esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Signal Preprocessing
_(7) Peak line correction_: This correction is performed when pulse wave peaks are found in BPEW. The form of the DPW rising edge trajectory (not the amplitude) carries information about systolic blood pressure as explained in Reference [29]. Therefore, it is important to ensure uniform time-amplitude proportions of all DPW rising edges. When the DPW sampling rate is 60 Hz, the variability of duration of the rising edge is low, as reported in Reference [47], which results in a constant value of abscissa  
Figure 10: (**a**) PPG signal after base line correction; (**b**) PPG signal after peak line correction. Figure 9: (**a**) PPG signal’s falling edge in PDW; (**b**, **c**) PPG signal’s falling and rising edge without a peak point in PDW; (**d**) PPG signal with a peak point in PDW. PDW: Peak detection window. (time) and does not affect the time-amplitude proportions. The motion of the arms or body of the patient (with sensors put on them) results in an uneven amplitude fluctuation during PPG registration and distorts the rising edge trajectory time-amplitude proportions. Therefore, the peak line (connecting pulse wave peak points) correction helps to eliminate random amplitude changes (see Figure 10b), without influencing the form of the pulse wave. This correction is done for each pulse wave (when base line correction is performed, and all pulse wave starting point values become zero) by dividing its values by maximum value, as explained in Reference [48].
## 1 Introduction
It may be disastrous to utilize all sensor network resources to locate hostile tanks at the cost of ignoring other enemy units. Consequently, a WSN should be able to allocate network resource to a specific kind of data according to the \"price\" the sink places on the type of data. Hence, resource consumption depends on the following factors: (1) properties of the events, such as priority, location, and frequency; (2) properties of the sensor network, such as topology and link quality, and (3) other categories of events. In Section 2., we provide a mechanism to estimate the success probability for transmitting a data packet from a sensor to the sink. The success probability is a good metric for resource consumption and includes both bandwidth and buffer. Based on the method in Section 2., we generalize our scheme to efficiently and fairly collect different categories of information when the WSN is congested. This is presented in Section 3. The following are the key contributions of this paper:  
1. In a WSN, packets with information of the desired event (_Event_ packets), such as fires in the forest, are more important and urgent than those without event information (_Non-Event_ packets). (Note that _Non-Event_ packets are inevitable since sensor nodes need to contact with the sink periodically to notify that they are alive.) Therefore, in _PCC_ we distinguish them with different priority thereby providing different throughput and dropping probability. 2. When congestion occurs, packets from nodes far from the sink have a smaller chance to reach the destination than those from the nodes close to the sink [6]. Without any control, the WSN can only collect the information from the nodes near the sinks. Therefore, in _PCC_, we assign packets an index to store the probability of a packet successfully reaching any node along its path to the sink. Then _PCC_ can dynamically adjust its dropping probability during congestion, to guarantee fairness for all nodes and coverage fidelity of the whole network. 3. In a large WSN, wireless link quality changes according to multiple factors, such as obstacles between transmitter and receiver, multiple-path transmissions, and interference among neighbor  
Figure 1: Collection of multiple classes of information in a WSN. links. In _PCC_, we consider the influence of link quality as an important parameter to indicate network resource utilization and the successful probability of transmissions. 4. We make use of cumulative survival probability of a packet reaching a node along its path to the sink and the priority of different event information to design a mechanism to efficiently and fairly collect different categories of information in a single WSN, called _Pricing System_. The remainder of this paper is organized as follows. In Section 2., we present the details of the _PCC_ mechanism, describe the design objectives, and present the simulation results. In Section 3., we propose a generalized pricing based scheme to efficiently collect multiple categories of information using one WSN. Related research is discussed in Section 4.. Finally, we conclude in Section 5..
## 2 Principle of Operation  
### Cr-Soa  
Illustrated in Figure 1a [7; 8; 9], the generation of the carrier reservoir (CR) layer adjacent to the active region (AR) in CR-SOAs is outlined. In a manner analogous to quantum dot (QD)-SOAs (See Figure 1b) [19; 20], CR-SOAs exploit the concept of a \"carrier reservoir\" to augment their operational speed, albeit through a distinct physical mechanism. The CR-SOA utilizes a wetting layer, serving as the CR layer, to facilitate a more rapid response. To elaborate, upon the application of a bias current, carriers initiate the process of filling energy levels in both the AR and CR layers. At lower current levels, the quasi-Fermi level aligns near the AR band edge, resulting in a higher carrier density within the AR compared to the CR. As the bias current intensifies, the quasi-Fermi level progresses beyond the AR band edge. Consequently, the CR becomes infused with carriers, allowing it to promptly supply the AR--depleted by an input optical signal--with carriers during a transition time estimated to be between 5 and 10 ps [7; 8; 9]. The dominance of this ultrafast transition governs the gain dynamics, causing the CR-SOA to exhibit an equally swift temporal gain and phase response, mirroring that of the QD-SOA. This observation implies that CR-SOAs may offer advantages in terms of potentially reduced cost and complexity, coupled with improved practicality. The feasibility of generating the required carrier reservoir with existing semiconductor processing techniques appears more promising for CR-SOAs, positioning them as a compelling alternative in the realm of advanced optical systems.
# 3D Ultrasound Mosaic of the Whole Shoulder: A Feasibility Study
## 2 Materials and Methods  
### Data Acquisition and Ultrasound System Parameters  
An in vivo sonography was performed on a healthy volunteer's right shoulder at the US research lab at the Queensland University of Technology (QUT) (Brisbane, Australia). The subject was a 30-year-old female, standing 1.70 m tall and weighing 58 kg, with a BMI of 20.1. She had no history of shoulder operations or injuries, and there were no distinctive features about her shoulder. Ethics approval for data collection was granted by the QUT Human Research Ethics Committee (No. 1700001110). The US data were acquired using a Philips EPIQ7 US system and a VL13-5 3D US probe (Philips Medical Systems, Andover, MA, USA), a mechanically swept 3D linear probe with an FOV of 38 mm \(\times\) 30 degrees of sweep and a frequency range of (5-13) MHz. The US system parameters were set by a certified sonographer to a shoulder preset, with a penetration depth of 4 cm, wide imaging, XRES image processing and SonoCT compound imaging. The US volume resolution was (512 \(\times\) 403 \(\times\) 256) voxels with spacings of 0.1229 mm \(\times\) 0.107 mm \(\times\) 0.2405 mm. A corresponding shoulder and upper arm MRI of the same subject was also already available from a previous study [29] using a 3-Tesla MR scanner (Ingenia, Koninklijke Philips N.V., Best, The Netherlands) at voxel dimensions of 0.4 \(\times\) 0.4 \(\times\) 0.8 mm. From this, MRI bone and muscle segmentation data were derived and included in this study. Bone segmentations comprised the humerus, scapula and clavicle. Muscle segmentations included trapezius, supraspinatus, infraspinatus, subscapularis, triceps, rhomboid, latissimus, pectoralis major, biceps and deltoid.
# Numerical Analysis of Laser Pattern Effects to Residual Stress on Metal 3D Printing
## 3 FEM Analysis Result
### Analysis Results Based on Rotation Angle  
To confirm the effect of the laser path, the analysis results were confirmed for the 45\({}^{\circ}\), 67\({}^{\circ}\), and 90\({}^{\circ}\) conditions of the laser pattern. In the analysis, all the setting values, except for the path rotation angle, were set to be the same, and the results were confirmed after stacking three layers. Figure 8 shows the analysis results for each angle, which are the results associated with the residual stress after the lamination of the three layers.  
Figure 8: Residual stress simulation results according to pattern rotation at third layer additive process: (**a**) same direction (180\({}^{\circ}\)); (**b**) 90\({}^{\circ}\) rotation; (**c**) 67\({}^{\circ}\) rotation; (**d**) 45\({}^{\circ}\) rotation.  
Figure 7: Simulation results of three-layer additive process: (**a**) Temperature; (**b**) residual stress.  
Based on the analysis results, it was discovered that the pattern of the residual stress changed significantly; in particular, for the 180\({}^{\circ}\) specimen, residual stress was formed in the lower solid region in a specific direction as the repeated pattern overlapped. As suggested in previous papers, these results show that twisting occurred in the same direction as the laser direction because of repeated thermal expansion and contraction, which was formed under conditions in which cracks are likely to occur [12].  
When comparing the applied stress to the underlying substrate and the average residual stress, the 90\({}^{\circ}\) rotation path results show that the stress concentration is more relaxed than the 180\({}^{\circ}\) rotation path condition. Additionally, similar to the results of 45\({}^{\circ}\) and 67\({}^{\circ}\) rotations, the concentrated residual stress dispersed as the overlap in the angle disappeared. In the 67\({}^{\circ}\) condition, the pattern angles of each layer were different and overlapped irregularly. The 45\({}^{\circ}\) condition looked like the 90\({}^{\circ}\) condition, but based on the maximum stress and average stress, it had a dispersion value similar to 67\({}^{\circ}\). Based on this residual stress distribution, the differences based on the laser path of 3D printing can be identified, and problems that may occur during specimen printing can be verified via the FEM model.
## 2 Material and Methods
### Statistical Analysis  
The sample size calculation was performed using the Excel calculator available on the Fisterra website ([[https://www.fisterra.com/formacion/metodologia-investigacion/determinacion-tamano-muestral/#sec4](https://www.fisterra.com/formacion/metodologia-investigacion/determinacion-tamano-muestral/#sec4)]([https://www.fisterra.com/formacion/metodologia-investigacion/determinacion-tamano-muestral/#sec4](https://www.fisterra.com/formacion/metodologia-investigacion/determinacion-tamano-muestral/#sec4)) (accessed on 3 September 2023).  
The parameters were as follows:  
* \"N\" (Initial Sample Size): Based on the sample size from the Halazonetis study [23].
* Measured Variable: The hue value obtained from the HueCheck Gum test.
* Precision (d): A margin of 0.15 points for accuracy.
* Significance Level (\(\alpha\)): 0.05 (95% confidence level).
* \(\beta\)): 90% (10% probability of type II error).  
Based on these parameters, the minimum required sample size was 19 patients. To account for dropouts or missing data, a 15% increase was applied, resulting in a final sample size of 22 individuals. This approach ensured adequate statistical power for robust and reliable conclusions.  
Figure 2: Viewgum® software (dHAL Software, Kifissia, Greece) for optoelectronic analysis of the samples. The interface displays, upon importing, two scanned images of a masticated gum specimen after 20 chewing cycles, capturing both planar surfaces of the flattened sample. The image was segmented into foreground (corresponding to the chewing gum) and background using a user-assisted, simple segmentation algorithm. Green markers indicate the foreground, while red markers indicate the background. The gum area identified by the software is delineated with a black contour and also shown in the thumbnail image below the main panels. On the right, the output reflects the colorimetric analysis. The red trace on the Gaussian distribution plot denotes a hue standard deviation significantly below the normative reference, indicating enhanced colour homogenization and, consequently, superior masticatory performance.  
Data were analysed using SPSS version 23.0 (SPSS, Chicago, IL, USA). Normality was assessed with the Shapiro-Wilk test. Descriptive and inferential statistics were analysed using [PERSON]'s exploratory data analysis and Student's _t_-test for the five questionnaire domains, foods-mastication, habits, steak, fruits, and vegetables, as well as for total QMFQ scores (pre- and post-treatment) and the hue value from ViewGum(r) software.  
In order to assess the influence of gender, facial pattern, and parafunctional habits on QMFQ and HueCheck Gum test scores, a one-way ANOVA was performed. Additionally, the Pearson correlation test was used to quantify the linear relationship between QMFQ and HueCheck Gum test scores. The significance level was set at \(p\) < 0.05.
###### Abstract  
Gadolinium-159 (\({}^{159}\)Gd) is a beta emitter with appropriate energy for therapeutic application. However, this radioisotope additionally emits gamma rays, enabling the distribution of \({}^{159}\)Gd to be detected by a gamma camera after each therapeutic administration. The current research is innovative in the investigation of \({}^{159}\)Gd as a the transonic radioisotope in the radioembolization of HCC using Monte Carlo (MC) simulation. For \({}^{159}\)Gd therapeutic investigation, various patient scenarios including varying tumour involvement (TI), tumour-to-normal liver uptake ratio (T/N), and lung shunting (LS) were simulated using Geant4 MC to estimate the absorbed doses to organs at risk. For \({}^{159}\)Gd planar imaging investigation, the SPECTHead example from GATEContrib (GitHub) was utilized, and inside a liver a tumour was created and placed inside a torso phantom and simulated using GATE MC simulation. The majority of \({}^{159}\)Gd absorbed doses by normal liver and lungs were less than the maximum dose limitations of 70 Gy and 30 Gy, respectively. Absorbed doses to other organs were observed to be below 1 Gy. The utilization of 58 keV and 363.54 keV photopeaks in combination produced optimal planar imaging of \({}^{159}\)Gd. This research gives new insights into the use of \({}^{159}\)Gd as a transonic radioisotope, with the potential to be used as an Yttrium-90 (\({}^{00}\)Y) alternative for liver radioembolization.  
\({}^{159}\)Gd; Monte Carlo simulation; theranostic; Geant4; GATE; radioembolization; HCC
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 3 Dynamic Model Setup
### Operational Conditions  
A trim condition must be established before conducting the flutter analysis. In this study, the trim is primarily defined by the propeller operational point, whereas the wing angle of attack and the corresponding lift generated are not considered trim parameters. This decision is based on the assumption that the wing flutter, in the subsonic regime, is a linear phenomenon depending on the lift perturbations rather than the lift generated at the reference condition. Furthermore, it must be remarked that the lift gradient with the angle of attack is constant at small angles of attack (i.e., at the higher flight speed where the flutter occurs), reinforcing the statement that the angle of attack at the trim condition and the corresponding lift generated remain negligible. Similarly, the choice of wing airfoil is expected to provide a second-order effect on the aeroelastic stability of the wing in subsonic regime [30].  
This paper focuses on two distinct propeller conditions, listed in Table 6. The wind-milling condition is chosen because it represents the most critical condition for propeller whirl flutter [31]. However, since the influence of the aerodynamic interaction between the propeller and wing on flutter is the object of the present study, a thrusting condition is also considered. Therefore, the thrust required by the aircraft is chosen as the trim point condition. Generally, the propeller's operational point is a function of the free stream velocity, rotational speed (i.e., the propeller advance ratio), and the blades' collective pitch angle. In this work, a constant propeller rotational speed is selected to maintain a constant  
Figure 3: DUST aerodynamic model for wing and propeller.  
angular momentum, while the free stream velocity is set as the initial condition. As a result, the only remaining parameter to achieve a trim condition is the collective pitch of the blades.
## 1 Introduction
### Sensitivity Analysis
#### 2.6.1 Impact of Roughness Constant  
In the parametric sensitivity analysis conducted, it is observed in Figure 8 that the U/U\({}_{\text{ref}}\) value remains constant despite different C\({}_{\text{s}}\) values as the approach to the building increases. However, the effect of changing this parameter is noticeable in the interior and exit areas of the building. To better understand this effect, the results obtained at different C\({}_{\text{s}}\) values presented in Table 4 were examined. When the C\({}_{\text{s}}\) value was reduced to 0.3, a 2.56% increase in the average U/U\({}_{\text{ref}}\) value and a 1.3% increase in the maximum U/U\({}_{\text{ref}}\) value were observed. At C\({}_{\text{s}}\) = 0.4, a 1.3% decrease in the maximum U/U\({}_{\text{ref}}\) was observed, while the average U/U\({}_{\text{ref}}\) remained unchanged. At higher values, when C\({}_{\text{s}}\) was 0.6, the maximum U/U\({}_{\text{ref}}\) decreased by 3.89%, while the average U/U\({}_{\text{ref}}\) decreased by a significant 10.25%. At C\({}_{\text{s}}\) = 0.7, the maximum U/U\({}_{\text{ref}}\) decreased by 1.3%, and the average U/U\({}_{\text{ref}}\) value decreased by 7.69%. Based on these results, C\({}_{\text{s}}\) = 0.5 was selected as the reference roughness coefficient, as it does not show excessive deviation in terms of both maximum (0.77) and average (0.39) U/U\({}_{\text{ref}}\) values and is widely preferred in the literature.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 3 The Proposed Protocol
### Assumptions  
* The network consists of three types of sensor nodes: source nodes, relay nodes, and sink nodes. Source nodes, also known as anchor nodes, are fixed at specific underwater locations and are responsible for collecting data from the underwater environment. They transmit the data to the sink nodes through relay nodes. Relay nodes are deployed at various underwater locations and forward the received data packets to the sink nodes. The sink nodes are located at the water surface and serve as the destination for all data packets in the network. The source nodes and sink nodes are relatively expensive, carrying more energy, while the relay nodes have simple functionality, lower cost, and carry less energy.
* Each node is equipped with a power control module and a signal strength measurement device. Nodes do not require location and distance information. Instead, they measure the received signal strength to estimate the path loss between communicating nodes. Based on the estimated path loss, nodes adjust their transmission power. All nodes have the same maximum transmission power, which corresponds to the maximum transmission distance in the network.
* Each node is equipped with a depth sensor, allowing them to obtain their own depth information when needed.
* A successful delivery is considered as long as the packet arrives at any of sinks on the water surface.
* The channel is reciprocal, meaning that the transmission signal experiences the same channel fading in both the uplink and downlink directions.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 4 Estimation of GHI from Other Meteorological Data
### Relationship between Input and Output Data  
The Pearson's correlation coefficient (R) between input variables and GHI and between inputs variables themselves were calculated. When the absolute value of R is near 1, there is a high degree of linear correlation between the two variables; if R = 0, there is no linear correlation, but other relation types can exist.  
Figure 2: ANN structure for GHI estimation.  
Computing R between input variables allows an estimation of whether the inputs are redundant and interdependent. The first objective is to rank the statistical linear dependences between the inputs and output; for a large sample of data, the R threshold from which there is a significant link between parameters is very low. Table 2 shows the values of the Pearson correlation coefficient between meteorological variables.  
We mainly see:  
1. The only high correlation is between GHI and S (69%), the other are just weak correlations;
2. The ranking of inputs from the R point of view (excepted S) is H (18%), EHI (13%), \(\theta_{z}\) (13%), and p (10%); and
3. A high value of R between inputs data, \(\theta_{z}\), \(\delta\), and EHI (between 34% and 95%); it was obvious because \(\theta_{z}\) and \(\delta\) are used in the calculation of EHI.  
This preliminary study allows to have an idea about the link between variables, but only linear ones. The results were not significant enough to avoid an exhaustive study for all combinations; the Pearson coefficient allows estimation of only the linear dependency between the data while the MLP is a non-linear model (sigmoid activation function), thus it is not surprising that the analysis of the Pearson coefficient is not sufficient to customize an MLP.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 9 Discussion
### Limitations and Hyperparameter Sensitivity  
A major limitation of our benchmarking experiment is the use of default hyperparameters across all fairness-sensitive models. While this choice was made to favor comparability and replicability across a wide range of methods and datasets, we are aware that fairness-accuracy trade-offs are typically very sensitive to hyperparameter choices. For instance, hyperparameters controlling regularization strength, reweighting coefficients, or adversarial loss functions may have a large influence on both predictive accuracy and fairness metrics. As such, reported performance may not approximate the optimal performance of each model in tighter-to-optimal scenarios. Future work would benefit from incorporating systematic sensitivity analysis or hyperparameter tuning--ideally as part of fairness-constrained cross-validation schemes--to better capture the range of potential trade-offs and to enable more systematic comparison. Incorporating this additional step would also allow for more realistic model selection and deployment recommendations in fairness-sensitive real-world applications.  
Our experimental assessment is primarily focused on typical tabular datasets that are widely utilized in the fairness literature due to their interpretability, relatively low dimensionality, and good documentation of sensitive attributes. While such a choice facilitates systematic comparison across fairness-aware models, it does limit the generalizability of our findings to more complex domains such as computer vision, natural language processing, and graph-based learning. Issues of fairness in these settings also tend to involve additional layers of complexity--e.g., word embeddings with hidden bias or biased representation in image features--that will not always be well represented by tabular data.