esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Finite Element Model Updating  
Making use of the modal model described in [14], the frequency response function \(H_{ik}(\omega)\) between input DOF \(k\) and output DOF \(i\) at angular frequency \(\omega\) can be written as  
\[H_{ik}(\omega)=\sum_{r=1}^{N_{m}}\frac{Q_{r}\psi_{ir}\psi_{kr}}{j\omega-\lambda _{r}}+\frac{Q_{r}^{*}\psi_{ir}^{*}\psi_{kr}^{*}}{j\omega-\lambda_{r}^{*}} \tag{6}\]  
where \(N_{m}\) is the number of modes, \(Q_{r}\) is the modal scaling factor, \(\lambda_{r}\) is the pole number, and \(r\), \(\psi_{ir}\) and \(\psi_{kr}\) are the mode shapes for mode \(r\) and \(*\) for the complex conjugate.  
We will use eigenfrequencies and mode shapes to correlate and update the model parameters to the real, optimized values.  
In this section, the automated updating method [22] used to update the finite element frequencies based on two updating parameters will be briefly explained, using poles (resonance frequencies) and corresponding mode shapes as reference data. A non-linear least squares problem is solved in order to minimize an objective function consisting of the sum of the squares of the differences in corresponding resonance frequencies and mode shapes between the test modes and FE modes. In the objective function, the resonance frequencies and mode shapes are both measured but are weighted differently, taking their importance and value into account [34].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Test (Hz)** & **FE\_init (HZ)** & **Relative Difference (\%)** & **FE\_opt (Hz)** & **Relative Difference (\%)** \\ \hline
1 & 102.813 & 93.581 & \(-\)8.98 & 98.252 & \(-\)4.35 \\
2 & 138.438 & 107.621 & \(-\)22.26 & 131.874 & \(-\)4.74 \\
3 & 206.563 & 190.744 & \(-\)7.66  
The parameter updating problem is stated as  
\[\begin{split}&\text{minimize }\ \ell(\vartheta)\\ &\text{with }\ \ell(\vartheta)=\sum_{i=1}^{nm}W_{fi}^{2}\left[f_{i} \left(\vartheta\right)^{FE}-f_{i}^{E}\right]^{2}+\sum_{i=1}^{nm}W_{\vartheta i}^ {2}\sum_{j=1}^{np}\left[\phi_{ji}\left(\vartheta\right)^{FE}-\phi_{ji}^{E} \right]^{2}\\ &\text{subject to }\ 0\leq\vartheta\leq 1\end{split} \tag{7}\]  
where \(f_{i}\) and \(\phi_{ji}\) are the \(i^{th}\) natural frequency and associated mode shape at measurement point \(j\), respectively. \(W_{f}\) and \(W_{\vartheta}\) are the weight coefficients of the frequency and mode shape, respectively; \(nm\) denotes the number of modes and \(np\) the number of measurement points. Subscripts \(FE\) and \(E\) denote the items from the FE model and experiments, respectively, \(\vartheta\) denotes the normalized parameter vector, which allows for variation within the interval [0,1], and \(\ell(\vartheta)\) is a real valued scalar objective function.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description
### TSAER: Operational Steps
#### 4.3.2 Routing Process  
When the source node has data to be transmitted, if there is a path to sink, they will be forwarded directly; if it does not exist, it will enter the routing process.  
In the routing process, the source node sends a routing request packet to its neighbor nodes. After receiving it, the neighbor node calculates the routing benefit and confirms the reply to the source node. Then, the source node compares the routing benefits of each neighbor node and selects the node with the maximum benefit function as the forwarding node transmits the data.  
The process of calculating the routing benefit: The node calculates the data reception probability corresponding to emergency data or normal data according to the information in the neighbor table and then calculates the corresponding benefit function value. As shown in Algorithm 2, the routing benefit calculation instructions for different types of data are shown. And further routing process is elucidated by Algorithm 3.
## 8 Results and Discussion
In addition, from the perspective of algorithm complexity, the complexities of the MM-AltOpt algorithm, PE-Altmin algorithm and OMP algorithm are \(O\left(I_{MM}\big{(}N^{3}+I_{Imre}2 NL^{2}+NL\big{)}\right)\), \(O\left(I_{PE}\big{(}N^{3}+L^{3}+NL\big{)}\right)\) and \(O\left(I_{OMP}\big{(}L^{4}N+L^{2}+N^{2}L^{2}+2L^{3}\big{)}\right)\), respectively, where \(I_{MM}\), \(I_{Imre}\), \(I_{PE}\) and \(I_{OMP}\) are the number of iterations of the corresponding algorithm. In conclusion, we can see that the complexity of the proposed MM-AltOpt algorithm is close to that of the other two algorithms, however, the system EE performance is higher, which can verify the effectiveness of the proposed algorithm. Figure 11: Change trajectory of system performance versus different \(U_{s}\). (**a**) Change trajectory of system EE versus different \(U_{s}\); (**b**) change trajectory of system SE versus different \(U_{s}\). Figure 12: Comparison of system performance of different algorithms for solving \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\). (**a**) Comparison of system EE of different algorithms for solving \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\); (**b**) comparison of system SE of different algorithms for solving \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\).
# PrimaVera: Synergising Predictive Maintenance
## 4 Predictive Maintenance Process Model  
To overcome the before mentioned obstacles and to orchestrate the different steps in the predictive maintenance workflow a generic applicable process model is proposed to facilitate this (Figure 1). The proposed model is similar to existing models [83; 84; 85; 86; 87], which are in essence all based on the generic model of [PERSON] et al. [82]. The model of [PERSON] et al. has three distinct stages; data acquisition, data processing and maintenance decision-making. As the goal is to define a generically applicable process model for predictive maintenance, not only should it be applicable at system level but also at fleet level [88; 89]. To ensure this genericity, the proposed model will also be based on the generic model of [PERSON] et al. To highlight the significance of diagnostics and prognostics within predictive maintenance, the proposed model explicitly breaks down the data processing step into these two elements. The importance of human and organisation factors is commonly overlooked by engineering disciplines [48; 61; 62; 63] but is well rooted within information systems research [90; 91]. Technology and behaviour are not dichotomous [90], therefore human and organisational factors have been added to the proposed model. This element is placed at the very centre of the model as it affects all other stages of the model. It is exactly this addition which sets the proposed model apart from the previously proposed models. The usability and applicability of the proposed model will be evaluated by applying it to the demonstrators described in Section 5.1.  
The generic model consists of five stages: (1) data are acquired from assets using sensors or other sources, (2) these data are then processed and turned into meaningful diagnostic information through data selection, cleaning and interpretation, (3) from this information predictions are made about the system's health (prognostics), (4) based on these prognostics, maintenance and associated logistics are optimised, (5) all information has to be incorporated into a strategic asset management plan. Decisions listed in the asset management plan are transformed into actions which will affect the asset being managed, hence closing the cycle. An asset management plan documents the activities, resources and timescales required to achieve the organisation's asset management objectives for an individual asset or group of assets [92]. Note that each of the stages relate to one or more of the before mentioned obstacles of predictive maintenance to overcome.  
The starting point within the predictive maintenance cycle depends on the motivation for asset management [93]. This motivation can be initiated by a technology push; existing technology isavailable which needs to be management, in this case the cycle would start with data acquisition. On the other hand motivation can be initiated by a decision pull; there is a certain economic necessity, in this case the cycle would start with an asset management plan.
## 3 Proposed Methodology
### Classification
#### 3.6.4 Random Forest  
An algorithm for collective learning called random forest (RF) is used for both classification [59] and regression tasks. In order to create more precise and reliable predictions, it builds multiple decision trees during training. In order to lessen overfitting and improve generalization, each tree is constructed using a random subset of features and data samples. Random forest creates a strong and adaptable machine learning model by combining the predictions of individual trees, which is widely used for numerous real-world applications [77].
## 3 Improved Point Cloud Comparison Method
### Model Tests  
In order to assess the applicability of the improved point cloud comparison method and analyze the calculation errors, this study conducted model tests to determine the model parameters with the highest measurement accuracy [49; 50].  
(1) Test model  
The main body of the test model was a rectangular gray iron plate of 1.2 m \(\times\) 1.8 m. The inclination of the iron plate was set to 45\({}^{\circ}\) to simulate the slope. A black cone magnet was placed on the iron plate to simulate the slope deformation by moving the position of the magnet. The displacement of the magnet was controlled by a Vernier caliper, as shown in Figure 5.  
Figure 4: Calculation principle of the improved point cloud comparison method.  
(2) Contents of tests  
When conducting the slope deformation measurement, the parameters that affect the accuracy mainly include the interval of scanning points \(t\), radius of the sphere used for normal estimation \(R\), side length of the prism \(d\), and height of the prism \(H\)[51; 52]. The commonly used values of these parameters are shown in Table 3.  
Displacement of the magnet was set to 0.30 m and two-phase scanning of the test model was conducted when \(t\) = 0.05 m, \(t\) = 0.10 m, and \(t\) = 0.20 m, respectively. Orthogonal design on \(R\), \(d\), and \(H\) was performed to obtain 27 combinations and 81 calculations of the deformation for the point cloud were performed, corresponding to each \(t\) through the improved point cloud comparison method [33]. Thirty additional calculations were added in the subsequent data analysis stage. A total of 111 calculations were performed to obtain 111 sets of deformations \(l\) and errors \(n\), where error \(n\) was calculated as shown in Equation (5).  
\[n=|l-0.30| \tag{5}\]
## 2 Fundamental Background
### Antenna Feeding Techniques  
The feed technique represents an important design choice due to its strong impact on the antenna performance parameters such as BW, return loss, gain, etc. Feed techniques are categorized into two types as contacting feed and non-contacting feed techniques [62]. In the contacting feed technique, the power is fed directly to the radiating patch through the connecting line, whereas in the non-contact feeding technique, the power is transferred to the radiating patch through an electromagnetic coupling. In general, the microstrip line and coaxial probe feed are widely used as contact feeding techniques. The coaxial probe feed technique, which is popular in many applications of a microstrip patch antenna, consists of an inner conductor, an outer conductor, and a dielectric material in between. The inner conductor of the coaxial probe feed is connected to the patch, while the outer conductor is connected to the ground plane, as shown in Figure 1. In the microstrip line feed method, the conducting strip is connected to the microstrip patch, and its width is smaller than the patch's width, as shown in Figure 2. On the other hand, the power is transferred between the radiating patch and the connecting element using the well-known non-contacting feed techniques of proximity coupling and aperture coupling, as demonstrated in Figures 3 and 4. Each of the four feeding networks is explained in detail in [62]. Table 1 summarizes their advantages and disadvantages [62; 84; 85; 86; 87; 88]. The comparison is primarily focused on the trade-off between fabrication complexity and antenna performance.  
Figure 4: Aperture-coupled feed.  
Figure 3: Proximity-coupled feed.  
Figure 2: Microstrip line feed.  
Figure 1: Coaxial probe feed.
## 2 Materials and Methods
### Method
#### 2.3.4 Correlation Analysis  
To address the limitations of random forest regression, we conducted a Pearson correlation analysis to determine the correlation between variables and provide an additional interpretation of the regression results. It is important to note that the effects of some ECIs on EIs may not be stable in the time series. ECIs are derived from temperature and precipitation data, which exhibit significant temporal and spatial heterogeneity. For example, short-term extreme events (e.g., droughts or heatwaves) may vary greatly between seasons or years, especially in regions with diverse climates like SWC. We attempted to mitigate this by using moving windows of three time scales (10, 15, and 20 years) and found no significant differences among the three scales. A 15-year moving window was utilized to analyze the temporal stability of the impact of ECIs on EIs. Correlations between ECIs and EIs were calculated using moving windows over the time series data. The results of the correlation analysis for the entire time series are presented in Tables S2-S5. The data were organized using the 'Pandas.DataFrame' function, and the'scipy.stats.pearson' function was used for calculations. The formula used is as follows:  
\[\mathrm{r}=\frac{\sum\left(\mathrm{X}-\overline{\mathrm{X}}\right)\left( \mathrm{Y}-\overline{\mathrm{Y}}\right)}{\sqrt{\sum\left(\mathrm{X}-\overline{ \mathrm{X}}\right)^{2}}\sqrt{\sum\left(\mathrm{Y}-\overline{\mathrm{Y}} \right)^{2}}}, \tag{7}\]  
where X and Y denote the value of the two variables, respectively, and \(\overline{\mathrm{X}}\) and \(\overline{\mathrm{Y}}\) denote the mean values of X and Y.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 4 Modeling Description  
### Data Preprocess and Variables Selection  
Data preprocess is an important stage in the data-driven modeling. Normally, missing values and outliers, should be removed or specially dealt with. In our research, the outliers and missing values are replaced by the average of the adjacent samples to guarantee the data continuity. On the other hand, the dimensional difference of input and output variables is eliminated by data standardization using Equation (13), which transforms different variables to same scale.  
\[x^{\prime}=\frac{x-\overline{x}}{\sigma} \tag{13}\]  
Before constructing the memory matrix, the variables need to be selected. Vibration variables are target vectors, and the relationship between the other variables is listed in Table 1 and target vectors need to be discussed. Grey relational analysis is an effective statistical method quantifying the relevance degrees, the \"grey relational degree\" of variables is calculated using consecutive data of variables, and the method is proved effective in many fields. Therefore, grey relational analysis is applied in variables selection in our paper.  
Consecutive 1000 samples are used for obtaining grey relational degree. Vibration vectors are set as target sequences, and the rest 9 variables are set as comparing vectors. The grey relational degree sequence for variable could be calculated as,  
\[\xi_{i}(k)=\frac{\min\limits_{i}\min\limits_{k}\left|y(k)-x_{i}(k)\right|+\rho \max\limits_{i}\max\limits_{k}\left|y(k)-x_{i}(k)\right|}{\left|y(k)-x_{i}(k) \right|+\rho\max\limits_{i}\max\limits_{k}\left|y(k)-x_{i}(k)\right|} \tag{14}\]  
where, \(\rho\) is discrimination coefficient, and is set as 0.6 in our research.  
The average grey relational degrees for compared vector are calculated as,  
\[r_{i}=\frac{1}{n}\sum\limits_{k=1}^{n}\xi_{i}(k) \tag{15}\]  
The results of grey relational analysis between the horizontal vibration, vertical vibration, and the compared vector are shown in Tables 2 and 3, respectively. According to the grey relational analysis results, except the vibration of horizontal and vertical itself, the temperature of end-side bearings, current air volume of unit, operation frequency of driving motor and current of driving motor (gray relational degree \(>\) 0.6), are chosen for vibration condition monitoring.
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 2 D2D, IOT, and Context-Awareness  
### D2D Communications in Smart Applications  
5G D2D is recently receiving more interest as a critical technology promoting the industrial 4.0 paradigm and many daily life applications with situation-adapted intelligence since D2D is able to support machine-type communication, flexible/extensible connectivity to the Internet, and temporal dynamic inter-connectivity. Figure 1 presents those scenarioswith D2D and other 5G connectivity techniques. Particularly, Figure 1a illustrates the smart factory scenario with various IoT devices such as drones, autonomous mobile robots, and producing robots which can be connected via a range of the 5G connectivity techniques such as small cells, D2D, and Drone-supported communication [9]. Then, D2D takes the role to inter-connect among diverse cells as well. In the other case, social networking is configured through the D2D technique, as shown in Figure 1b, where two social groups are formed via D2D. It means that the members of each group might be willing to open their own smart mobile device capacity to other members since D2D relies on sharing resources of devices in communication, which is the biggest reason to prevent this novel paradigm deployment nowadays [5; 6].  
Such D2D communication is fulfilled by not only radio access technologies, e.g., cellular series, WiFi, and Bluetooth, but also various routing techniques with many different network topologies according to diverse environmental and situational properties [12]. D2D communication can configure tree or mesh networks via inband or outband spectrums and be configured in conventional control and data planes or direct ways without support from an eNB in 4/5G. Moreover, it can support offloading from a macro cell to small cells, as well as isolated networking in out-of-network coverage [13]. Such D2D networks are minutely composed of three networking phases: Infrastructure-to-Device (I2D), Device-to-Infrastructure (D2I), and Device-to-Device (D2D) networking. Each networking phase can be performed in single-hop or multi-hop communication.  
D2D supports highly flexible and extensible networking technology based on the moving devices such as smartphones and smart IoT devices as represented in Figure 1. It means that the network topology is easily broken due to the mobility of various devices, which can affect communication performance such as latency and reliability. Thus, connectivity guarantee and fast network topology recovery should be taken into consideration for the smart applications that rely on D2D architecture for real-time and reliable communication. Fast connectivity is essential for D2D network sustainability because of the battery constraints of D2D to ensure network lifetime [14]. Furthermore, the mobility of devices may bring about the discontinuation of established connection sessions in D2D system [15]. As a result, there are several challenges associated with D2D communication.
## 3 Results
### Recovery Ratio of Bacteria from Contaminated Samples
The calculated reduction rates of the tested bacteria during experimental variant I are presented in Figure 3. The reduction rate of the bacteria reisolated from the glass surface without RCI treatment was not higher than 0.012 and no significant differences between strains and species were found. On the contrary, RCI most efficiently reduced the number of \(C\). _ jejuni_ (0.734). For \(L\). _monocytogenes_, the reduction rate was 0.636 and for \(S\). Enteritidis, 0.611. The differences calculated for tested species were not statistically significant. Figure 3: The reduction rate of _Camryylobacter jejuni_ (CJE), _Listeria monocytogenes_ (LMO), and _Salmonella_ Enteritidis (SENT) on non-RCI-treated (RCI-) and RCI-treated (RCI+) glass surfaces in experimental variants I, II, and III (a,b,c—values marked with different letters differ statistically significantly). The absolute RCI efficiency coefficient (E) values for the experimental variant I ranged from 0.584 (_S._ Enteritidis strain 2) to 0.725 (C. _jejuni_ strain 1). The differences between strains and species were not statistically significant (Table 1). Assessment of Effectiveness of Surface Disinfection Using Radiant Catalytic Ionization during Biofilm Maturation (Experimental Variant II)  
The average number of bacteria reisolated from the glass surface, after 72 h contact with the contaminated meat sample, for _C. jejuni_ was 4.729 log CFU \(\times\) cm\({}^{-2}\) (Figure 4). The greatest number of bacteria for _C. jejuni_ strains was noted for the reference strain (5.478 log CFU \(\times\) cm\({}^{-2}\)) and was significantly higher than the values obtained for the other strains (4.355 log CFU \(\times\) cm\({}^{-2}\)--strain 1, 4.354 log CFU \(\times\) cm\({}^{-2}\)--strain 2) The initial number of _L. monocytogenes_ and _S._ Enteritidis isolated from the contaminated glass surface ranged from 6.082 log CFU \(\times\) cm\({}^{-2}\) (strain 2) to 6.258 log CFU \(\times\) cm\({}^{-2}\) (ATTC strain) and 5.082 log CFU \(\times\) cm\({}^{-2}\) (for strain 1) to 5.213 log CFU \(\times\) cm\({}^{-2}\) (strain 2), respectively. The average number of bacteria for _L. monocytogenes_ and _S._ Enteritidis was 6.176 log CFU \(\times\) cm\({}^{-2}\) and 5.162 log CFU \(\times\) cm\({}^{-2}\), respectively. No statistically significant differences between the individual strains of both species were observed (Figure 4).
# Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method
###### Abstract  
To shorten the measurement period and reduce experiment costs, we investigated the parameter conversion between the experiment results of the controlled pass-by (CPB) method and alternative close proximity (A-CPX) method for automotive applications. The CPB and A-CPX methods were used to experiment with tire noise. The correlation between the tire noises of the two experimental methods was analyzed. Then, the quantitative transformation relationship between the tire noises of the two methods was obtained using an acoustic radiation propagation experiment in the semi-free field. The results indicate a good linear correlation between the experimental results of the two experimental methods. In the case of ignoring the shielding effect of the car body, the average difference between the measured value of the CPB method and the predicted value of the experimental tire is about 1.1 dB. When considering the shielding effect of the car body, the average difference between the measured value of the CPB method and the predicted value of the experimental tire is about 2.7 dB.  
controlled pass-by (CPB); alternative close proximity (A-CPX); tire noise; parameter conversion; acoustic radiation propagation +
Footnote †: journal: Applied Sciences