esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Approach to Manufacture an AUV Propeller by Additive Manufacturing and Error Analysis
## 3 Results and Discussion  
### FEM Analysis  
An FEM analysis of the developed component was carried out by using the software INVENTOR. Figure 2 shows the boundary conditions applied to the blade of the propeller. It is observed that at the fixed end, the maximum stresses accrued, and the displacement is maximum at the front end. Strain and stress are well within the permissible limits. Figure 3 depicts the stress distribution in the blade for the applied loading conditions. We have found that almost the whole blade was under a stress of 0.001 MPa due to the applied load. However, a small area near the hub was found to have developed the maximum stress of 4.19 MPa. For convenience, it can be said that the developed ABS blade is safe, as the Yield strength of the material is 20 MPa (Table 1), which is far higher than the maximum stress developed in the blade.  
Figure 1: 3D printing machine with manufactured blades.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & \multicolumn{2}{c}{**ABS Plastic**} \\ \hline \multirow{4}{*}{General} & Mass Density & 1.06 g/cm\({}^{3}\) \\  & Yield Strength & 20 MPa \\  & Ultimate Tensile & 29.6 MPa \\  & Strength & 75 MPa \\  & Flexural Strength & 2.24 GPa \\ \hline \multirow{2}{*}{Stress} & Young’s Modulus & 2.24 GPa \\  & Poisson’s Ratio & 0.38 ul \\  & Shear Modulus & 0.811594 GPa \\ \hline Part Name(s) & \multicolumn{2}{c}{Blade1} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material properties of ABS plastic.  
Figure 3: Von Mises stress distribution in the blade and stress contours.  
Figure 2: The boundary condition applied to the blade.  
Figure 4 depicts the deflection in the blade for the applied loading conditions. It was observed that the maximum deflection that occurred in the blade was 0.01797 mm. The maximum deflection was found at the free end of the blade, which was expected due to the fixed boundary condition at the other end. The deflection of 0.01797 mm is within the allowable limit when compared with the size of the component and its material properties.
# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 1 Introduction
Traditional approaches of sequence analysis have limitations that hinder their ability to accurately predict ABPs, which are essential for the discovery of novel therapies for disease. To address this critical challenge, this study develops a capsule network-based model, ABPCaps, to identify ABPs. By taking advantage of the unique architecture of capsule networks, the ABPCaps model overcomes the limitations of traditional neural networks and shows superior performance when compared to conventional baseline models. First, is the hierarchical representation. Capsule networks capture the hierarchical relationship between features. Unlike traditional neural networks, which use scalar output units, capsules are vector-valued units that encode properties of entities along with their instantiation parameters. This hierarchical representation allows capsules to capture more nuanced information. Second, is the dynamic routing mechanism. Dynamic routing mechanism can be viewed as a parallel attention mechanism. Capsule networks use a dynamic routing algorithm to determine the coupling coefficients between capsules in adjacent layers. This routing mechanism enables the network to learn to assign appropriate weights to different capsules based on their agreement, improving the network's ability to handle complex relationships and variations in data, ultimately achieving better classification performance. As such, the ABPCaps model has the potential to revolutionize the field of bioinformatics and contribute to significant advances in the understanding of biological processes and the development of novel therapies.
## 2 Related Research
### Precision Agriculture  
The development of WSN applications in precision agriculture makes it possible to increase efficiencies, productivity and profitability in many agricultural production systems, while minimizing unintended impacts on wildlife and the environment. The real time information obtained from the fields can provide a solid base for farmers to adjust strategies at any time. Instead of making decisions based in some hypothetical average condition, which may not exist anywhere in reality, a precision farming approach recognizes differences and adjusts management actions accordingly [15].  
[PERSON] deployed a WSN for fighting phytophtora in a potato field. Phytophtora is a fungal disease which depends on the climatological conditions. 868/916 MHz notes were used for measuring humidity and temperature. The aim of the system was to reveal when the crop was at risk and let the farmer treat the plants only when really needed [16].
## 4 Genome Editing for Autosomal Dominant Retinitis Pigmentosa
### Nr2e3  
The Nuclear Receptor subfamily 2 Group E Member 3 gene, _NR2E3_, encodes a transcription factor mainly expressed in photoreceptors [99]. It is essential for the differentiation of rods [100]. NR2E3 belongs to the family of orphan nuclear receptors, as no ligand has yet been identified. It interacts with CRX, NR2D1 and NRL in rod precursors as well in mature cells [101]. This interaction promotes the transcription of rod-specific genes, such as rhodopsin. At the same time, NR2E3 suppressesthe expression of cone-specific genes [102]. To date, 80 mutations have been reported for _NR2E3_. The majority of these mutations cause enhanced S cone syndrome, which is an autosomal recessive disorder [103]. Symptoms include vision loss with increased sensitivity to blue light.  
Interestingly, there is only one mutation in _NR2E3_, c.166G>A (p.Gly56 Arg; G56R), that has conclusively been linked to adRP [104]. This one mutation is the second most common mutation for adRP (after the _RHO_ P23H mutation) and accounts for 1%-2% of adRP in the US and 3.5% in Europe [104; 105; 106]. Therefore, one G56R-targeted strategy could potentially treat all _NR2E3_-related adRP patients. The glycine residue at position 56 of NR2E3 is located in its DNA-binding domain. When changed to an arginine residue, it has been proposed that the mutant protein cannot bind DNA or dimerize effectively with wild type NR2E3 [107]. However, the G56R NR2E3 protein can continue to interact with CRX. A hypothesis regarding its mechanism of action is that mutant NR2E3 recruits CRX but blocks its interaction with DNA. The G56R variant thus represents a gain-of-function mutation [108]. As a result, mutant NR2E3 cannot activate rhodopsin but it oversuppresses S and M cone opsins. This lack of rhodopsin would be detrimental for the cells, as mice with 50% reduction in rhodopsin show photoreceptor deterioration [109].  
The small size of the _NR2E3_ open reading frame (1230 bp), and the fact that there is only one mutation that causes adRP, which makes it an ideal target for gene therapy. As the mechanism of action seems to be gain-of-function, invalidation of the mutant allele, with or without gene supplementation, or specific correction of the mutant allele would be appropriate therapeutic approaches.
# On the Properties of WC/SiC Multilayers
## 3 Results and Discussion
In this equation \(\mathrm{T_{RBS}}\) is RBS thickness obtained via fitting the data and \(\mathrm{D_{RBS}}\) is the assumed density. In as-deposited sample 40 nm thick SiC appears to be homogeneous with a Si:C ratio of 47.6:52.4. However, WC shows an elemental composition gradient. Hence, the 40 nm layer was subdivided into three 13 nm thick layers and fitted with different elemental composition ratios. In addition to W and C this layer also showed measurable amount of Kr (1.4 at\(\%\)) sputtering gas. The sequence of bulk/WC/SiC layers can be described as bulk/(13 nm + 13 nm + 13 nm) WC/(40 nm) SiC. The W:C elemental composition is (46.0:52.6) + (37.2:61.4) + (43.8:54.8) as listed in Table 5. Tungsten is noticeably depleted in the middle of WC layer (Figure 14). Annealed sample has an identical elemental composition in the layers and sub-layers to within experimental error. The only difference is in the sub-layer thicknesses. The best fit could be obtained with bulk/(14 nm + 13 nm + 12 nm) WC/(40 nm) SiC (Table 6). Figure 13: Cross section TEM images of the same WC/SiC multilayer as deposited (**a**) and after annealing to 700 \({}^{\circ}\)C for 24 h (**b**); Annealing increased the multilayer period from 3.84 nm to 3.88 nm. The electron diffraction pattern in the upper right corner shows Bragg spots from the substrate (single crystal Si) and faint diffuse ring due to the multilayer. Difference in the intensity of this ring is due to different exposure times.
# RECRP: An Underwater Reliable Energy-Efficient Cross-Layer Routing Protocol +
## 3 RECRP Description
### Routing Updating Phase
The distance to the neighbor node is obtained from RSSI. Using RSSI to estimate distances for UWSNs needs to take into account multipath effects due to surface reflection, bottom reflection, and backscattering [24]. RSSI-based schemes can provide a ranging accuracy of a few meters [25]. Experiments reported in [26] have shown that multipath effects can be modeled as Rayleigh fading in shallow water. Therefore, RSSI is more suitable for deep water, with less reflection and refraction. For shallow water, we can use the peak value of signal intensity to estimate the distance. The relationship between Doppler scale shift and relative velocity between any pair of nodes is \(v=a\cdot c\), where \(v\) is the relative velocity, \(a\) is the Doppler scale shift, and \(c\) is the sound speed. Relative velocity estimations based on Doppler can achieve a promised performance from the obtained data. For example, [PERSON] et al. [27] reached 0.1 m/s deviation for node speed with a maximal velocity of 5 m/s (9.7 knot). We leveraged the Doppler measurement to estimate the relative velocity. In underwater sensor networks, the network topology changes with nodes drifting in water currents. Therefore, the routing table needs to be updated frequently. However, frequent updates to the routing table requires a large number of control messages to be sent, which increases energy consumption. Considering the limited energy of the sensor node, the update frequency initiated by sink nodes should be based on the information on ocean currents. ACK is not required in the updating phase to save energy. It is assumed that sensor nodes are clock synchronized and that messages must be sent within a single time slot. In order to reduce packet collisions caused by multiple nodes sending messages at the same time, sensor nodes randomly select the transmission time in each time slot. Therefore, the time slot is much longer than the time required to send data messages.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 5 Pricing: Creating Incentive for Caching
### Competition and Evolution  
Multiple VNOs share the bandwidth provided by a common RAN. Such an infrastructure sharing model results in competition among VNOs, which may bid for bandwidth through auctions. If a VNO fails in getting bandwidth when it has to launch on-demand transmissions, its users will suffer from poor QoS. A VNO providing poor QoS frequently will lose its users. As a result, a VNO has to spend a lot of money to win the bandwidth auction if an on-demand transmission is necessary.  
The greater the hit ratio is, the more profit the caching policy brings to a VNO [40]. With more profit, the VNO can afford a higher price to win the bandwidth auction when necessary, thereby assuring QoS. It may also reduce the service fee to attract cost-sensitive users. As a result, VNOs with low cache hit ratios will be either bankrupt due to high service cost or abandoned by users for poor QoS. In other words, the bandwidth auction not only brings more income for a RAN, but also motivates the evolution of prediction and caching policies.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.2 Cuckoo Search  
The Cuckoo Search (CS) metaheuristic is inspired by the brood parasitism behavior of some cuckoo species [38]. It was first introduced by [PERSON] in 2010 [39]. The algorithm is based on the cuckoo's behavior of laying eggs in the nests of other birds, and the host birds' behavior, which may discover these eggs and eject them or keep them [40]. It has been utilized for a variety of optimization problems, including feature selection [41; 42].  
CS follows three main rules/principles [38; 40; 41]:  
1. Each cuckoo randomly selects a nest to lay its eggs.
2. The number of available host nests is constant, and nests with high-quality eggs are passed on to the next generation.
3. If a host bird finds a cuckoo egg, it can either discard the egg or abandon the nest and construct a new one.  
These principles are then translated to an algorithmic approximation so as to use them in real use cases. In such cases, the key steps involved in CS are the following [38; 40; 41]:  
* Step 1: Various nests are initialized as a set of solutions. A set of eggs is initialized in each nest, each representing a solution.
* Step 2: A random nest is selected and a new egg (cuckoo's egg) is generated by executing a \"Levy Flight\", which involves random walks with step lengths derived from a heavy-tailed probability distribution, on its solution space. This new solution is then laid in this nest.
* Step 3: The fitness of this new egg is assessed using the fitness function in comparison to the existing eggs in the nest. If the new egg demonstrates better fitness than the least fit egg in the nest, it replaces that egg.
* Step 4: There exists a low chance of the host bird (function) detecting the cuckoo's egg, in which case it may discard the egg or leave the nest, therefore avoiding local optimization.
* Step 5: Steps 2-4 are repeated until a stopping criterion is fulfilled, such as reaching a maximum number of iterations or attaining a satisfactory solution.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Performance Analysis
#### 5.2.3 Impact of Device Density  
In Figures 10 and 11, we study the impact of the network density on the emergency call transfer under a fixed number of emergency calls and fixed emergency time interval. As observed in Figure 10, the success rate increases with the increment in the number of UEs since it increases the availability of more idle nodes under all the protocols considered. A higher number of idle nodes increases the probability of serving a large number of emergency calls compared to a low device density network. As in the previous section, initially the _EEL_ in 5G-SOS increases compared to M-HELP, since a higher latency is consumed to achieve a higher success rate under the constraints of increasing local congestion. It is reflected in Figure 9 where the _EEL_ increased against the number of UEs due to the increment in the local congestion and thus the waiting times \(T_{r}\) in 5G-SOS. The increment in local congestion increases \(T_{\text{max}}\) and causes a saturation in buffers, which result in lower relaying efficiency. However, the adaptive nature in 5G-SOS parameters allow faster relaying of calls compared to M-HELP and FINDER. It is observed that increasing the number of devices plays a positive role in the improvement of the success rate of 5G-SOS, M-HELP, and FINDER. The use of 5G-SOS significantly reduces the _EEL_ compared to M-HELP and FINDER, since 5G-SOS uses the local congestion data, RSSI, and buffering to increase the relaying efficiency in an adaptive manner.  
Figure 10: Comparison of successful rate against number of UEs (NoU) under 5G-SOS, M-HELP, and FINDER. Parameters: NoU: 6000 to 15,000, NEC: 5000, ETI: 1800 s (30 min).
## 3 Results and Discussion
### Normalization Analysis of Stress-Strain Curves  
The normalization of stress-strain curves is an important way to study the mechanical properties of soil. A hyperbolic function is widely used to express the stress-strain relationship of cohesive soil in the CU triaxial test [26]:  
\[\sigma_{1}-\sigma_{3}=\varepsilon_{1}/(b+a\varepsilon_{1}), \tag{3}\]  
which can be rewritten as  
\[\varepsilon_{1}/(\sigma_{1}-\sigma_{3})=(b+a\varepsilon_{1}), \tag{4}\]  
where \(\sigma_{1}-\sigma_{3}\) is deviator stress, \(\varepsilon_{1}\) is axial strain, and \(a\) and \(b\) are two parameters of the hyperbolic function. As shown in Equation (4), there was a linear relationship between \(\varepsilon_{1}/(\sigma_{1}-\sigma_{3})\)and \(\varepsilon_{1}\). The intercept and slope of the line are \(a\) and \(b\), respectively. The physical meaning of \(1/a\) and \(1/b\) is the ultimate strength of soil and the initial modulus. For the unloading stress path, initial deviator stress \(\sigma_{1c}-\sigma_{3c}\) needed to be subtracted in Equation (4) [12], which is rewritten as  
\[\varepsilon_{1}/[(\sigma_{1}-\sigma_{3})-(\sigma_{1c}-\sigma_{3c})]=(b+a \varepsilon_{1}), \tag{5}\]  
The stress-strain curves of reconstituted Hangzhou silt under different stress paths were hyperbolic; thus, Equations (4) and (5) were used to fit curves for normalization analysis of the stress-strain relationship. Figure 8 shows the normalized stress-strain relationship. R-squared values rangedfrom 0.9908 to 0.9998, which proves that the hyperbolic model is suitable for the normalization of the test results. Table 6 summarizes normalized curve parameters \(a\) and \(b\), and initial modulus \(E_{0}\) under different stress paths. The \(a\) and \(b\) values of the three soils were greater in the unloading path, indicating that the ultimate deviator stress and initial elastic modulus were less than those under the two other paths. In the unloading-reloading path, when unloading intensity was 10 kPa, the \(a\) and \(b\) parameters were nearly identical to those of the conventional triaxial test for silt and silty sand. The \(a\) and \(b\) values of silt and silty sand are \(a\) and \(b\) respectively. The \(a\) and \(b\) values of silt and silty sand are \(a\) and \(b\) respectively. sand increased as unloading intensity increased, indicating that the ultimate deviator stress and initial modulus decreased. The relationship between normalized initial modulus and unloading intensity in different paths is shown in Figure 9, where Group B tests used B\(-\)1 data as a reference, and Group A and C tests used A\(-\)0 data as a reference. Unloading intensity had a greater effect on the initial modulus of silt in the unloading path. When unloading intensity increased to 30 kPa, the initial modulus of Groups B and C decreased by 19% and 23%, respectively. In the unloading-reloading path, the initial elastic modulus of mucky silty clay increased by 56% with the increase in unloading intensity.
## 3 Results
### Homogeneous Layer Model  
In order to validate the efficacy of the Step-by-Step Inversion Algorithm, a Homogeneous Layer Model was constructed. In this model, the velocities from the shallow to deep regions are set as [3; 3.5, 4] km/s. Synthetic datasets were computed through forward propagation using a Vertical Seismic Profile (VSP) observation system, Orthogonal parameter points are 100 \(\times\) 100, with a spatial step of 10 m and a time step of 0.5 ms. The initial model has velocities set as [3; 3.4, 3.9] km/s from shallow to deep regions, simulating a scenario where the initial model is relatively accurate. The inversion was performed using the Adam optimization algorithm, with identical parameter settings for all tests. The Homogeneous Layer Model and its inversion results are depicted in Figure 7.  
As can be seen from Figure 7, the Step-by-Step Inversion Algorithm yields smoother results both in shallow and deep areas, especially near the source points close to the surface and the lateral regions. Slices were taken at depths of 500 m and 800 m for velocity profile comparisons, with the results shown in Figures 8 and 9, respectively.  
It can be deduced from Figures 8 and 9 that, in terms of inversion accuracy, the Step-by-Step Inversion generally achieves higher accuracy compared to the Sum-of-Losses Inversion. The inversion results are closer to the true model. Moreover, by employing a time-stepped inversion, which takes into account the propagation of waves, the accuracy of the preceding wavefield is ensured. This, in turn, leads to more precise calculations in subsequent wavefield propagations, manifesting in the inversion results as more accurate representations on the lateral sides and deeper regions of the model.  
Figure 6: Schematic diagram of the time-by-time inversion algorithm.  
Figure 8: Comparison of 500 m deep velocity profiles of Homogeneous Layer Model.  
Figure 7: Homogeneous layer model and inversion results. (**a**) True model. (**b**) Total loss inversion. (**c**) Time by time inversion.
## 4 Data Analysis Framework Application
### Discussion on Spectral Range  
Based on the findings in Tables 4-6, it appears that the features from two closely matched spectral ranges exhibit a commutative addition property. This means that the features obtained from scenario A can effectively be applied to scenario B, and vice versa. However, as the frequency range extends farther, this mechanism no longer holds true. This might be because the JONSWAP data are characterized by the peak period as the dominant period in the distribution. Therefore, any significant alteration in data distribution significantly impacts the spread, as evidenced in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario 1 and 2** & **32** & **256** & **Difference (\%)** \\ \hline Exp and Sim total & 0.0300 & 0.0282 & **6.1** \\ Exp and new feature total & 0.0163 & 0.0154 & 5.6 \\ Change (RMSE) \% & **59** & **59** & \\ Exp and Sim Black Plane & 0.0270 & 0.0278 & **2.9** \\ Exp and new feature Black Plane & 0.0164 & 0.0154 & 6.2 \\ Change (RMSE) \% & **48** & **57** & \\ Exp and Sim White Plane & 0.0086 & 0.0013 & **147** \\ Expand new feature White Plane & 0.0039 & 0.0014 & **94** \\ Change (RMSE) \% & **75** & **7** & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative outcomes of proposed feature operation technique for stochastic input between scenarios 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario 2 and 1** & **32** & **256** & **Difference (\%)** \\ \hline Exp and Sim total & 0.0302 & 0.0286 & 5.4 \\ Exp and new feature total & 0.0163 & 0.0154 & 5.6 \\ Change (RMSE) \% & **59** & **60** & \\ Exp and Sim Black Plane & 0.0272 & 0.0261 & 4.1 \\ Expand new feature Black Plane & 0.0164 & 0.0154 & 6.2 \\ Change (RMSE) \% & **49** & **51** & \\ Exp and Sim White Plane & 0.0080 & 0.0007 & 167 \\ Exp and new feature White Plane & 0.0040 & 0.0012 & 107 \\ Change (RMSE) \% & **66** & **50** & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative outcomes of scenarios 2 and 1.  
Nevertheless, the key takeaway is that features obtained from a specific frequency can be added to a limited spectral range in the vicinity. For instance, features from the peak period 1.4 can be applied to periods 1.3 and 1.5, as well as any increments in between. While a more comprehensive understanding would require further investigation, such depth is beyond the scope of the current work and will be addressed in future publications. It is anticipated that if the wave experiment had been formulated based on the zero-crossing period, the results could have been more spectrally inclusive. This could potentially serve as a goal for future experiments.