esa-sceva/satcom-chunk-collection huggingface dataset:
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF  
We aim at supporting the simulation of thousands (or more) devices participating in previously unforeseen/existing IoT scenarios that have not been examined before in more detail (e.g., in terms of scalability, energy efficiency or management costs). As this aim requires a high performance resource sharing mechanism, we have chosen to extend the DISSECT-CF [4] simulator because of its unified resource sharing foundation.  
DISSECT-CF is a compact open source simulator [15] focusing on the internals of IaaS (Infrastructure as a Service) systems. Figure 1 presents its architecture including our extensions (denoted with grey colour). There are six subsystems (encircled with dashed lines) implemented, each responsible for a particular functionality: (i) event system--the primary time reference; (ii) unified resource sharing--models low-level resource bottlenecks; (iii) energy modelling--for the analysis of energy-usage patterns of resources (e.g., CPUs) or their aggregations; (iv) infrastructure simulation--for physical/virtual machines, sensors and networking; (v) cost modelling--for managing IoT and cloud provider pricing schemes, and (vi) infrastructure management--provides a cloud like API (Application Programming Interface), cloud level scheduling, and IoT system monitoring and management.  
Our current extension performed within this work focuses on IoT and cost management. We introduced the following new components to model IoT systems: Sensor, IoT Metering and IoT Controller. Sensors are essential parts of IoT systems, and usually they are passive entities (actuators could change their surrounding environment though). Their performance is limited by their network gateway's (i.e., the device which polls for the measurements and sends them away) connectivity and maximum update frequency. Our network gateway model builds on DISSECT-CF's already existing Network Node model which allows changes in connection quality as well. Our model of the Sensor component is used to define the sensor type, properties and connections to a cloud system. IoT Metering is used to define and characterize messages coming from sensors, and the IoT Controller is used for sensor creation and management.  
Figure 1: The architecture of the extended DISSECT-CF simulator.  
To incorporate cost management, we enabled defining and applying provider pricing schemes both for IoT and cloud part of the simulated environments. These schemes are managed by the IoT and Cloud Pricing components of the Cost modeling subsystem of DISSECT-CF, as shown in Figure 1.
# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Tracking Phase  
For robust detection and in order to increase the accuracy of the information used in identifying traffic signs, the signs are tracked using a simple motion model and temporal information propagation. This tracking process is very important for real-time applications, by which the TSDR system verifies correctness of the traffic sign and keeps tracking the sign to avoid handling the same detected sign more than once [21; 83]. The tracking process is performed by feeding the TSDR system with a video recorded by a camera fixed on the vehicle and monitoring the sign candidates on a number of consecutive frames. The accepted sign candidates are only those shown up more than once. If the object is not a traffic sign or a sign that only shows up once, it can be eliminated as soon as possible, and thus, the computation time of the detection task can be reduced [84]. According to [85] and as shown in Table 5, the most common tracker adapted is the Kalman filter, as in [82; 85; 86; 87; 88]. The block diagram of a TSDR system with a tracking process based on the Kalman filter as proposed in [82] is shown in Figure 11. In the figure, SIFT, CCD and MLP are abbreviations of scale-invariant feature transform, contracting curve density and multi-layer perceptrons, respectively.
# A Micro-Coordinate Measurement Machine (CMM) for Large-Scale Dimensional Measurement of Micro-Slits
## 4 Conclusions  
Dimensional measurement of micro-slit gap width of a precision slot die coater with a length of 200 mm and a nominal width of 85 \(\upmu\)m has been carried out by using a specially designed and constructed micro-CMM. A shear-mode micro-probing system was employed in the micro-CMM. A calibrated artifact, which was a calibrated gauge block with a thickness of 100 \(\upmu\)m, was placed on the same stage table as a \(Y\)-directional linear slide for the on-line qualification of the effective diameter of the probe tip ball prior to the gap width measurement of the slot die. The proposed micro-CMM, which combined the micro-probing system and a laser interferometer, was able to achieve nanometric resolution and repeatability of length measurement. The effective diameter \(D_{e}\) of the probe tip ball was evaluated to be 53.781 \(\upmu\)m, and the expanded uncertainty in \(D_{e}\) was estimated to be 52.8 nm (\(k=2\)). After the qualification procedure, the gap width measurement of the slot die was carried out immediately. As a result, the expanded uncertainty of the gap width measurement was estimated to be 66.6 nm (\(k=2\)), which can satisfy the required measurement accuracy. The use of an air-bearing linear slide with a stroke of 300 mm has made it possible for the micro-CMM to cover the entire length of the precision slot die. Furthermore, a micro-stylus with an effective working length of larger than 1 mm, which was composed of a capillary glass shaft and a micro-glass sphere, has been employed to measure the gap width from the inside of the micro-slit of the precision slot die.  
On the other hand, the shear-mode micro-probe was utilized as a touch-trigger probe in the developed micro-CMM and the total measurement time for the uniformity evaluation of the gap width was approximately 200 min. Reduction of the measurement time will be an important future work.  
This research was supported by KAKENHI. The authors would like to acknowledge the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS).  
[PERSON] and [PERSON] conceived the experiments and wrote the paper; H.K. designed the experimental apparatus and performed the experiment; [PERSON] and [PERSON] analyzed the data. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to prepare slot die sample.  
The authors declare no conflict of interest.  
Figure 19: Gap width uniformity over the entire slot die length (\(Z\)-direction).
## 3 Results
### GEP Method
However, as illustrated in Figure 12, the values of the statistical indicators (\(R^{2}\), \(RMSE\), and \(MAE\)) for those models are different. Based on those values, the accuracy of the different methods was deduced as follows: GEP (the highest accuracy), MLR, Equation (1), and Pickett equation. To sum up, it can be expressed that the nonlinear equations extracted by the GEP and MLR methods deliver the best results. Thus, it is deduced that the GEP approach can be successfully applied in \(V_{s}\) estimation for the study area as it delivers the most accurate results with the lowest \(RMSE\) and \(MAE\) values. The application of artificial intelligence techniques such as MLR and GEP has its own benefits and drawbacks. A number of researchers have already developed some correlations for \(V_{s}\) prediction using the different AI techniques such as fuzzy logic, ANN, ANFIS, genetic algorithm, polynomial neural networks, etc. [5; 69; 70; 71]. This research confirms the findings of those researches which reported the significant capability of AI techniques in \(V_{s}\) estimation. In this research, Equations (10) and (11) were established based on the MLR and GEP techniques, respectively. In evaluating the performance of the GEP and MLR models, it is essential to consider the trade-off between accuracy and robustness. The GEP model, with its ability to capture complex relationships within the data, has demonstrated  
Figure 11: The real \(V_{s}\) data of the DSI logs versus the estimated values obtained by Equation (1), Pickett, MLR, and GEP model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** \\ \hline GEP & 0.972 & 0.000290 & 0.000208 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The calculated values of \(R^{2}\), \(RMSE\), and \(MAE\) corresponding to the GEP model. commonable accuracy in predicting outcomes. However, it is imperative to acknowledge the potential challenges associated with model robustness, especially in the presence of outliers or noisy data. On the other hand, the MLR model, being a simpler linear approach, may exhibit greater robustness in the face of such challenges but might sacrifice some accuracy in capturing intricate patterns. The choice between these models depends on the specific characteristics of the dataset and the goals of the predictive task. Future research will delve into refining the GEP model for enhanced robustness without compromising its predictive accuracy, striking a balance that aligns with the specific requirements of the application domain. Regarding the Aboozar oilfield, by employing the GEP method that ensures precise \(V_{s}\) estimation, the geomechanical risks can be proactively managed, leading to safer and more efficient drilling practices. The reduction in drilling costs is a direct outcome of the improved predictability afforded by accurate \(V_{s}\) estimations, as it enables better planning and resource allocation.
## 3 Results
### Burned Area Map
#### 3.2.2 Threshold Selection and Accuracy Evaluation
The results prove that the new method achieved a better performance than the traditional empirical formula method. We also applied the new burned area mapping method to the Cinder Butte Fire and Tohakum 2 Fire. The pixel-wise comparison results presented by color mixing of the MODIS-based and OLI-based burned area maps in the two fires are shown in Figure 11. The CE and OE are 8.07% in Cinder Butte Fire, and they are 14.40% and 17.16% in Tohakum 2 Fire, respectively. Figure 11: The pixel-wise comparison results of the MODIS-based and OLI-based burned area maps. The images on the left of (**a**) and (**b**) are the true-color composition RGB image of OLI bands 4, 3 and 2, while the images on the right side are the comparison results: the OLI burned area is symbolized by blue, MODIS burned area is red, and the agreement between them is purple. (**a**) OLI image on 17 August 2017 (path/row is 44/30) and the comparison result in Cinder Butte Fire; (**b**) OLI image on 27 September 2017 (path/row is 43/32) and the comparison result for Tohakum 2 Fire. Figure 10: Change trends of commission error and omission error along with the thresholds of [0, 0.9] range in the wildfire cases using the proposed method. According to the accuracy assessment results between the MODIS-based and OLI-based burned area maps that are shown in Table 7 and Figure 11, it demonstrates that the new method achieves high accuracy in burned area detection. The success is mainly due to the combination of the training samples of optimal spectral indices and the BPNN algorithm. In the application of the wildfire cases, the omission errors illustrate an underestimation of the burned area detected in the MODIS-based map. This can be mainly ascribed to the conservative threshold used to separate the burned and unburned pixels in the BPNN output layer. In addition, the impact of the spatial pattern of burned patches at high spatial resolution data can lead to the false detection [68]. Moreover, because the MODIS data was corrected to the transverse Mercator projection, the pixel shift and the resampling method could also bias the commission and omission errors between the MODIS-based and OLI-based burned area map. Considering that the OLI image used in the Tohakum 2 Fire is 22 days later than the applied MODIS image, the loss of ash and restoration of herbaceous plants may account for the discrepancy in the northeast of the fire.
# Improvement of the Zienkiewicz-Zhu Error Recovery Technique Using a Patch Configuration
## 6 Square Plate Example
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh Size (1/h)**} & \multirow{2}{*}{
\begin{tabular}{c} **FEM Error** \\ **(\(\times\)10\({}^{-3}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**ZZ (Standard Patch)**} & \multicolumn{2}{c}{**ZZ (Modified Patch)**} & \multicolumn{2}{c}{**LS (Modified Patch)**} \\ \cline{3-8}  & & **Error (Stress)** & **Error (\(\times\)10\({}^{-3}\))** & **Effectivity** & **Error (Stress)** & **Error (\(\times\)10\({}^{-3}\))** & **Effectivity** \\ \hline
1/4 & 13.1683 & 4.9321 & 0.95024 & 9.7741 & 1.10601 & 12.2577 & 1.25556 \\
1/12 & 1.51492 & 0.29653 & 0.95295 & 0.25279 & 0.97335 & 0.52885 & 1.04045 \\
1/24 & 0.3802 & 0.0553 & 0.95729 & 0.0316 & 0.98134 & 0.0698 & 1.01265 \\ \hline Rate of Conv. & 1.97848 & 2.50622 & & 3.19768 & & 2.88449 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Solution error and global effectivity for the plate problem using stress/displacement recovery and patch configuration (regular quadratic triangular element). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh Size (1/h)**} & \multirow{2}{*}{
\begin{tabular}{c} **FEM Error** \\ **(\(\times\)10\({}^{-3}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**ZZ (Standard Patch)**} & \multicolumn{2}{c}{**ZZ (Modified Patch)**} & \multicolumn{2}{c}{**LS (Modified Patch)**} \\ \cline{3-8}  & & **Error (Stress)** & **Error (\(\times\)10\({}^{-3}\))** & **Effectivity** & **Error (Stress)** & **Error (\(\times\)10\({}^{-3}\))** & **Effectivity** \\ \hline
88 & 410 & 3.4529 & 1.4263 & 0.93743 & 1.3562 & 0.92285 & 2.8478 & 1.24041 \\
395 & 1696 & 0.7502 & 0.2302 & 0.95072 & 0.1300 & 0.95475 & 0.6746 & 1.09216 \\
925 & 3876 & 0.3190 & 0.0965 & 0.95235 & 0.0468 & 0.96886 & 0.0775 & 1.02221 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Solution error and global effectivity for the plate problem using stress/displacement recovery and patch configuration (regular quadratic triangular element). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh Size (1/h)**} & \multirow{2}{*}{ \begin{tabular}{c} **FEM Error** \\ **(\(\times\)10\({}^{-3}\))** \\ \end{tabular}  } & \multicolumn{2}{c}{**ZZ (Standard Patch)**} & \multicolumn{2}{c}{**ZZ (Modified Patch)**} & \multicolumn{2}{c}{**LS (Modified Patch)**} \\ \cline{3-8}  & & **Error (Stress)** & **Error (\(\times\)10\({}^{-3}\))** & **Effectivity** & **Error (\(\times\)10\({}^{-3}\))** & **Effectivity** \\ \hline
1/4 & 60.2610 & 19.0382 & 1.09566 & 55.5321 & 1.39422 & 13.6672 & 1.08099 \\
1/16 & 15.0154 & 2.1981 & 1.01340 & 1.7520 & 1.01266 & 0.8131 & 1.00512 \\
1/32 & 7.5071 & 0.6082
## 3 Remaining Useful Life Prediction Algorithm Based on Double Bidirectional Long Short-Term Memory
### RUL Prediction Model
#### 3.4.2 Bayesian Optimization for Hyperparameters  
We automatically search for hyperparameters of the proposed remaining useful life prediction model and use the Bayesian optimization method [45] to achieve automatic optimization, thereby avoiding the cumbersome process of manually tuning hyperparameters.  
The objective function of Bayesian optimization is the prediction error on the validation set. In this paper, RMSE (Root Mean Squared Error) is adopted; the formula for RMSE is [5]:  
\[RMSE=\sqrt{\frac{1}{n_{y}}\sum_{i=1}^{n_{y}}\left(Y_{t}-\hat{Y}_{t}\right)^{2}} \tag{36}\]  
where \(n_{y}\) represents the number of samples in the test set.  
Formulas related to Bayesian optimization are obtained from Reference [45]:  
1. The objective search formula is designed as: \[f(\theta)=Error(Y_{t},\hat{Y}_{t})\] (37) where \(\theta\) represents the combination of hyperparameters (learning rate, number of hidden layer neurons, batch size, dropout rate, and number of iterations).
2. Bayesian optimization models the objective function through a Gaussian process, with the assumption that: \[f(\theta)\sim GP(m(\theta),k_{h}(\theta,\theta^{\prime}))\] (38) where \(m(\theta)\) is the mean function (usually set to 0), and \(k_{h}(\theta,\theta^{\prime})\) is the kernel function, whose formula is: \[k_{h}(\theta,\theta^{\prime})=\sigma_{f}^{2}\exp(-\frac{\|\theta-\theta^{ \prime}\|^{2}}{2l_{l}^{2}})\] (39)
3. Given the observed data \(D=\left\{(\theta_{ii},f(\theta_{ii}))\right\}_{i=1}^{n_{y}}\) (where \(n_{y}\) represents the number of samples in the test set, and \(ii=1,2,\,\ldots,\,n_{y}\)), the posterior distribution remains a Gaussian process, with its mean and variance given by:  
Figure 6: Network structure of A-DBiLSTM.  
\[\mu(\theta)=K_{0}^{T}(K_{00}+\sigma_{n_{y}}^{2}I)^{-1}f\] (40) \[\sigma^{2}(\theta)=k_{h}(\theta,\theta)-K_{0}^{T}(K_{00}+\sigma_{n_{y}}^{2}I)^{-1 }K_{0}\] (41) where \(K_{0}\) is the vector of kernel function values between the new point \(\theta\) and the observed points \(\theta_{ii}\), \(K_{00}\) is the kernel function matrix among the observed points, and \(\sigma_{n_{y}}^{2}\) is the variance of the observation noise.
4. An acquisition function is used to select the next evaluation point. The main formula is: Expected Improvement: \[a_{EI}(\theta)=E[\max(0,f_{best}-f(\theta))]\] (42) where \(f_{best}\) is the current optimal value. Upper Confidence Bound: \[a_{UCB}(\theta)=\mu(\theta)+\kappa\sigma(\theta)\] (43) where \(\kappa\) controls the balance between exploration and exploitation and is the uncertainty coefficient.  
The search ranges when optimizing hyperparameters using Bayesian optimization are as follows: number of neurons in hidden layers: [16; 512]; initial learning rate: [10\({}^{-4}\), 1]; batch size: [16; 512]; number of iterations: [50; 3000]; dropout rate: [0, 0.5].
## 5 Results
### Simulation Results
#### 5.2.5 Poisson's Ratio Analysis  
To investigate the effect of the Poisson's ratio on prediction of the model displacement in the two boundary conditions, an analysis in the range of 0.399 to 0.499 was performed in the two boundary conditions. The range 0.399 to 0.499 is extracted from the previous studies [45; 54; 55] for the mixture 1:1:2, which the model is based on. The range was chosen to cover the [PERSON]'s ratios determined by tensile test [45] and by dynamic measurement setup [54; 55] from 0.45 to 0.499. The model predictions of the displacement \(z\)-component by the four different Poisson's ratios are demonstrated in Figure 15.  
Figure 14: Displacement amplitude versus different damping factors in the two boundary conditions; 0.1 to 0.4 were varied in the (\(f_{1}\)) and 0.225 to 0.4 were varied in the (\(f_{2}\)); (**a**) connected pipette; (**b**) pipette with gap.  
Figure 15: Displacement amplitude versus different Poisson’s ratios in the two boundary conditions; (**a**) connected pipette; (**b**) pipette with gap.  
High dependency on the Poisson's ratio is visible from the plots in both boundary conditions, especially in the pipette with gap condition. Apart from the previous studies [54; 55] in which the Poisson's ratio of a harmonically excited sample determined close to 0.5, the measured displacement which is denoted with the black dotted line in the graphs fits better with the predictions of displacement using [PERSON]'s ratio 0.499. Therefore, we use [PERSON]'s ratio 0.499 for the model in the simulation. Both graphs show that the Poisson's ratio can be assumed constant in the frequency range as is assumed in the previous studies [54; 55].
# Arm Posture Changes and Influences on Hand Controller Interaction Evaluation in Virtual Reality
## 4 Methods
### Design  
In this study, a 2 \(\times\) 3 \(\times\) 15 repeated measures within-participant design was adopted. The independent factors included hand-use choice (left, right), arm-bending degree (wholly-bent, half-bent, stretched) and hand-operational height (3 target heights \(\times\) 5 horizontal angles = 15 different positions).  
According to the experimental design, targets were located at different horizontal angles and vertical heights surrounding the shoulder joint. Earlier studies on kinematics have found that people can flexibly perform arm movements in body-front space, but can hardly perform any arm movements in body-behind space [43]. Because of this, in the horizontal direction, all target positions were located within 180 degrees of the visual field and were distributed at 5 different angles: \(-90^{\circ}\), \(-45^{\circ}\), \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\), respectively, from the left side to the right side, as shown in Figure 4a. In the vertical direction, target positions were located at the overhead height, the shoulder height and the waist height, respectively, as shown in Figure 4c. There were 3 \(\times\) 5 = 15 target positions in the experiment.  
Figure 4: An illustration of the independent variables: (**a**) 15 target positions; (**b**) three operational distances corresponding to three bending degrees of the arm; (**c**) hand-operational heights (overhead, shoulder, and waist).  
In terms of hand-use choice, the participant used the left hand and the right hand, respectively, to complete one task block. The order of hand use was counterbalanced across the participants: one half used the left hand first, while the other half used the right hand first. Arm-bending degree included wholly-bent, half-bent and stretched arm postures. As illustrated in Figure 4b, three bending degrees of the arm corresponded to three interaction distances between the shoulder joint and the target. To make all targets reachable, the interaction distances were defined as 30 cm, 45 cm and 60 cm. Each participant completed 2 hands \(\times\) 3 bending degrees of the arm \(\times\) 15 target positions = 90 task blocks. Table 1 presents a summary of independent variables in this experiment.  
Target size, i.e., the diameter of the spherical target shown in the experiment program was defined as 2.0 cm. The presentation order of the target positions, as well as the order of the arm used, were counterbalanced across the participants.