esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Research results have shown that modeling-guided wave propagation using the finite element method (FEM) is effective [15]. Therefore, [PERSON] [16] applied this method to simulate the generation and propagation of ultrasonic signals in cylindrical waveguides. [PERSON] [17] simulated ultrasonic-guided wave propagation across waveguide transition. [PERSON] [18] applied the numerical simulation method to analyze the propagation of ultrasonic waveguide waves through the lap-welded plate. A study that modeled the ultrasonic cleaning technique for cylinders was conducted by [PERSON] [19]. Based on the simulated pressure distribution data, [PERSON] completed the optimal design of the sensor and achieved good application results [20]. [PERSON] [21] modeled the propagation of ultrasonic-guided waves in anchor rods partially embedded in concrete and used simulation results to evaluate the potential of ultrasonic-guided waves for bolt debonding detection. [PERSON] [22] and [PERSON] [23] modeled ultrasonic propagation in concrete and acoustic attenuation in hollow concrete bricks, respectively. The simulation data are in good agreement with the experimental test data. In summary, it is desirable to analyze the propagation of ultrasound in solids by numerical simulation. Thus, we expect to improve our understanding of the ultrasonic transmission loss in [PERSON] rods through numerical simulations and experimental measurements. The organization of this paper is as follows. The theoretical background is given in Section 2, while Section 3 consists of experimental laboratory settings and FEM. Experimental and numerical results are discussed in Section 4, followed by conclusions in Section 5. Further work is suggested in Section 6. The novelties and contributions of this paper are listed as follows:  
1. We proposed an ultrasonic non-destructive measurement method for consumable electrode length;
2. We developed a FEM model which is based on the successful ultrasonic measurement results.
# SEA and GATOR 10 Years Later
## 7 Deletion Phenotypes of the SEA/GATOR Components across Different Species  
In unicellular yeast _S. cerevisiae_, SEA genes (apart from Sec13) are non-essential [3] and in rich media, SEA deletion mutants grow practically with the same rate as wild type yeast [3]. In fission yeast _S. pombe_, deletion of any GATOR1 as well as GATOR2 component Sea3 results in a severe growth defect [22,28]. Homozygous deletions of _nprl2_ and _nprl3_ in _Drosophila_ are semi-lethal and deletions of _iml_ are lethal, with GATOR1 activity required for animals to transit the last stage of pupal development [130]. In addition, _nprl2_ null flies have a significantly reduced lifespan [165]. Similarly, _depdc5_ knockout in zebrafish resulted in premature death at 2-3 weeks post-fertilization [167]. In mice homozygous knockouts of Seh1 [168], Wdr59 and Wdr24 are embryonically lethal [169]. Constitutive knockout homozygous and heterozygous GATOR1 rodent models differs significantly. Thus, GATOR1 homozygous animals _Nprl2-/-_ mice [20], _Nprl3-/-_ mice [41], _Depdc5-/-_ rats [21] and _Depdc5-/-_ mice [170] are embryonically lethal. Mice embryos deficient for NPRL2 expression show a compromised liver hematopoiesis, which has a negative impact on embryonic viability [20]. Although mutations in GATOR1 genes are associated with epileptic disorders and brain malformations, heterozygous _Depdc5+/-_ rats and mice did not present spontaneous epileptic seizures, but _Depdc5+/-_ rats have subtle cortical malformations [21; 170]. Several tissue specific knockouts have also been investigated. Neuron-specific conditional homozygous Depdc5 knockout mice lived till adulthood, but had larger brains and exhibited a decreased survival [171]. The hepatic deletion of _Depdc5_ in mice resulted in mild liver inflammation and decreased fat level [172]. Skeletal muscle-specific Depdc5 depletion in mice resulted in muscle hypertrophy, but neither the physical nor contractile muscle function of these mice improved [151]. Similarly, mice with Nprl2 deletion in skeletal muscles had larger muscle fibers and exhibited altered running behavior [149]. In conclusion, deletions of SEA/GATOR components in every organism studied thus far provoked severe defects on growth and viability.
# Exosomal miR-92a Concentration in the Serum of Shift Workers
## 2 Materials and Methods  
### Subjetct Enrollment  
The concentration of exosomal miR-92a was determined in the serum of 30 workers enrolled among the nursing staff of Area Vista 2 (Ancona, Italy). All nurses were female, fifteen subjects worked a rotating shift schedule that included night shifts (SW nurses), whereas the other 15 workers were involved exclusively in daytime shifts (DT nurses).  
Selection criteria were the following: fertile age; no current use of medications; a negative history of psychiatric disorders, insomnia, cancer or chronic systemic diseases, such as degenerative, cardiovascular or autoimmune disorders; absence of artificial light when sleeping at home; no occupational tasks involving exposure to ionizing radiation or antiblastic drugs. SW nurses had to be enrolled in the current shift schedule, including more than 60 night shifts/year, for more than 2 years without schedule breaks in the previous 6 months. DT nurses had to go to bed habitually around 23:00 and to wake up around 06:00 without sleepless nights for more than 3 weeks prior to the study.  
Workers provided their consent after receiving information about the purpose and procedures of the study, which was conducted according to the Helsinki Statement of Ethical Standards. Being performed as part of required, routine health surveillance, the study required no formal approval by the local ethics committee. The SW nurses worked a fast clockwise shift schedule: day 1, 07:00-14:00; day 2, 14:00-22:00; day 3, 22:00-07:00; rest for 48 h; resumption of the cycle. The DT nurses worked from 07:00 to 14:00 for 6 days/week. To exclude the acute alterations related to night shift, fasting venous blood samples were taken from both SW and DT nurses at the beginning of the morning shift after a regular night's sleep on a day off. Blood samples were processed immediately after collection. An aliquot of serum was stored at \(-80\)\({}^{\circ}\)C until miR-92a analysis. Blood was analyzed the same day for glycemia, total and HDL cholesterol, and triglycerides.
# Advanced Medical Image Segmentation Enhancement:
## 2 Related Works
### Fusion of Optimization and Image Enhancement  
The fusion of optimization techniques with image enhancement methods signifies a rapidly evolving research area with significant implications for medical image analysis [16; 73; 74]. This emerging field explores the seamless integration of optimization strategies and image enhancement techniques to collectively address the challenges encountered in medical image segmentation [75]. Notably, it highlights the potential of optimization-enhanced approaches to substantially improve segmentation accuracy, particularly for extracting critical structures such as brain tumors from MRI images [76]. This avenue of research has yielded methods that enhance the visibility of structures within medical images while concurrently optimizing the precise delineation of regions of interest [77].  
At the same time, the growing interest in machine learning techniques and their application within the medical imaging domain has provided valuable insights into their role in computer-aided diagnosis and deep-learning-based image segmentation [78]. These advances have catalyzed innovation in image analysis techniques and offer valuable perspectives for addressing the complexities presented by intricate medical images [79]. However, deep learning for medical image analysis presents challenges due to computational cost and processing time, along with an increased risk of overfitting due to limited data [80]. Our research builds upon this evolving body of knowledge by introducing a pioneering approach that synergizes PSO and HE to enhance medical image segmentation. In the following sections, we delve into the technical intricacies of our proposed method and present empirical findings: presenting a fresh and innovative perspective to address the challenges associated with noisy and non-uniform medical images.
## 3 Results and Discussion
### Thermodynamic Properties  
We calculated the properties of phonons to find the thermodynamic properties of Debye temperature, heat capacity, entropy, enthalpy, and free energy that vary with temperature. It is worth noting that for all the thermodynamic temperature ranges we chose 0-1000 K. Figure 11(a) shows the change of Debye temperature with thermodynamic temperature under the pressure of 0 GPa and \(-\)0.5 GPa. We found that the Debye temperature increased with the increase of thermodynamic temperature, but it increased significantly faster under the pressure of \(-\)0.5 GPa, indicating that the thermal conductivity of CsGeI\({}_{3}\) was higher after pressure. However, within the thermodynamic temperature of 1000 K, neither of them reached the maximum Debye temperature. Figure 11(b) shows the change of heat capacity with thermodynamic temperature. The heat capacity and Debye temperature and the thermodynamic temperature satisfy the following relation:  
\[C_{V}=\frac{12\pi^{4}}{5}R(\frac{T}{\theta_{D}})^{3} \tag{11}\]  
where \(R\) is Rydberg constant and \(\theta_{D}\) is Debye temperature. From Figure 11(b), we can see that the heat capacity increases with the increase of temperature and finally tends to be stable. The maximum heat capacity corresponding to 0 GPa and \(-\)0.5 GPa are 29.6 J/cell.k and 29.1 J/cell.k, so the heat capacity of 0 GPa is slightly higher. Combined with Equation (11) and Figure 11(a), the Debye temperature of 0 GPa is lower at the same temperature, so the heat capacity is higher, which is consistent with the results obtained in Figure 11(b). Finally, their heat capacity approaches a constant value (\(C_{V}=3R\)), independent of temperature, satisfying Dulong-Petit's Law.  
Figure 11(c) shows the relationship between Gibbs free energy, enthalpy, and entropy with temperature under both pressure conditions. Gibbs free energy, enthalpy, and entropy satisfy the following relationship:  
\[\Delta G=\Delta H-T\Delta S \tag{12}\]  
It can be seen from Figure 11(c) that entropy and Gibbs free energy have great influence after pressure, while enthalpy has little influence. As the temperature rises, the thermal motion and lattice vibration of particles will increase, and the degree of chaos in the system will increase, eventually leading to the increase of entropy. The change of enthalpy with temperature is less than TS, which leads to the decrease of free energy with the increase of temperature. The variation trend of the three is consistent with Equation (12). The Gibbs free energy without pressure decreases faster with the increase of temperature, indicating that Gibbs free energy has higher stability without pressure.
# Isolation and Characterization of Starch from Different Potato Cultivars Grown in Croatia
## 3 Results and Discussion  
### Basic Chemical Composition of Tested Potato Cultivars  
The chemical composition of potatoes is very important for the potato starch industry. The most important parameter is the amount of starch present in the potato so that at the end of the process, the economy and utilization are as high as possible. The type and cultivar of the potato itself, as well as the growing conditions (soil, climate, fertilizer addition, etc.), should also be considered; for instance, potatoes grown on silty soil have the highest, while those grown on sandy soil have the lowest proportion of dry matter [8]. For this research, starches were isolated from eight potato cultivars, whose properties are shown in Table 1. As Table 1 shows, the Stilleto cultivar had the highest proportion of dry matter, and thus the highest proportion of starch, which could have been assumed as most of the dry matter consists of starch. The Senata cultivar had the lowest proportion of dry matter. However, the lowest proportion of starch was found in the SL 13-25 cultivar. The content of dry matter in potatoes is a very important factor, with an average value of 22.3%, and can vary significantly depending on the cultivar, conditions during growth and the degree of maturity [7]. Changes also occur during storage itself. The share of water in all of the cultivars was above 78.34%. [PERSON] and [PERSON] [7] state that, on average, water makes up more than 70% of the total mass of potatoes, which was confirmed by the above results. Potatoes used for processing into starch must be healthy, must not sprout and must contain at least 14% starch [9]. In this research, the proportion of starch ranged between 17.26 \(\pm\) 0.37 and 22.49 \(\pm\) 0.71%, which confirms the suitability of these cultivars for starch production. The proportion of protein ranged between 1.79 \(\pm\) 0.09 to 3.47 and 0.16%. The Stilleto cultivar had the highest protein content, while Senata had the lowest. The highest proportion of fat was found in the SL 13-25 cultivar, and the lowest proportion in the Senata cultivar. The Sereno cultivar had the highest, while the Sofista cultivar had the lowest share of crude fiber.
## 2 Subgrid Stress Tensor Equation Closure and Calibration
### Standard Subgrid Viscosity Models  
In the current paper, we compare the performance of the proposed DSM with that of Smagorinsky model [14], dynamic Smagorinsky model [4] and WALE model [15], each extended with diagonal stress terms. These three models express the subgrid stress tensor as follows:  
\[\overline{u_{i}^{\prime}u_{j}^{\prime}}=\frac{2}{3}k\delta_{ij}-2\
u_{ggg}S_{ ij}. \tag{18}\]  
In the Smagorinsky model, subgrid viscosity is defined as  
\[\
u_{ggg}=(C_{s}H)^{2}S, \tag{19}\]  
where \(H\) is the computational grid spacing (equal to characteristic filter size \(\Delta\)), \(S=\sqrt{2S_{ij}S_{ij}}\) and \(C_{s}\) is a constant coefficient. In dynamic Smagorinsky model, \(\
u_{ggg}=C_{dyn}H^{2}S\), where  
\[C_{dyn}=\min\left\{\max\left\{\frac{1}{\alpha}\frac{\mathcal{L}_{ml}S_{ml}}{ H^{2}\widehat{SS}_{kn}S_{kn}-\alpha^{2}H^{2}\widehat{SS}_{pq}S_{pq}},\ C_{dyn}^{min}\right\},\ C_{dyn}^{max}\right\}, \tag{20}\]  
\[\mathcal{L}_{ij}=\overline{\tilde{u}_{i}\tilde{u}_{j}}-\overline{\tilde{u}_{ i}}\overline{\tilde{u}_{j}}.\]  
Tilda denotes averaging over a larger star-shaped filter of seven cells. Its size exceeds that of the original filter by a factor of \(\alpha=\sqrt[3]{7}\). The coefficient is clipped on both sides to stabilize the model.  
In the WALE model,  
\[\
u_{ggg}=(C_{W}H)^{2}\frac{\left(f_{ij}^{d}f_{ij}^{d}\right)^{3/2}}{\left(S _{ij}S_{ij}\right)^{5/2}+\left(f_{ij}^{d}f_{ij}^{d}\right)^{5/4}},\]  
where  
\[f_{ij}^{d}=\frac{\left(g^{2}\right)_{ij}+\left(g^{2}\right)_{ij}}{2}-\frac{1} {3}\left(g^{2}\right)_{kk}\delta_{ij},\ \ \ \ g_{ij}=\frac{\partial\overline{u}_{i}}{\partial x_{j}},\ \ \ \ \ \left(g^{2}\right)_{ij}=g_{ik}g_{kj}\]  
and \(C_{W}\) is a constant coefficient. There are different approaches to determine the coefficient values [27; 28]. In the next section, we find \(C_{s}\), \(C_{dyn}^{min}\), \(C_{dyn}^{max}\) and \(C_{W}\) from the requirement of obtaining the correct behavior of the energy spectrum in the inertial range.  
In subgrid viscosity models, the subgrid kinetic energy can be evaluated by referring to the second invariant, \(A_{2}\), of the anisotropy tensor, see Equation (6). According to the filtered DNS data, the volume-averaged \(A_{2}\) value is \(0.31\pm 0.04\) for the filter widths chosen in Section 2.3. From (18), \(A_{2}\) is expressed as \(2\big{(}\
u_{ggg}S/k\big{)}^{2}\). Equating this to 0.31, we obtain  
\[k=\left(\frac{2}{0.31}\right)^{1/2}\
u_{ggg}S\approx 2.54\
u_{ggg}S.\]We use this formula in the Smagorinsky and WALE models. In dynamic Smagorinsky model, we restrict the kinetic energy to non-negative values:  
\[k=\max\{2.54v_{sgsg}S,\ 0\}. \tag{21}\]  
Finally, note that, when the DSM is employed, we approximate the subgrid viscosity which appears in the mixture fraction equations (see below) by the Smagorinsky formula (19) and determine \(k\) according to its definition, \(k=\overline{u^{\prime}_{i}u^{\prime}_{i}}/2\).
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms
### Self Organizing Maps (SOM)  
Self-Organizing Maps (SOM) are artificial neural network architectures that are adjusted using unsupervised learning methods to cluster data sets, including missing values. They can be used to analyze and visualize complex multivariable data with multiple parameters [46].  
The network presents advantages over other multivariate approaches because it can deal with nonlinearities in a system [47; 48]. Because it can be elaborated using data without mechanical knowledge of the system, it can deal with noisy, irregular, or missing samples. In addition, it is easy to update and interpret information with many variables or parameters using visualization resources [49; 50; 51].  
The SOM network is a matrix of \(M=m\times m\) of artificial neurons. If these \(m^{2}\) neurons are arranged in a grid or a plane, the network is called two-dimensional since it maps high-dimensional input vectors to a two-dimensional surface. For a given network, the input vector \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{n})\) has a fixed dimension \(n\). These \(n\) components are connected to each neuron in the matrix. A synaptic weight \(\mathbf{w}_{ij}\) is defined for connecting the \(i\)-th component of the input vector to the \(j\)-th neuron. Therefore, a \(n\)-dimensional vector \(\mathbf{w}_{j}\) of synaptic weights is associated with each neuron \(j\)[52].  
Equation (7) shows the function of the closest distance to the winning neuron \(j\):  
\[D_{min}(t)=\begin{array}{c}min\{D_{i}(t)\}\\ i\end{array}\ =\begin{array}{c}min\\ i\end{array}\ \left\{\sum_{j}(\mathbf{x}_{j}(t)-\mathbf{w}_{ij}(t))^{2}\right\} \tag{7}\]  
where \(\mathbf{w}_{ij}\) are the weights at the initial iteration \(t=0\), which are small randomly generated values, \(t\) is the number of the current iteration, and \(\mathbf{x}_{j}\) is a randomly selected vector from the training data set.  
Equation (8) shows the update function of the winning weight vectors and their neighbors:  
\[w_{i}(t+1)=w_{i}(t)+\alpha(t)\times(x-w_{i}(t)),\ \forall i\ \in\ N_{j} \tag{8}\]  
where \(\alpha(t)\) is the function of the learning rate that decreases exponentially with the iterations; it is calculated by Equation (9):  
\[\alpha(t)=\alpha_{0}e^{\frac{-t}{N}} \tag{9}\]  
where \(\alpha_{0}\) is the initial learning rate and \(T\) the number of iterations, both user-defined data.  
Figure 2: Groups formed in the application of the DBSCAN algorithm with Min. Points = 4.  
In Equation (10), the neighborhood order function can be observed:  
\[d(t)=\left[d_{0}e^{\frac{\pi t}{\hbar}}\right] \tag{10}\]  
\(d_{0}\) being the initial topological neighborhood.
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 2 Method
### Monte Carlo Simulation
#### 2.1.1 Photon Transmission Distance  
Determine the transmission distance of photons before the next interaction with the medium. The attenuation law of a collimated beam with radiance \(L(r,\overrightarrow{\xi})\) with distance is [39]  
\[L(r,\overrightarrow{\xi})=L(0,\overrightarrow{\xi})\cdot e^{-\sigma} \tag{3}\]  
where \(r\) is the geometric transmission distance of the photon in the direction \(\overrightarrow{\xi}\), and \(r\) is 0 in the reference point.  
The mean free path \(1/c\) is the average distance traveled by a photon between two interactions. Therefore, the probability of a photon interacting within a length \(\mathrm{d}r\) is \(c\cdot\mathrm{d}r\), and the probability of no interaction occurring is \(1-c\cdot\mathrm{d}r\). If the transmission distance \(r\) is divided into \(N\) segments equally, the probability of no interaction occurring within the transmission distance \(r\) is  
\[\Psi(r)=\left(1-\frac{cr}{N}\right)^{N}=e^{-cr}=e^{-\tau} \tag{4}\]  
where, the optical path length \(\tau=cr\), and the probability of interaction occurring within the optical path length \(\tau\), i.e., the cumulative probability distribution, is  
\[\Psi(\tau)=1-e^{-\tau} \tag{5}\]  
\begin{table}
\begin{tabular}{c l l} \hline
**Symbols** & **Descriptions** & **Unit** \\ \hline \(p(x)\) & Probability density distribution function of a certain sampling variable & \\ \(\Psi(x)\) & Cumulative distribution function of a certain sampling variable & \\ \(L\) & Radiance & W m\({}^{-2}\) sr\({}^{-1}\) \\ \(\theta\) & Scattering angle & \({}^{\circ}\) \\ \(\overrightarrow{\sigma}\) & Azimuth angle & \({}^{\circ}\) \\ \(\overrightarrow{\xi}\) & Direction vector of a photon & \\ \(\omega_{0}\) & Single scattering albedo (SSA), \(\omega_{0}=b/c\) & \\ \(\beta\) & Volume scattering function (VSF) & m\({}^{-1}\) sr\({}^{-1}\) \\ \(\widehat{\beta}\) & Scattering phase function (SPF), \(\widehat{\beta}=\beta/b\) & sr\({}^{-1}\) \\ \(K_{\mathrm{d}}\) & Downwelling diffuse attenuation coefficient & m\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The optical path length is sampled as  
\[\tau=-ln(1-\zeta) \tag{6}\]  
Since \(1-\zeta\) is also evenly distributed between 0 and 1, Equation (5) can also be written as  
\[\tau=-ln(\zeta) \tag{7}\]  
If \(r\) is the transmission distance, then  
\[r=\frac{\tau}{c}=-\frac{ln(\zeta)}{c} \tag{8}\]
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 3 SDN-Based SIN Management Architecture
In the OpenFlow protocol, an OpenFlow switch can connect multiple network controllers, but at the same time, only one controller has control over it, and other controllers have read-only function. In the SDN-based SIN management architecture, all switches in each single domain can only be managed by its single domain controller. Based on the establishment of SDN-based SIN management architecture, the routing algorithm flow of spatial information network based on a snapshot sequence is designed as shown in Figure 3.  
Figure 3: Routing algorithm flow based on snapshot sequence. Let the topological period of the SIN be T, and the topological structure of the network after time T is consistent with that before. The cycle T is divided into a series of time slices \([t_{0},t_{1}]\), \([t_{1},t_{2}]\)... \([t_{n-1},t_{n}]\). The time slot of the time slice is small enough, so it can be assumed that the slot is a static topological structure, and the topological structure only switches at the time point. After time T, the network repeats its original state. Thus, the whole dynamic network topology is divided into several static topologies. We only need to analyze the static routing in each slot and the routing handover strategy in the link handover [23]. When the time slice is switched, due to the high dynamic characteristics of SIN nodes, the link changes, and the routing needs to be recalculated. If the recalculated routing strategy is different from the original, only the original transmission link is cut off, and then a new transmission link is re-established, which requires a certain delay. The original data transmission will be interrupted, which is intolerable for tasks with high delay requirements, which is not tolerated for the task with high delay requirements. Therefore, a soft handoff strategy is needed. When two time slices are switched, the original transmission path is cut off after the new routing strategy is established and run to avoid the interruption of data transmission.