esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calculation of the Loss Contributions  
An adequately precise estimation of the losses and the heat inputs during cable operation is crucial for accurately sizing the refrigeration system and the corresponding capital and operation costs. Moreover, losses directly impact the temperatures of the cryogenic fluid and the HTS tapes, consequently affecting all parameters of the cable's design.  
As described in Section 3.1, the nitrogen flows within the former and the annular gap share the heat inputs in each cable segment. The cooling stations deal with these losses consistently with the LN\({}_{2}\) flows they manage, with the termination ones also handling the heat generated by the current leads, as explained in Equation (24) using the arbitrary reference scheme from Figure 2.  
Although adopting finite element methods (FEMs) for computing the thermal inputs in a complex cable structure could lead to quite accurate outcomes, this would demand considerable computational times, especially within a genetic optimization algorithm which recursively evaluates a great number of potential results at each step. Hence, in this context, the preference is for widely accepted analytical formulae, which offer faster computational solutions. The following equation describes the calculation of \(Q_{f}\) and \(Q_{an}\) over a cable segment of a length \(d_{stat}\), as performed with the tool:  
\[\left\{\begin{aligned} & Q_{f}\Big{(}T_{max_{f}},T_{max_{an}}\Big{)}= \frac{Q_{AC}\big{(}T_{max_{f}},T_{max_{an}}\big{)}+Q_{ins}+Q_{ed}}{2}\\ & Q_{an}\Big{(}T_{max_{f}},T_{max_{an}}\Big{)}=Q_{crgu}(T_{ max_{an}})+\frac{Q_{AC}\big{(}T_{max_{f}},T_{max_{an}}\big{)}+Q_{ins}+Q_{ed}}{2} \end{aligned}\right. \tag{30}\]  
where \(Q_{AC}\) represents the AC losses produced in the HTS tapes, \(Q_{ins}\) represents the losses generated in the dielectric layers, \(Q_{ed}\) is the eddy current losses in the normal conducting materials, and \(Q_{crgu}\) is the heat input from the cryostat walls. Equation (30) further details the correlation between \(Q_{f}\) or \(Q_{an}\) with the temperatures of the two fluids, which was already explicated in Equation (2).  
Furthermore, it is assumed that the losses generated in the conductor, \(Q_{AC}\), \(Q_{ins}\), and \(Q_{ed}\) are equally shared between the two fluids in contact. On the contrary, the thermal inputs coming from the cryostat are assumed to be absorbed only by the nitrogen flowing in the annular gap, given its proximity to the cable's exterior. Further analysis of the heat distribution along a cable cross-section could lead to different repartitions of the heat contributions between the two fluids.  
Regarding the losses generated in the current leads at the terminations (\(Q_{cl}\)), these are computed by multiplying the rated maximum current per phase by a parameter \(q_{cl}\), corresponding to the losses per unit of current. This term is then multiplied by the requirednumber of current leads, which in the case of an AC coaxial cable corresponds to three current leads per cable side [20].  
The next sections will detail the single loss contributions reported in Equation (30).
_Algorithms_**2013**, \(6\), 636-677; doi:10.3390/a6040636  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Sublinear Time Motif Discovery from Multiple Sequences**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Computer Science, University of Texas-Pan American, 1201 W University Dr.,  
Edinburg, TX 78539, USA; E-Mails: [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (Y.X.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-956-665-3635; Fax: +1-956-665-5099.  
_Received: 11 June 2013; in revised form: 30 September 2013 / Accepted: 1 October 2013/_  
_Published: 14 October 2013_  
**Abstract:** In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are \(k\) background sequences, and each character in a background sequence is a random character from an alphabet, \(\Sigma\). A motif \(G=g_{1}g_{2}\ldots g_{m}\) is a string of \(m\) characters. In each background sequence is implanted a probabilistically-generated approximate copy of \(G\). For a probabilistically-generated approximate copy \(b_{1}b_{2}\ldots b_{m}\) of \(G\), every character, \(b_{i}\), is probabilistically generated, such that the probability for \(b_{i}\
eq g_{i}\) is at most \(\alpha\). We develop two new randomized algorithms and one new deterministic algorithm. They make advancements in the following aspects: (1) The algorithms are much faster than those before. Our algorithms can even run in sublinear time. (2) They can handle any motif pattern. (3) The restriction for the alphabet size is a lower bound of four. This gives them potential applications in practical problems, since gene sequences have an alphabet size of four. (4) All algorithms have rigorous proofs about their performances. The methods developed in this paper have been used in the software implementation. We observed some encouraging results that show improved performance for motif detection compared with other software.  
**Keywords:** motif discovery; sublinear time; randomized algorithm; deterministic algorithm
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 3 Optimisation Algorithm  
The flowchart of the power allocation algorithm based on the partitioning strategy is given in Figure 3. The input is the coverage area and the number of base stations. The output is the allocated power, aggregate interference, and throughput.
## 3 Results and Discussions
### HRV Features  
#### 3.2.1 Time Domain  
RMSSD and PNN50 do not require any interpolation to obtain reliable estimations for all the percentages of missing values, while SDNN need quadratic interpolation on time (see Table 3). A possible explanation of this result is that RMSSD and PNN50 capture fast changes in heart activity, i.e., high spectrum frequencies, and SDNN captures slow changes, i.e., very low spectrum frequencies. Moreover, interpolation methods, especially interpolation on duration, act as low pass filters, affecting the signal measured by the HRV features (Figure 5). No interpolation changed the spectrum, but did not introduce fictuous durations, thus minimizing the impact on successive differences of durations, that were the first computation step of both RMSSD and PNN50.
## 2 Materials and Methods
### Control Structures
#### 2.2.1 PI Cascade Control Structure  
The most popular aeration control structure is a feedback controller, often set up in a cascade. Figure 2 shows a PI cascade control structure. It defines two control loops, outer and inner, in the aeration process of the tank and two PI controllers in its structure. The internal loop controls the air flow rate and the external loop controls the dissolved oxygen.  
WWTP is a non-linear process with increasing response times and a measured controlled variable in the internal loop (air flow rate \(Q_{air}\)). Air flow rate control and dissolved oxygen (DO) control obtain benefit from a simple cascade control structure. The advantage of the cascade control is that the inner loop compensates for the sensitivity and nonlinearity of the plant in the closed loop, reducing the parameter perturbations in the outer loop. In this structure, the DO set-point is decided by the operator.
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 4 Experiments
### Dataset  
OpenSARShip is a wildly used dataset in SAR ship classification research and was released by [PERSON] et al. in 2018. OpenSARShip contains 11,346 SAR ship images covering many categories of ships from Sentinel 1 satellite. Furthermore, the single-look complex data which contain both VV and VH polarization images with a high range resolution for targets constitute the final datasets in our experiments. The number of different categories of ships in OpenSARShip are distributed in a wide range. We set two tasks to verify the effectivity of the proposed SBNN following the researches about SAR ship classification [16; 38]. Those two tasks are the three categories SAR ship classification and the six categories Dual-Polarization SAR ship classification respectively.  
The three categories SAR ship classification task includes bulk carrier, container ship and tanker which own approximately 80% of international routes [39]. To mitigate the imbalance number of different categories, we use the same way of HOG-ShipCLSNet [16] to create the dataset. We take 70% of the least number of all three categories as the number of training targets for each category. The rest targets are set in the test set. In addition, the VV and VH SAR images of a same target are treated as two independent samples. Table 3 shows the sample numbers for each category. The six categories Dual-Polarization SAR ship classification task includes bulk carrier, container ship, tanker, cargo, general cargo and fishing. We take 80% of the least number of all six categories as the number of training targets for each category. Similarly, the rest targets are set in the test set. In the six categories task, each sample consists of both the VV and VH SAR images of a same target. Table 4 shows the sample numbers for each category in the six categories Dual-Polarization SAR ship classification. Some samples used in experiments are shown in Figure 6.  
Figure 5: The illustration of patch shift processing.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
### Literature Review  
The authors presented an exhaustive and critical review of the available documentation on finite difference simulation for oil extraction [16], where guidelines were given for reservoir simulations that aimed to improve and reactivate oilfields. In addition, other authors, such as those cited in [17], applied the exploiting reflectors technique that used the acoustic wave equation using the finite difference method. Likewise, authors, such as those mentioned in [18], developed simulations through finite difference mesh refinement, achieving typical oilfield structures. The authors used other methods, such as those presented in [19], including the finite volume method. This method allows the discretization and numerical resolution of reservoirs' heat and fluid flow. Other authors [20] analyzed failure theories in oil reservoirs contemplating the dynamic effects and applications of finite element formulations. Further investigations [21] used the finite element method to simulate different scenarios of an oil pipeline section, considering the pipeline's operating conditions (internal pressure) and variations in diameters, thicknesses, and temperatures. The analysis results showed the stresses and deformations in the pipeline, having optimal working and safety conditions. The contour element simulation method was used in reference [22]; the researchers used the boundary element simulation method to determine the state of stresses acting on cement in an oil well at a given depth. The combined material model was appropriate in obtaining Von Misses stresses at the interface; although, it did not provide displacements. Several methods address oilfield situations, allowing companies to optimize maintenance, reactivation, and improve oil extraction processes. The research related to the topic of study is mainly available in Colombia and Venezuela, where there is a diversity of simulation methods applied to reservoir study. However, these simulations require extensive data processing and present considerable complexities in treating the information [23]. Some authors state that there are few works developed in computational simulations of oil exploitation [24]. After reviewing the literature, the researchers observed that seismic exploration studies lacked computational developments that could be utilized to optimize the acquisition parameters in prospective studies. The research presented here is conducted with realistic elements and parameters, which can significantly contribute to improved oil-well activation, enabling a more reliable exploitation and recognition of oil in the subsurface based on the simulation results. The authors confirm that there are several reliable methods to conduct this study (Table 1); however, they have certain disadvantages compared to the finite difference method, especially in data processing and technical requirements.
## 3 Amphiphilic Miktoarm Star Polymers: Self-Assembly
### Non-Spherical Micelles  
Due to a wide range of the hydrophilic fractions (\(f>0.45\)) that conventionally permit amphiphilic polymers to form spherical micelles, and as a result of their simple morphology, micelles constitute the bulk of assemblies that have been explored for drug delivery. However, through the alteration of hydrophilic fractions of constituent amphiphilic polymers, it is possible to assemble nanocarriers with different morphologies. When the hydrophilic fraction is lower than 0.45, polymers tend to form polymersomes, inverted nanostructures, and cylindrical micelles, amongst other morphologies [120; 121]. This rule is not universally followed, as for example, in a study with AB (A = PEG, B = PCL) type diblock copolymers, spherical micelles were obtained with PEG fractions of 0.5, 0.3, and 0.17. AB\({}_{2}\) polymers with hydrophilic PEG fractions of 0.55, 0.32, and 0.20, instead formed fiber-like cylindrical micelles at \(f\leq 0.32\)[54]. Generally, the transition from spherical to cylindrical micelles occurs as the hydrophilic fraction of the constituent polymers decreases, as a result of a change in the curvature. The increase in the size of the hydrophobic segment decreases the interfacial curvature, resulting in fiber-like micelles. Unlike linear AB block copolymers, the miktoarm stars transitioned to form cylindrical assemblies at higher hydrophilic fractions as a result of the lateral crowding of PCL arms. Additionally, owing to their branching, miktoarm stars had generally lower CMCs except in the case of the 0.2 fraction polymers; and higher DOX loading efficiencies, reaching 73%, as opposed to 55% for diblock co-polymers. A comparison of DOX release between the cylindrical \(f_{\text{PEG}}=0.32\) micelles and spherical 0.30 micelles, showed 27 and 48% cumulative release, respectively. Another AB\({}_{2}\) (A = PEG, B = PCL) miktoarm star containing a \(\beta\)-aminoacrylate junction, was found to form spherical micelles at \(f_{\text{PEG}}=0.71\), cylindrical micelles at \(f_{\text{PEG}}\) = 0.56-0.33, and platelet-like structures for \(f_{\text{PEG}}\) = 0.23. Interestingly, loading the \(f_{\text{PEG}}\) = 0.56 polymers with chlorin-e6 (Ce6) and DOX resulted in a spherical morphology [74].  
Rather than assembling a structure defined by fractional hydrophilicity, (AB)\({}_{2}\)C\({}_{2}\) (A = PS, B = poly(vinylidene fluoride) (PVDF), C = PEG) miktoarm stars were observed to self-assemble into 99 nm micelles in aqueous solution with distinct wrinkled cores [69]. PVDF in the micellar inner core blocks had both lipo-philicity and -phobicity. Cryo-TEM evaluation revealed that micelles had a \"frustrated\" wrinkled core, due to the resultant immiscibility of PVDF and PS. As a proof of concept for drug delivery, these micelles were loaded with Nile Red, and due to imperfect packing of PS and PVDF, the cargo could be encapsulated in empty pockets within the core, potentially allowing for high loading of drugs.
## 3 Results and Discussion
### Water Tank Energy Charging  
The water tank charging process is depicted in Figures 9 and 10. One can see that tests 1 and 2 are very correlated in terms of temperature levels and the energy charged to the water storage tank. The drop of charging performance for both tests occurred when the temperature at the bottom of the tank was going to rise, even before the heat recovery was switched on (see Figure 9, curves with \"\(\Diamond\)\" and \"\(\triangle\)\" markers).  
The increase of the water temperature measured at the bottom part of the tank represents the loss of the stratification inside the water storage tank. Although, the charging process started at different initial temperatures as it is shown in Figure 9, the reasonable moment to activate the heat recovery was related to the loss of the stratification in the water storage tank.  
The energy charging process mostly depended on the stratification state of the water storage tank. The maximum of the thermal power, being transmitted from the reactor to the tank, corresponded to the maximum temperature difference between the top and the bottom parts of the water storage (see Figures 9 and 10).  
Concerning the fixed packed bed, the decrease in the thermal power in Figure 10 was due simultaneously to the attenuation of the sorption reaction (see Figure 8) and to the loss of the stratification in the tank (see Figure 9). The activation of the heat recovery had the positive effect on the global performance, that will be shown in the next subheading.  
On the account of the moving bed test, the loss of the stratification was the main reason of the decrease of the thermal power. It can be seen in Figure 10 (test 2), that the charging thermal power started to decrease even before the activation of the heat recovery. But it remained constantly stable around the ~320 W level from 30 min to 55 min (see Figure 10, test 2), without considering the outliers at 44 min. However, starting from 57 min, the charge of the water storage was not very effective for the both tests because of the significant uniformization of the tank temperature and because the hydration reaction was in the terminal state.  
Figure 10: Energy and heat flow during the charging process of the water tank.  
Figure 9: Temperature profiles during the charging process of the water tank storage.
## 3 Observations and Discussions
### Contact Pressures of Soils  
The corresponding stresses of the soil reactions (i.e., the contact pressures) are plotted in Figure 6 for rafts with different dimensions in clays and in sands. It can be obviously  
Figure 5: Foundation settlements obtained for square raft foundations with different dimensions: (a) 16 m, (b) 26 m, (c) 36 m in clays; (d) 16 m, (e) 26 m, (f) 36 m in sands.  
seen that the contact pressures from the soil elements were varied at different locations under the foundation. For the smallest raft in clay, the contact pressures appeared to be more consistent (Figure 6a). For such rafts in very soft clays, the stresses of the soil reactions were found to be much smaller than those exerted in stiffer clays. Notice that the load pressures applied to the smallest raft were around 264 kPa, adding up with the weight of the concrete slab, the total load transmitted to the bottom of the foundation should approximate 288 kPa. The soil reactions for Clay 2 and Clay 3 with the smallest raft can reflect such levels of the pressure. It is believed that the total amount of energy carried out by the underneath soils is key in presenting the settlements and the contact pressures. When the raft became larger (Figure 6b,c), load pressures applied to the raft became 100 kPa and 52 kPa, respectively. The contact pressures of Clay 1 appeared to be larger at the edge and smaller at the center. Sharp changes in the contact pressures near to the edge are believed to be activated by the discontinuity of the foundation geometry in these cases. For the cases in Clay 2, the distributions of the contact pressures were varying from Figure 6b.c. Higher contact pressures were found in the center of the foundation for Clay 2. For the rafts in Clay 3, larger contact pressures were found at the center of the foundation. These observations implied that the contact pressures of a raft foundation in clay are far more complicated than those revealed commonly.  
For the rafts in sand (Figure 6d-f), more consistent distribution of contact pressures can be found. Larger contact pressures at the center and lower at the edges were observed for rafts with dimensions of 26 m and 36 m. The stresses at the foundation edge are smaller compared to the center one especially for very stiff sand. For the smallest raft, larger stresses occurred at the edge. The effects of soil stiffness (or strength) of the sand were found to be negligible to the contact pressures. Once again, sharp changes in the contact pressures of the soil near to the edge were observed for rafts in sand.  
Figure 6: Contact pressure under square raft foundations with different dimensions: (**a**) 16 m, (**b**) 26 m, (**c**) 36 m in clays; (**d**) 16 m, (**e**) 26 m, (**f**) 36 m in sands.
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 6 Conclusions  
This paper focused on the development of an underwater crack detection system for structural integrity assessment of submerged structures, emphasizing the use of machine learning approaches. This highlights the significance of advanced technology in addressing the challenges associated with underwater crack detection.  
The literature review conducted in the paper provides insights into the current methods for underwater surface crack detection, highlighting the existing challenges and potential opportunities for improvement. This suggests that the proposed system takes into account the limitations of current approaches and aims to overcome them.  
The paper introduces an image augmentation approach for creating underwater optical effects. By utilizing augmented images, the proposed system enhances the performance of existing network-based machine learning approaches in detecting cracks in underwater conditions. This showcases the innovative use of data augmentation techniques to improve detection accuracy. The main innovations in this study are an augmented database and an improved underwater concrete crack detection technique.  
The experimental results presented in the paper demonstrate the effectiveness and accuracy of the developed system in detecting cracks in underwater structures. This  
Figure 10: Confusion matrix with underwater crack images dataset (overall accuracy = 99.7%).  
Figure 9: Training progress plots and training parameters for Case 3 using AlexNet, with training, testing, and validation data = 30% of the total dataset.  
indicates that the proposed system has the potential to significantly contribute to the safety and reliability of submerged structures, such as offshore oil and gas installations, underwater pipelines, and underwater foundations for bridges and dams.  
The implementation of the proposed system has the potential to prevent catastrophic failures by detecting cracks in underwater structures at an early stage. This emphasizes the importance of proactive maintenance and inspection strategies, which can ultimately save costs and ensure the long-term integrity of underwater infrastructure.  
Conceptualization, M.R.; Methodology, U.O., V.K. and M.P.; Software, M.P.; Data curation, U.O.; Writing--original draft, U.O., V.K., M.P. and M.R.; Writing--review & editing, U.O., V.K., M.P. and M.R.; Supervision, M.P. and M.R.; Project administration, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.