{6}\]  
The value of companding error (\(r\)) is calculated as [14]:  
\[r=|x|-\left|E_{Q}\left(C_{Q}(X)\right)\right| \tag{7}\]  
Though there is a companding error (\(r\)) in the digital case, the present scheme neither does nor requires the information '\(r\)', to loss lessly restore the image, unlike other techniques found in the literature. So in the present scheme, the effective capacity is equal to the size of the payload. It is also to be noted here that due to integer coefficients of lifting based DWT, companding technique would return low loss in watermarked image fidelity (due to floor function used in Equation (5)) than the traditional DWT that returns fractional value of coefficients after transformation.  
Assume we have value \(x=250\), we would like to embed one bit \(W=1\). First, the value '\(x\)' is divided by two based on Equation (5.) i.e., \(C_{Q}(x)=\text{sign}(x)\times\left(\frac{|250|}{2}\right)=125\). Next, we represent the quantized value \(C_{Q}(x)\) into its binary representation i.e., \(C_{Q}(x)=125=\left(0111101\right)_{2}\). Then we append 'W' into the binary representation of \(C_{Q}(x)\) after the least significant bit (LSB), the new modified value \(C^{\prime}_{Q}(x)\) will be \(\left(01111101W\right)_{2}=\left(01111011\right)_{2}\). Mathematically, this is equivalent to \(2\times 125+1=251\), which is considered as a watermarked coefficient. From the embedded coefficients, we can extract the embedded bit and restore the original coefficients. The watermark bits are extracted by investigating the least significant bit (LSB) of the watermarked signal.
## 5 Engineering Case Study
### Evaluation of Application Effectiveness  
In this study, the Y-coordinate of the station YK3 + 229 was set to 0, while YK3 + 233 was assigned a value of 400, and so on in equal intervals. To evaluate the interpolation accuracy of the reconstructed structural surface, cross-sectional slices were generated at Y = 300 and Y = 700, corresponding to intermediate locations between the actual tunnel stations YK3 + 232 and YK3 + 236. These interpolated cross-sections in the X-Z plane were compared to manually collected feature point data from the corresponding stations. As illustrated in Figure 18, solid lines represent the interpolated sections at Y = 300 and Y = 700, while the scatter points denote the actual measured feature points.  
To quantify the surface fitting accuracy, the mean relative error (MRE) was used, calculated as follows:  
\[MRE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|z_{\text{pred},i}-z_{\text{true},i} \right|}{\left|z_{\text{true},i}\right|} \tag{9}\]  
where \(n\) is the number of sample points, and \(Z_{\text{pred},i}\) and \(Z_{\text{true},i}\) represent the predicted and actual elevation values at the iii-th point, respectively.  
The MRE values calculated by the Python-based analysis were 0.0234 and 0.0226 for the respective sections, demonstrating that the reconstructed surfaces closely match the actual geological data and validating the reliability of the proposed modeling approach.  
The root mean squared error (RMSE) was calculated to quantify the average deviation between the predicted and true elevation values of the reconstructed surface. It is defined as  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Z_{\text{pred},i}-Z_{\text{ true},i}\right)^{2}} \tag{10}\]  
In this study, the calculated RMSE was 3.12 mm, indicating a high level of surface fitting accuracy.  
The Hausdorff Distance (HD) was also employed to evaluate the maximum  
The resulting Hausdorff Distance in our test case was 8.57 mm, which further confirms the spatial consistency of the fitted surface.
## 4 Effect of the Injection Rate Shape
### Evaporation Process
Figures 18 and 19 show that the spray penetration area is cooler than the surrounding combustion chamber, as the fuel absorbs heat for vaporization. The RECT and QIGD injection rates show better vapor penetration at the beginning when considering the temperature distribution contours and comparing them with the other injection rate shapes. This phenomenon occurs due to the higher initial injection rate compared to the GIGD and GIQD injection rates. In addition, the area near the nozzle exit showed different vapor penetration values for each case. The very high injection rate that resulted in initial vapor penetration also occurred further away from the nozzle exit. In Figures 18 and 19, under the GIQD injection rate, the vapor penetration at the nozzle exit is farther away from the nozzle exit and clearly farther from the injector exit than other shapes after the EOI. This is because the gradual increase in the injection rate results in fuel breakup capability. These phenomena occur because faster injection rates can accelerate the evaporation of fuel, with the airflow in the cylinder having a great impact on the evaporation and atomization of the fuel. Therefore, under the conditions of higher injection rates, the droplets will be smaller and evaporate faster, resulting in better acceleration in the formation of the air-fuel mixture. It can be predicted that a high initial injection rate would result in a decrease in the ignition delay period due to better fuel atomization at the beginning of the injection, which may more quickly lead to the start of combustion. The influence of injection rate shapes on the characteristics of the mixture properties will be studied in the next section. Figure 17: Comparison of the Sauter mean diameter (SMD) for different injection rate shapes. Figure 18: Comparison of the temperature distribution during injection for different injection rate shapes.
## 1 Introduction
They indicate that to cover the cooling load of a house of 120 m\({}^{2}\) the best air-conditioning system configuration consisted of a single-effect Yazaki absorption chiller of 10 kW, 28 m\({}^{2}\) flat plate collectors with 35\({}^{\circ}\) inclination, and a hot storage tank of 0.8 m\({}^{3}\). They concluded that the ratio of the collector area per kW cooling was 2.80 m\({}^{2}\)/kW. In Iraq, the conventional electricity grid is not working well as the country struggles to recuperate from years of war [25]. However, Iraq is blessed with an abundance of solar energy, which is evident from the average daily solar irradiance, ranging from 6.5-7 kWh/m\({}^{2}\) (which is one of the highest in the world). This corresponds to total annual sunshine duration ranging between 2800-3000 h [26]. Accordingly, solar cooling technology promotion in Iraq appears to be of high importance, concerning development, and is part of the government's new strategy for promoting renewable energy projects. It is clear from the literature that solar energy has a great influence on refrigeration and/or air conditioning processes. Different types and configurations of solar collectors have been applied for such purposes. The most used type was the evacuated tube collector (ETC). Moreover, it was noticed that LiBr-H\({}_{2}\)O have been used for most of the research activities in this regard [27; 28]. The aim of this work is to provide (1) a valuable roadmap related to solar-driven cooling systems operating under the Iraq climate to allow for sustained greenhouse gas emission reductions in the residential air conditioning sector, and (2) energetic performance analysis of solar driven cooling systems to investigate the best system design parameters.
## 4 Discussion  
### Uncertainties in NPP Estimates  
Net primary productivity (NPP), an indicator of the accumulation of atmospheric CO\({}_{2}\) in terrestrial ecosystems, plays a crucial role in global change [54]. The accurate estimation of NPP is a crucial step for reliably quantifying carbon fluxes between the atmosphere and terrestrial ecosystems [31], especially for regions with different topography and climatic conditions. Although deficiency and biases exist in the MODIS derived NPP, the validation illustrated that the model applied by MODIS was superior to the Miami model and Thornthwaite Memorial model, with an increased R\({}^{2}\) value and a decreased RMSE and MAE. The RMSE and MAE between the MODIS derived NPP and observations were only 0.19 kg C m\({}^{-2}\) and 0.14 kg C m\({}^{-2}\), respectively. This indicated that the MODIS derived NPP may be suitable for analysis with relatively low regional biases in China for the period of 2001-2014 to some extent despite coarse global climate data applied in MOD17. Additionally, it illustrated that the parameters input and algorithms may affect the results. For instance, the Miami model is one of the first global empirical models that only utilizes temperature or precipitation in the model [55], while the Thornthwaite Memorial model determines NPP for a particular location as the actual evapotranspiration functions [36]. The interactions among climatic factors and other influential factors such as vegetation types are not included in these two models [11]. Furthermore, there is no mechanism to account for changing vegetation density in the two models [56]. However, the model applied by MODIS incorporates biogeochemical principles in a mechanistic modeling environment and the vegetation feedback to climate conditions through changes in Leaf Area Index and absorbed radiation, and has the advantage of providing spatially continuous estimates with a consistent methodology, which is important for any large-scale studies [35]. It should be noted that NPP is not easy to measure and only 26 field sites were included in this study, which may cause biases due to the larger study area. In addition, heterogeneity, stand density, and pixel resolution may also  
Figure 8: Structural equation model relating climatic factors, Length of Season (LOS) and Net Primary Productivity (NPP) in China. Single headed arrows indicate directional relationships, while double headed arrows indicate covariances. Numbers in brackets are \(R^{2}\) values.  
affect validation results. Therefore, increased observation sites and an extended observation period has become essential for NPP estimation. Additionally, forest inventory data is the only data source that provides large-scale consistent productivity assessments [57]. Therefore, the application of forest inventory data rather than numerous field observed data in this study would be more preferable to validate the suitability of MODIS NPP in China, and more efforts should be made in the future. What is more, temporal analysis of match between models and observations based on a long time period were also a good choice for NPP estimates and comparison.