esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Application of SOMRS Model with a Multivariate t-Distribution Approach  
Based on the experimental result using CCD, it is obtained the mean of the Paracetamol tablet's fragility is 0.52% (according to specifications, which is less than 1%), the mean of the tablet's hardness is 90.33 Newtons (according to specifications, which is 39.23-98.07 Newtons), and the mean of tablet's disintegration time is 242.98 s or 4.05 min (according to specifications, which is less than 15 min). A complete data description is presented in Table 4.  
With the help of R Language version 4.1.0 and RStudio 2021.09.0, the SOMRS model with a multivariate t-distribution approach is obtained as follows:  
\[\hat{y}_{1}= 0.5239+0.0675x_{1}+0.0287x_{2}-0.1453x_{3}+0.0492x_{1}^{2}+ \tag{53}\] \[+0.0191x_{2}^{2}-0.0675x_{3}^{2}+0.0063x_{1}x_{2}-0.0462x_{1}x_{3} +0.0862x_{2}x_{3}\] \[\hat{y}_{2}= 87.6758-4.7906x_{1}-5.9001x_{2}+18.7743x_{3}+0.4714x_{1}^{2}+0.50 14x_{2}^{2}+\] (54) \[+2.9174x_{3}^{2}+2.7912x_{1}x_{2}+1.9987x_{1}x_{3}-0.7088x_{2}x_{3}\] \[\hat{y}_{3}= 244.8791+49.0306x_{1}+9.6788x_{2}+7.5927x_{3}+2.5301x_{1}^{2}+13. 6342x_{2}^{2}+\] (55) \[-18.9430x_{3}^{2}-23.2938x_{1}x_{2}+12.6212x_{1}x_{3}-18.0863x_{2} x_{3}\]  
The Equation in the coded level can be used to predict the response value based on each factor's levels and to identify each factor's relative impact by observing the corresponding parameters (models).  
Furthermore, the R-square values for the model in Equations (53)-(55) are 0.6572, 0.8483, and 0.7109, respectively. Based on R-square values, it is possible to conclude that the model adequately describes the relationship between the input and response variables.  
By using the DRS technique, the tablet's disintegration time (\(Y_{3}\)) is considered the most important among the three responses, while the tablet's friability (\(Y_{1}\)) and the tablet's hardness (\(Y_{2}\)) are considered the same level of importance. The three response variables, optimal characteristics, and specifications are presented in Table 1. Thus, the optimal level set obtained is 1.32% binder, 2.32% disintegrant, and 8445.91 N compression force. In this setting, Paracetamol tablets have a friability of 0.73%, a hardness of 96.63 N, and a disintegration time of 44.14 s, with a minimum MSE of 1988.21. The three response values obtained are in accordance with the desired specifications.
## 2 Biomedical Image Segmentation
### Introduction of Probabilistic Models in Biomedical Image Segmentation
#### 2.3.2 Hidden Markov Models (HMM)  
HMMs are another class of probabilistic models with a sequential framework, making them particularly suitable for tasks involving ordered data, such as temporal sequences in medical imaging. In the context of segmentation, HMMs model the underlying dynamics of image intensities by considering a sequence of hidden states, each corresponding to a different tissue or structure.  
HMMs have found applications in dynamic imaging modalities, such as functional MRI (fMRI) or video sequences. By considering the temporal dependencies of image intensities, HMMs can discern transitions between different states, facilitating the segmentation of dynamic structures over time. This is especially valuable in functional imaging where the evolution of tissue properties is a key factor [39; 40].
## 3 Analysis of Damping Layer Parameters
### The Effect of the Damping Layer's Thickness (\(h_{d}\) Is Variable)  
In this subsection, the impact of the damping layer thickness distribution on the overall system damping is explored through the establishment of various functions representing changes in damping layer thickness. The damping layer is positioned within the range \([x_{d1},x_{44}]=[0.14,0.26]\). For ease of comparison, the effect of a damping layer with a constant thickness of \(0.0002\,m\) is used as the reference. The thickness of the damping layer, \(h_{d}\), is considered as a function of \(x\). For the sake of comparison, two restrictions for \(h_{d}(x)\) are assumed as follows:  
* The mass of the damping layer remains constant during the variation in \(h_{d}(x)\): \[\int_{x_{d1}}^{x_{d1}}h_{d}(x)dx=\int_{x_{d1}}^{x_{d1}}0.0002\,dx\] (24)
* The thickness variation function \(h_{d}(x)\) is symmetric about \(x=x_{b2}\), and it satisfies \[h_{d}(x_{b2})=0,\quad or\quad h_{d}(x_{d1})=h_{d}(x_{d4})=0.\] (25)
# Design of a Modular Energy Production-Storage System for a Sustainable Bicycle
## 2 Materials and Methods
### Design Premises  
Once the context of use was identified (long-distance or intensive bike users), and after the in-field observation, several interviews with bike users were performed, obtaining a set of desirable features that users would like the newly designed model to comply with, as follows:  
* Aesthetics--users would like maximum integration within the bike with the least visibility of the product;
* Battery capacity--users find this of vital importance to feed their electronic devices;
* Current output--one of the most important aspects as it will determine the amount of time necessary to charge the electronic devices plugged into the USB charger;
* Weight--users did not reach a consensus here, although most of them like to have light devices mounted on their frames;
* Easy battery replacement--users like to have easy access to batteries, so they can replace or repair them by themselves;
* Solar charging--users would like their devices to be charged not only when they are pedaling but also when they are stopped at traffic lights or resting during long rides;
* Drag force--users regret that drag force is variable when a dynamo is used. They would like the charging system to present a stable drag force for a wide range of speeds.
* Price--users want it as reduced as possible;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Battery Type**} & **Capacity [mAh]** & **Temperature** & \multirow{2}{*}{**Lifetime**} & \multirow{2}{*}{**Price [EUR]**} \\  & & **Voltage [V]** & & **Range [\({}^{\circ}\)C]** & & \\ \hline \multirow{2}{*}{Plug5 Plus [46]} & \multirow{2}{*}{LiFeO3} & 1100 & \multirow{2}{*}{\(-\)15–60} & \multirow{2}{*}{\(>\)2000 cycles} & \multirow{2}{*}{259} \\  & & 3.2 & & & \\ \hline \multirow{2}{*}{Appcon3000 [47]} & Li-ion: LG & 3000 & & & \\  & INR18650 & 3.63 & & & \\ \hline \multirow{2}{*}{USB-P5 [48]} & Li-ion: Samsung & 2 \(\times\) 2500 & & & \\  & INR18650-25R & 3.6 & & & \\ \hline \multirow{2}{*}{Forumslader V5} & LiMn: Efest & 3 \(\times\) 700 & & & \\  & INR18350 & 3.7 & & & \\ \hline \multirow{2}{*}{USB-Werk [50]} & LiFePO3 & 2 \(\times\) 150 & & & \\  & 3.2 & \(-\)15–60 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical characteristics of the most-used charging–storage systems on the market.  
* Bluetooth/App--this is merely an informative aspect that some users would like the final model to include. Then, a benchmarking process was made to find out which of these characteristics were present in each of the main market models. In Table 2, previous aspects are compared, assigning a numerical score (from 1 to 4) for qualitative aspects, and 1 or 0 for features that the different devices either have or do not have, respectively.  
The final design candidate should aim to beat the USB-P5 and Forumslader V5 Ahead models, which obtained the highest scores.
## 6 Conclusions  
In this paper, we have proposed and evaluated the use of ISO as a routing path optimization technique for the stretch reduction in the ICN framework in 5G and next-generation TI. We tested our simulation using Q-learning. We evaluated our results for different reinforcement learning parameters like \(\alpha\), \(\gamma\), and \(\varepsilon\). We have compared the average  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Discounted Factor (\(\gamma\))** & **Learning Rate (\(\alpha\))** & **Convergence Attained at Respective Episode** \\ \hline \multirow{3}{*}{0.1} & 0.1 & 121 \\  & 0.5 & 117 \\  & 0.9 & 128 \\ \hline \multirow{3}{*}{0.5} & 0.1 & 115 \\  & 0.5 & 116 \\  & 0.9 & 115 \\ \hline \multirow{3}{*}{0.9} & 0.1 & 452 \\  & 0.5 & 118 \\ \cline{1-1}  & 0.9 & 115 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal stretch comparison of learning agent.  
Figure 9: ISO link delay at \(\gamma\) = 0.5 and comparison with random routing and HARP.  
and instantaneous results with three experiments of \(\gamma\), and hence the optimal routing path selection in ICN using Q-learning algorithm proves to be efficient. The path with the lesser stretch gives the higher reward. Therefore, the performance and efficiency of the system is greatly improved. We designed the MDP model for our problem statement and solved that MDP with the help of Q-learning. The algorithm decides the optimal path between consumer and publisher based on previous experiences. We have evaluated our algorithm to illustrate the efficiency of our proposed algorithm towards stretch reduction. The proposed algorithm discusses the identification of requested content for AR/VR users at the certain content router in RCR and retrieves the content from the certain content router back to the AR/VR user. Moreover, we evaluated our performance and compared our proposed ISO strategy with random routing algorithm and HARP. The best optimal path lead to better QoS, QoT, and QoE for the proposed infrastructure, for haptic-driven applications of TI. Our Results show that our ISO algorithm converges to the optimal stretch within a lesser number of episodes with less link delay and is 33.69% better and 33.33% better than random routing and HARP, respectively.  
Methodology, H.A., R.A. and A.H.; Software and coding, H.A. and M.Z.I.; Experimentation and formal analysis, H.A., A.H. and R.A.; Writing--original draft preparation, H.A. and A.H.; Writing--review and editing, H.A., A.H., M.Z.I. and R.A.; Supervision, H.K.; Funding acquisition, A.H. and H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Nos. 2019R1A4A1023746, 2019R1F1A1060799) and the Strengthening R&D Capability Program of Sejong University.  
Not applicable.  
Authors declare no conflict of interest.
## 3 Results and Discussion
### DSC Analysis  
The overlay DSC thermograms of GEF pure, SLS alone, TPGS alone, HP \(\beta\)-CD alone, GEF-TPGS SDs (F1), GEF-SLS SDs (F2) and GEF-TPGS HP \(\beta\)-CD inclusion complex (F3) were represented in Figure 4. The DSC thermogram of GEF pure exhibited a sharp endotherm at 195.82 \({}^{\circ}\)C, which corresponds to the melting endotherm of GEF. The thermograms of used excipients such as SLS (104.15 \({}^{\circ}\)C), TPGS (36.24 \({}^{\circ}\)C) and HP \(\beta\)-CD (133.10 \({}^{\circ}\)C) have shown their characteristic melting peaks. The GEF-SLS has shown a small peak of endotherm at 193.01 \({}^{\circ}\)C very near to the melting point of GEF pure (195.82\({}^{\circ}\)), which was attributed to the less crystalline state of GEF in the GEF-SLS SDs form. In the case of GEF-TPGS SDs (35.23 \({}^{\circ}\)C), no melting peak was observed for GEF at around 195.82 \({}^{\circ}\)C. This suggested the complete solubilization of GEF in TPGS aqueous solution. In the case of the GEF-TPGS HP \(\beta\)-CD inclusion complex, the sharp crystalline peak of GEF was almost diminished and a very small intense peak at 193.87 \({}^{\circ}\)C was found, while a less intense broad pelting peak at 120.11 \({}^{\circ}\)C was found, which was due to the presence of HP \(\beta\)-CD.  
The reduction in the peak intensity of GEF in the case of GEF-SLS SDs might be associated with the incomplete solubilization of GEF, which can be further substantiated by the lower yield (%) of GEF SDs when SLS was the stabilizer [26]. In contrast, when the GEF SDs were prepared with TPGS as a stabilizer, the thermogram did not show an endothermic peak of GEF at around the melting endotherm of GEF, which was attributed to the good molecular dispersion of GEF in the GEF-TPGS SDs. The broad peak of endotherm for HP \(\beta\)-CD near to 133 \({}^{\circ}\)C might be due to its thermal degradation, which was also reported during the development of the HP-\(\beta\)-CD inclusion complex of lipophilic drugs, such as desloratadine [45] and posaconazole [49]. Overall, the reduction in and absence of endothermic peak intensity of GEF in the SDs and HP-\(\beta\)-CD-complex manifested the increased solubilization of the drug due to its amorphous conversion from crystalline state. These findings suggested the feasible manifestation of an inclusion complex between the GEF-TPGS SDs and HP \(\beta\)-CD. Moreover, the diminished sharp melting endotherm of GEF in inclusion complex indicated the amorphous conversion of GEF (in GEF-TPGS SDs)  
Figure 4: The overlay DSC thermograms of GEF pure, SLS alone, TPGS alone, HP \(\beta\)–CD alone, GEF–TPGS SDs (F1), GEF–SLS SDs (F2) and F1 complexed with HP \(\beta\)–CD (F3).  
during its complexation with HP \(\beta\)-CD and confirms the formation of the complex, which could be substantiated by previous reports [45; 50].
###### Abstract  
In this paper, the improved extended finite element method (XFEM) for analyzing the three-dimensional (3D) electric field is presented. The interface between two media is described by using a four-dimensional (4D) level set function. For elements with multiple interfaces, the local level set method is used to improve the accuracy. By using weak discontinuous enrichment function and moving level set function, the interpolation function is modified. The new interpolation function makes it unnecessary to repeat the mesh generation when a moving interface occurs. The cost of calculation is greatly reduced. The reliability of 3D improved XFEM in the electric field is verified through numerical calculation examples of single bubble, multi-bubbles, and moving deformed bubble in liquid nitrogen.  
XFEM; 3D electric field; 4D level set function; enrichment function; moving deformed bubble bubble
## 2 Brief Review of the Curvilinear Coordinate System  
Figure 1 illustrates a doubly-curved shell with thickness \(h\), defined in a curvilinear orthogonal coordinate system (\(\alpha_{1}\), \(\alpha_{2}\), \(\zeta\)), where \(\alpha_{1}\) and \(\alpha_{2}\) refer to the principal lines of curvature on the neutral surface (\(\zeta=0\)), whereas \(\zeta\) is perpendicular to the neutral surface itself. In addition, \(R_{1}\) and \(R_{2}\) are the curvature radii along \(\alpha_{1}\) and \(\alpha_{2}\) directions, respectively.  
Figure 1: Geometry of doubly-curved shell and its coordinate systems.  
By considering a generic point P on the neutral surface, its location is defined through the position vector \(\mathbf{r}\):  
\[\mathbf{r}=f_{1}(\alpha_{1},\alpha_{2})\mathbf{e}_{1}+f_{2}(\alpha_{1},\alpha_{2})\mathbf{e }_{2}+f_{3}(\alpha_{1},\alpha_{2})\mathbf{e}_{3}, \tag{1}\]  
where \(\mathbf{e}_{1},\ \mathbf{e}_{2},\ \mathbf{e}_{3}\) are the unit vectors standing for the principal directions in a three-dimensional setting. Meanwhile, \(f_{1},\ f_{2},\ f_{3}\) are smooth functions that characterize the parametric surface.  
The distance between point \(P\) and a reference point in orthogonal curvilinear coordinates is given by:  
\[\left(ds\right)^{2}=A_{1}^{2}(d\alpha_{1})^{2}+A_{2}^{2}(d\alpha_{2})^{2}, \tag{2}\]  
where \(ds\) stands for the infinitesimal curvilinear length that can be measured between P and the reference point at issue, within the shell middle surface. The symbols \(A_{1}\) and \(A_{2}\) are the Lame parameters defined by  
\[\left\{\begin{array}{l}A_{1}^{2}=\frac{d\mathbf{r}}{d\alpha_{1}},\frac{d\mathbf{r}} {d\alpha_{1}}\\ A_{2}^{2}=\frac{d\mathbf{r}}{d\alpha_{2}},\frac{d\mathbf{r}}{d\alpha_{2}}\end{array} \right.. \tag{3}\]  
The distance \(dS\) between two close points within the three-dimensional shell element in orthogonal curvilinear coordinates can be deducted through the following compact expression  
\[\left(dS\right)^{2}=\sum_{i=1}^{3}g_{ii}(\alpha_{1},\alpha_{2},\zeta)(d\alpha _{i})^{2}, \tag{4}\]  
where  
\[g_{11}=A_{1}^{2}\bigg{(}1+\frac{\zeta}{R_{1}}\bigg{)}^{2} \tag{5}\]  
\[g_{22}=A_{2}^{2}\bigg{(}1+\frac{\zeta}{R_{2}}\bigg{)}^{2} \tag{6}\]  
\[g_{33}=1. \tag{7}\]  
It should be recalled that the Lame parameters \(A_{1}\), \(A_{2}\) and the two curvature radii \(R_{1}\), \(R_{2}\) must fulfill the Gauss-Codazzi conditions shown below:  
\[\begin{array}{l}\frac{1}{R_{2}}\frac{\partial A_{1}}{\partial a_{2}}=\frac {\partial}{\partial a_{1}}\Big{(}\frac{A_{1}}{R_{1}}\Big{)}\\ \frac{1}{R_{1}}\frac{\partial A_{2}}{\partial a_{1}}=\frac{\partial}{ \partial a_{2}}\Big{(}\frac{A_{1}}{R_{1}}\Big{)}\\ \frac{\partial}{\partial a_{1}}\Big{(}\frac{1}{A_{1}}\frac{\partial A_{2}}{ \partial a_{1}}\Big{)}+\frac{\partial}{\partial a_{2}}\Big{(}\frac{1}{A_{2}} \frac{\partial A_{1}}{\partial a_{2}}\Big{)}=-\frac{A_{1}A_{2}}{R_{1}R_{2}}. \end{array} \tag{8}\]  
Further details concerning the analytical description of doubly-curved surfaces can be found in the book by [PERSON] and [PERSON] [38].
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 2 Synchronization Issues in LoRa
### Insights on Strategies Used to Synchronize LoRa Signals
#### 2.3.2 Synchronization Process in LoRa  
Given the specific structure of the synchronization signal as presented in the previous paragraph, an estimation of the integer parts of the STO and the CFO (\(L\) and \(C\), respectively) can be jointly performed. As explained in [14; 19; 28], a system of two equations using the estimated \(argmax\) of each FFT module in the preamble and the SFD is used to that end. If we denote the latter estimated frequencies \(\hat{S}_{up}\) and \(\hat{S}_{down}\) respectively, we have:  
\[\hat{S}_{up}=(-L+C)\mod M \tag{18}\] \[\hat{S}_{down}=(L+C)\mod M\]  
Combining the two equations of (18), \(L\) and \(C\) can be easily determined. However, since \(\hat{C}=\frac{\hat{S}_{up}+\hat{S}_{down}}{2}\) and \(\hat{S}_{up}+\hat{S}_{down}\) is also modulo \(M\), \(\hat{C}\) can only be defined modulo \(\frac{M}{2}\). Given the definition of \(C\), \(\Delta f\) should be modulo \(\frac{B}{2}\). As a result, the receiver will be able to recover a CFO only in the range \([-\frac{B}{4},\frac{B}{4}]\). Nevertheless, the authors in [22] have proposed a new method of synchronization that allows to overcome the latter constraint. To this end, they proposed using the sampling frequency \(2f_{min}\), so that the estimated FFTs \(argmax\) are modulo \(2M\). This processing would resolve the latter problem, but it is done at the expense of the computational complexity.  
Although this limit of the maximum CFO estimable is not really a problem, with regard to the deployed bandwidths and the carrier frequencies and the local oscillator precision, it prevents reducing the bandwidth of the transmitted signals. In fact, if the bandwidth is reduced, it is more likely to obtain a CFO that exceeds \(\frac{B}{4}\), especially when considering LEO communications. The eventual bandwidth reduction makes it possible to increase the sensitivity of the receiver, as we will see later in this paper. It also leads toward increasing the number of possible channels, which could increase the capacity of the technology. However, it reduces the data rate and, thus, increases the time on air of the packets, which increases the probability of collision. Some applications, such as satellite IoT, lend themselves well to this need for long range communications. The contribution developed in the following section is not limited to this application case, but is particularly well adapted to it.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Analysis of Haze and Rain Models for Creating Synthesized Training Dataset  
Training dataset for the proposed network is created based on the rain model as shown in Figure 3. We also propose haze removal method based on the haze model. Therefore, to fully understand FHRR, it is crucial to know the haze and rain model. In this section, we describe the haze model and the rain model in detail.  
Figure 3: Example comparison of synthesis methods on road image using rain-models. (**a**) Using linear addition model, (**b**) Using screen blending model.  
Figure 2: Schematic diagram of the proposed frequency-based haze and rain removal network (FHRR-Net) framework.
# Sonic Crystal Noise Barrier with Resonant Cavities for Train Brake Noise Mitigation
## 3 Results
### Application of the Insertion Loss Obtained via In Situ Measurements of the TBN  
In this section, a practical application is developed to assess the impact of isolating a barrier in real train scenarios. This approach is intended to facilitate the evaluation of the actual sound pressure level (SPL) reduction resulting from the implementation of the barrier in a real situation. To assess the performance of these barriers, the IL obtained from the measurements described in Section 3.2 was added to the sound pressure level in the cases found in the literature regarding train noise. In particular, [PERSON] et al. [6] concluded that maximum levels in this frequency range were found during braking at different speeds (93, 89 and 58 km/h), and [PERSON] et al. [7] measured the noise spectrum at 84 km/h and 72 km/h. Based on their measurements of the TBN, an estimate of the improvement resulting from the installation of these barriers is presented.  
We start by extracting the sound pressure level (SPL) data for each case study from the existing literature. We then subtract this value the IL of the SNCB barriers measured experimentally in Section 3.2 from the TBN within the same frequency range. This method provides a clearer understanding of the barriers' performance in realistic scenarios and allows for the assessment of their applicability. Finally, we obtain the A-weighted SPL. The results are shown in Table 1.  
Figure 6: Insertion loss of the experimental measurements, with experimental 0\({}^{\circ}\) SCNB (blue dot-dashed), experimental 90\({}^{\circ}\) SCNB (red dot-dashed) and experimental CB (black dot-dashed).  
The perceived SPL is lower in all cases when considering the IL provided by the 0\({}^{\circ}\) and the 90\({}^{\circ}\) SCNB barriers. The overall improvement is greater than the just-noticeable difference (JND) of 1 dBA, indicating that the reduction is not only measurable but also perceptibly significant [48]. These results provide an overview of the performance of the SCNB insulation and show that the SCNB at 0\({}^{\circ}\) outperforms the SCNB 90\({}^{\circ}\) in all cases evaluated.
## 4 Experimental Results
### Evaluation of the Extrinsic Calibration  
We proceed to evaluate the extrinsic calibration by considering all scenes that meet the preliminary conditions. The calibration is performed image-wise and evaluated separately for translation and rotation. For each case, we compute the average \(L2\)-norm relative to the ground truth provided in the dataset. To evaluate rotation, the given quaternion is converted into Euler angles for comparison. The results are presented in Figure 10a. Table 2 presents the deviation of the final averaged extrinsic parameters.