esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis  
### Data Description  
To evaluate the proposed low and high frequency fusion criteria and their performance in the NSCT domain, three sets of side-scan sonar images are used to verify the performance of the fusion criteria. Data 1 is a small-scale image of aircraft debris with clear target profile features. Data 2 is a medium-scale pipeline detection image, which contains more information, such as oil pipeline targets, rich terrain texture, and different types of sediment characteristics. The above source images were subjected to median filtering, and local Gaussian blurring (sigma = 5) is performed on the left and right sides, as shown in Figures 4 and 5. This procedure is an imitation process that the overlapping area image generated by the echo intensity of adjacent strips is not relatively clear in complex waters.  
The phenomenon of clear underwater topography and small noise interference exists in the right strip, but the image information of left strip is the opposite.  
Data 3 is the sonar images with a high overlap rate, which is collected from the port area of Rhode Island, USA, in 2011. Two sonar images are processed by seabed tracking, time variable gain, image de-noising, and image registration to obtain the large-scale overlapping areas. Stacking the overlapped area image together reflects the information differences between two images (Figure 6). Partially enlarged areas of Figure 6 indicate that underwater protective embankment and the position of underwater target reef have basically overlapped after strict registration. In addition, image color values display that the amount of information is different with the overlapped area of adjacent strips, and local area information is missing. Moreover, the emphasis of the measured underwater target is different and the terrain relief texture details are diverse in the local area.  
Figure 4: Aircraft wreckage. (**a**) Original image. (**b**) Left region blurred. (**c**) Right region blurred.  
Figure 5: Oil pipelines. (**a**) Left region blurred. (**b**) Right region blurred.  
Figure 6: Overlapping image of underwater port. Purple is the left strip and green is the right strip.
## 4 Results and Discussion  
The computational complexity of the proposed model can be expressed in terms of the number of components (K), the dimensionality of the data (D), the number of iterations required for convergence, and the specific operations involved in the E step and M step of the EM algorithm. The overall complexity is considered to be on the order of O(K\(\times\) D\(\times\)N\(\times\)I), where N is the number of data points, and I is the number of iterations. In addition, in most cases, parsing NMEA sentences is a relatively low-complexity task. The straightforward comma-separated format simplifies the parsing process, and many programming languages provide built-in functions or libraries to facilitate this task. The overall time complexity is often linear, O(N), where N is the length of the NMEA sentence.
## 1 Introduction
### Titanium Degradation  
Aged titanium impaired the migration, attachment, spread, and proliferation of osteoblasts, and such effect is unrelated to the surface topography or texture of implant-based biomaterials [22; 40; 41]. Biological aging or time-dependent degradation of titanium occurs due to surface contamination over time under ambient conditions upon storage and transfer before reaching the end-users. The mechanism of the degradation process is unknown because of the stability of the oxide layer of titanium, however, progressive accumulation of hydrogen and carbon compounds occurs over time on the surface exposed to ambient temperature [2; 28]. When exposed to atmosphere, the TiO\({}_{2}\) surface can bind to hydrocarbons in the air through interactions with carboxyl and amine groups, regardless of the type of surface treatment [42]. This progressive accumulation of organic molecules on the titanium surfaces cannot be avoided [25]. The deposition of hydrocarbons onto the titanium surface is inversely proportional with osteoblast activity [25; 28]. In comparisons of UV-treated and untreated titanium surfaces, osteoblast attachment in the former was found to be more profound and was higher in number [43], demonstrating lamellipodia-like actin projections in multiple directions [21] and possessing mature cytoskeletal development on UV-treated surfaces. Osteoblasts on the untreated surface were found to be rounded, lacking cytoskeleton formation, and presented delayed cellular proliferation [41; 44]. Time-dependent degradation also caused the implant surface to become more electronegative [45] and hydrophobic [25; 40]. The two proposed mechanisms of time-dependent titanium degradation are discussed in this section. The first mechanism involves hydrocarbon compound contamination on the external layers of TiO\({}_{2}\). The second mechanism involves changes in surface energy that resulted from alterations in the electrostatic status of the titanium surface.
Previous research has demonstrated that the global search capability of GAs facilitates the development of an efficient methodology through the identification of optimal solutions within a complex parameter space for energy management. Furthermore, the algorithm exhibits particular suitability for addressing multi-objective optimization problems. The algorithm simultaneously takes into account multiple performance metrics, such as power, fuel economy, and emission performance, enabling it to achieve multi-objective optimization. This comprehensive optimization capability not only enhances the robustness of energy management systems for hybrid vehicles, but also offers promising applications for improving energy efficiency and vehicle performance. This paper presents a novel review that focuses on the following aspects:  
1. A comprehensive review and analysis of the classification of hybrid vehicles and their associated EMSs. This section presents the structural features, operating principles, and performance advantages and disadvantages of each type of hybrid vehicle, based on their classification. This provides readers with a clear understanding of the basic classification of hybrid vehicles. 2. The paper will delve into the classification and application of EMSs. The EMS is the cornerstone of hybrid vehicle technology, determining the power performance, fuel economy, and emission levels of the vehicle. This paper compiles and examines the main categories of contemporary energy management technologies, including rule-based and optimization-based approaches. Furthermore, the paper presents the advantages, disadvantages, and applications of these different approaches. 3. The paper focuses specifically on the optimization effectiveness of GA-based EMSs. As a review, the paper provides an overview of the potential and current status of GA applications in optimization, offering readers a comprehensive perspective on the research dynamics and trends in the field. The objective of this paper is to provide readers with a comprehensive understanding of the classification methods and energy management techniques of hybrid vehicles, while also serving as a reference and inspiration for further research and applications in related fields.
## 1 Introduction  
The Capacitated Vehicle Routing Problem (CVRP), which is the focus of the study presented, is a classical operations research problem that can be described as a problem of designing routes for several vehicles traveling from a depot to a set of geographically dispersed customers and then returning back to the depot. The goal is to choose routes to serve all customers at a minimum total transport cost, without exceeding the vehicle capacity.  
CVRP falls into the category of NP-hard problems, which are computationally difficult, and no algorithm is known to have solved them in polynomial time. Depending on their size, two main approaches are considered to handle this class of problems. For fairly small-sized instances, researchers usually use exact methods. Exact methods are guaranteed to find an optimal solution, but they have a high time complexity. Therefore, heuristics and particularly metaheuristics are applied to large-scale problems. These methods can obtain good results within shorter execution time. However, they do not guarantee the solution optimality. For a few decades, hybrid algorithms that combine different algorithmic concepts have been successfully applied to a wide range of optimization problems. A special class are matheuristics, where metaheuristics are hybridized with exact methods based on mathematical programming. Since the matheuristics have the advantages of both exact and heuristic methods, they are usually very effective at solving large complex optimization problems.  
One of the recently developed matheuristics is Kernel Search (KS). Its strategy is to cleverly divide the solution space into smaller regions and explore them separately to find different feasible solutions. Because the most laborious part of the search relies on an optimization solver, KS is less demanding to implement than other heuristics. The matheuristic method presented in this paper applies the KS general framework to CVRP.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
Specific inclusion and exclusion criteria were set by two independent researchers who agreed in the selection of the studies.  
The inclusion criteria were:  
* RCTs published in English and in full text;
* The sample of the studies had to include women over 18 years old;
* Women had to have stress urinary incontinence (SUI) or urge urinary incontinence (UUI) or mixed incontinence (MUI) of any severity;
* PFM exercises combined with BF were part of the therapeutic intervention for at least one (experimental) group and were compared with a control group, which followed PFM exercises alone (without BF).  
The exclusion criteria were:* Studies including men;
* Women who in the past had undergone surgery for their incontinence problem;
* The control group followed a placebo or no intervention or any other kind of 'passive' intervention (such as booklets and guides);
* PFM exercises with the combination of BF were part of the intervention in both the experimental and control groups.
# Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
## 2 Materials and Methods
### Case Study #1: Residential PVSCS
#### 2.2.1 Description of the Alternative PVSCS Layouts  
This PVSCS was built in a single-home detached building with no important shadings from nearby constructions, so there were several options for installing a PVSCS on it. The orientation was 30\({}^{\circ}\) E for the larger facade and 60\({}^{\circ}\) W for the shorter one. The roof was flat, and was divided into two sections connected through a corridor. The owners decided to install the PV modules on the southern part of the roof in two rows of 5 modules each with portrait orientation, as is shown in Figure 3a. For this study, three alternatives are considered: the first one uses canopies with an inclination of 30\({}^{\circ}\) on the southeast and southwest facades, as shown in Figure 3b; the second one uses modules attached over the same facades, as shown in Figure 3c; and the third is mixed, using a canopy on the southeast facade and modules attached on the southwest facade.  
The original PVSCS uses power optimizers (one for each PV module), and they are connected to the inverter in a single-string configuration. For the other options, a single-string configuration is also possible, but two strings are preferred. In this case, a minimum length of 6 modules was mandatory for this inverter and power optimizers. It is important to note that, for the two orientations configurations, the peak power was flattened so the inverter can easily accommodate the sum of both strings. These inverters can drive a peak power (DC) up to 50% higher than the nominal AC output. Thus, the cost of the alternatives using several orientations is only increased by the additional modules needed.  
The original PVSCS, the three alternative layouts, and the energy optimal one are simulated using PVSYT 7.1 [28; 39]. This software provides the results on an hourly basis. The meteorological database used for this location was the Meteoromr 7.3 because it is near Madrid, and the models for the PV modules and the SolarEdge P405 power optimizers and SE4000H inverter were provided with the software. Thermal parameters, mismatch, and incidence angles were selected by default. No ageing degradation was used in the simulations because a 0.8% linear degradation was assumed in the financial analysis. The simulations were performed using the detailed electrical model considering the design of the module.
## 3 Proposed Method
### Loss Function
During the training process of the IDP-YOLOV9 network, various data augmentation techniques were utilized, including image flipping, cropping, and transformations, to extend the training dataset. Additionally, random resizing of images to (128 n \(\times\) 128 n), where n \(\in\)[9; 19], was performed to enhance the model's adaptability to different input sizes. The RAdam (Rectified Adam) optimizer was employed for better convergence performance during training. Algorithm 1 summarizes the training process of our proposed method, as shown below. ```
Initialize the Improved YOLOv9 model and optimizer with RAdam parameters, lr = 0.0001. Initialize the TRA \(P_{\theta}\) and the DLIE \(P_{\varphi}\) with random weights \(\theta\) and \(\varphi\). Training parameters: epochs = 150, batch size = 8. Prepare the training dataset. fori in range(num_epochs): forbatch_idx, (images, objects) in enumerate(train_loader): optimizer.zero_grad()  Reset optimizer gradients. Calculate DIP parameters \(PN=P_{\theta}(image\_batch)\). Apply TRA filtering to image_batch: \(image\_batch=TRA(image\_batch,PN)\). Calculate DLIE parameters \(PM=P_{\varphi}(image\_batch)\). Apply enhancement filtering to image_batch: \(image\_batch=DLIE(image\_batch,PM)\). outputs = model(images) \(L_{det}\) = criterion_detection(outputs, objects) \(L_{region}\) = criterion_region(model_region_routing_output, objects) \(L_{token}\) = criterion_token(model_token_routing_output, objects) \(L_{global}\) = criterion_global(model_global_routing_output, objects) \(L_{total}=L_{det}+\lambda_{region}\cdot L_{region}+\lambda_{token}\cdot L_{token }+\lambda_{global}\cdot L_{global}+\lambda_{\text{IoU}}\cdot L_{\text{IoU}}\)  total_loss_backward()  optimizer.step() endfor Update TRA, DLIE parameters \(P_{\theta}\), \(P_{\varphi}\), and YOLOv9 network based on \(L_{total}\). endfor
```  
**Algorithm 1** Training Procedure Pseudocode of IDP-YOLOV9  
During the training phase, a comprehensive loss function was employed, providing a holistic optimization approach to enhance detection performance under various weather conditions.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 3 Delay-/Disruption-Tolerant Networking
### Tests with a Full Network Scenario  
With an emulation based on the Interplanetary Overlay Network (ION) software [53], a virtualized testbed for a DTN of a communication system between Earth and Mars is provided [36]. This system analyzes the performance of the network with the implementation of BP, LTP, and Contact Graph Routing (CGR) algorithm. By analyzing the DTN architecture as a whole, the authors had the opportunity to present a comprehension of the problems from the network. The results drew attention to the role of intermittent connectivity and bundle priority to safeguard the delivery of timely and ordered bundles. The authors show that frequency and volume of contacts increase hop-by-hop. The main challenge of the implementation of these systems is congestion control and network management. A significant safety margin is needed to prevent network saturation. It is also suggested that the LTP aggregation mechanism needs optimization, as well as the code reliability of ION.  
This study was realized on a realistic and complex end-to-end scenario, which bridged the research gap of DTN performance evaluation that were focused on individual components of DTN, or simple test cases. This is the case of Reference [54], in which the DTN architecture was used for communication with the International Space Station (ISS), thus validating the flight qualification of DTN software for a simple test configuration.
## 2 System Model and Problem Formulation  
### System Model  
We assume an anti-jamming relay communication scheme consisted of a base station (BS), a user, a jammer and a relay group, as shown in Figure 1. The user collects nearby information and sends it to the BS. Considering the serious channel fading of wireless communication, the user cannot transmit messages to BS directly, which communicates with BS through a relay group. In phase 1, the user transmits messages to the relay group that contains its collected information and instruction which relay is acquired to assist the user to forward messages to BS. In phase 2, the relay adopts apply and forward (AF) mode, and retransmits the messages received from the user to BS. The user and relay use the same channel. The jammer sends malicious noise-like signals to decrease communication quality. Moreover, both the user and jammer have ability to change power to achieve a better communication or jamming effect. Note that for simplicity, we consider relay's power remains unchanged, because relay as the fixed station on the ground, the power constraint is not obvious compared to the user.  
Figure 1: Anti-jamming relay communication system.  
We consider the user and jammer update their respective strategies with different time scales. On the one hand, jammer updates its power strategy every epoch. On the other hand, in order to improve anti-jamming ability, the user updates its joint relay and power strategy quicker than jammer to adjust timely and obtain the optimal strategy. As shown in Figure 2, each epoch is divided as T time slots, and at the end of each time slot, BS feeds the signal-on-interference-plus-noise ratio (SINR) information of current time slot back to the user as the communication reward, which is also eavesdropped by jammer [40; 41; 42] to measure the jamming effect at the current epoch.  
In the formulated model, we assume that the user has \(M\) power levels and its power set is \(\mathcal{P}=[P_{1},P_{2},...,P_{M}]\). Jammer has \(L\) power levels and its power set is \(\mathcal{J}=[J_{1},J_{2},...,J_{L}]\). The relay group contains \(N\) relays and the relay set is \(\mathcal{R}=[R_{1},R_{2},...,R_{N}]\). The \(n\)th relay's power is a fixed value and defined as \(Q_{n}\), and all relays' power set is \(\mathcal{Q}=[Q_{1},Q_{2},...,Q_{N}]\). The distance between the user, jammer, BS and the \(n\)th relay are \(d_{u,r_{n}},d_{j,r_{n}},d_{B,r_{n}}\), respectively. The distance between BS and jammer is \(d_{Bj}\). For convenience, some used notations are listed in Table 1.
###### Abstract  
The liquid fraction from the dewatering of digested sewage sludge (LF-DSS) represents a major processing complication for wastewater treatment facilities, thus necessitating new and effective methods of LF-DSS neutralization. This pilot-scale study examined the evolution of a _Chlorella_ sp. monoculture into microalgal-bacterial granular sludge (M-BGS) during treatment of LF-DSS in a hybrid photo- bioreactor (H-PBR). The M-BGS reached a stable taxonomic and morphological structure after 60 days of H-PBR operation. The biomass was primarily composed of _Chlorella_ sp., _Microthrix parvicella_, and type 1851 and 1701 filamentous bacteria. A greater abundance of bacteria led to a faster-growing M-BGS biomass (to a level of \(4800\pm 503\) mgTS/dm3), as well as improved TOC and COD removal from the LF-DSS (\(88.2\pm 7.2\)% and \(84.1\pm 5.1\)%). The efficiency of \(\mathrm{N/P}\) removal was comparable, since regardless of the composition and concentration of biomass, it ranged from \(68.9\pm 3.1\)% to \(71.3\pm 3.1\)% for N and from \(54.2\pm 4.1\)% to \(56\), \(2\pm 4.6\)% for P. As the M-BGS taxonomic structure evolved and the C/N ratio improved, so did the anaerobic digestion (AD) performance. Biogas yield from the M-BGS peaked at \(531\pm 38\) cm3/gVS (methane fraction = \(66.2\pm 2.7\)%). It was found that final effects of AD were also strongly correlated with the N and TOC content in the substrate and pH value. A mature M-BGS significantly improved settleability and separability through filtration.  
microalgal-bacterial consortia; microbial granules; microalgae-bacterial granular sludge; taxonomic evolution; digestate treatment; anaerobic digestion; biogas; methane +
Footnote †: journal: Applied Sciences  
Accepted: 12 January 2023  
13 January 2023
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** \\ \hline \hline \multirow{4}{*}{Average} & Logistic Regression (LR) & 0.8239 & 0.7264 & 0.7758 & **0.8014** & 0.8148 \\ \cline{2-6}  & XGBoost (XGB) & 0.8225 & 0.8283 & 0.8252 & 0.5668 & 0.8227 \\ \cline{2-6}  & PyCaret & 0.8024 & 0.8324 & 0.8170 & 0.6889 & 0.7971 \\ \cline{2-6}  & H2O.ai & 0.8112 & 0.8458 & 0.8281 & 0.6903 & 0.7775 \\ \cline{2-6}  & Average of All Single Classifiers and AutoML & 0.8120 & 0.7612 & 0.7770 & 0.6200 & 0.7658 \\ \cline{2-6}  & **Proposed Model (CSS Type 2)** & **0.8717** & **0.8724** & **0.8720** & 0.6930 & **0.8198** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average performance results of Type 2 severity for all groups. In Groups 1-3, we observed an improvement in performance associated with an increase in the number of clinical variables. However, a significant improvement in performance was observed in Groups 4 and 5, which included blood test results. This indicates that clinical variables related to blood tests significantly affect classification performance. However, the two AutoML approaches, PyCaret and H2O.ai, showed only slight improvements in Groups 4 and 5. This is because PyCaret and H2O.ai select the optimal single classifier rather than constructing stacking ensemble models for each group. Additionally, these two AutoML approaches typically streamline the parameter tuning process and utilize a limited selection of single classifiers to enhance user convenience. However,
## 3 Numerical Modelling and Validation
### Model Validation  
To validate the feasibility of the numerical model, we compare its simulation results with the analytical solution of the classical KGD model, as shown in Figure 3. The fracture length in the KGD model is calculated using the following equation [37]:  
\[L_{f}(t)=2\left[0.539\left(\frac{E^{\prime}q^{3}}{\mu_{w}h_{f}^{3}}\right)^{ \frac{1}{t}}t^{\frac{1}{5}}\right] \tag{9}\]  
where \(q\) is the pumping rate; \(E^{\prime}\) is the elastic modulus under plane strain, \(E^{\prime}=\frac{E}{1-p^{2}}\); \(\mu_{w}\) is the fluid viscosity; \(h_{f}\) is the fracture height (in the 2D model, \(h_{f}=10\,\mathrm{m}\)); \(t\) is time. Comparative results show that the fracture length of the numerical model is consistent with the trend of the KGD model.
## 6 Strengths, Limitations, and Future Research Directions
### Limitations  
Despite their many advantages and the breakthroughs made over the years. There are still some drawbacks to the architectures studied. This section focuses on these.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Terrestrial Links
#### 3.4.2 Antenna Characteristics  
The user can change antenna characteristics such as pool height, dish diameter, and radiation efficiency. Using the data made available by Telewave [19], the user is presented with the option to select between different radiation patterns, as presented in Figure 4.  
Figure 3: Path definition window.