esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Boundary Condition Models on the Seismic Responses of a Container Crane
###### Abstract  
In recent years, several large earthquakes have caused the collapse of container cranes, which have resulted in halting of freighting, and significantly affected the economy. Some reports are concerned the uplift and derailment events of crane legs, and the collapse of the crane itself. In this study, the effects of different boundary conditions used in the numerical method are investigated for a container crane under seismic excitation. Three different boundary conditions are considered in terms of the connection of the crane's legs (wheels) and the ground (rails), namely pin support (PIN), gap element (GAP), and Friction contact (FC) elements, by using the SAP2000 program for a typical container crane. Then, time history dynamic analyses are conducted using nine recorded ground motions. Dynamic behaviors of the container crane are studied in terms of the total base shear, portal drift, and relative displacement of legs, by investigating the three types of base boundary conditions. The results of the study show that when the intensity of earthquakes is large enough to create uplift and derailment events, the selection of the boundary condition model considerably affects the dynamic responses of the container crane. In addition, when uplift and derailment of the crane occur, the FC support condition is the most compatible with the real behavior of the crane. On the other hand, under low seismic excitation, there is no significant difference of the crane behavior according to the choice of boundary condition model.  
container crane; friction contact; boundary condition; portal drift; total base shear; uplift; derailment; ground motion; spectral acceleration; time history analysis Article
## 3 Results
### Analysis of Meteorological Factors
#### 3.2.2 RHU and SSD  
The time series of the annual average RHU and SSD with the corresponding 5-year sliding averages and spatial distributions are presented in Figures 5 and 6, respectively. Figures 5a and 6a show that RHU and SSD presented a slight downward trend in the Loess Plateau over the past 34 years. RHU varied from 54% to 64%, whereas SSD fluctuated from 6.3 h to 7.3 h. RHU was relatively high north and west of Loess Plateau, but low in the cities of Wuzhong, Taiyuan, and Changzhi (Figure 1). SSD was only approximately 4-5 h in the north of Loess Plateau but increased to 7-9 h in the middle of this region. This phenomenon may be associated with the latitudes and topography. RHU and SSD further showed a significant negative correlation in Figures 5b and 6b in the Loess Plateau. For areas with a long SSD, the corresponding RHU was low, whereas areas with a short SSD had a high RHU.  
Figure 4: Elevation distribution of the Loess Plateau region.  
Figure 5: Time series of the average RHU (**a**) and spatial distribution over the Loess Plateau region (**b**).
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.2 Gap Filling  
Linear and spline interpolations methods are widely applied to fill the time series gaps [18; 26]. For each sampling point, the gaps in the time series were filled using the three gap-fill algorithms presented in this paper. The weighted average method is used to fill gaps in the time series [44], the spurious midpoints can be filled by the weighted average of the two nearest points. As shown in Figure 3, points a and d can be filled by the known points b and c according to Equation (3), where \(D_{ab}\) is the time interval between points a and b, \(D_{bd}\) is the time interval between points b and d; \(P_{a}\) is the weight of point a; and \(V_{a}\) is the value of point a.  
\[\begin{array}{l}P_{a}=\frac{D_{bd}}{D_{bd}+D_{ab}}P_{d}=\frac{D_{ab}}{D_{bd }+D_{ab}}\\ V_{b}=P_{a}\times V_{a}+P_{d}\times V_{d}\end{array} \tag{3}\]  
The trend-fill method with a decay coefficient alpha can be used to fill the gaps in the edges of a time series. If there are fewer than five deficient points in one edge of the time series, they can be predicted according to Equation (4), where delta is the difference between points b and a and alpha is the change ratio.  
\[\begin{array}{l}delta=P_{b}-P_{a}\,alpha=(1-abs(\frac{delta}{P_{a}})) \times\frac{P_{b}}{P_{a}}\\ predict_{value}=P_{b}+delta\times alpha\end{array} \tag{4}\]As in Figure 4, point c can be predicted from points a and b, while point d can be predicted from points b and c. If there are more than five spurious points at the edge of the time series, a quadratic polynomial function is used to predict the deficient values. Precision can be assured by using three times the number of missing points to construct the polynomial model. The specific gap-filling strategy follows Equation (5), where \(x\) is the serial number of the time series point, \(\beta\) is the coefficient matrix, and \(\bar{x}\) is the index of the deficient point in the time series.  
\[y=ax^{2}+bx+c\quad predict\_values=\beta\overline{A} \tag{5}\] \[\beta=(A^{\mathrm{T}}A)^{-1}\big{(}A^{\mathrm{T}}y\big{)}=\left[ \begin{array}{cc}a\\ b\\ c\end{array}\right]\] \[A=\left[\begin{array}{ccc}1&x&x^{2}\\ \vdots&x&x^{2}\\ 1&x&x^{2}\end{array}\right]\]
## 4 Experiments and Analysis
### Ablation Study  
In this subsection, we conduct ablation studies to assess the functionality of the CT-Transformer, Variational Mode Decomposition, CSPNet-Attention, and TCN-LSTM within the fusion model presented in this paper. We evaluate five ablation variants of the fusion model: (1) Ours: the complete CT-Transformer model incorporating Variational Mode Decomposition fused with TCN-LSTM; (2) no-V: the complete CT-Transformer model excluding Variational Mode Decomposition, fused with TCN-LSTM; (3) no-C: the CT-Transformer model devoid of CSPNet-Attention, coupled with TCN-LSTM in the fusion model; (4) no-T: the complete CT-Transformer model without TCN-LSTM integration; (5) no-VT: the complete CT-Transformer model without Variational Mode Decomposition and TCN-LSTM; and (6) no-VCT: the model excluding Variational Mode Decomposition, CSPNet-Attention, and TCN-LSTM. Figure 10 below illustrates the results of the ablation studies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**PeMS Traffic Dataset**} \\ \cline{4-7}  & & & **96** & **192** & **336** & **720** \\ \hline \multirow{3}{*}{NHiTS} & \multirow{2}{*}{AAAI2022} & MAE & 0.314 & 0.315 & 0.324 & 0.328 \\ \cline{3-7}  & & MSE & 0.222 & 0.229 & 0.220 & 0.227 \\ \hline \multirow{3}{*}{SCINet} & \multirow{2}{*}{NeurIPS2022} & MAE & 0.458 & 0.449 & 0.429 & 0.400 \\ \cline{3-7}  & & MSE & 0.423 & 0.374 & 0.370 & 0.324 \\ \hline \multirow{3}{*}{GBT} & \multirow{2}{*}{NeuralNetworks2023} & MAE & 0.253 & 0.257 & 0.260 & 0.299 \\ \cline{3-7}  & & MSE & 0.160 & 0.160 & 0.168 & 0.205 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 10: Results of ablation studies across four prediction window sizes.  
A in Figure 10 represents the results of the ablation experiment when the predicted window size is 96, B represents the results of the ablation experiment when the predicted window size is 192, C represents the results of the ablation experiment when the predicted window size is 336, and D represents the results of the ablation experiment when the predicted window size is 720.  
Table 4 below illustrates that the \"Ours\" method outperforms the alternative methods in predictions using the PeMS Traffic dataset. Upon removal of the Variational Mode Decomposition, CSPNet-Attention, and TCN-LSTM, individually or collectively, we observed increases in MAE values of 8.7%, 14.7%, 19.5%, and 34.2% and MSE values of 17.5%, 18.9%, 30.7%, and 134%, respectively.  
The findings reveal that omitting three critical elements from the fusion model substantially reduces its effectiveness owing to various negative impacts. This evidence underscores the vital importance of each component in boosting the model's performance within the context of this research.
###### Abstract  
The data collection of Acoustic Emission (AE) method is typically based on threshold-dependent approach, where the AE system acquires data when the output of AE sensor is above the pre-defined threshold. However, this approach fails to detect flaws in noisy environment, as the signal level of noise may overcome the signal level of AE from flaws, and saturate the AE system. Time-dependent approach is based on streaming waveforms and extracting features at every pre-defined time interval. It is hypothesized that the relevant AE signals representing active flaws are embedded into the streamed signals. In this study, a decomposition method of the streamed AE signals to separate noise signal and crack signal is demonstrated. The AE signals representing fatigue crack growth in steel are obtained from the laboratory scale testing. The streamed AE signals in a noisy operational condition are obtained from the gearbox testing at the Naval Air Systems Command (NAVAIR) facility. The signal addition and decomposition is achieved to determine the minimum detectable signal to noise ratio that is embedded into the streamed AE signals. The developed decomposition approach is demonstrated on detecting burst signals embedded into the streamed signals recorded in the spline testing of the helicopter gearbox test rig located at the NAVAIR facility.  
Acoustic Emission (AE); streamed signals; laboratory scale testing; gearbox spline Article
## 2 Data and Methods
### Model Development
#### 2.3.3 Sample Size Sufficiency for Model Estimation  
To quantify the effect of the number of training samples on the NEE prediction of site-level, four EC sites (US-MMS, US-Var, DE-Geb, NL-Loo) with different biomes and  
Figure 1: Flowchart of datasets and model setup. NEE is assumed to be the nonlinear function of TA, SW_IN, LW_IN, VPD, PA, P, WS, NETRAD, TS, and SWC. (NEE: Net ecosystem exchange; TA: Air temperature; SW_IN: Shortwave radiation, incoming LW_IN: Longwave radiation, incoming; VPD: Vapor pressure deficit; PA: Atmospheric pressure; P: Precipitation; WS: Wind speed; NETRAD: Net radiation; TS: Soil temperature at the shallowest; SWC: Soil water content at the shallowest).  
more than 12 years observations were selected. Two approaches were used to assess the robustness of the model prediction results with different training sample sizes. The first approach is to use the last 2 years dataset of the site as the test samples and the number of training samples is increased by the annual sample size (BAS) to build the model. The details of the annual sample size of each evaluated EC site are shown in Table S3.  
Another method is to increase the number of total samples gradually by a fixed step of 100 (BFS). First, we merged the annual samples for each evaluated EC site. The total samples for each evaluation were randomly selected from the merged data, then split into 70% for training and 30% for testing. Determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the prediction results. The definitions of three metrics are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{1}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{2}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_ {i}-\overline{y}_{i})^{2}} \tag{3}\]  
where \(y_{i}\) is the \(i\)th observed validation sample, \(\hat{y}_{i}\) the predicted value of the \(i\)th validation sample, \(\overline{y}_{i}\) denotes the mean value of observed value y, and n is the number of validation samples. When R\({}^{2}\) is larger and RMSE and MAE are smaller, it means that the predicted value is closer to the observed value. Evaluation of the models using these three indices can provide criteria for hyper-parameter correction and model performance comparisons.
## 5 Experiment Result  
### RMS of NSRE and Iteration Coefficient  
The RMS of NSRE of the iteration with modified strategy, simplified strategy, rise-up manual strategy, constant manual strategy and RMS based manual strategy, as well as the iteration coefficient \(\mu_{k}\) are shown in Figure 10:  
As shown in Figure and Table, the RMS of NSRE of the modified strategy namely method 1 is the best result, which has smallest minimum value and almost doesn't diverge after the minimum point. The coefficient of the modified method is large at the beginning, then gradually decreases, and finally drops to very low. The result of the simplified strategy, namely method 2, is a little worse than the modified method, which has greater minimum than the modified strategy and slightly diverges after minimum point. Compared with the modified strategy, the coefficient of the simplified strategy still increases after minimum point of RMS of NSRE, which causes the divergence of the RMS of NSRE.  
The RMS of NSRE of the rise-up manual strategy, namely method 3, diverges significantly after the minimum, and the minimum is larger than the modified and simplified strategies. The RMS of NSRE of the constant manual strategy, namely method 4, converges too slowly and the minimum is too large. The RMS of NSRE of the RMS based manual strategy, namely method 5, converges as fast as the modified strategy, but the minimum is much larger.  
For RPC, with the progress of iteration, if the RMS value of NSRE can be stabilized at a relatively small value, then the iteration is considered to be convergent. The value of RMS is calculated based on the expression in Section 3.5, and related to the iterative control strategy, the experimental environment interference and the weighted characteristics in error evaluation.  
The iteration results are listed in Tables 2 and 3:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy No.** & \begin{tabular}{c} **Minimum** \\ **RMS (\%)** \\ \end{tabular} & \begin{tabular}{c} **Convergence** \\ **Times** \\ \end{tabular} & \
\begin{tabular}{c} **RMS of 10 th** \\ **Iteration (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Divergent** \\ **Proportion (\%)** \\ \end{tabular} \\ \hline
1. Modified & 21.76 & 6 & 23.41 & 0.4125 \\
2. Simplified & 23.57 & 5 & 26.55 & 0.5960 \\
3. Rise-up & 25.14 & 5 & 31.22 & 1.2160 \\
4. Constant & 30.54 & 8 & 31.19 & 0.7250 \\
5. RMS based & 27.42 & 7 & 27.80 & 0.1333 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Iteration results.  
Figure 10: RMS of normalized spectrum of response error (NSRE) and iteration coefficient of vehicle response with Road A, and \(\mu_{0}=0.5\); (solid line represents the RMS of NSRE and dashed line represents the iteration coefficient; the marker X O \(\square\Diamond\Diamond\triangle\) and number 1–5 represent the method 1–5)  
Figure 11: _Cont._
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 3 Energy-Based RPL Routing for IoT-Fog-Enabled Forest Region
### Contiki--Rpl  
RPL [28] construction comprises destination-oriented directed acyclic graph (DODAG) discovery, formation, and monitoring. RPL is formed with the exchange of ICMPv6 control messages for route formation. These control messages help for exchanging the DODAG information and, finally, forms a routing topology. Every node first sends the DODAG information solicitation message (DIS) to enquire about its neighborhood. Destination advertisement object (DAO) is used to identify and send the control message in the reverse direction to mark the visited nodes towards the root.  
Every node position is fixed concerning the DODAG root node. The rank of the node increases towards the leaf and decreases towards the root. Rank 0 node acts as the root node. The rank of the node is calculated using an objective function. The formation of topology starts from the root node. First, the root sends the DODAG information object (DIO) message to all the neighbor nodes, which will receive and process the same. The process of broadcasting the DIO to the one-hop neighbors is repeated until the leaf nodes.  
Once any of the one hop nodes join the DODAG, it will extend the path of routing towards the DODAG root. All the intermediate nodes calculate their RANK in DODAG and share their rank with the root node. If any of the nodes are not in the transmission range, it will send the DIS message at regular intervals to its neighbors. The process is repeated until all the nodes join the DODAG.
## 3 Results
### Robotic Manipulation with TIAGo
#### 3.2.1 Training  
We conducted several training runs using the proposed method outlined in this work, which we refer to as EF-RD-PPO (AL-IRL with Expert-Trajectory-Based Features, Reverse Discount, and PPO), as well as an alternative approach to AL-IRL for continuous spaces [12] and the foundational AL-IRL algorithm [9].  
The AL-IRL approach for continuous spaces utilizes K-means clustering to partition the state space and generate cluster centers. These centers are then used to compute Gaussian RBF distances, which define the feature space. Policy optimization in this approach is performed using the DDPG algorithm, tailored for continuous environments. We refer to this implementation of AL-IRL as CF-DDPG (Cluster Features with DDPG).  
On the other hand, the foundational AL-IRL algorithm was tested as a baseline with some adaptations. For feature definition, we implemented a similar approach involving the uniform discretization of the state space, resulting in one-hot vector features, to enable comparison with more generalized feature representations. In the original implementation of AL-IRL, a known model and value iteration methods were employed for policy optimization. However, in this work, due to the absence of a model, we trained the policy using both model-free DDPG and PPO algorithms. We refer to this implementation of AL-IRL as DF-PPO and DF-DDPG (Discretization Features with PPO and DDPG).  
To evaluate whether a run was successful, we defined a success metric based on the Euclidean distance between the goal position and the final position of the box in a trajectory, which varies depending on the environment. If this distance metric, denoted by \(d_{g}\), is  
Figure 6: Simulation of the TIAGo environments showing the initial state for each task: (**a**) TiagoPush, (**b**) TiagoInsert, and (**c**) TiagoRemove.  
at least \(d_{g}<\text{tolerance}=0.01\,\text{m}\), we consider the trajectory successful. We performed 100 runs for each task and AL-IRL implementation, with the parameters and hyperparameters detailed in Appendix A. The success rates achieved are presented in Table 1.  
For successful runs, we can observe how the algorithm approximates the agent's feature expectations to the expert's feature expectations over episodes in the graphs in Figure 7 for the tasks TiagoPush, TiagoInsert, and TiagoRemove. The distance decreases with each episode and converges to a specific value.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Environment** & **EF-RD-PPO** & **CF-DDPG** & **DF-PPO** & **DF-DDPG** \\ \hline TiagoPush & 54\% & 24\% & 28\% & 24\% \\ \hline TiagoInsert & 72\% & 8\% & 24\% & 0\% \\ \hline TiagoRemove & 64\% & 18\% & 12\% & 10\% \\ \hline \end{tabular}
\end{table}
Table 1: Success rates of training using different approaches to AL-IRL in Tiago environments.  
Figure 7: The distance between feature expectations of the expert and the agent (\(t\)) over training episodes for (**a**) TiagoPush, (**b**) TiagoInsert, and (**c**) TiagoRemove. The mean and standard deviation are calculated from 10 successful training runs using different approaches to AL-IRL.
# Electrical and Optical Properties of Laser-Induced Structural Modifications in PbSe Films
## 3 Results and Discussion
### Electrical and Optical Characterization
\[q=P\pi r^{2}=4P\pi d^{2} \tag{1}\]  
\[t=d\
u \tag{2}\]  
\[w=qt \tag{3}\]  
The PbSe film optical band gap was determined under various processing conditions by the expression: \((\Delta hv)^{2}=B^{2}\) (\(h\
u-E_{g}\)), where \(h\
u\) is photon energy, \(B\) is the Tauc parameter, \(E_{g}\) is the band gap [16]. As a result of two straight sections extrapolation, the values \(E_{g}\) = 1.33 and 1.38 eV were determined (Figure 8). The obtained values are in good agreement with the previously published results of the scientific group [PERSON] et al. [19], where the band gap varied from 1.46 to 1.62 eV. The decrease in the band gap is associated with an increase in the probability of charge carriers' passage from defect states in the modified regions of the film induced by thermal processes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{P}\), \(\mathbf{mW}\) & \(\mathbf{V}\), \(\mathbf{mm/s}\) & \(\mathbf{q}\), \(\mathbf{kW/cm^{2}}\) & \(\mathbf{t}\), \(\mathbf{\mu s}\) & \(\mathbf{w}\), \(\mathbf{\mu J/cm^{2}}\) & **Regime Name** \\ \hline Regime 1 & 11.7 & 1.1 & 15 & 9 & 134 & Modification threshold \\ \hline Regime 2 & 17.2–26.4 & 0.9–1.2 & 22–33.6 & 8–11 & 241–269 & Photodarkening \\ \hline Regime 3 & 13.4 & 0.25 & 17 & 40 & 682 & Burn \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scanning modes with continuous UV radiation. Figure 8: Optical band gap \(E_{g}\) graphical determination (orange line) for PbSe films various treatments: untreated (black curve), laser modification (red curve).
# Effect of the Increasing Operator's Experience on the Miniscrew Survival Rate
## 3 Results  
Total success rate reached 79.46% (321/404). Separately, it equaled 80% (128/160) for JA-S, 76.25% (122/160) for MS, and 84.52% (71/84) for JL.  
The success rate with the following groups of 40 TAD insertions is demonstrated in the Table 1. Evaluating each orthodontist's separately, after the first 40 insertions, JA-S, MS, and JL achieved 72.5%, 70%, and 82.5% of stable TADs, respectively. Then, the discussedpercentage increased up to 90%, 87.5%, and 85%. As can be seen on the overall learning curve, 80% and 85% TAD survival rates occurred after 74 and 118 insertions, respectively (Figure 1).  
From that point, the TAD total success rate still presented an ascending tendency.  
As can be seen from the learning curves, the postgraduate student JL exceeded 80% success rate from the start, while for JA-S and MS, the 80% threshold was reached after 76 and 98 TAD insertions, respectively. A success rate of 85% was achieved by JL, JA-S, and MS after 48, 112, and 130 TAD insertions, respectively (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Success Rate \%**} \\ \hline  & **First Group of** & **Second Group** & **Third Group of** & **Fourth Group** \\  & **40 TADs** & **of 40 TADs** & **40 TADs** & **of 40 TADs** \\ \hline
**JAS** & 72.5\% & 77.5\% & 80\% & 90\% \\ \hline
**MS** & 70\% & 67.5\% & 80\% & 87.5\% \\ \hline
**JL** & 82.5\% & 85\% & & \\ \hline
**Total** & 75\% & 76.7 & 80\% & 88.8\% \\ \hline \end{tabular}
\end{table}
Table 1: Success rate of TAD insertions (according to groups of 40 TADs) for each doctor separately and in total.  
Figure 1: Total learning curve in TAD insertion—logistic regression model.  
Statistical analysis proved that effectiveness in achieving stability increased by 1.016 (\(p=0.006\)) times with every TAD placement (Table 2), and it was significantly affected by the number of executed procedures or--in other terms--the operator's gradually gathered experience. The chance of a successful procedure did not depend on a particular doctor.