esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Orbit Association Methods
The approach, characterized by linearizing the mapping process and discretizing the feasible domain, effectively mitigates computational costs and maintains robustness even in the presence of observation errors. Nevertheless, it does not apply to the GEO association problems [50]. [PERSON] et al. reframed the orbit association problem into an optimization process by employing a loss function and corresponding optimization methods. Depending on whether the optimization problem used in the trajectory association process is an initial value problem (IVP) or a boundary value problem (BVP), the associated methods are categorized into two classes [51; 52]. Among them, the BVP-based approach utilizes distance assumptions and Lambert problem solvers to determine feasible orbits for observed targets, exhibiting better completeness and efficiency. On the other hand, the IVP-based methods determine the orbits of the targets through the admissible region method, offering advantages in terms of clarity. It is notable that a significant challenge in existing orbit association methods lies in the high rate of erroneous orbit associations for distinct targets within the same satellite constellation. To enhance the accuracy of orbit associations for different targets within the same constellation, [PERSON] et al. introduced a novel approach known as the \"Common Ellipse Method\". This method addresses the orbit association problem by examining the proximity of the hypothetical ellipses representing the target trajectories in the observational data to a common ellipse. Simulation results demonstrate a notable improvement in the accuracy of orbit associations using this method [43].
## 4 Transponder Calibration Results
Figure 26: Transponder signal responses for Jason-2 (**lower left**), Jason-3, CryoSat-2 (**upper**), and Sentinel-3A (**lower right**) at different calibration dates.
## 5 Conclusions  
In this study, we proposed a simpler SentenceTransformers model combined with balancing the dataset and fine-tuning the hyperparameters of the model to handle an automatic short answer grading system. Our recommended SentenceTransformers model has a relatively small size, resulting in manageable resource requirements. We also balanced the dataset using GPT data augmentation, employing prompt engineering in the GPT model to generate new sentences based on existing student answers or desired answers. Additionally, we also fine-tuned the model by combining appropriate hyperparameters to achieve optimal grading performance. From the experiments conducted, the new balanced dataset significantly improved the performance of the grading system, as observed through RMSE, F1-score, accuracy, and Pearson correlation metrics. The newly generated answer data from GPT also display satisfactory results, with a cosine similarity score reaching 0.8 for the correct category and 0.3 for the false category. This data augmentation aims to create a more balanced distribution of grade labels in the dataset. The implementation of this new balanced dataset also resulted in a significant performance improvement. The best result we obtained reached a Pearson correlation value of 0.9586 from the implementation of the all-distilroberta-v1 model. This model has a relatively small size and underwent a fine-tuning of hyperparameters, as well as the utilization of the new balanced dataset. Key hyperparameters include (1) the use of gradient checkpointing to reduce memory consumption; (2) the split-size ratio for the training and testing datasets, with 80% for training and 20% for testing; (3) a pre-processing step involving the removal of special characters and converting text to lowercase. Other parameters remained fixed across all the experiments, as mentioned earlier. Furthermore, the RMSE, F1-score, and accuracy score also consistently achieved better results compared to the previous research. This has also been demonstrated by additional experimental results. Although the performance increase for the new dataset is not very significant, there is still an improvement. This difference can be attributed to the varying characteristics of the datasets themselves. In terms of future works, there are several areas that might be explored further. Currently, the dataset used consists only of English-language data. Hence, future research
## 3 Comparative Flexural Test of FRP-Concrete Hybrid Beam  
### Material Properties and Setup for the Flexural Test  
Two beams (Figure 4) were constructed and tested under a monotonic bending experimental program. Beam 1 consisted of a concrete slab connected to an I-shape FRP girder by a steel bolt connection. Beam 1 was designed with SIP forms with ribs. These ribs can increase the contact area with concrete, thereby creating more natural bonding, so the benefits of using SIP forms can be seen more clearly. The SIP form has a hook at the right side and an extrusion at the left side, so the two of them can be assembled into one unit (Figure 4b). The concrete used in the beam test was the same as those for the push-out test. The reinforcing bars had a yield strength of 235 MPa. Both of the hybrid beams were cured under the same conditions as the material testing specimens. The length of the beams was 4 m with a span of 3.8 m. The cubic strength of the concrete was 36.5 MPa, the tensile strength of the FRP profiles was 365.4 MPa, the tensile modulus of the FRP was 12.8 GPa, and the shear modulus of the FRP was 8.1 GPa. The cross sections of the hybrid beams can be seen in Figure 4. Timber stiffeners were bonded to both sides of the support ends and the loaded end, which can be seen in Figure 5. Therefore, the local buckling of flanges and webs could be prevented.  
The beam specimens were instrumented with strain gauges and linear variable displacement transformers (LVDTs) (Figure 5). The strain gauges for concrete and FRP components had a length of 30 mm, with a resistance of 120 \(\Omega\). The strain gauges for the steel bars had a length of 5 mm, with a resistance of 120 \(\Omega\). The dial gauges at the beam ends can measure the interfacial slip of the concrete relative to the FRP beam. The loading was force controlled and applied using a 1000 kN testing machine in small increments. Both of the beams had three loaded stages, namely, stage-I: from 0 to 100 kN with a loading rate of 10 kN/min with data collected every 10 kN; stage-II: from 100 to 200 kN with a loading rate of 5 kN/min with data collected every 5 kN; and stage-III: from 200 kN to failure (if it does not fail before 200 kN) with a loading rate of 2 kN/min with data collected every 2 kN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Non-Slip Stage**} & \multicolumn{2}{c}{**Debonding Stage**} & \multicolumn{2}{c}{**Bearing Stage**} \\ \cline{2-7}  & **Load (kN)** & **Slip (mm)** & **Load (kN)** & **Slip (mm)** & **Load (kN)** & **Slip (mm)** \\ \hline S-3 & 184 & 0 & 101 & 1.08 & 220 & 4.61 \\ S-4 & 200 & 0 & 114 & 0.84 & 345 & 6.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic load and slip of the three stages of specimens S-3\(A\).
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 3 Materials and Methods
### System Architecture  
The proposed CNN structure can be seen in Figure 1 and the layer details in Table 6. It consists of the following steps:  
* The accelerometer data, sized N \(\times\) 3, are fed to the first convolutional layer with 192 convolutional filters and a kernel size of 12, and the stride of the convolution is 1. The ReLU function is applied to its output.
* A max-pooling layer follows with a kernel size of 3 \(\times\) 1 and a stride of 3, which reduces the feature representation by 3.
* Another convolutional layer is added with 96 convolutional filters and a kernel size of 12, and the step of the convolution is 1. This will help to learn more abstract and hierarchical features. The ReLU function is applied to its output.
* A final max-pooling layer has a kernel size of 3 \(\times\) 1 and a stride of 3, which further reduces the feature representation by 3.
* The output of the max-pooling layer is then flattened and concatenated with the statistical features described in Section 3.2. The joint vector is passed to a fully-connected layer that consists of 512 neurons. The ReLU function is applied to its output.
* A dropout layer is added with a dropout rate of 0.5 to avoid overfitting.
* Finally, the output of the fully-connected layer is passed to a softmax layer, which computes a probability distribution over six activity classes.  
Figure 1: Detailed network architecture.
## 3 Results and Discussion
### Hardness  
Figure 12 illustrates the overall results of the measured Rockwell hardness on the top and side surfaces, showing an overall higher hardness on the top surface.  
Considering the range of the error bars of the values measured on the side, the results could not be regarded as solid and reliable. Therefore, no linear regression analysis was conducted for the effect of the process parameters on the hardness on the side surface. This high standard deviation of the values can be attributed to the layer profile on the side surface, meaning that due to the layer-by-layer addition of material, there can be locations on the side surface, such as the areas in between layers, where there might be pores, or the presence of unmelted powder, etc., which will affect the side hardness measurement. Therefore, during indentation, the tip might contact solid material or porous areas, which creates the large variations due to different surface conditions at the indentation points (as seen in Figure 13).  
Figure 12: Measured Rockwell hardness HRC on the top and side surfaces.  
Figure 13: Possible indentation points on the side surface.  
Hardness of Top Surface  
As abstracted from Figure 14 and ANOVA, the three process parameters had no significant influence on the hardness of the top surface. The most important parameter obtained from ANOVA, though not significant, was the scan speed with a \(p\)-value of 0.579.  
Additionally, the interaction plots illustrated in Figure 15 show no clear interaction effects between the parameters. Only the effects of laser power and hatch spacing are more distinctive, which was validated by being the second-most important term in ANOVA (\(p\)-value = 0.626). It can be said that when applying small hatch-spacing values, the laser power can make a small difference regarding the hardness. The effect of scan speed at certain laser powers causes slight deviations in hardness.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 6 The Soft-Decision Helper Data Scheme  
The performance increase of the MO helper data scheme, regarding the traditional single enrollment scheme, is mostly due to the fact that it can distinguish between reliable and unreliable SRAM cells. A well-known helper data scheme that also considers the reliability information of the SRAM cells is the Soft-Decision scheme introduced by [PERSON] et al. [7]. In this section, we first describe the SD scheme and derive its achievable performance. We note that the SD scheme assumes that the one-probabilities of the SRAM cells are observable which in practice is often not the case. Therefore, a pre-processing step is required that estimates the one-probabilities of the SRAM cells. Based on this observation, we propose a variation on the SD scheme that instead directly considers the binary SRAM-PUF observations as an input. We show that the newly proposed strategy results in the same decoder LLRs as the MO scheme (for equal key and SRAM observations). This implies that both schemes achieve the same reconstruction performance, and thus, since the MO scheme is provably optimal, the newly proposed strategy for the binary SD scheme is optimal as well.  
To the best of our knowledge, we are the first to propose an optimal strategy for the SD scheme with binary enrollment observations. Furthermore, we show that very few observations (less than 10) are sufficient to achieve an acceptable performance, whereas in the literature 64 observations are used [8] for the same statistical model and parameter settings that are used in the current work.  
Figure 6: Reconstruction error probability FER for the MO helper data scheme, with 128 bit key and 128/\(R\) SRAM cells.
# A Study of Constitutive Model of Rock Damage under the Joint Effect of Load and Moisture
## 3 The Mechanical Test of Sandrock Containing Water
### The Mechanical Test Results of Sandrock Containing Water  
As demonstrated in Figures 4 and 5, respectively, the full stress-strain curves of sandstone specimens under different loading stress paths and different water content are plotted based on the experimental data. The statistics data of rock mechanical parameters in uniaxial and triaxial compression tests are shown in Tables 1 and 2.  
Figure 4: The uniaxial compression test results of sandrock containing water. (**a**) Dry Sample; (**b**) Sample Submerged for 0.75 h; (**c**) Sample Submerged for 1.5 h; (**d**) Sample Submerged for 6 h\({}_{2}\) (**e**) Sample Submerged for 24 h; (**f**) Sample Submerged for 720 h.  
Figure 3: The correlation between water content and soaking time.  
According to Figure 4 and Table 1, the comparison of the stress and strain values of each specimen group indicates that the average uniaxial compressive strength of each group of rock specimens decreases significantly with the increase in water content. In addition, the variation of the strain value \(\varepsilon\) corresponding to the yield point of each group of rock specimens is not obvious.  
As shown in Figure 5 and Table 2, the stress-strain curves of each group of specimens can be divided into three levels based on different confining pressures, indicating that the increase of confining pressure can enhance the specimens. In addition, under the same confining pressure conditions, with the increase of water content, the failure strength of the sandstone specimens significantly decreased, indicating that a smaller confining pressure leads to a faster decrease of the peak strength.
## 3 Proposed Scheme
### Overview of Iteration Method
#### 3.1.1 Original SOR Method
Therefore, if the spectral radius \(\rho(\mathbf{G}_{SOR})\) of \(\mathbf{G}_{SOR}\) is less than 1, then iterative convergence is guaranteed. Interested readers can refer to advanced linear algebra books for the more detailed and rigorous mathematics. When the spectral radius is less than 1, the error can gradually approach zero in each iteration, thereby ensuring the convergence of the iterative method. Therefore, the SOR iteration method is guaranteed to converge when \(\omega\) is in the interval [0, 2].
# A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction
## 3 Proposed Method: DenseNet-LSTM
### Dataset and Preprocessing  
#### 3.2.1 Dataset  
The CHB-MIT dataset used in the paper is a scalp EEG recording measured from 23 pediatric patients at Children's Hospital Boston, which is a public dataset and is avail  
Figure 2: The system model of proposed method.  
able with open access at PhysioNet.org. This record was measured at a 256 Hz sampling rate using 22 electrodes placed according to the International 10-20 Electrode Positioning System and contains a total of 983 h of consecutive EEG recordings and 198 seizures [25]. As can be seen from the annotation file of the dataset, we can see that the patient's channel changes frequently. Therefore, we used 18 channels (\"FP1-F7\", \"F7-T7\", \"T7-P7\", \"P7-O1\", \"FP1-F3\", \"F3-C3\", \"C3-P3\", \"P3-O1\", \"FP2-F4\", \"F4-C4\", \"C4-P4\", \"P4-O2\", \"FP2-F8\", \"F8-T8\", \"T8-P8\", \"P8-O2\", \"F2-CZ\", \"CZ-PZ\") that are commonly used by 24 patients out of a total of 22 electrode channels. Although there are some differences according to the patient's data, it must be a certain distance from the ictal PHASE to be regarded as interictal. If the distance is too close, seizure waves may be included within the interictal period. Since the distance from the ictal varies from patient to patient, there are two cases considered as interictal. First, patients with close distances used the interictal as far from the ictal as possible. On the other hand, patients with sufficient distance used the interictal at a distance more than a certain distance from the ictal. In addition, we assume the preictal length to be 5, 10, and 15 min because the preictal phase is not clearly distinguished. As shown in Figure 3, there exist the preictal length plus the 5 min interval before the ictal period. Since the model is trained with the preictal data for seizure prediction, the 5 min interval preceding the seizure is excluded from the preictal length purposefully. In real situations, if a seizure can be predicted in advance before the ictal period and the patient can be treated immediately, a certain amount of time (e.g., 5 min) is needed to ensure that the patient has some effect on seizure.
# Spatial Data Analysis for Deformation Monitoring of Bridge Structures
## 2 Deformation Analysis Using TLS
### Geometry-Based Approach for Deformation Analysis of Brgidge Structures
The vertical component is calculated as a quadratic mean of differences, \(\Delta\)Z, between the identical reference points after the registration of point clouds into a common coordinate system. From the orthogonal distances of the subset of points from the plane, the standard deviation of the regression plane is calculated. The dispersion of the points around the plane reflects the noise of the point cloud, which is mainly affected by the rangefinder (distance measurement) of the instrument used. To minimize the effects of systematic errors, the measurements should be performed under equal conditions in each epoch (position of the scanner, temperature, etc.) if possible. The vertical displacements of the monitored points are calculated as the Z coordinate differences of these points (calculated by (4)) in each measurement epoch. Finally, the vertical displacements can be transformed into the normal direction to the object's surface based on the estimated normal vectors. Figure 1: Modelling the height of the monitored point. Definition of a local coordinate system (**left**), pre-segmentation of the point cloud by fencing box and projection of the monitored point onto the modelled regression plane (**right**). The benefit of this procedure is that the monitored object itself defines the direction of the displacement and therefore it is not dependent on the scanner position or on the coordinate system axis orientation.
## 4 Experiments and Results
We also show in Figure 9 only the convergence curve graphs on train samples because the curve graphs on test samples are very similar in shape but have different scales. We can see in Figure 9,b that the Cascade-ICA and SingleElite-ICA ensembles inherit slower convergence from the ICA individual optimizer. The Cascade-TLBO and SingleElite-TLBO ensembles inherit faster convergence from the TLBO individual optimizer, but the Cascade-SHLO and SingleElite-SHLO ensembles show no promising improvement. Therefore, we can say that Cascade and SingleElite ensembles are sensitive to an unbalanced number of iterations for weak optimizers. On the other hand, we can see in Figure 9c that the MultipleElite-ICA ensemble has a better convergence in the first seven ensemble iterations compared to the balanced ensemble, which is not an intuitive outcome. In the case of the MultipleElite-TLBO and MultipleElite-SHLO ensembles, we observe slightly poorer performance in the first seven  
Figure 9: Convergence curve graphs of ensembles using a balanced number of iterations and an unbalanced number of iterations for weak optimizers: (**a**) cascade strategy; (**b**) single elite solution strategy; (**c**) multiple elite solutions strategy. ensemble iterations. However, we can see that the MultipleElite ensemble is stable because MultipleElite-ICA, MultipleElite-TLBO, and MultipleElite-SHLO perform similarly to MultipleElite-balanced after ensemble iteration 7. The performance stability of the MultipleElite ensemble provides the advantage of using an unbalanced number of iterations. In other words, in the MultipleElite ensemble, we can favor the weak optimizer with less computational effort without losing ensemble performance. We show in Table 3 the performance of each ensemble in the four scenarios (at ensemble iteration 20). Based on the results in Table 3, we can see that the MultipleElite ensemble performs similarly for balanced and unbalanced number of iterations for weak optimizers. Additionally, the MultipleElite-ICA ensemble inherits a lower computational effort (i.e., 38,261) from the individual optimizer ICA (37,159). The presented results demonstrate that our proposed hybrid multiple elite solutions ensemble is a simple and promising ensemble strategy that benefits from the best performance of the base optimizers in terms of accuracy and computational effort.
## 4 Experiments
### Comparison with State-of-the-Art Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**(a)** The performance results on cervical cancer classification} \\ \hline
**Application (Task)** & **Approach** & \(\text{TL}^{+}\) & \multicolumn{3}{c}{**Metrics**} \\  & & & **Acc (\%)** & **Precision (\%)** & **Sensitivity (\%)** \\ \hline \multirow{8}{*}{Cervical cancer classification (Image classification)} & [PERSON] et al. [45] & ✗ & 49.3 & - & - \\  & [PERSON] et al. [48] & ✓ & 77.3 & - & - \\  & [PERSON] et al. [49] & ✓ & - & 49.0 & 36.0 \\  & [PERSON] et al. [45] & ✓ & 75.4 & - & - \\ \cline{1-1}  & \(T_{sim}\) & ✓ & 74.2 & **56.0** & **61.6** \\  & \(T_{sl}\) & ✓ & 73.4 & 55.1 & 43.2 \\  & \(T_{roi}\) & ✓ & **77.7** & 55.9 & 58.7 \\  & \(T_{multi}\) & ✓ & 20.4 & 12.0 & 14.4 \\ \cline{2-6}  & \multicolumn{2}{c}{Ensemble\({}^{**}\)} & ✓ & **86.0** & **77.3** & **75.7** \\ \hline \multicolumn{5}{c}{**(c)** The performance results on pressure ulcer segmentation} \\ \hline
**Application (Task)** & **Approach** & \(\text{TL}^{+}\) & \multicolumn{3}{c}{**Metrics**} \\  & & & **Acc (\%)** & **IoU (\%)** & **DSC (\%)** \\ \hline \multirow{8}{*}{Pressure ulcer segmentation (Segmentation)} & [PERSON] et al. [62] & ✗ & 95.0 & 47.3 & - \\  & [PERSON] et al. [66] & ✗ & 96.0 & - & - \\  & [PERSON] et al. [57] & ✗ & - & 83.0 & 87.0 \\  & [PERSON] et al. [64] & ✓ & - & - & 89.9 \\  & [PERSON] et al. [67] & ✓ & 97.8 & - & 85.0 \\ \cline{1-1}  & \(T_{sim}\) & ✓ & **98.9** & **99.9** & **93.3** \\  & \(T_{sl}\) & ✓ & 87.2 & 99.8 & 63.0 \\  & \(T_{roi}\) & ✓ & 91.4 & 91.2 & 91.2 \\  & \(T_{multi}\) & ✓ & 85.4 & 87.2 & 21.0 \\ \cline{1-1}  & \multicolumn{2}{c}{Ensemble\({}^{**}\)} & ✓ & **99.0** & **99.9** & **93.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overall results compared to state-of-the-art approach results for the three applications: (**a**) cervical cancer classification (image classification), (**b**) skin lesion classification (object detection and classification), (**c**) pressure ulcer segmentation (segmentation). \(\text{TL}^{+}\) denotes transfer learning, and \({}^{*}\) denotes reproduced results following their parameter settings for our dataset. Ensemble\({}^{**}\) performances marked in bold are compared to state-of-the-art approaches.