esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transmitter and Receiver Structure
### Complex Rotation Matrices  
A GSM symbol vector forming a PT-GSFIM is subject to the influence of the channel on a single subcarrier since, according to the signal model of Equation (8), \(\mathbf{H}_{g}^{u}\) is block diagonal. However, we can incorporate SSD techniques into the design of PT-GSFIM in order to take advantage of the inherent diversity of frequency-selective channels commonly seen in mobile propagation situations. SSD was first proposed in [41] and can be used to increase performance without requiring more power or bandwidth. In the proposed approach, we apply SSD by spreading each GSM symbol that makes up the primary PT-GSFIM symbol over different subcarriers through the use of CRMs. Working with a rotating super-symbol is part of the CRM application procedure. In this situation, we only apply the spreading in the frequency direction, which means that all of the spatial components are rotated simultaneously. For each user \(u\), the rotated length-\(N_{s}N_{f}\) super-symbol can be expressed as  
\[\widetilde{\mathbf{s}}_{g}^{u}=\Big{(}A_{N_{f}}\otimes\mathbf{I}_{N_{s}} \Big{)}\cdot\mathbf{s}_{g}^{u} \tag{9}\]  
and the transmitted signal becomes (1)  
\[\mathbf{x}_{g}=\sum_{u=1}^{N_{u}}\mathbf{F}_{u}\Big{(}\mathbf{A}_{N_{f}} \otimes\mathbf{I}_{N_{s}}\Big{)}\mathbf{s}_{g}^{u}=\mathbf{F}\Big{(}\mathbf{ I}_{N_{u}}\otimes\Big{(}\mathbf{A}_{N_{f}}\otimes\mathbf{I}_{N_{s}}\Big{)} \Big{)}\mathbf{s}_{g} \tag{10}\]  
Matrix \(\mathbf{A}_{N_{f}}\in\mathbb{C}^{N_{f}\times N_{f}}\) can be chosen from the family of orthonormal complex matrices (OCRMs), which is defined as  
\[\mathbf{A}_{M_{CRM}}=\begin{cases}\left[\begin{array}{cc}e^{i\varphi}&je^{- j\varphi}\\ je^{i\varphi}&e^{-i\varphi}\end{array}\right]/|\mathbf{A}_{2}|^{1/2},M_{CRM}=2\\ \left|\mathbf{A}_{2}\right|=\det(\mathbf{A}_{2})=2\\ \left[\begin{array}{cc}\mathbf{A}_{M_{CRM}/2}&\mathbf{A}_{M_{CRM}/2}\\ \mathbf{A}_{M_{CRM}/2}&-\mathbf{A}_{M_{CRM}/2}\end{array}\right]/\left| \mathbf{A}_{M_{CRM}}\right|^{1/M_{CRM}},M_{CRM}>2\end{cases} \tag{11}\]  
where \(M_{CRM}=2^{n}\), \(|\mathbf{A}_{M_{CRM}}|=\det(\mathbf{A}_{M_{CRM}})\) and \(\varphi\) is the rotation angle [42]. To get the most out of frequency diversity, a subcarrier-wise interleaver is applied so that the effective group of subcarriers assigned to each PT-GSFIM symbol is spread out across the entire bandwidth. This interleaver is similar to the subcarrier-level block interleaver in [15]. However, in the case of PT-GSFIM, its effect is leveraged since with the use of CRMs, and each individual GSM symbol is spread over the different subcarriers allocated to the corresponding PT-GSFIM subblock.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 3 Proposed Time Classification Algorithm
### Time Classification  
A reference color histogram representing each time period is generated based on only the sky region extracted from the training dataset. We use four methods to compare the histogram generated from test image with the reference color histogram: Correlation, Intersection, Bhattacharyya, and Chi-square. We find the optimal comparison method and parameters among them. We apply a weighted sum comparison between the color histograms.
## 3 Results
### The Biological Effects of the Isolated Compounds
Both phytotoxic compounds have been reported as carotenoid metabolites [55]. Dehydrocomifoliol is reported in different plants such as _Phaseolus vulgaris_ L. [56], rice husks [35], _Beta vulgaris_ var. [57], _Cucumis sativa_[36], _Arctium lappa_[58], and _Nitraria sibirica_ [PERSON]. [59]; it has also been obtained by synthesizing C9-hydroxy ketone [60]. In addition, dehydrocomifoliol has been shown to have cytotoxic effects against human cancer cells [61]. In 1964, [PERSON] and [PERSON] [37] first reported on loliolide in the plant _Lolium preenne_, and thereafter it was found in both land and sea ecosystems in different plant species and animal species [62], such as _Helianthus tuberosus_[55], _Heliotropium angiospermum_[63], _Digitaria sanguianis_[64], _Eichhornia crussipes_[65], _Sargassum horneri_[66], and _Oryza sativa_ L. ssp. _indica_ (indigenous rice variety \"Goria\") [67]; it has also been obtained by synthesizing C11-aldehyde [60]. Loliolide has various pharmaceutical functions [62]. It has been applied for its antioxidant [68], anticancer [58], antiviral, anti-melanogenic, anti-inflammatory, anti-aging [69], antituberculosis [70], antidiabetic [71], antibacterial, antifungal [64], and cell senescence inhibition activities [58]. Previously, the compounds dehydro-drovomifoliol and loliolide have also been identified from the plants _Rollinia emarginata_[72], _Paspulum commerciali_[73], and _Vitex leptobortys_[74]. However, there are no reports found in the literature suggesting that dehydrocomifoliol and loliolide have been isolated from _Albizia richardiana_ leaves. The I50 values indicate that inhibitory effect of loliolide against cress shoot and root growth was more potent than that of dehydrocomifoliol. The difference in allebotathic activity may be due to the disparity in the chemical structures of the substances, because the allebotathic potential of phytotoxic substances is measured based on structural differences [75]. Dehydrocomifoliol contains 13 carbon atoms, in which cyclohexanone (2-cyclohex-1-one) is substituted at positions 3, 5, and 5 by methyl groups, and by both a hydroxy group and a 3-oxobut-1-en-1-yl group at position 4. Alternatively, loliolide has 11 carbon atoms, in which 1,3-dihydroxy-3,5,5-trimethyl-substituted cyclohexylidene is connected to acetic acid lactose at position 4. Additionally, in dehydrocomifoliol, an OH group is situated at the C-4 position, whereas in loliolide, an OH group is situated at the C-1 and C-3 positions. [PERSON] [76] suggested that the OH group at the C-3 position of loliolide is responsible for its phytotoxic effects. For these reasons, the cress seedlings might exhibit greater sensitivity to loliolide than dehydrocomifoliol. Therefore, the inhibitory activity of dehydrovomifoliol and loliolide indicate the allelopathic potential of _Albizia richardiana_. Accordingly, the allelopathic activity of _Albizia richardiana_ could lead to this tree being used for the development of bioherbicides to increase sustainable agricultural production.
## 3 The Proposed Model
### Weed Detection and Classification  
For the weed recognition process, the MBMODL-WD technique exploited the ENN model. The ENN model is a common example of a dynamic recurrent network, and its architecture comprises an input layer with specific context nodes, an output, and hidden layers [21]. The key advantage of ENN is that the context node might be employed for remembering the prior hidden node activation, which makes it appropriate in the fields of dynamic system identification and prediction control. Consider the external input of the network as \(u\). The output is \(y\), and the output of the hidden layer is \(\chi\). Hence:  
\[x(k)=f\Big{[}w_{k}^{1}x_{c}(k)+w_{k}^{2}u(k-1)\Big{]} \tag{11}\]  
\[x_{c}(k)=x(k-1) \tag{12}\]  
\[y(k)=g\Big{[}w_{k}^{3}x(k)\Big{]} \tag{13}\]  
where \(w_{k}^{1}w_{k}^{2}\), and \(w_{k}^{3}\) indicate the weight connection matrices from context to implicit layers, the input to hidden layers, and hidden to output layers, correspondingly. Now, \(f\) and \(g\) represent the transfer function of implicit and output layers.  
\[x_{c}(k)=x(k-1)=f\Big{[}x_{k-1}^{1}x_{c}(k-1)+x_{k-1}^{2}u(k-2)\Big{]} \tag{14}\]  
Then,  
\[x_{c}(k-1)=x(k-2) \tag{15}\]  
where \(x_{c}(k)\) depends on \(w_{k-1}^{1},w_{k-2}^{2}\) at distinct moments; hence, \(x_{c}(k)\) represents the dynamic recursive method. Consequently, the BP model applied to Elman regression NN training is the dynamic BP learning mechanism.
## 3 Results and Discussion
### Radiation Pattern Performance
#### 3.2.2 Side Lobe Level Performance  
Figure 8a shows the Side Lobe Level (SLL) of element RPT, 1 \(\times\) 2 of RPT, 1 \(\times\) 2 of FCPT, MCPT, BCPT, CCPT, LTPT, RTPT, and HFSP. Meanwhile, Figure 8b presents the SLL of element RPT, SLL 1 \(\times\) 2 of RPT, 1 \(\times\) 2 of KSPT, ESEPT, VWSPT, HWSPT, RLPT, ELPT, and  
Figure 7: Directivity of (**a**). Element and 1 \(\times\) 2 Regular Palm Tree- (RPT-CVA), 1 \(\times\) 2 Exponential Slot Edge Palm Tree (ESE-CVA), 1 \(\times\) 2 Vertical Wave Structure Palm Tree (VWPT-CVA), 1 \(\times\) 2 Horizontal Wave Structure Palm Tree (HWPT-CVA), and (**b**). Element and 1 \(\times\) 2 Regular Palm Tree-Coplanar Vivaldi Antenna (RPT-CVA), 1 \(\times\) 2 Regular Lens Palm Tree (RLPT-CVA), 1 \(\times\) 2 Elips Lens Palm Tree (ELPT-CVA), And 1 \(\times\) 2 Metamaterial Lens Palm Tree (MLPT-CVA).  
MLPT-CVA. From the simulation result, It shows that the best SLL performance is reached for RPT of 11.94 dB at 1 GHz, followed by 1 \(\times\) 2 HFSPT of 10.357 dB at 3 GHz. Furthermore, most antennas have an SLL greater than \(-\)5 dB at frequencies above 1.5 GHz. Although mutual scaring parameters of the antenna at high frequencies are good, it produces a high-level sidelobe. This is because the higher the frequency, the greater the distance between the elements relative to the wavelength, therefore, the grating lobe will occur, enlarging the SLL However the SLL of a single element of RPT is better than others at frequency 2, 2.5, 3.5 and 4 GHz. But at frequency 1.5 GHz shows that 1 \(\times\) 2 RPT has better performance of SLL than a single element. For wideband antennas, by arranging the antennas into MIMO, the sidelobe level performance can increase at low frequencies but at high frequencies, the SLL performance decreases due to the presence of grating lobes because the distance between elements becomes greater. Figure 8b shows that the 1 \(\times\) 2 ELPT got the best SLL performance at 1 GHz of \(-\)13.757 dB. At 2 GHz, the 1 \(\times\) 2 KFSPT results in the best performance of SLL of \(-\)9.426 dB. Meanwhile, at 2 GHz, the worst SLL of \(-\)0.677 dB was obtained for RPT. This means an SLL improvement of 8.749 dB between 1 \(\times\) 2 KFSPT and 1 \(\times\) 2 RPT. The best SLL of \(-\)6.89 dB was obtained at 2.5 GHz for 1 \(\times\) 2 MLPT, while at 4 GHz, the 1 \(\times\) 2 ELPT has the best performance of \(-\)4.688 dB. SLL performance can also be improved by adding a structure and lens at both antenna heights.
## 4 Experimental Verification
### Validation of Measured Datasets  
To further verify the diagnostic effect of the proposed method on the motor bearing fault, the motor bearing shaft current damage simulation test bench is used for verification. The bearing experimental platform is shown in Figure 5. The rolling bearing used in the experiment is a detachable deep groove ball bearing. The specific parameters of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** & **IMF9** & **IMF10** \\ \hline
0.00 & 0.00 & 0.01 & 0.03 & 0.08 & 0.60 & 0.01 & 0.01 & 0.01 \\ \hline \end{tabular}
\end{table}
Table 1: Correlation coefficient.  
\begin{table}
\begin{tabular}{c c c} \hline
**Processing Method** & **SNR** & **RMSE** \\ \hline No noise reduction & \(-8\) & \(1.71\) \\ \hline VMD-NLMS noise reduction & \(1.59\) & \(0.67\) \\ \hline \end{tabular}
\end{table}
Table 2: SNR and RMSE values between the signal with added noise and the signal after denoising with VMD-NLMS.  
Figure 4: Squared envelope spectra of different methods.  
6205 EKA bearing are shown in Table 3. The fault signals of the motor bearing's inner ring and outer ring with loads of 500 N and 1500 N are collected, respectively. The load was adjusted using a load bolt, which applied controlled forces to the bearing housing, mimicking the variable load conditions typically encountered in industrial machinery. The motor used in the experiment was a three-phase induction motor with a rated power of 1.5 kW, a rated speed of 1800 rpm, and a rated voltage of 380 V. The sampling frequency is 16 kHz, and the motor rotation frequency is 30 Hz. The outer and inner ring fault frequencies are calculated to be 101.25 Hz and 158 Hz, respectively. To simulate realistic fault conditions, pitting corrosion faults were introduced on the inner and outer rings of the bearing using electrical discharge machining (EDM), as shown in Figure 6. The inner ring fault had a pit diameter of about 0.5 mm and a depth of 0.2 mm, while the outer ring fault had a pit diameter of about 0.6 mm and a depth of 0.25 mm. These fault sizes represent moderate-to-severe pitting damage, which is common in bearings subjected to high stress and poor lubrication conditions.
## 5 Simulation Results  
Accordingly, the process of implementation of the proposed approach is performed in two phases, i.e., implementing Phase I and then feeding the outcome of Phase I while simulating Phase II. Phase I includes the experiments related to the transmission strategy formulation mostly carried out using spreadsheets and R [34] software. In Phase II, a WBAN is simulated using Castalia 3.2 [35] simulator where each node is incorporated with the pre-computed strategy developed in Phase I. Nodes are programmed to follow Algorithm 1 to route data to the sink. Several experiments are performed in this phase to estimate the performance of the entire process in terms of the identified performance metrics.
## 4 Theoretical Framework
### Methodology  
For the choice of the models described above the Box-Jenkins [26] methodology was used, which can be summarized in three steps:  
* Identification and selection of the model (stationarity, seasonality, autoregressive components, and moving averages);
* Estimation of the coefficients that best fit the parameters chosen by means of computational algorithms; and
* Validation of the model obtained.
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 3 Proposed EOMF Structure
### Structure of an EOMF with Multiuser Detection
^{T}\) be the weight vector of the array antenna for beamforming for desired users 1 and 2, respectively, the signals input to MF\({}_{1}\) and MF\({}_{2}\) are as follows in Equations (17) and (18):  
\[y_{\text{beam}}^{1}=Re\Big{(}\big{(}\mathbf{U}^{1}\big{)}^{H}\mathbf{X}\Big{)}=\sum_{k= 1}^{K}Re\Big{(}\  
in the proposed structure. The reason is that there are multiple desired users. The values \(\alpha^{1}\) and \(\alpha^{2}\) for normalizing the desired user signals 1 and 2 are as follows in Equations (24) and (25), respectively:  
\[\alpha^{1}=\sum_{k=1}^{K}N\sqrt{P_{1}}Re\Big{[}e^{-j\phi_{k}(\theta_{1})}\big{(} U_{k}^{1}\big{)}^{*}\Big{\}} \tag{24}\]  
\[\alpha^{2}=\sum_{k=1}^{K}N\sqrt{P_{2}}Re\Big{[}e^{-j\phi_{k}(\theta_{2})}\big{(} U_{k}^{2}\big{)}^{*}\Big{]}. \tag{25}\]  
Letting \(y_{\text{MF}_{1}}\), \(y_{\text{MF}_{2}}\), and \(y_{\text{MF}_{i}}\) be normalized for desired user 1 yields \(y_{\text{MF}_{1}}^{1}\), \(y_{\text{MF}_{2}}^{1}\), and \(y_{\text{MF}_{i}}^{1}\); these are expressed by Equations (26)-(28), respectively:  
\[y_{\text{MF}_{1}}^{1}=\frac{y_{\text{MF}_{1}}}{\alpha^{1}}=\frac{{c_{1}}^{T}y _{\text{beam}}^{1}}{\alpha^{1}} \tag{26}\]  
\[y_{\text{MF}_{2}}^{1}=\frac{y_{\text{MF}_{2}}}{\alpha^{1}}=\frac{{c_{2}}^{T}y _{\text{beam}}^{2}}{\alpha^{1}} \tag{27}\]  
\[y_{\text{MF}_{i}}^{1}=\frac{y_{\text{MF}_{i}}}{\alpha^{1}}=\frac{{u_{i}}^{T}y _{\text{null}}}{\alpha^{1}}. \tag{28}\]  
Thus, when the output of MF\({}_{2}\) to MF\({}_{N}\) in the LC 1 of the proposed structure is \(y_{\text{MF}_{2n\
eq 1}}^{\prime}=\Big{[}y_{\text{MF}_{2}}^{1},\ y_{\text{MF}_{3}}^{1},\ \dots,\ y_{\text{MF}_{N}}^{1}\Big{]}^{T}\) and the weight vector of LC 1 is \(w^{1}=\Big{[}w_{1}^{1},\ w_{2}^{1},\ \dots,\ w_{N-1}^{1}\Big{]}\), the output of LC 1 in the proposed structure, \(y^{1}\), is as follows in Equation (29):  
\[y^{1}=y_{\text{MF}_{1}}^{1}+\Big{(}w^{1}y_{\text{MF}_{n\
eq 1}}^{\prime}\Big{)} ^{T}=y_{\text{MF}_{1}}^{1}+\sum_{i=1}^{N-1}w_{i}^{1}y_{\text{MF}_{i+1}}^{1}. \tag{29}\]  
Even in LC 2, the output, \(y^{2}\), can be obtained by the same procedure. Further, the above procedure can be extended even when the desired number of users is \(M_{d}\).
# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 5 Discussion
In this study, the identification of the population, the magnitude and the spatial distribution of the potentially unstable rock blocks was executed with a 3D approach, based on the accuracy of the point cloud and the knowledge of the optimal failure model. It should be underlined that due to the absence of data from former rockfall-related events, limitations and uncertainties of this approach have been recognized. Firstly, the limitations could occur from the specifications of the 3D point cloud dataset (i.e., pointsdensity, ground resolution, reprojection error, etc.) and of course the inexperience of the user. When it comes to the uncertainties of the proposed methodology, these are subsequent to the lack of pre-existing data which could be used to validate the results. As was shown in the Kipinas Monastery case study, no limitations regarding the quality of the constructed point cloud (consisting of about 104 million points with a ground resolution of 1.27 cm/pixel and a reprojection error of 0.157 pixels) have been identified, according to the standards set by previous related analyses [46; 47; 48; 49; 50; 109]. In order for the aforementioned uncertainties to be mitigated, it was decided to identify and analyze an additional parameter, the already failed rock blocks. The monitoring of the previously fallen rock blocks and the estimation of their volumes enabled the construction of a substitute for the previously non-existent inventory of prior rockfall events in the area of interest. With the establishment of this preliminary rockfall-related dataset, the distribution and volumes of the potentially unstable rock blocks were validated and, in some cases, calibrated. As a result of the reduction in the analysis incertitude, a more accurate simulation of reality is achieved. To conclude, it is important to highlight that this proposed rating system reflects the result of an initial attempt, and further development and validation by back-analyses is encouraged for the optimum adjustment of the weight for various contained factors. Specifically, its potential application in different areas and rock slopes, where most of the parameters may have a greater range, could help to evaluate the sensitivity of each factor in the final estimation of the hazard and risk level.
# Design and Testing of Torveastro: An Outer Space Service Robot
## 4 Dimensional Design  
In addition to autonomous tasks, an astronaut robot may interact with astronauts on building, maintenance, and monitoring, or aid in these areas. Therefore, significant robot mobility is required, since it ensures that robots can work outside of space stations. The robot can move slowly while retaining consistent action outcomes and a stable posture. Therefore, the Torveastro robot is designed to navigate along the handrails intended for human space travel. Along the surface of the space station, railings are constructed so astronauts may undertake maintenance and service operations in orbit. The handrails feature a small (\(34.95\times 15.9\)) mm structure, are highly stable and easy to grip, and are separated by 1 m [22]. An example of an installation of handrails in space is represented in Figure 2.  
It has been determined that the body of the service robot will be cylindrical and consist of three limbs 120\({}^{\circ}\) apart that are equally spaced. Additionally, the limb will feature two links and an end effector. The arc-shaped links in Figure 3 are intended for a home setup that is suited for launch storage.  
Figure 3: A scheme for dimensional design.  
Figure 2: Example of Handrails for astronaut movements detail [22].  
Using the data in Figure 3, it is possible to perform a geometric optimization according to Equations (1)-(3) to find the robot body and link dimensions:  
\[c=2(R+d)\sin\alpha \tag{1}\]  
\[l^{\prime}=c \tag{2}\]  
\[{l^{\prime}}^{2}+L^{2}-2l^{\prime}L\cos\theta=(R+f)^{2}, \tag{3}\]  
where  
* \(c\) is length of the entire limb;
* \(R\) is the radius of the body;
* \(d\), \(f\) are the distance from the connection of the limb to the body;
* \(\alpha\) is the rotation of the body in respect to the horizontal reference frame;
* \(2L\) is the distance between the handrails;
* \(l^{\prime}\) is the sum of the cord of the limb link;
* \(\theta\) is the inclination of the cord of the limb link in respect to the horizontal reference frame.  
The horizontal reference frame represents the distance between handrails, with the beginning and terminating points denoting the grasping points. The configuration with the body rotated 120.00 degrees and the arm extended and slanted 25.62 degrees was selected because it reflects the worst case situation in which the robot is navigating between two known locations utilizing handrails.  
In addition, \(L\) is 500 mm, or half the distance between the handrails, whereas \(d\) and \(f\) measure 40 mm apiece.  
Assuming \(l^{\prime}=c\), it is possible to derive the ideal radius of the body \(R\) and length of each link of the limb \(c/2\) by solving Equations (1) and (3) with Carnot's Theorem as expressed in Equation (3). The results indicate that the best body radius is \(R=184.78\) mm and that the optimal limb link length is \(c/2=199.67\) mm. Using these characteristics, a kinematic simulation was performed to compute the mechanism's workspace, angular velocities, and angular accelerations to evaluate the design's feasibility and to characterize its performance.
## 5 Results  
We assume that 100 APs and 10 terminals are identically and uniformly distributed within a square of size \(1\times 1\) km\({}^{2}\). The channel model is the 3 rd Generation Partnership Project (3 GPP) UMi channel model [27]. The noise variance at the receiver is assumed to be \(\sigma_{w}^{2}=T_{0}\times\kappa\times B\times NF\), where \(\kappa\), \(B\), and \(NF\) are the Boltzmann constant, bandwidth and noise figure, respectively. The pilot overhead \(\tau_{p}\) equals \(M\) for downlink training or \(K\) for uplink training. Other parameters are summarized in Table 3.
## 3 Bridge Vibration Response Based on Vehicle-Bridge Coupling
### Bridge Vibration Response Based on Vehicle-Bridge Coupling
#### 3.2.4 Bridge Vibration Response at Different Levels of Smoothness  
Among the factors influencing the vehicle-bridge coupling system, the unevenness of the bridge deck is also essential, in addition to the vibration characteristics of the vehicle and the bridge itself. In a coupled vehicle-bridge system, the excitation of the bridge deck is mainly caused by the vehicle and the random excitation is caused by the unevenness of the bridge deck when the vehicle is in motion, without taking into account the effects of external factors such as seismic and wind effects. The excitation caused by the unevenness of the bridge deck is random and is a significant influence in the coupled vehicle-bridge system. This section calculates the bridge vibration force response for bridge decks when smooth and with Class A, Class B, and Class C unevenness.  
Figure 20: Variation of peak dynamic deflection in the span of bridge and parapet for different numbers of transverse vehicles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **One Car** & **Two Cars** & **Three Vehicles** \\ \hline Spanwise dynamic deflection/(mm) & 0.54 & 0.59 & 0.72 \\ Guardrail side span dynamic deflection/(mm) & 0.66 & 0.75 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak dynamic deflection in bridge spans and parapet spans.  
When travelling on a bridge at a speed of 60 km/h, the poorer the smoothness of the bridge deck, the greater the excitation caused to the bridge by the vehicle movement. As can be seen from the graph above, as the smoothness of the bridge deck decreases, the more significant the change in dynamic deflection of the nodes in the bridge span. The peak dynamic deflection is 0.58 mm when the bridge deck is smooth and 0.62 mm, 0.64 mm, and 0.69 mm when the bridge deck is not smooth for classes A, B, and C, respectively.