esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 5 Ultrasound  
US is a cheap, easily accessible, and well-recognized image modality for diagnosing and assessing OC; it is a non-invasive exam without radiation [42].  
To facilitate the sonographer's assessment to differentiate benign from malignant adrenal masses, the International Ovarian Tumor Analysis group developed the Assessmentof Different Neoplasia in the Adnexa (ADNEX) model [43; 44; 45; 46; 47; 48]. The Society of Radiologists in Ultrasound consensus statement [49] and the Gynecologic Imaging Reporting and Data System, also known as GI-RADS [50], are other proposed characterization and management systems of ovarian masses (OM). A standardized lexicon including all appropriate descriptors of the characteristic US appearance of normal ovaries and lesions of the ovaries was made available by the Ovarian-Adnexal Reporting and Data System (O-RADS), published in 2018. In 2020, based on this reporting system, US guidelines for management of lesions related to the ovaries were proposed, including all risks and strategies that correspond to their management [51].  
However, about 18% to 31% of such lesions of the adnexa detected by ultrasound are unknown.  
Radiomics has emerged as a new powerful method that is capable of quantifying features from ultrasound images [52; 53]; these features contain information that reflects the underlying pathophysiology of a cancer tissue [54; 55].  
We report ultrasound studies deploying radiomic analysis and their technical characteristics (Table 1).  
[PERSON] et al. aimed to test the performance of radiomic analysis in automatically distinguishing malignant and benign OMs on patients diagnosed with OMs.  
They analyzed three types of OMs--solid, cystic, and mixed. The best accuracy was reported for cystic OMs with an 87% accuracy compared to 80% for solid OMs and 81% for mixed OMs.  
Therefore, the application of radiomics to ultrasonography might improve the diagnosis of OC, and the main advantage of this algorithm is that it does not depend on the experience of the ultrasound examiner [42].  
In another study, [PERSON] et al. focused on the segmentation accuracy and its effect on radiomic features compared to manual segmentation performed by expert radiologists.  
They applied multiple U-net models to realize automatic segmentation of target volumes for OC on US images.  
U-Net architecture is a convolutional neural network that was developed for biomedical image segmentation and consists of a contracting path to capture context and a symmetric expanding path that enables precise localization [57].  
These models achieved a relatively high accuracy on target representation with a high Pearson correlation (0.87, 95% CI 0.84-0.90) and intraclass correlation coefficients (0.85, 95% CI 0.82-0.88) in correlation with features extracted from manual contours [56].
## 4 Hydraulic-Thermal Coupling Modeling and Analysis for HCS
### Calculated Results Analysis of Analytical and Coupled Models  
Referring to the heat-generation power, heat-dissipation power, and thermal equilibrium calculation formulas (Formulas (2)-(17)) obtained in the previous section, the steady-state oil temperature is solved by using mathematical analytical methods under different ORFRs and working-load conditions. The results are compared with those obtained from the hydraulic-thermal coupled model, as shown in Figures 9 and 10.  
Under various operating ranges and working-load torque conditions, the steady-state temperature solution error ranges for the HPP are 0.53 \({}^{\circ}\)C to 4.46 \({}^{\circ}\)C and 2.67 \({}^{\circ}\)C to 4.81 \({}^{\circ}\)C, respectively, while those for the HOT are 3.72 \({}^{\circ}\)C to 3.98 \({}^{\circ}\)C and 3.14 \({}^{\circ}\)C to 4.28 \({}^{\circ}\)C, respectively. The accuracy and stability of the mathematical analytical method pose challenges to meeting engineering requirements.  
The primary reason for this is that mathematical analytical methods have simplified numerous parameters. For instance, the leakage coefficient, convective heat-transfer coefficient, volumetric elastic modulus of hydraulic oil, and local resistance coefficient of the hydraulic components are all influenced by multiple practical factors, such as working pressure, temperature difference, temperature, and effective heat-dissipation area. In the process of mathematical calculation, these parameters are treated as constants, leading  
Figure 8: Temperature variations under different cooling powers.  
Figure 10: Temperature variations and errors obtained by the two methods under different loading torques.  
Figure 9: Temperature variations and errors obtained by the two methods under different ORFRs.  
to significant deviations in the calculation results. While the hydraulic-thermal coupling model offers high solution accuracy, its modeling and solution process is exceedingly intricate and time-consuming. Additionally, the economic cost of modeling software is substantial. Henceforth, there exists an urgent necessity to establish an efficient and precise oil temperature prediction model for HCS.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 1 Introduction
The DSCS will operate the independent four-wheel drive electric vehicle using the input torque from all four wheels without Conventional Steering Mechanism (CSM) [15]. A design method for an integrated chassis controller was established for four-wheel independent steering (4 WIS) with front slip angle constraint [16]. Simulation results by Car-sim software show that this integrated chassis controller can maintain lateral stability and maneuverability under the constraint without performance degradation. An adaptive control method for the four-wheel independent drive (4 WID) unmanned sprayer was designed to track accuracy degradation due to the influence of side-slip in autonomous navigation of the high clearance sprayer [17]. A four-wheel independent steering drive control system was designed for wheel-type agricultural robots to address the problems of single steering, challenging adapting to the complex environment in the field, and high cost [18]. Either method is based on the assumption that the steering center of the prototype is on the lateral extension line of the vehicle center, or the steering modes such as crab walk, diagonal, and in situ steering of the 4 WIS-4 WID structured vehicle are artificially divided and switched. These methods inhibit the flexibility and rapidity of the 4 WIS-4 WID structured vehicle regarding position correction. In this paper, based on the analysis of the relationship between the steering center and deviation correction, a tracking model of the 4 WIS-4 WID structure and fuzzy control is proposed to determine the position of the steering center according to the vehicle's positional state, which expands the vehicle's steering center from 1 DOF control to 2-dimensional plane, thus better improving the quality of path tracking for 4 WIS-4 WID agricultural machinery. The vehicle test results demonstrate that the proposed methods improve the vehicle navigation and path following of 4 WIS-4 WID agricultural machinery at low speed.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 5 Conclusions  
In this paper, the maximum transmit power \(P_{\text{max}}\) for a small cell uplink was investigated for OL/TPC in various deployment scenarios. We showed that, to achieve better network performance, \(P_{\text{max}}\) = 18 dBm is the appropriate choice in a uniform small cell deployment, which provides room for FPC operation to compensate for the pathloss. Furthermore, the HetNet environment was investigated with the three most efficient small cell deployments within the existing macro layer. For the HetNet environment, we observed a deviation of \(P_{\text{max}}\) from 18 dBm by \(-\)0.52 dBm in the first, +0.03 dBm in the second, and +3.29 dBm in the third HetNet deployment due to the variations in interference. However, significant SIR gain was achieved with an increase in \(P_{\text{max}}\) for the HetNet deployment as compared to the uniform small cell deployment. Use of FPC mode for the HetNet environment was also validated by our analysis. Further research will be conducted to consider the joint optimization of OL and CL/TPC schemes by employing system-level simulations, along with downlink energy efficiency enhancement schemes and event-specific HetNet deployment.  
[PERSON] and [PERSON] contributed to the main idea of this research work. [PERSON] performed the simulations. [PERSON] and [PERSON] contributed to the writing of this article. This research activity was planned and executed under the supervision of [PERSON].  
This work was supported by a grant from the Institute for Information and Communications Technology Promotion (IITP) of the Ministry of Science, ICT, and Future Planning (MSIP), Rep. of Korea (R0101-15-224, Development of 5G Mobile Communication Technologies for Hyper-Connected Smart Services).  
The authors declare no conflicts of interest regarding the publication of this article.
## 3 Electromagnetic and Thermal Finite Element (FE) Model  
### Overall Structure  
In order to apply an active lateral suspension system to a railway vehicle under the aforementioned design constraints, it is important to choose an appropriate topology from among the various geometric arrangements available for the TPMA design, as shown in Figure 3. In a tubular machine, there are two main components: the moving element (referred as the mover) and the stationary element (referred as the stator). The overall configuration is classified as either a'moving magnet' or a'moving coil.' In a moving magnet configuration, magnets are placed on the mover and coils are placed on the stator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Stator outer diameter & 0.200 m \\ Axial length & 1.000 m \\ Bandwidth & 10 Hz \\ Stroke & \(\pm\)0.050 m \\ Maximum thrust force & 7700 N \\ Allowable maximum temperature & 130 \({}^{\circ}\)C \\ Maximum current limit & 100 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the tubular permanent magnet actuator.  
Figure 2: Real track experimental results with conventional lateral passive dampers: (**a**) relative lateral velocity; (**b**) relative displacement.  
In an active lateral secondary suspension, a moving magnet configuration is more suitable than a moving coil configuration since its interior magnet topology eliminates the cable slab and its thermal characteristics are excellent because the exterior coils (which are primary heat sources) effectively expel the heat.  
For comparable sizes and weights, the force density attainable by a long tubular mover arrangement is higher than that attainable by a short tubular mover arrangement. However, a short tubular mover arrangement is more appropriate for this application because of the large stroke of the TPMA in the limited design space.  
In addition, tubular topologies considering the core shape are divided into slotless and slotted types. A slotted actuator has a large cogging ripple compared to a slotless actuator, but a slotted actuator can generate higher thrust forces because of the magnetic field generated by the interaction between the magnets and the core. Also, the cogging force of a slotted actuator can be sufficiently reduced by design optimization.  
Figure 4 shows the overall structure of the TPMA. The mover consisted of segmented magnets, a yoke, and a shaft. The stator consisted of five pairs of three-phase U, V, and W coils, a laminated core, and an aluminum housing that contained the cooling fin. Laser displacement sensors were used to measure the axial position of the mover. Figure 5 shows the topology and the design variables of the TPMA. The initial design variables of the TPMA are summarized in Table 2.  
Figure 3: Various tubular configurations: (**a**) moving magnet; (**b**) moving coil; (**c**) short mover; (**d**) long mover; (**e**) slotless; (**f**) slotted.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology
### Evaluation Criteria
#### 3.2.3 Jeffries-Matusita Distance  
In order to evaluate the extent to which timbral descriptors lie at opposing ends of the mapped parameter space, we can measure the extent to which the timbre classes are separable using a distance metric. Typically, this can be done by finding the divergence between class distributions using a technique such as Kullback-Leibler Divergence (KLD), as we proposed in [26]; however, as explained in [27], this does not satisfy the triangle inequality based on the measurement's asymmetry. While two-sided KLD addresses this, as explained in [28], [29] proposes Jeffries-Matusita Distance (JMD) as a more appropriate alternative. JMD (as shown in Equation 4) is a metric derived from the Bhattacharya (BH) distance, as in Equation (3), which bounds the output of the measure from 0 (no separability) to 2 (perfect separability).  
\[BH_{i,j}=\frac{1}{8}(m_{i}-m_{j})^{T}(\frac{S_{i}+S_{j}}{2})^{-1}(m_{i}-m_{j}) +0.5\ln(\frac{0.5(\left|S_{i}+S_{j}\right|)}{\sqrt{\left|S_{i}\right|\left|S_{ j}\right|}} \tag{3}\]  
\[JMD_{1,2}=\sqrt{2(1-e^{-BH_{ij}})} \tag{4}\]  
Here \(m\) represents the mean and \(S\) represents the covariance of classes \(i\) and \(j\), respectively.
## 3 Test Results
### Structural Deformation  
As the structural deformation monitoring scheme has been described in Section 2.5, there are four tunnel structural deformation monitoring sections. Vertical and horizontal deformations were both monitored for the small circular opening, while only vertical deformations were monitored for the other three monitoring sections. The variations of the deformations versus time under different buried depths are shown in Figure 10.  
Figure 10: Cont.  
It can be found that the results of the deformations also showed obvious step variations versus test time, similar to the earth pressures, but stabilized faster. In addition, the fluctuations at the stable stages are greater, which may be induced by the sensor accuracy and the vibration of equipment during centrifuge operation. The average values of the displacements at each stable stage were calculated for analysis. The ratios of deformation to diameter versus the buried depth are plotted in Figure 11.  
It can be seen from Figure 11 that, the vertical deformations of the four sections increased approximately linearly with the increase of buried depth. The four sections, in descending order of the vertical deformations, are big circular opening, small circular opening, rectangular opening, and non-opening. Thus, the larger the opening size, the greater the vertical deformation of the tunnel. For the big circular opening, the vertical deformation ratio to the diameter reached 3.86%, under the buried depth of 2.0 D, which  
Figure 11: Variations of the ratio of deformation to diameter versus buried depth.  
Figure 10: Variations of deformations versus test time: (**a**) 1.0 D, (**b**) 1.5 D, (**c**) 2.0 D.  
almost reached the limit of 4%, specified in Code for construction and acceptance of shield tunnelling method [28]. Therefore, when considering making a large opening in a shield tunnel, it is necessary to check the structural deformation and take necessary control measures to avoid exceeding the limit.  
For the horizontal deformation of the small circular opening, it increased as the buried depth increased from 1.0 D to 1.5 D, but when the buried depth continued to increase from 1.5 D to 2.0 D, the increase was insignificant. The reason may be that as the laser sensor support is fixed on the invert of the tunnel when the elliptical deformation occurs, the horizontal displacement measuring point may not be located on the diameter line of the lining ring, resulting in the errors.
## 1 Introduction
### Organization and Notation  
The rest of this paper is organized as follows. Section 2 describes the system model with the RIS by which each impedance is interconnected. In Section 3, the proposed scheme for impedance network grouping and the solution of the RS power-splitting ratio is provided. In Section 4, the simulation results of proposed system are presented. Finally, the conclusion of this paper is presented in Section 5.  
In this paper, vectors and matrices are denoted with bold lowercase and bold uppercase letters, respectively. \(\mathbf{A}^{T},\mathbf{A}^{H},\mathbf{A}^{-1}\), \(\left[\mathbf{A}\right]_{i,j}\), and \(\left\|\mathbf{A}\right\|\) refer to the transpose, conjugate transpose, inverse, \((i,j)\)-th element, and spectral norm of matrix \(\mathbf{A}\), respectively. \(\mathbb{R}\) and \(\mathbb{C}\) denote the real and complex number sets, respectively. \(j=\sqrt{-1}\) denotes the imaginary unit. \(\mathbf{0}\) and \(\mathbf{I}\) denote an all-zero matrix and an identity matrix, respectively, with appropriate dimensions. \(\mathcal{CN}\big{(}\mathbf{0},\mathbf{I}\big{)}\) denotes the distribution of a circularly symmetric complex Gaussian random vector with mean vector \(\mathbf{0}\) and covariance matrix \(\mathbf{I}\), and \(\sim\) stands for \"distributed as\". \(\text{diag}\big{(}\mathbf{A}_{1},\cdots,\mathbf{A}_{N}\big{)}\) refers to a block diagonal matrix with blocks with \(\mathbf{A}_{1},\cdots,\mathbf{A}_{N}\).
## 3 Background
### Reproducibility  
Reproducibility is often understood as experimental reproducibility. This is also a widely researched topic [31; 32; 33; 34; 35], but our aim is to obtain bitwise identical results of an application run with the same input parameters regardless of the level of parallelism, be it the number of threads or processes executed simultaneously. Non-reproducibility is not caused by the roundoff error but by the non-determinism of accumulative roundoff error. Due to the non-associativity of floating-point addition, accumulative roundoff errors depend on the order of evaluation, which is almost always relaxed in parallel and distributed environments. In a distributed MPI environment, there are multiple possible sources of non-associativity: number of MPI nodes, MPI reduction tree shape, number of cores per node, and data ordering. The histogram in Figure 1, which runs the Aero benchmark of the OP2 library, shows the relative differences (\(\frac{(a-b)}{a}\mid a>b\)) of a non-reproducible application run with different numbers of MPI processes. In general, some of the causes might be efficiently addressed, such as the reduction tree shape, which can be defined by network interface cards [36], but changing the number of processes can cause issues that are not as easily addressed. A general solution might be to fix the order of evaluation but that is, in many cases, incompatible with parallelization, and running sequentially is prohibitively costly. Another solution is to eliminate rounding errors. We can use exact arithmetics [37], but that will substantially increase the memory usage and the cost of the computations, as well as the amount of communication when applied to more complicated operations such as matrix multiplication. Higher precision can be used, but it will be reproducible only with higher probability [38].
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Reduce Traffic Congestion in Cities  
Flying taxis have the potential to reduce traffic congestion in cities by providing an alternative mode of transportation that is faster and more direct than traditional ground transportation. With flying taxis, passengers can travel in a straight line between their point of origin and destination, avoiding the need to navigate through crowded streets and intersections. This can help to reduce travel time, as well as the amount of vehicle emissions and noise pollution associated with traditional ground transportation. Additionally, flying taxis can access areas that are not easily accessible by ground transportation, such as islands, remote areas, or areas with limited infrastructure [153].
## 6 Biofilms and Healthcare-Associated Infections
### Dental Health  
In dental health, biofilms mostly appear as plaque, a complex microbial formation that develops on tooth surfaces. The development of dental plaque starts with the attachment of the first colonizing bacteria to the pellicle layer, a proteinaceous film on the tooth surface. As the biofilm develops, it becomes a highly organized microbial community encased in EPS matrix. This matrix provides structural integrity and protection, making bacterial colonies resistant to mechanical removal. Thus, managing biofilms continues to be a significant issue in dental hygiene [220; 221; 222].  
The adverse effects of dental biofilms are most apparent in their contribution to developing dental caries and periodontal disorders. Dental caries arise from the metabolic processes of acidogenic bacteria, especially \(S\). _mutans_. These bacteria digest dietary carbohydrates, producing lactic acid as a byproduct. The localized acidification in the biofilm environment decreases pH, resulting in the demineralization of dental enamel. This process triggers cavity development, advancing as enamel integrity deteriorates, allowing bacterial infiltration into more profound dental components, including the dentin and pulp. This development intensifies structural damage and elevates the likelihood of infection [223; 224].  
Periodontal disorders, including gingivitis and periodontitis, are intimately associated with biofilm formation at the gingival edge. Gingivitis, the preliminary phase of periodontal disease, is characterized by gum inflammation induced by the immune response to biofilm-related bacteria. If left untreated, gingivitis may advance to periodontitis, a more serious disorder characterized by the subgingival infiltration of biofilms. This advanced stage leads to the degradation of the periodontal ligament and alveolar bone, resulting in persistent inflammation, tissue degeneration, and tooth loss [225; 226].  
Biofilms provide considerable obstacles in dental implantology. The creation of biofilm on dental implants may result in peri-implantitis, a severe inflammatory illness linked to implant failure. Biofilms on implants provoke a persistent immunological response that, if unregulated, leads to the gradual deterioration of adjacent soft tissues and alveolar bone. This degradation undermines the structural integrity of the implant, eventually leading to its collapse. These problems underscore the need for appropriate biofilm control solutions in implant dentistry to improve long-term results and avert complications such as peri-implantitis [227; 228].