esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Outcomes
### Area under the Curve and Number of Parameters Trade-Off  
During the experimental phase, the authors came across an interesting trend between the mean AUC (computed on the test set) and the number of parameters of the employed architectures. A plot illustrating the relationship between AUC and the number of parameters for the eight considered CNNs is presented in Figure 8. It is observable that the VGG-16 holds a gigantic number of parameters but without yielding the corresponding improvement in the AUC. The SqueezeNet, on the contrary, is a small architecture in terms of number of parameters, but fails to realize commendable AUC among the contemplated models. The best trade-off between the number of parameters and the performance can be seen in ResNet-like models, with ResNet-50 winning the dispute.  
Figure 8: The relationship between area under the curve and number of trainable parameters for the eight CNN architectures considered throughout this study.  
Figure 7: The train and validation loss for the fold = 0 of cross-validation. Figure (**a**) DenseNet-161 with _Adv Aug_ configuration; (**b**) SqueezeNet with _Adv Aug_ configuration. The reported behavior of train and validation loss trends is comparable to that of the other folds.
###### Abstract
Meanwhile, we propose a novel spatial-spectral dimensional transformation (SSDT) strategy to complete the all-channel interaction of spectral information and extend the range of information interaction of CNN-based methods. Then, we construct a fully convolutional network with two branches based on 3D-FCN and 2D-FCN which extracts joint spatial-spectral features and the novel spectral correlation features separately. Finally, we fuse two features into the classifier to obtain the classification results. The main contributions of our proposed method are summarized as follows:  
1. The pixel-to-pixel prediction strategy is utilized in the proposed method to enhance the utilization of the limited labeled sample. Meanwhile, a more reasonable data partition strategy is employed to ensure the accuracy of the sample labels, which can also prevent information leakage between the training and testing data. 2. We propose the SSDT strategy and define the concept of the spectral self-correlation matrix by which the correlation between each spectral band within a small region is calculated one by one. Therefore, the correlation feature between each spectrum is fully utilized without being limited by the size of the convolution kernel. 3. An FCN with two branches is constructed to implement the extraction and fusion of the proposed spectral correlation feature and classic spectral-spatial information by which the fused feature can realize the complementary advantages of the two parts. Extensive experiments show that the proposed SSDT-net achieves competitive classification performance on several HSI datasets.
# Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
## 3 Experiments  
This experiment uses Manga109 dataset [24; 25] and CIFAR-10 dataset [26] to appraise the performance of two proposed algorithms. In experiments, label noise is artificially generated in the training data, and the accuracy for the test set of the model constructed by the proposed method is measured. Label noise is generated so that it represents labeling mistakes that are likely to be caused by humans. As such, the label noise is created based on the classification model Confusion Matrix, which is trained based on training data that do not contain noise. In the C-classes classification problem, the confusion matrix is a \(C\times C\) matrix that summarizes the results. The \(i\)th row shows how the cases of the \(i\)th class were classified. First, the correct original dataset is used to train a model. Then, the trained model is used to the classify that original dataset and this prediction is the noisy label.  
To evaluate the effect of the proposed method, the experiments are performed with six algorithms as follows:  
1. Normal learning algorithm. The classification model is performed without removing label noise.
2. Classification Filtering [29] (CF) is an algorithm on filter framework which use a model that trained from the original noisy training set to classify the training set itself, and remove samples that its predicted output differs from its label
3. LC-True algorithm [27].
4. LC-Est algorithm [27].
5. Rule-Remove algorithm.
6. Rule-Weight (RW) algorithm.  
The third and fourth algorithms are derived from the Loss Correction Approach [27]. In their approach, loss function is changed based on the noise transition matrix \(T\) to make the model robust with label noise. The noise transition matrix \(T\) is the matrix whose element \(T_{ij}\) in row \(i\)th column \(j\)th present the probability of a data sample from classes \(i\) is incorrectly labeled to class \(j\). [27] proposed two approaches to refine loss function during training in the noisy data: forward and backward. In the forward case, the refined prediction is normal prediction multiply with T and in the backward case, multiplying \(T^{-1}\) with loss during back-propagation give the refined loss. Only LossCorrection forward is compared in this article since it work better than backward, and it is denoted by LossCorrection for short. The LC-True algorithm ideally assumes that the \(T\) matrix is well-known while LC-Est algorithm has to estimate it. Here, the LC-True is used as a benchmark algorithm because it is very rare for the T matrix to be known in advance in practice.
## 3 Materials and Methods
### Data Collection Instruments  
#### 3.3.1 Questionnaire  
After the participants completed the Four Seasons VR experience, they filled in a questionnaire comprising questions for collecting demographic information and five-point Likert items related to presence and their experience with the gesture-based UI of Four Seasons. The presence-related items were constructed based on the following presence factors: immersion [30], information/presentation quality [9; 10], pictorial/social realism [10], storytelling [31], enjoyment/fun [16], emotional responses [11], the richness of information [10], soundscape [32], and cybersickness [17]. The items regarding gesture-based interaction were formulated according to the presence factors related to interaction with the environment and the richness of sensory information [10], as well as overall usability of the gesture-based interaction. The questionnaire was prepared in English and then translated into Korean.  
We used a custom set of items rather than a set of standard questionnaires for presence and gesture-based interaction to keep the questionnaire length appropriate. Moreover, we aimed to include items that are specific to Four Seasons and do not appear in standard questionnaires. We acknowledge that this decision reduces the comparability of the results.
## 3 Construction of the Risk Assessment Model
### Triangular Fuzzy Analytic Hierarchy Process
#### 3.2.3 Construction of Triangular Fuzzy Judgment Matrix  
Using the expert system analysis approach, expert opinions are collected and then filtered and aggregated. Following the principles of the Analytic Hierarchy Process (AHP), pairwise comparison matrices \(\mathrm{A_{r,k}}\) are determined to assess the importance of indicators at each level. The corresponding comparison matrices for each level, based on expert opinions, are summed and averaged to obtain the average comparison matrix \(\mathrm{A_{r}}\). By interpreting the tendency information carried by the numerical values in the average matrix \(\mathrm{A_{r}}\), simplified  
Figure 6: Triangular fuzzy distribution.  
Figure 7: Hierarchical structure diagram.  
triangular fuzzy numbers are used to represent the ratios. Based on this, the corresponding simplified triangular fuzzy judgment matrix is constructed as shown in Equation (2).  
\[\text{R}_{\text{N}}=\begin{bmatrix}1&\widetilde{\text{r}}_{12}&\text{L}& \widetilde{\text{r}}_{1n}\\ \widetilde{\text{r}}_{21}&1&\text{L}&\widetilde{\text{r}}_{2n}\\ \text{M}&\text{M}&\text{O}&\text{M}\\ \widetilde{\text{r}}_{n1}&\widetilde{\text{r}}_{n2}&\text{L}&1\end{bmatrix} \tag{2}\]  
The values of simplified triangular fuzzy numbers should closely reflect the importance information contained in the \(\text{U}_{\text{i}}/\text{U}_{\text{j}}\) and \(\text{U}_{\text{j}}/\text{U}_{\text{i}}\) of the average comparison matrix \(\text{A}_{\text{r}}\). The resulting simplified triangular fuzzy judgment matrix should satisfy consistency. According to the matrix definition, in Equation (2), \(\widetilde{\text{r}}_{\text{ij}}\) represents the fuzzy ratio of the importance level between factor \(\text{x}_{\text{i}}\) and factor \(\text{x}_{\text{j}}\), and its values are still represented using 1, 2, 3,...9.  
By using triangular fuzzy numbers instead of their corresponding simplified triangular fuzzy numbers, and based on the obtained triangular fuzzy judgment matrix, the weights of evaluation criteria can be determined. In this context, the simplified triangular fuzzy number \(\widetilde{\text{r}}\) represents \"approximately equal to \(\text{r}\)\", and its specific definition is presented in Table 3[28]. For a given fuzzy judgment matrix, if the matrix composed of its simplified triangular fuzzy numbers satisfies the consistency requirements, it can be approximately considered that the triangular fuzzy judgment matrix also satisfies the consistency requirements.  
By substituting triangular fuzzy numbers, a triangular fuzzy judgment matrix can be constructed as shown in Equation (3).  
\[\text{R}_{\text{N}}=\begin{pmatrix}\text{l}_{\text{ij}},\text{m}_{\text{ij}}, \text{u}_{\text{ij}}\end{pmatrix}_{\text{r}\times n} \tag{3}\]
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Link Characteristics Validation
#### 5.3.2 Experimental Results  
In the above scenario, ten bundles of 50 kB were transferred from the Moon Lander to the MCC. As illustrated in Figure 13, ten bundles were first generated and taken into custody on the Lander. Upon the first Lander-Sat contact started at 20 s, therefore six bundles were transferred and taken into custody at the Moon Sat. Then these six bundles were delivered to GW when the Sat-GW contact was on at 70 s. As the GW-MCC link was continuous, these data were immediately transferred to the MCC. The remaining four bundles were transferred to Sat during the second Lander-Sat contact, working at 100 s, and taken into custody as before.Finally, when the Sat-MCC contact first opened at 150 s, they were directly delivered to MCC according to CGR. The comparison of obtained experimental results between [33] and our study indicates the reliability of the proposed platform.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Required Level (\%)** & **Measured Level (\%)** & **Absolute Deviation** & **Relative Deviation** \\  & **(Mbps)** & **(Mbps)** & **(\%)** \\ \hline
50.00 & 55.28 & \(-5.28\) & 10.56 \\
100 & 108.20 & \(-8.20\) & 8.20 \\
500 & 530.00 & \(-30.00\) & 6.00 \\
1000 & 1085.44 & \(-85.44\) & 8.54 \\
1200 & 1300.48 & \(-100.48\) & 8.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of measurement of bandwidth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & **Contact** & \
\begin{tabular}{c} **Start-Stop** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Speed** \\ **(Downlink)** \\ \end{tabular} & **Latency (s)** \\ \hline Lander-Sat & 1 & 20–40 & 128 kbit/s & 0 \\  & 2 & 100–120 & 128 kbit/s & 0 \\ Sat-GW & 1 & 70–80 & 1 Mbit/s & 1.3 \\  & 2 & 160–170 & 1 Mbit/s & 1.3 \\ Sat-MCC & & 150–180 & 1 Mbit/s & 1.3 \\ GW-MCC & & 0–180 & 10 Mbit/s & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contact plan excerpt.
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 4 Experiment
### Compare with State-of-the-Art Methods  
Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used as the image-quality metrics. We compare the result generated by our proposed TAN with Bicubic, A+ [2], and deep learning-based methods including SRCNN [3], LapSRN [5], MemNet [8], EDSR [4], and RDN [7]. Table 1 shows the quantitative results on Set5 [23], Set14 [24], BSD100 [25], Urban100 [12], and Manga109 [26] with scale factor \(\times\) 2, \(\times\) 4, and \(\times\) 8. We also illustrate a visual quality comparison to show the reconstructed details on BSD100 and Urban100 datasets. As shown in Figure 6, the small red rectangle represents where the sub-image was taken from and the zoomed-in ground truth is located on the bottom-right marked with a larger red rectangle. The example images shown in Figure 6 are img_052 from BSD100 [25], img_074, and img_005 from Urban100 [12]. The textures of these images were reconstructed clear with our proposed TAN, while images generated from other methods were blurred, distorted, and have errors with the image details. This illustrates that our proposed method could generate high-resolution images with more accurate details. In addition, as shown in Figure 7, our proposed TAN only contains 7.2 M parameters which are 28% fewer than DBPN(10 M) [27] 67.3% fewer than RDN(22 M) [7], and 83.3% fewer than EDSR(43 M) [4].  
Figure 6: Visual comparison for details of reconstructed images. Compared with Bicubic, A+ [2], SelfExSR [12], SRCNN [3], VDSR [13], DRCN [14], LapSRN [5], EDSR [4] and RDN [7], our proposed TAN shows the best visual quality.
# In Silico Modeling the Impact of Cartilage Stiffness on Bone Tissue Stress
## 3 Numerical Results and Discussion
### Comparison of Stress Values and Discussion  
The diagram representing the obtained values of maximum stresses under different loading conditions is shown in Figure 14. As can be seen from the values, in the case of running, the stresses on bone caused by mild cartilage are lower than those in case of walking caused by moderate and severe cartilage, obtained at loading typical of normal walking.  
Figure 14: Maximum von Mises stress values on cortical bone in case of various cartilage stiffness and loading conditions.  
Figure 13: Von Mises stress plots on trabecular bone in case of running: (**a**) With mild cartilage stiffness; (**b**) With moderate cartilage stiffness; (**c**) With severe cartilage stiffness.  
The percentage increase in stress in cases of running is shown in Figure 15. Interestingly, in the case of soft cartilage, the growth of stress reached 31%, whereas in case of medium and high cartilage stiffness, the percentage increase in stress values was approximately the same and reached 50%.  
The analysis of the obtained results allows us to state that taking into account the non-linear properties of soft tissues is extremely important for assessing the stress state of the entire biological subsystem. The main difficulty, however, is still the lack of data regarding the mechanical behavior of tissues in certain diseases. Despite the fact that general trends, such as an increase in the stiffness of cartilage in osteoarthritis, are known, modern science either does not have information on the specific values of the required constants or parameters, or this information is contradictory.  
The advantages of in silico studies were emphasized in the work; however, for their full-scale application, an information base based on experimental data is necessary.  
As for this specific study and the proposed methodology, its improvement can be performed in several directions. Firstly, the numerical model geometry can be improved. To more thoroughly study the processes occurring in the knee joint, the model can be supplemented with important anatomical components, such as ligaments. The presence of ligaments will allow the verification of more complex loading cases, such as flexion. Secondly, a more complex mathematical apparatus can be applied. Accounting for properties such as viscoelasticity and damping will allow for research on impact loads that the knee joint is subjected to during normal life cycles. To obtain more reliable results, it would be desirable to use more advanced material models that take into account the anisotropy of mechanical properties. The mathematical models of such materials themselves have been developed and mathematically defined, but the main difficulty is that each bone is strictly individual, and the absence of a template severely limits the application of more accurate models.
## 5 Conclusions  
This paper presented studies on the effect of the process time delay and damping ratio on the performance of MPC and PID controllers. The study has shown that, for the four second-order systems investigated in this paper, the performance of MPC was better than PID in terms of both normalized TV and IAE for all damping ratios when the time delay is approximately three times the time constant. Furthermore, for systems with a low dead-time-to-time-constant ratio, MPC also showed improved performance for underdamped systems with a damping ratio of less than 1. The MPC controller was compared with the best of three commonly used industrial PID control algorithms, and steps were taken to make the MPC controller tuning transparent. However, the method adopted for comparison does not quantify the improvements provided by MPC. As such the results may need to be more definitive, especially since MPC comes with additional costs related to plant testing and software. However, since the low TV demonstrated by some of the systems directly translates to actuator wear, this could further support the conclusions of this work. Moreover, the MPC formulation adopted for this work uses few tuning parameters, and information embedded in routine plant data could easily be harnessed for controller design. More importantly, the work guides when to consider deploying MPC, which presents a good starting point for further analysis and studies. Practitioners can leverage these findings to anticipate plants that may or may not benefit from MPC implementation. The results obtained can thus be summarized in Table 15. Note that the boundary presented in Table 15 is not crisp, and zones may infringe into an adjoining zone. Therefore, our results suggest that, if steps are taken to adopt more advanced controllers on second-order processes that may not be well-suited for PID, then performance improvements are possible. However, this work did not quantify the amount of control improvements. Also, nonstandard PID schemes may offer improved performance over the standard PID techniques considered in this study. Therefore, quantifying the improvements and considering nonstandard PID schemes are possible directions for future studies.  
Another dimension which is critical to MPC is that of constraint handling and multi-variable systems. These are outside the scope of this work as it is well-known that MPC is more suitable for large, multivariable constrained systems. Hence this work focused on SISO unconstrained processes.  
This research received no external funding.  
Not applicable.  
The author wishes to acknowledge MIT libraries for supporting this research with open access funds.  
The authors declare no conflict of interest.
## 1 Introduction
The droplet size of PE can be influenced by several parameters, such as emulsification time and energy, oil to solid particle volume ratio, and concentration of oil phase [24]. The fungal hypha damages the nail structure, creating pores in \(\upmu\)m size range [25], with porosity in the 5-20% range depending on pretreatment of the nail [26]. An emulsion droplet with the appropriate size could penetrate into the porous nail structure and retain the antifungal drug on the nail bed for a longer period, which is the main site of reinfection [27], and thereby a targeted drug delivery can be achieved. Inert and biocompatible particles should be chosen as stabilizing particles in PEs drug formulation. Silica nanoparticles (SNPs) are widespread in pharmaceutical technology in topical treatments because of their favorable chemical and surface properties, thermal stability, mechanical resistance, and biocompatibility [28; 29]. The effects of topically applied SNPs have been examined in detail [30], and it has been found that they have no toxic effect even after prolonged usage. Because of the above-mentioned advantageous properties of SNPs, they can be suitable for PEs stabilization. The PEs are most stable when the partial wetting conditions of stabilizing particles are the same for the oil and water phase [31]. The native SNPs are hydrophilic because of the high number of free silanol groups at their surface [32], which has to be modified with organic ligands to achieve appropriate wettability and strong stabilizing effect. In the present study, our aim was to formulate PE of an azole derivative and antifungal essential oil as an alternative formulation for onychomycosis topical treatment. Ticonazole (TIO) has a broad antifungal activity for common dermatophytes, which has proved to be efficient for the topical treatment of fungal infections [33]. [PERSON] et al. determined that _Melaleuca alternifolia_ (MA) EO (tea tree EO) inhibited the growth of several clinical fungal isolates, so they suggested its use in the topical treatment of fungal infections [34]. For PE formulation, we used the solution of TIO in MA EO. Synthesis and surface modification of SNPs with different sizes were performed, and they were used as stabilizing agents of PEs. We characterized the stability, droplet size, and emulsion type of PEs. In vitro diffusion studies were also performed through artificial membranes. The aim of the in vitro diffusion test was to compare drug delivery characteristics of different formulations in the membranes that have similar porous structure and surface properties as the nail plate and nail bed. The antifungal activity against _Candida albicans_ and _Trichophyton rubrum_--the species mainly responsible for fungal nail infections--has been investigated [3].
## 3 Description of Information Technologies for Order Picking and Formation of an MCDM Model
### Order-Picking Technologies
#### 3.2.4 Pick-to-Vision Technology  
Pick-to-vision technology represents a new concept in storage systems made possible by using smart glasses, which transmit information from the server directly to the observer's field of vision via WLAN technology. Through the barcode scanner, when the warehouse worker receives the product, his glasses will light up green if it is the right item or red if it is the wrong product.  
Smart glasses can also be used to determine the position of an observer in a warehouse. Based on this, they can be used to manage the virtual warehouse using graphic symbols that lead an employee to a specific location, see Figure 5.  
The paper [37] presents certain conclusions about the application of smart glasses in the field of logistics and order picking in warehouse systems. The conclusions state that the use of this technology affects walking performance in terms of the risk of slipping or falling. Then, workers point out that the glasses are too heavy and sometimes uncomfortable to wear, and their adjustment can be difficult. Moreover, in certain situations, there are problems related to fitting the glasses because the entire field of vision is not available.  
Figure 5: Vision order picking [36].  
Another problem of a technical nature implies that the glasses lose their orientation, so the order picker has to move a few steps backward in order to be able to identify the objects that are in front of him. There are numerous advantages to applying this technology, but it still requires configuration improvement, especially in terms of ergonomics [37].