esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 3 Acoustic Channel Analysis  
### Numerical Model  
The electro-mechanical model used in this paper follows the approach developed by [PERSON] et al. [20], which was implemented on an electrical circuit simulator. The whole acoustic channel, composed of piezoelectric transducers and intermediate layers, can be simulated by considering the electro-mechanical analogy between voltage and current to pressure and velocity, respectively. The Opiezoelectric transducer component is modeled as a three-port device, being one electrical and two mechanical (shown in Figure 2). The electric signal is applied or sensed in the electrical port (EP), whereas the two mechanical ports (MP), represent the front and back faces of the transducer, which vibrate when ultrasonic waves are generated or impinge upon them. One of the mechanical ports is terminated by a resistor, simulating the back face of the transducer in air. The intermediate layers are implemented as a purely two-port mechanical device.  
Figure 3 shows the diagram of the model designed in LTSpice(r). As one can notice, in the acoustic channel block, the intermediate layers are modeled as transmission lines. The power analysis is performed by means of S-parameters, which are typical network parameters that relate the amount of power reflected or transmitted in each pair of ports [22]. In the present case, the S21 parameter of the whole channel indicates how much power a load in the inside block absorbs when a certain amount of power is injected in the outside block. Moreover, this circuit model approach can provide transient analysis such as instant voltage, current and power, which are useful to analyze the data communication system. Therefore, this model helps with understanding some behaviors of the electro-acoustic channel, such as power transfer and data communication, by evaluating the electrical signals on the electrical ports named EP2 and EP1. The material properties can be easily modified by changing the model parameters. An important advantage of the model is that multiple layers can be added or removed by simply connecting/disconnecting the new transmission lines.  
Figure 2: Equivalent electrical model of a piezoelectric ceramic transducer.
## 1 Introduction
Research on technologies for water production from air conditioning units has identified areas with interesting collection potential, represented mainly by the coastal regions of the Arabian Peninsula, Sub-Saharan Africa areas, and South Asian countries [5]. A sustainable way of approaching this topic should simultaneously consider maximizing water production, the internal thermal comfort provided by the air conditioning system, and the efficiency of the system depending on local climate conditions and variations. Other aspects related to heat exchange should also be considered in order to optimize the whole system. Studies on the possibility of extracting water produced by air conditioning units, based on an experimental setup in a laboratory climate chamber, were carried out with a split system, considering its air-cooling target design [6]. Other preliminary studies have shown promising potential for technological improvement towards more sustainable processes and systems [7; 8]. The current work presents a real application that embodies both energy and drinking water savings. It represents an evolution of an integrated system [9], which provides both air conditioning and water to a hotel. The system [9] behavior was analyzed, taking into account the climatic conditions of the Arab Emirates coast. The results show that, for the simulated case study, the water extracted from air by condensation covers approximately half of the hotel's water requirements. Energy efficiency was also taken into account to consider the sustainability of the process from various points of view. The system analyzed in the present research is a real installation developed on the basis of the results of previous research: its primary duty is to meet drinking water requirements, and, secondly, to provide domestic water heating and primary air for a non-conditioned zone (the laundry area of the hotel). The integrated system was built and placed on the roof of a hotel in Villahermosa, Tabasco, Mexico. In the following paragraphs its features are outlined, the needs of the hotel are described, and the calculations of water and energy savings are presented. Moreover, a simulation tool was developed with the aim to evaluate possible water savings in a one-year period and to improve the efficiency of the system. With this tool, validated by means of experimental results, it is possible to simulate the real behaviour of the whole system. A method to verify the effectiveness of the integrated system is also described.
## 2 Geological Setting  
The Ordos Basin is a basin formed on an ancient and relatively stable continental plate, which has undergone multiple geological cycles and features layered structural overlays due to prolonged tectonic movements, sedimentation, and possible erosion. It is composed of six secondary structural units: the Yimeng Uplift, the Weibei Uplift, the Jinxi Nappe Belt, the Yishan Slope, the Tianhuan Depression, and the western erosion belt [15; 16; 17]. The basin has a simple structure with gently sloping strata, and the main feature is the Yishan Slope, which tilts westward at an angle of less than 1\({}^{\circ}\)[18]. The Middle to Upper Triassic Yanchang Formation deposition period is a critical sedimentary phase in the basin, forming 800-1200 m of inland river-lake facies strata, which are the main targets for oil and gas exploration in this area [19]. The Yanchang Formation is divided into 10 oil reservoir groups from top to bottom. After the deposition of the Chang 9 oil reservoir group, the Chang 8 oil reservoir group formed with mainly delta-front subfacies sediments [20; 21; 22].  
The Luo 1 well area is located in the eastern part of the Tianhuan Depression in the Ordos Basin, neighboring the Shaanxi North Slope (Figure 1). It is an important oil-producing block of the Jiyuan Oilfield. Structurally, the area is characterized by a monoclinic that slopes from east to west, with no significant faulting or folding within the region. The oil-bearing area spans 33.9 km\({}^{2}\) and is considered a delta-front subfacies, with underwater distributary channel sedimentary microfacies forming the framework sandstone, which is a favorable oil and gas accumulation zone. Due to diagenesis, the permeability is extremely low, making it a typical ultra-low-permeability reservoir with poor reservoir properties and strong heterogeneity. In terms of development, acid fracturing is the primary engineering method used for such reservoirs [23]. However, there has been limited research on the internal structure and interfaces of the reservoir itself, leading to poor correspondence between injection and production during extraction and low production efficiency. The sand body architecture directly influences the development results. Therefore, it is urgently needed to conduct a detailed characterization of the internal architecture of single sand bodies, identify how different architectural styles impact the oil-water connectivity of well groups, and reveal the oil-water flow patterns to provide a reference for the future recovery of remaining oil [24]. To enhance the explanation of oil-water flow patterns for more effective recovery of residual oil, it is imperative to integrate sophisticated simulation models with empirical field data. A thorough analysis of fluid dynamics, reservoir pressure, and saturation levels should inform the optimization of well placement and the application of enhanced oil recovery techniques. Continuous monitoring and adaptive strategies, guided by real-time data, are crucial for maximizing extraction efficiency.
# The Influence of Chamfered and Rounded Corners on Vortex-Induced Vibration of Super-Tall Buildings
## 8 A Brief Analysis of the VIV Reduction Mechanism  
Because a large number of researchers have studied the wind load of super high-rise buildings with different rounded corners based on rigid pressure models and force balance tests, this paper does not repeat such tests but uses the existing test data to assist the analysis. Figure 14 shows the comparison between the wind-induced response in this paper and the base bending moment in the existing reference [19]. As seen from Figure 14, the reduction of the base moment is not obvious when the rounded ratio is less than 12.5%, while the reduction of the base moment becomes quite significant when the rounded ratio increases to 15%. This phenomenon is similar to the wind response results of the aeroelastic model at small reduced wind speeds (\(V_{r}=6\), \(V_{r}=7\), etc.). However, when \(V_{r}\) is close to \(V_{c}\) (\(V_{r}=10\), \(V_{r}=10.5\), etc.), the response of each rounded model decreases; especially after the rounded ratio increases to 15%, the crosswind response decreases significantly, and the decrease of wind-induced response is more significant than that of the base bending moment.  
Figure 15 shows the aerodynamic damping ratio of the models with different rounded ratios. It can be seen from Figure 15 that when the rounded ratio is 12.5%, the negative aerodynamic damping phenomenon near the VIV wind speed ceases to exist, and the aerodynamic damping ratio becomes positive at high wind speed. This phenomenon is more obvious when the rounded ratio increases to 15%. As is well known, the aerodynamic damping ratio must be dependent on the vibration amplitude; in other words, these characteristics of aerodynamic damping and wind-induced response are mutually causal.  
Figure 14: Comparison between the result of this paper and that of [PERSON]’s [19].  
Figure 13: _St_ number of rounded model by existing studies [14; 20].  
The above analysis indicates that the rounded corner not only reduces the wind load but also improves the aeroelastic effect significantly. Generally speaking, when the rounded ratio reaches 15%, the crosswind vibration of super high-rise buildings can be effectively suppressed.
# Hybrid Ring- and Tree-Topology RoF Transmission System with Disconnection Protection
###### Abstract  
This paper proposes a hybrid ring- and tree-topology radio over fiber (RoF) transmission system with self-disconnection protection that can support the high distribution density of base stations (BSs) in a metropolitan area and strengthen the network quality of service through self-disconnection protection. The number of supportable BS in the system can be increased significantly by integrating the time- and wavelength-division multiplexing techniques and properly utilizing a new-generation single-line bidirectional add/drop multiplexer (SBOADM) into the proposed system. Moreover, when the ring-fiber link of the system is interrupted for any reason, the system operator can recover the broken connections quickly only by transforming an optical switch state at the CO end to allow the downlink optical signals to transmit along the clockwise and counterclockwise directions of the ring-fiber link simultaneously. In this case, the downstream optical signals can be delivered to each set of BS-groups through the two-way transmission characteristics of the SBOADM automatically, and the uplink optical signals, originally, from each set of BS-groups can be transmitted back to the CO end along the opposite direction of the downlink signal-routing path. In this way, the interference caused by fiber breakage can be avoided immediately, and the entire transport system can be reconnected to ensure the quality of network services. Our experimental results prove that the overall transmission performances are similar to those under normal circumstances.  
disconnection protection; optical add/drop multiplexer; radio over fiber; wavelength division multiplexing +
Footnote †: journal: _Photonics_  
1
Footnote 1: [[https://www.mdpi.com/journal/photonics](https://www.mdpi.com/journal/photonics)]([https://www.mdpi.com/journal/photonics](https://www.mdpi.com/journal/photonics))
# Mid-Flexion Instability in Total Knee Arthroplasty: Insights from Robotic-Assisted Surgery
## 8 Clinical Results and Evaluation  
It is well established that prosthetic components' malalignment and soft tissue imbalance primarily contribute to patient dissatisfaction with mediolateral laxity at 60\({}^{\circ}\) and MFI, leading to negative patient expectations [4; 5] (Figure 3).  
Robotics were also developed to solve this problem [2; 36]. Even if their increased precision and knee balancing are well established [2; 6; 7; 8; 36], it remains to be proven if their high accuracy leads to better PROMs [2; 14; 36].  
As already mentioned, [PERSON] et al. performed a study in which the same patient underwent a staged bilateral TKA with one conventional manual TKA and an rTKA to assess PROMs and patient satisfaction [14].  
This study underlined higher patient satisfaction, a reduced time to independent ambulation, and improved joint perception during daily activities measured through the Forgotten Joint Score-12 (FJS-12) with rTKA [14]. Patients felt the robotic-operated knee was less painful and more natural, and when asked which procedure they would prefer for surgery again, the majority preferred robotic [14]. Furthermore, 76.4% of the patients would recommend rTKA to others who should undergo knee surgery for osteoarthritis [14].  
However, manual and robotic TKA were associated with comparable PROMs evaluated through the Oxford Knee Score [14]. [PERSON] et al., in a recent systematic review, described no differences in medium- and long-term functional outcomes between manual TKA and rTKA [36].  
Figure 3: An example of MFI: (**a**) a posterior stress X-ray at 40\({}^{\circ}\) flexion position showing a posterior subluxation of the tibia; (**b,c**) the same size implants simulated in model bone in 40\({}^{\circ}\) flexion position. The source is published under a Creative Commons License from [PERSON] et al. [44].  
[PERSON] et al. conducted a prospective study analyzing intraoperative balance and laxity in rTKA and their association with 2-year KOOS subscores [45]. The authors reported that intraoperatively measured balance and joint laxity are linked to PROMs at least two years after TKA [45]. Using these measurements, clinically laxity and balance limits were recognized throughout flexion, isolating a subgroup of TKAs with significantly improved pain outcomes [45].
# Sirtuin 1 and Hormonal Regulations in Aging
## 4 Natural Activators of SIRT1 and Their Use in the Prevention of Aging
### Delivery of SIRT1 Activators  
The therapeutic potential of SIRT1 activators, such as resveratrol, curcumin, and paeonol, is often limited by their poor bioavailability, rapid metabolism, and low permeability across biological barriers, including the blood-brain barrier (BBB) [86]. This limits their use for the prevention of aging and CNS stress, as well as in potential drugs against Alzheimer's and Parkinson's diseases as observed in preclinical studies [86; 104]. To overcome these difficulties, various delivery strategies have been developed to improve their stability, absorption, and targeted delivery. Nanotechnology-based approaches, such as covalent binding to nanoparticles and incorporation into liposomes and lipid-based carriers, are promising for improving the bioavailability and targeted delivery of SIRT1 activators [86; 105; 106]. For example, curcumin-loaded nanostructures improve oral bioavailability and allow efficient passage across the BBB, enhancing neuroprotective effects in models of neurodegenerative diseases [86; 104]. Similarly, resveratrol encapsulated in nanoparticles, nanoemulsions, cubosomes, and liposomes demonstrates enhanced stability and targeted delivery to the brain, effectively enhancing its antioxidant and anti-aging properties [86; 105; 107].
## 3 Methodology
### Coverage Threshold Analysis  
The signal coverage radius is estimated from a statistical analysis of the data collected in the measurements referring to distances, as illustrated in Figure 7.  
Therefore, quartiles are values extracted from a set of data observed in ascending order and divide the distribution into four equal parts. The first quartile (Q1) corresponds to the first 25% of the data; the second quartile (Q2) corresponds to the range between 25 and 50% (median); the third quartile (Q3) corresponds to the range between 50 and 75%; the fourth quartile (Q4) corresponds to the ranges between 75 and 100%.  
In this context, the 75 th percentile value of 0.66268 km or 662.68 m was defined to determine the coverage radius threshold for a mobile object uplinking data between end nodes and the gateway to ensure reliable communication to the proposed link. For values greater than the defined, the signal becomes non-continuous, for smaller values, it would be a very conservative coverage radius.
## 3 Results
### Study Characteristics
Several studies also implemented custom neural network architectures, including Approximation-Aware Neural Network (_AXNet_) (Shanghai Jiao Tong University, Shanghai, China), Margin and Modality-Aware Network (_MMANet_) (Huazhong University of Science and Technology, Wuhan, China), and Dual Attention Dual-Path Network (_DADPN_) (City University of Hong Kong, Hong Kong, China) [70; 86; 94], as well as multi-domain neural networks [62; 71; 72]. Moreover, model integration strategies were adopted, including ensemble learning [31; 38; 90] and hybrid approaches combining CNN outputs with GPA or TW3 scores [28; 43; 73; 88]. Region-based processing techniques were also applied, such as segmentation via _U-Net_ (University of Freiburg, Freiburg, Germany) [32; 33; 46; 85], attention mechanisms [32; 64; 71; 81; 94], and region localization using _YOLOv3_ ([PERSON], Seattle, WA, USA) and _YOLOv5_ (Ultralytics, London, UK) [47; 48; 71; 85]. Among the metrics employed, the most frequently utilized were, firstly, the mean absolute error (MAE) or median absolute deviation (MAD) [24; 25; 26; 27; 28; 29; 31; 32; 33; 34; 35; 36; 37; 38; 39; 43; 44; 45; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 71; 73; 75; 77; 79; 85; 97; 99], secondly, the root mean square error (RMSE) [25; 26; 27; 28; 89; 90; 91], thirdly, the accuracy within predefined temporal windows [25; 26; 88; 90; 91; 94], and finally, the correlation with established reference standards such as GPA and TW3 [24; 26; 45; 88]. In most studies, follow-up was limited to a single time point, with BA estimates for the AI-models compared directly against expert assessments or commercial software outputs immediately after image acquisition [24; 28; 43; 49; 88]. A subset incorporated intermediate follow-up intervals of 6 to 12 months (mos.) to evaluate the reproducibility, inter- and intra-observer agreement, and temporal stability of predictions, reporting significant improvements in RMSE and MAD within these windows [45; 54; 56; 69; 76]. Only a few studies carried out a long-term follow-up, for instance, [PERSON] et al. correlated baseline AI predictions with final adult height outcomes [69], while [PERSON] et al. linked initial assessments to subsequent growth disorder detection over multi-year periods [61], underscoring the prognostic value of DL in pediatric growth monitoring. See details in Table S2: Study characteristics.
## 2 Materials and Methods
### Interpretation of Results  
The final step in the methodology of this study is the explanation of the optimal prediction model. This analysis is of critical importance for all black-box prediction models, as it offers the users of such systems valuable insight into the decision-making process of the AI. This is especially true with AI systems that are being deployed in medical applications. Specifically, in the healthcare field, ML systems that lack explainability may not be perceived as trustworthy by patients or medical personnel [48, 49, 50] and they may fail to comply with certain rules and regulations, which have explanations as a mandatory requirement for automated decision-making systems [51]. Therefore, if AI is to be considered as a valuable tool for health experts, it is of the outmost importance it and its prediction process be less opaque.  
This study takes on the matter of explaining the chosen black-box ML prediction model, by utilizing two main analytical approaches in tandem: [PERSON]'s effect sizes and SHAP analysis. These are two widely established techniques that are being used in a variety of scientific fields to analyze datasets and explain the results of experiments [52, 53, 54, 55]. Moreover, the explained results can be compared to established bibliographies and knowledge in the field of the application and offer further validation to the process of prediction/classification of the particular computer-aided decision-making tool.  
[PERSON]'s effect size is a statistical metric that indicates the magnitude of the difference between two groups. It is calculated by dividing the difference between the means of the groups by the pooled standard deviation. [PERSON] (1988) [56] suggested that an effect size of 0.2 should be considered small, 0.5 medium, and 0.8 large. This measure is widely used in different fields, including psychology, medicine, and other sciences, to interpret the results of experiments and studies. It provides a valuable tool for assessing the practical significance of the findings and comparing them to previous research.  
On the other hand, SHAP analysis [57] is a widely used technique that aims to increase the interpretability and transparency of machine learning models. This approach is based on cooperative game theory concepts and treats each feature as a \"player\" in a game where the prediction is the goal. By measuring the impact of each feature on the prediction outcome, SHAP analysis assigns an importance value to each feature, which allows for the identification of the most relevant features in the model. This feature-level importance assessment provides a valuable tool for understanding and interpreting the model's predictions and can help increase user trust in the model. Furthermore, SHAP analysis can be used to detect potential biases and help ensure that ML models are fair and reliable.