esa-sceva/satcom-chunk-collection huggingface dataset:
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 5 Finite Element Analysis (FEA) of PMs
In conclusion, for the high \(\omega\) limit of 120 krpmn considered in this FEA, depending on the values of \(n\) and \(R\) (which are imposed by the application, as discussed in Section 6), \(\sigma_{\max}\) can pass the limit of the tensile yield strength, as determined from Figures 12 and 13, respectively. To avoid this, appropriate modifications of the design must be considered, such as a smaller diameter of the central hole, 2\(\tau\)--Figure 14(c2). Other parameter adjustments include increasing the thickness of the PM or slightly altering the position, diameter, and number of supplemental mounting holes, as studied in Figure 15. All these measures should be combined to be able to increase the upper value of \(\omega\) that can be applied to the PM. However, despite measures such as the above, for the upper limits of \(\omega\), materials such as beryllium may be necessary, despite its disadvantages, to keep \(\sigma_{\max}\) lower than the yield limit. Figure 14: FEA of a hexagonal PM (with \(R\) = 25 mm) for two levels of \(\omega\), (**1**) 60 krpm and (**2**) 120 krpmn, for a change in the width \(b\) from (**a**) 5 mm to (**b**) 10 mm, as well as for a change in the radius \(r\) of the central shafts, from (**c**) 2.5 mm to (**b**) 5 mm. For (**a**) \(r\) = 5 mm, while for (**c**), \(b\) = 10 mm.
## 2 Materials and Methods
### Excitation Modelling
#### 2.1.5 The Mining-Induced Shock Registered in the Upper Silesian Coal Basin  
In this work, a mining tremor recorded by a seismic station located in the USCB was used [6] as a kinematic excitation. The duration of the intense vibration phase was about 3.5 s. The energy of the shock was 1.10\({}^{7}\) J, which puts it in the high-energy event category. The maximum PGA level of the acceleration reached 0.35 and 0.28 m/s\({}^{2}\) for the west-east (WE) and the north-south (NS) directions, respectively. Hence, the maximum value of the resultant PGAH10 horizontal acceleration was 0.46 m/s\({}^{2}\). The maximum vertical accelerations (Z) were relatively small and did not exceed 0.12 m/s\({}^{2}\). The spectral analysis of the shock revealed that the main frequencies fall into the range of 2.4-4.0 Hz, with the dominant frequency of 3.516 Hz. As discussed (see Section 2.1.4), the maximal surface acceleration in different areas of the USCB depends on local geological conditions. The analyzed bridge is located close to the Libiaz mining area, in which several strong shocks with energies about 10\({}^{9}\) J and epicentral amplitudes PGAH10 1.4-1.7 m/s\({}^{2}\) have lately been registered [42]. Thus, the shock amplitudes of the analyzed shock have been scaled to PGAH10 = 1.5 m/s\({}^{2}\). Figure 1 shows the time histories of shock accelerations in three directions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subsurface Type**} & \multicolumn{3}{c}{**Amplification Factor for Tremor Energy:**} \\ \cline{2-4}  & **10\({}^{5}\) [J]** & **10\({}^{6}\) [J]** & **10\({}^{7}\) [J]** \\ \hline Rock & 1.0 & 1.0 & 1.0 \\ Quaternary 22 m & 0.7 & 1.4 & 2.2 \\ Quaternary 15 m & 2.1 & 4.4 & - \\ Quaternary and & 0.6 & 1.4 & 1.4 \\ tertiary 70 m & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Amplification factor depending on the tremor’s energy and subsurface soil type and thickness.  
Figure 1: The time history of the ground acceleration registered in the USCB region: (**a**) horizontal direction WE; (**b**) horizontal direction NS; (**c**) vertical direction Z.
## 4 Discussion  
Analysis of the Impact of a Cognitive Task on the Biomechanics of Drop Landing Movements in Individuals with FAI  
A pivotal finding of this study is the observation that, during a single-leg drop landing task, individuals with FAI significantly increased the plantarflexion angle of the ankle joint on the affected side when influenced by the cognitive task. This increase in the plantarflexion angle, likely a strategic adaptation, suggests that individuals with FAI may significantly adjust their motor control to accommodate the cognitive demands during the landing task, which could be a response to their impaired motor control capabilities [10]. The increase in the plantarflexion angle may serve as a natural protective mechanism for FAI individuals to counteract the attentional diversion caused by cognitive tasks. Given the potential reduction in proprioceptive acuity associated with FAI, these individuals may rely more heavily on enhanced motor control to maintain postural stability [32]. In the absence of a cognitive load, FAI individuals may have developed a more conservative \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **FAI Group** & **Control Group** & \multicolumn{2}{c}{**Main Effect**} & \multicolumn{1}{c}{**Interaction Effect**} \\  & **Affected Side** & **Matched Side** & **Group** & **Cognition** & **Group \(\times\) Cognition** \\ \hline IC & & & & & \\ Ankle Dorsiflexion/Plantarflexion Angle (\({}^{\circ}\)) & & & & & \\ Baseline & \(-14.65\pm 8.01\) & \(-22.00\pm 5.95\) & \(p=0.014\)\({}^{*}\) & \(p=0.037\)\({}^{*}\) & \(p=0.040\)\({}^{*}\) \\ Cognitive Influence & \(-20.68\pm 4.19\) & \(-22.06\pm 4.82\) & \(E\)\(\bar{n}^{2}=0.198\) & \(E\)\(\bar{n}^{2}=0.147\) & \(E\)\(\bar{n}^{2}=0.142\) \\ Ankle Inversion/Eversion Angle (\({}^{\circ}\)) & & & & \\ Baseline & \(3.15\pm 2.78\) & \(2.33\pm 3.62\) & \(p=0.536\) & \(p=0.711\) & \(p=0.872\) \\ Cognitive Influence & \(2.74\pm 2.94\) & \(2.17\pm 5.05\) & \(E\)\(\bar{n}^{2}=0.014\) & \(E\)\(\bar{n}^{2}=0.005\) & \(E\)\(\bar{n}^{2}=0.001\) \\ Peak vGRF Moment & & & & & \\ Ankle Dorsiflexion/Plantarflexion Angle (\({}^{\circ}\)) & & & & & \\ Baseline & \(12.45\pm 3.47\) & \(14.26\pm 3.65\) & \(p=0.195\) & \(p=0.565\) & \(p=0.572\) \\ Cognitive Influence & \(12.44\pm 3.42\) & \(13.30\pm 3.77\) & \(F=1.763\) & \(F=0.339\) & \(F=0.326\) \\ Ankle Inversion/Eversion Angle (\({}^{\circ}\)) & & & & & \\ Baseline & \(1.38\pm 2.78\) & \(1.51\pm 4.07\) & \(p=0.587\) & \(p=0.782\) & \(p=0.597\) \\ Cognitive Influence & \(0.72\pm 3.70\) & \(1.72\pm 3.68\) & \(E\)\(\bar{n}^{2}=0.012\) & \(E\)\(\bar{n}^{2}=0.003\) & \(E\)\(\bar{n}^{2}=0.010\) \\ \hline \hline \end{tabular} Note: Ankle dorsiflexion (\(+\))/plantarflexion (\(-\)), ankle inversion (\(+\))/eversion (\(-\)); \({}^{*}\); \(p<0.05\).  
\end{table} 
Table 4: Ankle joint angle at IC and peak vGRF moments during drop landing.
# Elite Multi-Criteria Decision Making--Pareto Front Optimization in Multi-Objective Optimization
## 3 Methodology
### Algorithm
#### 3.1.1 Calculating Weights of Each Objective  
In the eMPF method, the weight of each objective is calculated in the process as shown below [16]:  
* The first step is to normalize the fitness values. The fitness values are normalized using Equation (2), \[F_{ij}=\frac{f_{ij}}{\Sigma_{i=1}^{N}f_{i}}\] (2) where \(i\) is the individual policy, \(j\) is the objective number, \(N\) is the total number of individual policies, \(f\) is the actual fitness value, and \(F\) is the normalized fitness value.  
* The next step is to find the entropy values (\(e_{j}\)) of each objective using Equation (3), \[e_{j}=-h\sum_{i=1}^{N}F_{ij}ln(F_{ij})\] (3) where \(h=\frac{1}{ln(N)}\), \(j\) is the objective fitness, \(N\) is the total number of individual policies, \(F\) is the normalized fitness value from Equation (2), and \(i\) is the individual policy.
* Lastly, the weights (\(w_{j}\)) of the objective are calculated using Equation (4), \[w_{j}=\frac{1-e_{j}}{\sum_{j=1}^{M}\left(1-e_{j}\right)}\] (4) where \(e_{j}\) is the entropy value from the above step, \(j\) is the number of objectives, and \(M\) is the total number of objectives.  
By following the above listed steps, the weight of each objective function is calculated for each generation in an MOEA.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 1 Introduction
In addition, it achieves high write stability, short write latency, and low hold power consumption. In this paper, the structural characteristics and design methodology of the WRTU-16T cell are detailed in Section 2, while performance comparisons with the conventional cells are presented in Section 3.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 3 Optical Filter Shift Detection and Data Augmentation
### Experimental Setup
The structure of the CNN model used for optical filter shift detection is shown in Figure 11. The dataset sizes for the constellation data, both before and after demodulation, are configured as 64 \(\times\) 64. Since there are two polarizations (X and Y), the input data for the CNN model are represented as a three-dimensional tensor with the dimensions 2 \(\times\) 64 \(\times\) 64. This structure is depicted in the accompanying figure, where each polarization contributes its own 64 \(\times\) 64 grid of constellation points to form the input layer. This design enables the model to process and learn from both polarization components simultaneously, improving its performance in analyzing and detecting impairments in optical signals. After the input layer, those data are input into DenseNet [29] (DenseNet 121) implemented by PyTorch. Finally, classification values are obtained through the fully connected layer. In this experiment, the magnitude of the filter shift was divided into two cases: the first filter shift is large (51 GHz) and the second filter shift is small (26 GHz). Accumulation and Min-Max normalization were performed on the captured data. We utilized Python and PyTorch as the primary machine learning platform and library for developing and training the models. The Docker containers hosting these components were centrally managed using the Kubernetes orchestration tool. For real-time monitoring of various parameters within the Cassini system, a dedicated webserver container was implemented. This container allows for real-time telemetry and visualization of critical \begin{table}
\begin{tabular}{c c} \hline \hline
**Parts** & **Descriptions** \\ \hline OS & Ubuntu 20.04 LTS \\ CPU & Intel® Xeon® W processor \\ Memory & 32 GB (8 GB \(\times\) 4) DDR4-2666 ECC \\  & Registered DIMM (PC4-21300) \\ GPU & GeForce RTX 3090 24 GB GDDR6X \\ Storage & 1 TB NVMe support M.2 SSD \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Workstation performance. Figure 11: Pre-demodulation input to DenseNet. Figure 10: Software stack structure in our WhiteBox. metrics such as transmitter (Tx) and receiver (Rx) power, bit error rate (BER), and the pre-/post-demodulation constellations. A snapshot of the telemetry interface is shown in Figure 12. Additionally, Kubernetes ensures high availability and fault tolerance. If any container encounters an issue and becomes non-operational, Kubernetes automatically detects the failure and restarts or recovers the container, maintaining the system's overall functionality and reliability. This robust orchestration capability significantly enhances the resilience and manageability of the system.
## 2 Methodology  
### Theoretical Basis  
#### 2.1.1 BRDF Model  
The BRDF is a function of the observation and illumination angle and can describe the reflection distribution of the surface. The semiempirical kernel-driven model is usually obtained by linearly weighting the scattering kernel with a certain physical meaning [28]. The RossThick-LiSparseR kernel-driven BRDF model (the so-called RTLSR model) was selected in this research [29] in which surface reflection consists of three parts: isotropic scattering, volume scattering, and geometric optical scattering. The mathematical description of its model is as follows:  
\[R(\theta_{v},\theta_{s},\varphi,\lambda)=f_{iso}(\lambda)+f_{vol}(\lambda)k_{ vol}(\theta_{v},\theta_{s},\varphi)+f_{geo}(\lambda)k_{geo}(\theta_{v}, \theta_{s},\varphi) \tag{1}\]  
where \(k_{vol}(\theta_{v},\theta_{s},\varphi)\) and \(k_{geo}(\theta_{v},\theta_{s},\varphi)\) represent the volume scattering kernel and the geometric optics scattering kernel, respectively, and the isotropic scattering kernel is defined as 1. \(f_{iso}(\lambda),f_{vol}(\lambda)\) and \(f_{geo}(\lambda)\) correspond to the kernel coefficients of each kernel. \(\theta_{v}\) is the observation zenith angle, \(\theta_{s}\) is sun zenith angle, and \(\varphi\) is the relative azimuth angle. \(\lambda\) is the wavelength. The RossThick kernel is selected to describe the volume scattering, and the LiSparseR kernel is selected to describe the geometric optical scattering. Figure 1 shows the 3D shapes for the RossThick volume scattering kernel (Figure 1a,b) and LiSparseR geometric optics scattering kernel (Figure 1c,d) at two given solar zenith angles (15\({}^{\circ}\) and 45\({}^{\circ}\)). The X-Y plane in the figure is the polar coordinate drawing plane, the polar diameter of the polar coordinates represents the observed zenith angle, and the polar angle of the polar coordinates represents the relative azimuth angle. The blue-green-red colors indicate the low-to-high transition of kernel values and distinctly reflect the dome-bowl variability of kernel shapes at different illumination geometries. The RossThick kernel shows a typical \"bowl-shaped\" curve, and the reflectance increases with increasing observation zenith angle. The LiSparseR kernel curve is similar to the dome-shaped curve in the backscattering direction, especially in the hotspot direction, with prominent peak reflectance. The actual surface BRDF curve is the linear weighting of these two shapes.
# Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization +
## 3 Proposed Algorithm  
### Classification via Discrete Cosine Transform  
To overcome the limitations of orthogonal sparse coding, we used several orthogonal dictionaries. In contrast to [10], we divided the input data and allocated them into one dictionary. To classify the input data, we based our method on the direction of the patches. Two reasons for classifying data based on direction are that it provides more structured information than division randomly or evenly in order and that this method is simply implemented by using a discrete cosine transform.  
Since DCT is popular in image compression fields, analyzing and extracting information from images in the compressed domain for fast implementation are important. In particular, [PERSON]. [11] designed an edge model in the DCT domain based on the DCT's characteristics. As mentioned earlier, DCT provides optimal performance for horizontal and vertical directional data because the bases of the DCT represent the horizontal and vertical directions or the diagonal directions made by their combinations. The two directional bases have the same edge complexity according to their order, as shown in Figure 1. The bases in the red box show the same complex edge information in different directions. Ref. [11] directly extracted low-level features, such as edge orientation, edge offset, and edge strength, from DCT compressed images. Ref. [11] also suggested four metrics for edge orientation, with coefficients based on the \(8\times 8\) block DCT. We used and introduced one of the metrics in our study.  
For simple and efficient implementation, our proposed method exploits the DCT matrix in two ways. First, we discerned the patches in the DCT domain with the following formulation:  
\[\theta=\begin{cases}\tan^{-1}(\left|\frac{C_{01}}{C_{10}}\right|),&\text{ where}\quad C_{01}C_{10}\geq 0\\ 90^{\circ}-\tan^{-1}(\left|\frac{C_{01}}{C_{10}}\right|),&\text{where}\quad C _{01}C_{10}<0,\end{cases} \tag{5}\]  
Here, \(C_{01}\) and \(C_{10}\) are the DCT coefficients in (0, 1) and (1, 0), which correspond to the bases in the red box in Figure 1. We restricted the range of directions from \(0^{\circ}\) to \(90^{\circ}\).  
Then, we quantized the \(\theta\) in the \(L\) levels. \(L\) is the number of orthogonal dictionaries used in the proposed method. As in [10], we constructed the dictionary as a set of several orthonormal dictionaries that is \(D=[D_{1}|D_{2}|\ldots|D_{L}]\), in which \(D_{i}\)s are orthogonal square matrices. Then, we classified the input data into \(L\) groups and assigned each to an orthogonal dictionary. When the value of \(L\) increased, the compression performance also improved, as is generally natural.  
Figure 1: The basis of two-dimensional discrete cosine transform. Each basis includes horizontal or vertical directional information.
## 2 DEOS Mission Scheduling Model
### Optimization Objective of Satellite Mission Scheduling  
Satellite mission scheduling is a combined optimization problem with multiple constraints and objectives. Through scheduling, we want to obtain a solution that not only prioritizes observing high-value targets (high priority) and a high task completion rate but also ensures less energy consumption among satellites. Based on this consideration, we set following sub-goals and use a linear weighted sum method to set the optimization objective for solving this model:  
(1) Sub-objective 1: Maximize the priority of the target observations.  
\[y1=a\times Max(\sum_{i=1}^{n}\sum_{j=1}^{num_{i}}X_{i}^{j}\times pri^{j})/\sum_{ j=1}^{m}pri^{j} \tag{9}\]  
(2) Sub-objective 2: Maximize the number of target observations.  
\[y2=b\times Max\sum_{i=1}^{n}\sum_{j=1}^{num_{i}}X_{i}^{j}/m \tag{10}\]  
(3) Sub-objective 3: Balance resource usage among satellites.  
\[y3=c\times Min\sum_{i=1}^{n}P_{i}y3=c\cdot Min\sum_{i=1}^{n}P_{i} \tag{11}\]  
On the basis of the above three sub-goals, the total optimization goal of this paper is obtained by a linear weighted sum method.  
\[y=\alpha\times y1+\beta\times y2+\gamma\times y3 \tag{12}\]  
Due to different measurements of priority, task execution number, and energy consumption, it cannot be directly evaluated at the same time. This paper introduces the dimensional parameters \(a\), \(b\), and \(c\) to unify each index, and the values are \(a=1/4\), \(b=1\), and \(c=1/5\), in order to ensure that the benefit of observing a target must be higher than the loss of energy consumption. In equation (12), \(\alpha\), \(\beta\), and \(\gamma\) are weight indicators, and the sum of them is 1. The value of the weight determines the importance of the sub-objective in the overall optimization goal. This paper takes the priority of the task as the key factor, and we set \(\alpha=0.7\), \(\beta=0.2\), and \(\gamma=0.1\).
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm
### Atmospheric Radiative Correction  
To perform atmospheric correction on the MSC L1 product, the first step is to calculate the Top-of-Atmosphere radiance for each band of the multispectral image using the radiometric calibration coefficients. The calculation is expressed as follows:  
\[L=K\cdot DN+B \tag{3}\]  
where \(DN\) is the pixel gray value for each band of the multispectral image, \(K\) is the slope of the radiometric calibration coefficient, \(B\) the intercept of the radiometric calibration coefficient, and \(L\) represents the TOA radiance.  
Based on the cloud detection results, cloudy pixels are treated differently from cloud-free pixels. For cloudy pixels, the TOA reflectance is directly calculated as the atmospheric correction result using the following formula:  
\[\rho_{AOD}=\frac{\pi L}{\cos\theta_{s}F_{s}} \tag{4}\]  
For cloud-free pixels, the influence of atmospheric scattering and absorption is removed to retrieve the surface composite reflectance \(\rho_{1}\), which incorporates the adjacency effect. Equation (1) can be rewritten as follows:  
\[\rho_{1}=\frac{L(\theta_{s},\theta_{v},\varphi)/T_{g}(\theta_{s},\theta_{v})- L_{path}(\theta_{s},\theta_{v},\varphi)}{T(\theta_{s})T(\theta_{v})F_{s}\mu_{s}/ \pi+\big{(}L(\theta_{s},\theta_{v},\varphi)-L_{path}(\theta_{s},\theta_{v}, \varphi)\big{)}S} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Range** & **Step Size** & **Number** \\ \hline Altitude & 0\(-\)4 km & 1 km & 5 \\ SZA & 0\(-\)80\({}^{\circ}\) & 5\({}^{\circ}\) & 17 \\ VZA & 0\(-\)65\({}^{\circ}\) & 5\({}^{\circ}\) & 14 \\ RAA & 0\(-\)180\({}^{\circ}\) & 10\({}^{\circ}\) & 19 \\ CWV & 0\(-\)10 & 1 & 11 \\  & 0.0\(-\)0.2 & 0.05 & 5 \\  & 0.2\(-\)1.0 & 0.1 & 8 \\ AOD & 1.0\(-\)2.0 & 0.2 & 5 \\  & 2.0\(-\)2.9 & 0.3 & 3 \\ Aerosol model & 6 types & & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Atmospheric correction lookup table.  
In the above equation, the parameters \(T_{g}\), \(T(\theta_{v})\), \(T(\theta_{s})\), \(L_{path}\), and \(S\) can all be obtained by referencing a pre-constructed atmospheric correction lookup table.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 5 Proposed Scheme
### Dithering  
As discussed, it is important to adopt a measure to avoid synchronization like CoAP which dithers the retransmission timer value. We set the actual retransmission timer by adding a random duration to the retransmission timer. The interval for generating a random duration depends on the retransmission timer value. To avoid setting the actual retransmission timer to an excessively large value, we set the interval by multiplying the retransmission timer value by 1, 1/2, or 1/4 with the thresholds of 4 and 12 s. The reason for choosing the multipliers of 1, 1/2, and 1/4 is that the multiplication can be implemented using the shift operation. The thresholds were chosen empirically. For other kinds of networks, the multipliers and thresholds may be optimized according to the RTT distribution. It is important to note that, since uIP TCP uses the time granularity of 0.5 s for the retransmission timer,the effect of dithering is limited. Nevertheless, the simulation results show that even the limited dithering is effective in improving the performance of uIP TCP.  
Our algorithm to set the actual retransmission timer is summarized in Table 2.
## Nomenclature  
\begin{tabular}{l l} \(\gamma\) & tool rake angle \\ \(V_{c}\) & cutting velocity \\ \(f\) & cutting feed \\ \(\mu\) & contact friction coefficient \\ \(\overline{\sigma}\) & equivalent stress \\ \(A\) & material yield strength (MPa) at room temperature \\ \(B\) & material hardening modulus \\ \(n\) & work hardening exponent \\ \(C\) & material constant for strain hardening rate \\ \(m\) & thermal softening coefficient \\ \(T_{room}\) & reference ambient temperature \\ \(T_{melt}\) & melting temperature \\ \(\overline{\varepsilon}\) & equivalent plastic strain \\ \(\Delta\overline{\varepsilon}\) & increment of equivalent plastic strain during damage evolution \\ \(\overline{\varepsilon_{f}}\) & equivalent strain to fracture \\ \(D_{1}\) & initial failure strain \\ \(D_{2}\) & exponential factor \\ \(D_{3}\) & triaxiality factor \\ \(D_{4}\) & strain rate factor \\ \(D_{5}\) & temperature factor \\ \(P\) & average normal stress \\ \(E\) & modulus of elasticity of material \\ \(v\) & Poisson ratio \\ \(q_{p}\) & heat generation rate \\ \(q_{p}\) & inelastic heat fraction \\ \(\tau_{f}\) & shear stress by Coulomb friction law \\ \(\overline{\varepsilon}\) & plastic strain rate \\ \(\overline{\varepsilon}_{o}\) & reference strain rate \\ \(W\) & synaptic weights \\ \(y\) & input signals to the respective neutron's \\ \(a\) & output layer of ANN \\ \(b\) & hidden layer of ANN \\ \(t\) & represents a target value (desired activation) \\ \(E\) & error \\ \(A\) & learning rate \\ \(N\) & iteration (epoch) \\ \(H\) & momentum \\ \(RF\) & reaction force \\ \(RF\_Pd\) & predicted reaction force \\ \(Err\) & error \\ \(\%\)\(Err\) & percent error \\ \(T\) & chip-tool interface temperature \\ \(T\_Pd\) & chip-tool interface predicted temperature \\ ANN\_Sim\(RF\) & ANN simulated reaction force \\ Exp\(\_RF\) & Experimental value of reaction force \\ ANN\_Sim\(T\) & ANN simulated temperature \\ Abq Sim \(T\) & Abaqus simulated chip-tool interface temperature \\ \end{tabular}