esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Dynamic Threshold \(\eta\)  
In the CFSFDP algorithm, the threshold \(\eta\) is fixed. However, in a WSN, the local density, distance and energy of each node are changing as the number of rounds increases. Therefore, it is necessary to choose an appropriate dynamic threshold to determine the number of cluster heads. According to LEACH, we set \(p=0.1\), which represents the possibility of being selected as cluster head is 0.1. In the experiments above, we choose 10% of the number of surviving nodes as the number of cluster heads. It is not an optimal threshold for this algorithm. It's necessary to choose a dynamic threshold to deal with three changeable factors.  
Firstly, we set \(t_{dc}(r)=[d_{c}(r)-\delta_{\min}(r)]/[\delta_{\max}(r)-\delta_{\min}(r)]\), which represents the normalization of cutoff distance, where \(\delta(r)_{\min}\), \(\delta(r)_{\max}\) is the minimum and maximum value of distance in the current round explained in (16). The dynamic threshold should also take residual energy, local density and distance into consideration. In CFSFDP-E algorithm, we have already set \(\omega^{\prime}=\rho^{\prime}E_{f}\). Thus, we define dynamic threshold as Equation (18):  
\[\eta(r)=\alpha\cdot\omega^{\prime}_{\max}(r)\cdot t_{dc}(r) \tag{18}\]  
where \(\alpha\) is a constant to determine the threshold. \(\omega^{\prime}_{\max}(r)\) represents the maximum value of \(\omega^{\prime}(r)\) in the current round, that is, \(\omega^{\prime}_{\max}(r)=\max[\omega^{\prime}(r)]\). In order to get a better performance of WSN, Table 8 shows the performance of network lifetime with different \(\alpha\).  
When the value of the threshold is too small, the number of nodes with the \(\gamma_{i}\) above the threshold is large, resulting in the formation of too many clusters. In the contrast, if threshold is too large, there will be little or no clusters in the network. Both situations will cause more energy consumption. From Table 8, the performance of different values of \(\alpha\) varies from each other. By comparing with the previous experiments, we choose \(\alpha=2\), which means the dynamic threshold of this experimental  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threshold \(\eta\)** & **FND** & **HND** & **LND (90\%)** \\ \hline
10\% alive nodes & 1001 & 1019 & 1030 \\ \(\alpha=0.5\) & 978 & 1002 & 1011 \\ \(\alpha=1\) & 995 & 1016 & 1023 \\ \(\alpha=1.5\) & 996 & 1017 & 1024 \\ \(\alpha=2\) & 1003 & 1025 & 1031 \\ \(\alpha=2.25\) & 1002 & 1027 & 1034 \\ \(\alpha=2.5\) & 1000 & 102 scenario is \(\eta=2\omega^{\prime}_{\max}t_{dc}\). Figure 5 describes distributions of the number of cluster heads in CFSFDP-E and LEACH. In the LEACH protocol, the number of clusters is almost between 6 and 12, and in the CFSFDP-E algorithm, the most densely distributed region is 6 to 10, which is similar to LEACH. Therefore, the dynamic and adaptive threshold is suitable for WSNs.
# Designing Pedagogically Effective Haptic Systems for Learning: A Review
## 3 Haptics Applications for Learning
### Medicine
#### 3.2.2 Surgical Training
The reduced level of force feedback can be combated by incorporating haptic feedback into laparoscopic surgical tools, thus providing the surgeon with increased sensitivity and ability to more accurately perform the surgical procedure, while potentially freeing up cognitive load [27]. A study completed by [PERSON] et al. [36] investigated the skill transfer from a VR haptic simulator to a porcine organ model in a box trainer, which is a physical surgical training environment. Students in the haptic group first underwent training on a haptic training simulation where they were trained on five common surgical tasks: coordination, clip applying, lifting and grasping, fine dissection, and pattern cutting. The control group had no training experience prior to performing surgical tasks in the porcine organ model box trainer. Both groups of participants were then evaluated on their ability to perform a cholecystectomy in a porcine organ model box trainer. Video evaluations were rated by two surgeons unaware of the subject's training condition (haptic training or no prior training). It was found that the haptic simulation was not directly transferable to performance in the porcine organ model box trainer; this negative result was speculated to be a result of poor mechanical performance of the simulated haptic feed. Further studies must be conducted between groups of haptic devices to determine the educational results various haptic devices may provide. A study conducted by [PERSON] et al. [37] investigated whether incorporating haptic training early-on (prior to other training) during surgical simulation would improve resident performance. 38 surgical residents were randomly assigned to two groups: early-haptic and late-haptic. The former group was designated the \"early-haptic\" group because in the first hour of training they received haptic feedback. The latter started the first hour of their training with the Procedius abdomen simulator and were termed the \"late-haptic\" group. After the completion first session, the groups switched training methods (haptic or non-haptic) types for the second session of training. The simulation tasks being completed were manipulation and diathermy for 30 min and point diathermy for 30 min, with the surgical context being a virtual upper abdomen. They found that the experimental group receiving the \"early\" haptic intervention preformed significantly better on the two diathermy tasks and was the only group to improve their skills on the second session. From these results, [PERSON] et al. concluded \"haptic feedback could be important in the early training phase of skill acquisition during a surgical simulator\" [37].
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 2 Burst Error, OFDM, and Interleaving
### Interleavers
#### 2.3.1 Helical Interleaver  
The helical interleaver algorithm can be summarized as follows [28; 29; 30; 31]:  
1. Primary interleaver generation: This refers to arranging the data sequence in a matrix with \(N_{r}\) rows and \(N_{c}\) columns, such that \(N_{r}\). \(N_{c}=L\). \(L\), in this context, refers to the length of the primary interleaver, as given in Figure 2a.
2. Helical interleaver: It is constructed based on the primary interleaver through reading the interleaver indices column-wise, as given in Figure 2b.
3. By cyclically reading the interleaver indices from the diagonals of a matrix with decreasing slope, other interleavers can be generated, as indicated in Figure 2c. The _ith_ helical interleaver can be represented in Equation (5) as follows: The _i'th_ helical interleaver can be represented in Equation (5) as follows: \[\pi[k]=\pi[l_{madL}],\;0\leq k<L\] (5) where, \(l=k_{madN_{r}}.N_{c}+\Big{(}\Big{[}\frac{k}{N_{r}}\Big{]}+\big{(}k_{madN_{r}}.(i-1)\big{)}\Big{)}\).  
In Equation (5), it is seen that the helical interleaver can be managed in a very short time. If the parameters are chosen correctly, the interleaver indices can be spread out quite well. Now, the best way to even better optimize Equation (5) is to add layer-specific shifts to it, as given in Equation (6).  
\[\pi[k]=\pi\Big{[}(l+i.S)_{madN_{c}}\Big{]} \tag{6}\]  
where \(S\) is a constant integer, which describes the shift between the interleavers.  
Figure 2: _Cont._
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 6 Generic Dynamic ABMS (GDABM) for Risk Analysis
### External Failure Agent Communication (EFAC)  
An External Failure Agent Communication (EFAC) governs the communication among different agents (connection **d**). A failure of an agent \(i\) could be propagated to other surrounding agents. When a failure mode \(i_{FM}\) of an agent \(i\) becomes active, this agent broadcasts a message to all surrounding agents. The propagated message contains complete information about the failure. This failure will be added to the set of external failure elements of the surrounding agent's failure modes.  
For example, if we consider a multi-agent environment where the agents are trucks moving in a highway. If a truck \(t1\) travels from a point A to a point B, a collision between two other trucks \(t2\), and \(t3\) in the same path of the truck \(t1\), might cause a significant delay to truck \(t1\). This collision information will be shared with \(t1\) and it is considered as an external agent failure for \(t1\).
## 3 Numerical Results
### Probabilistic Analysis of the Reference Case
#### 3.3.1 \(Pf\) Estimation  
The \(Pf\) estimation by \(SS\) in each support element is conducted using the corresponding LSF defined in Equations (1) and (2). The values of \(\epsilon_{v\_max}=45\%\) and \(\sigma_{e\_max}=45\) MPa are chosen correspondingly as the threshold of the volumetric strain in the flexible liner and the maximum equivalent stress in the concrete liner.  
The estimated \(Pf\) values of the tunnel liners at different locations (every \(15^{\circ}\) from \(0^{\circ}\) to \(90^{\circ}\) with respect to the springline) are presented in Figure 5. Due to symmetry, only a quarter of the tunnel is presented. Consistent with the previous deterministic analyses in the case \(\lambda<1\), the failure probability in the outer flexible liner increases from \(0^{\circ}\) to \(90^{\circ}\) with a maximum value of \(3.2\%\) found at the roof. Inversely, the \(Pf\) in the concrete liner decreases from the springline to the crown with the highest value of \(17.5\%\). Under the configuration of the selected thresholds, the adopted parameters in Table 1, as well as the uncertainty modelling, these results show that the more critical failure mode is in the concrete liner.
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 3 Proposed Fusion Methods
### Tightly Coupled Fusion  
In the tightly coupled approach, it is proposed to use an EKF to fuse the raw distance measurements from the three existing ULPSs, in order to estimate the final receiver's position. Note that, in this approach, the fusion method does not deal with the intermediate positions obtained for each ULPS independently, but merges the distance differences determined by every ULPS into a single EKF to obtain the final position.  
Simulations have been carried out for the seven positions P1-P7 at \(z_{1}\) and \(z_{2}\), with a typical deviation \(\sigma_{w}=0.2\) m. As before, Figure 10 plots the clouds of positions, which are more concentrated around the ground-truth points, with lower dispersions. The mean errors and the standard deviations are listed in Table 7. Finally, the errors for 90% of the cases for the seven studied points P1-P7 are between 0.107 and 0.3 m at \(z_{1}\), and between 0.105 and 0.2 m at \(z_{2}\), as can be derived from Figure 11.  
Figure 10: (**a–f**) Clouds of estimated positions with an EKF and three ULPSs for \(\sigma_{w}=0.2\) m, at \(z_{1}=1.35\) m (**left**) and \(z_{2}=1.93\) m (**right**): successively, 3D representation of clouds of points; Y–X projections; and Z–X projections.  
Summing up, the first approach proposed here is based on the fusion of the obtained positions with the so-called loosely coupled fusion method, where two different algorithms have been applied: the LKF and AKF. On the other hand, from the point of view of the tightly coupled fusion, an EKF has been proposed for the raw measurements from the three ULPSs. Table 8 summarizes the errors for 90% of the estimated positions. It is possible to observe that the tightly coupled method is more accurate than the loosely coupled ones.
# Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method
## 4 Conclusions  
In the present work, a two-stage hybrid method was proposed to efficiently identify the parameters of RBF neural networks. In the first stage of the method, a technique rooted in particle swarm optimization was used to efficiently identify a reliable interval of values for the neural network parameters. In the second stage of the method, an intelligent global optimization technique was used to locate the neural network parameters within the optimal value interval of the first stage. In this work, a genetic algorithm was used in the second phase, but any global optimization method could be used in its place.  
The method was applied to a multitude of classification and regression problems from the relevant literature. In almost all cases, the proposed method significantly outperformed the other machine learning models, and on average, the improvement in the error on the test sets was of the order of 40% relative to the established RBF training method. Moreover, the method is quite robust with respect to the basic parameters since any changes in the parameter values do not significantly affect its performance. Furthermore, the method can efficiently locate the value interval of network parameters without any prior knowledge about the type of training data or whether it is a classification or a regression problem. However, the proposed technique is significantly more time-consuming than the traditional training technique, as it requires computational time for both of its phases. However, this effect can be overcome to some extent by the use of modern parallel computing techniques.  
The method could be extended by the use of other techniques of training the parameters in RBF networks, such as, for example, the differential evolutionary method [107]. Furthermore, more efficient methods of terminating the first stage of the method could be used, as finding a suitable interval of values for the network parameters requires many numerical calculations.  
[PERSON] and [PERSON] conceived of the idea and methodology and supervised the technical part regarding the software. [PERSON] conducted the experiments, employing datasets, and provided the comparative experiments. [PERSON] performed the statistical analysis and prepared the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The experiments of this research work were performed at the high-performance computing system established at the Knowledge and Intelligent Computing Laboratory, Department of Informatics and Telecommunications, University of Ioannina, acquired with the project \"Educational Laboratory equipment of TEI of Epirus\" with MIS 5007094 funded by the Operational Programme \"Epirus\" 2014-2020, by ERDF and national funds.  
The authors declare no conflict of interest.
# Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
###### Abstract  
For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial runs during the manufacturing process. However, despite the progress made in CAE simulation results, there still exists a slight deviation from actual conditions. Therefore, relying solely on CAE simulations cannot entirely prevent product defects, and businesses still need to implement real-time quality checks during the production process. In this study, we developed a Back Propagation Neural Network (BPNN) model to predict the occurrence of short-shots defects in the injection molding process using various process states as inputs. We developed a Back Propagation Neural Network (BPNN) model that takes injection molding process states as input to predict the occurrence of short-shot defects during the injection molding process. Additionally, we investigated the effectiveness of two different transfer learning methods. The first method involved training the neural network model using CAE simulation data for products with length-thickness ratios (LT) of 60 and then applying transfer learning with real process data. The second method trained the neural network model using real process data for products with LT60 and then applied transfer learning with real process data from products with LT100. From the results, we have inferred that transfer learning, as compared to conventional neural network training methods, can prevent overfitting with the same amount of training data. The short-shot prediction models trained using transfer learning achieved accuracies of 90.2% and 94.4% on the validation datasets of products with LT60 and LT100, respectively. Through integration with the injection molding machine, this enables production personnel to determine whether a product will experience a short-shot before the mold is opened, thereby increasing troubleshooting time.  
BPNN; machine learning; transfer learning; injection molding; short-shots +
Footnote †: journal: Applied sciences
## 4 Experiments
### Hyperparameters
#### 4.4.2 Exponential Constants  
The strong suppression weight function of MA-NMS contains a hyperparameter \(C_{1}\), which is used to enhance the suppression of duplicate proposals. Additionally, \(C_{2}\) and \(C_{3}\) are utilized as large suppression factors in strong and weak suppression to further remove duplicate proposals, respectively. Extensive experiments are conducted to evaluate the performance of MA-NMS using the control variables method. More concretely, when \(C_{1}\) is  
Figure 4: Results of NMS algorithms with varying values of rigid threshold \(N_{t}\). The experiments are conducted on the CrowdHuman validation set. To ensure a fair comparison, these NMS algorithms are applied separately to the same baseline, with the same settings stated in Section 4.2. The higher the \(AP\) value, the better the result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **SS** & **WS** & **SF** & **AP** & **Recall** & \(\text{MR}^{-2}\) \\ \hline Baseline & & & & 85.0 & 88.1 & 44.8 \\  & \(\surd\) & & & 88.6 & 92.7 & 45.1 \\ MA-NMS(ours) & \(\surd\) & \(\surd\) & & 89.1 & 94.2 & 43.7 \\  & \(\surd\) & & \(\surd\) & 89.3 & 93.5 & 42.5 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **90.2** & **94.6** & **42.0** \\ \hline \hline \end{tabular} Better performance is indicated by the table’s bold font. This also applies to subsequent tables.  
\end{table}
Table 3: Ablation experiments evaluated on CrowdHuman validation set. The baseline (the first line) corresponds to Faster R-CNN [8] with ResNet-50, FPN, and Greedy NMS. MA-NMS—Multi-Attribute NMS. SS—strong suppression. SW—weak suppression. SF—suppression factors.  
varied, \(C_{2}\) and \(C_{3}\) are set to 3 and 4. When \(C_{2}\) is varied, \(C_{1}\) and \(C_{3}\) are set to 4. When \(C_{3}\) is varied, \(C_{1}\) and \(C_{2}\) are set to 4 and 3. Figure 5 shows the results of MA-NMS with different values of these exponential constants. As depicted in Figure 5a, the best performance is obtained when \(C_{1}\) is set to 4. Furthermore, it can be seen from Figure 5b,c that the optimal performance is obtained when \(C_{2},C_{3}\) are set to 3 and 4.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Perceptually Uniform Color Spaces
#### 2.1.1 HDR-Lab  
One of the most popular uniform color spaces is the CIE 1976 \(L^{*}a^{*}b^{*}\) or CIELAB which is suited for SDR content. An extension of this color space for HDR images was proposed in [20]. The proposition is to tailor CIELAB for HDR by changing the non-linear function applied to the pixel \(XYZ\) values. This color space is calculated as follows:  
\[\begin{cases}L_{HDR}=&f(Y/Y_{n})\\ a_{HDR}=&5[f(X/X_{n})-f(Y/Y_{n})]\\ b_{HDR}=&2[f(Y/Y_{n})-f(Z/Z_{n})]\end{cases} \tag{1}\]  
where \(X_{n}\), \(Y_{n}\) and \(Z_{n}\) are the \(XYZ\) coordinates of the diffuse white. The non-linear function \(f\) is used to output perceptually linear values. \(f\) is defined for HDR as follows:  
\[f(\omega) =247\frac{\omega^{\epsilon}}{\omega^{\epsilon}+2^{\epsilon}}+0.02 \tag{2}\] \[\epsilon =0.58/(sf\times lf)\] (3) \[sf =1.25-0.25(Y_{s}/0.184)\] (4) \[lf =\log(318)/\log(Y_{n}) \tag{5}\]  
where \(Y_{S}\) is the relative luminance of the surround and \(Y_{n}\) is the absolute luminance of the diffuse white or reference white. The diffuse white corresponds to the chromatic coordinates in the \(XYZ\) domain of a 100% reflectance white card without any specular highlight. In HDR imaging, the luminance \(Y\) of the diffuse white is different from the luminance of the peak brightness. Light coming from specular reflections or emissive light sources can reach much higher luminance values. The luminance of the diffuse white is often chosen during the color grading of the images.  
The use of \(HDR\)-\(Lab\) color space is somewhat difficult since it requires to know the relative luminance of the surround, \(Y_{S}\), as well as the diffuse white luminance, \(Y_{n}\). Unfortunately these two parameters are most of the time unknown for HDR contents. To cope with this issue, we considertwo different diffuse whites to compute the \(HDR\)-\(Lab\) color space, i.e., 100 cd/m\({}^{2}\) and 1000 cd/m\({}^{2}\). These two color spaces are named \(HDR\)-\(Lab_{100}\) and \(HDR\)-\(Lab_{1000}\), respectively.  
In addition to the \(HDR\)-\(Lab\) color space, [PERSON] et al. [20] also proposed the \(HDR\)-\(IPT\) color space, which aims to extent the IPT color space [23] to HDR content. This color space is not studied in this article due to its high similarity with \(HDR\)-\(Lab\).