esa-sceva/satcom-chunk-collection huggingface dataset:
# Spine, Pelvis and Hip Kinematics--Characterizing the Axial Plane in Healthy and Osteoarthritic Hips
###### Abstract  
Abnormal spinopelvic movements are associated with inferior outcomes following total hip arthroplasty (THA). This study aims to (1) characterize the agreement between dynamic motion and radiographic sagittal assessments of the spine, pelvis, and hip; (2) determine the effect of hip osteoarthritis (OA) on kinematics by comparing healthy individuals with pre-THA patients suffering from uni- or bilateral hip OA. Twenty-four OA patients pre-THA and eight healthy controls underwent lateral spinopelvic radiographs in standing and seated bend-and-reach (SBR) positions. Lumbar-lordosis (LL), sacral-slope (SS), and pelvic-femoral (PFA) angles were measured in both positions, and the differences (\(\Delta\)) between SBR and standing were computed to assess spine flexion (SF), pelvic tilt (PT), and hip flexion (HF), respectively. Dynamic SBR and seated maximal trunk rotation (STR) tasks were performed at the biomechanics laboratory. Peak sagittal and axial kinematics for spine, pelvis, and hip, and range of motion (ROM), were calculated for SBR and STR. Radiograph readings correlated with sagittal kinematics during SBR for ALL and SFnax (\(r\) = 0.66, \(p\) < 0.001), \(\Delta\)PT and PTmax (\(r\) = 0.44, \(p\) = 0.014), and \(\Delta\)PFA and HFmax (\(r\) = 0.70, \(p\) < 0.001), with a satisfactory agreement in Bland-Altman analyses. Sagittal SBR spinal (\(r\) = 0.33, \(p\) = 0.022) and pelvic (\(r\) = 0.35, \(p\) = 0.018) flexions correlated with the axial STR rotations. All axial spinopelvic parameters were different between the OA patients and controls, with the latter exhibiting significantly greater mobility and less variability. Bilaterally affected patients exhibited lower peak and ROM compared to controls. The biomechanics laboratory performed reliable assessments of spinopelvic and hip characteristics, in which the axial plane can be included. The sagittal and axial pelvic kinematics correlate, illustrating that pelvic rotation abnormalities are likely also contributing to the inferior outcomes seen in patients with abnormal spinopelvic flexion characteristics. Axial rotations of the pelvis and spine were least in patients with bilateral hip disease, further emphasizing the importance of the hip-pelvic-spine interaction.  
hip osteoarthritis; kinematics; axial plane; motion analysis; spinopelvic mobility +
Footnote †: journal: Applied Sciences
## 4 The Algorithms
### The DSR-Fle Variant Algorithm
#### 4.2.2 The Approximation Algorithm
For any delivery plan \(Opt_{spl}^{i}\), and any consecutive three virtual orders \(\tilde{r}_{jo}\), \(\tilde{r}_{kp}\) and \(\tilde{r}_{hq}\) served by \(a_{i}\), it is feasible for \(a_{i}\) to serve any two virtual orders of \(\tilde{r}_{jo}\), \(\tilde{r}_{kp}\) and \(\tilde{r}_{hq}\). This is because the triangle inequality \(\tau_{j_{0}k_{p}}+\tau_{k_{p}h_{q}}\leq\tau_{j_{0}h_{q}}\), where \(j_{0}\), \(k_{p}\), and \(h_{q}\) are the virtual orders with the drop-off period \(t_{j}^{e}+o-1\), \(t_{k}^{e}+p-1\) and \(t_{h}^{e}+q-1\), respectively. This indicates that, for any delivery plan \(Opt_{spl}^{i}\), removing any number of virtual orders does not break the shareability of other remained virtual orders. Therefore, in Algorithm 3, for each real order \(r_{j}\) (with \(\kappa_{j}\) virtual orders) that are served in the \"split\" DSR-Fle variant, at least one of its virtual orders \(\tilde{r}_{jh}\) remains. Therefore, we have \(Alg_{spl}=\sum_{r_{j}}1_{r_{j}\in R^{\prime}}\geq\sum_{\tilde{r}_{j}}\sum_{j_{ \#}\in\tilde{r_{j}}}\frac{1_{r_{\tilde{\#}}\in\tilde{R^{\prime}}}}{\kappa_{j}} \geq\frac{Opt_{spl}}{\kappa}\), where the function \(1_{f(\cdot)}\) returns value \(1\) when the function \(f(\cdot)\) is true; otherwise, it returns zero, \(\tilde{R}^{\prime}\) is the set of orders served in \(Opt_{spl}\), and \(R^{\prime}\) is the set of orders served in \(Alg_{spl}\). Combining the above two conclusions, we have that \(\frac{Alg_{spl}}{Opt}\geq\frac{Alg_{spl}}{Opt_{spl}}\geq\frac{1}{\kappa}\). Theorem 2 generalizes the DSR-Fix variant (i.e., \(\kappa=1\)) that Algorithm 1 can achieve the optimal solution [6].
## 3 Description of Research Methods
### Reconstruction Error  
Reconstruction error is a measure used to quantify the difference between the original version of data and their reconstructed version. For many models, especially AEs, the objective is to learn an encoding function and a decoding function such that the input data, after being encoded and subsequently decoded, can approximate their original form as closely as possible. Let us assume that we have an input data \(x\) and their corresponding reconstruction \(\hat{x}\). The reconstruction error \(E\) is typically defined as the difference between them. For continuous data, the most common reconstruction error is the Mean Squared Error (MSE), which can be defined as:  
\[E(x,\hat{x})=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2} \tag{3}\]  
where \(N\) represents the dimensionality of the data. For discrete or categorical data, other types of error functions, like cross-entropy loss, can be employed. In any form, the reconstruction error offers us a means to quantify the discrepancy between the original data and their reconstructed version.
###### Abstract  
As technologies advance and applications for uncrewed aircraft increase, the capability to conduct automated air-to-air refueling becomes increasingly important. This paper provides a review of required sensors to enable automated air-to-air refueling for an uncrewed aircraft, as well as a review of published research on the topic. Automated air-to-air refueling of uncrewed aircraft eliminates the need for ground infrastructure for intermediate refueling, as well as the need for on-site personnel. Automated air-to-air refueling potentially supports civilian applications such as weather monitoring, surveillance for wildfires, search and rescue, and emergency response, especially when airfields are not available due to natural disasters. For military applications, to enable the Air Wing of the Future to strike at the ranges required for the mission, both crewed and uncrewed aircraft must be capable of air-to-air refueling. To cover the sensors required to complete automated air-to-air refueling, a brief history of air-to-air refueling is presented, followed by a concept of employment for uncrewed aircraft refueling, and finally, a review of the sensors required to complete the different phases of automated air-to-air refueling. To complete uncrewed aircraft refueling, the uncrewed receiver aircraft must have the sensors required to establish communication, determine relative position, decrease separation to asterm position, transition to computer vision, position keep during refueling, and separate from the tanker aircraft upon completion of refueling. This paper provides a review of the twelve sensors that would enable the uncrewed aircraft to complete the seven tasks required for automated air-to-air refueling.  
automated air-to-air refueling; A3R; computer vision; autonomous aerial refueling; AAR; UA; RPA; UAV; UAS 202095
## 2 Materials and Methods
### Characterization of the SPL  
The chemical composition of the SPL aluminosilicates was measured using energy dispersive X-ray fluorescence on a powder sample (ED-XRF). The phase composition was quantitatively obtained with X-ray diffraction (XRD) using an external standard. An Empyrean diffractometer from PANanalytical was used with a Co target and as parameters during the measurement: 40 kV, 45 mA, 0.0131\({}^{\circ}\) 20 step size and counting time of 0.02 s/step. Qualitative analysis and Rietveld refinement were performed in HighScore X'Pert software. He-pycnometry was done in an AccuPyc II 1340 from Micromeritics to obtain the density of the material. The specific surface area was obtained through BET calculations from a nitrogen sorption experiment on an Autosorb iQ from Quantachrome Instruments. The microstructure of the SPL was studied using scanning electron microscopy (SEM) combined with energy dispersive X-ray spectroscopy (EDS) on a FEI Nova NanoSEM 450. The material was embedded in an epoxy resin, polished and coated with carbon. Images were taken in backscattered electron mode with an acceleration voltage of 20 kV. This voltage setting was also applied during the EDS measurements.
## 3 Results and Analysis  
In this section, we show the GNSS-R soil moisture retrieval performance analysis from two different points of view. The first one is to utilize the XGBoost algorithm to analyze the importance of the input variables. The input variables in GNSS-R are taken as different input features in XGBoost algorithm. The simulated data set are shown considering the GNSS-R bistatic soil moisture retrieval equations in the case of flat surface. Then, the variable importance of the input variables is shown and analyzed by means of three different parameter issues. To investigate the contribution of the different input variables to permittivity and soil conditions (including two typical soil types) respectively. For the second part, it focuses on ground-truth measurements. Two different soil compositions and moisture terrain were chosen to do GNSS-R experiments. Some input variables are analyzed mathematically to validate the results obtained by XGBoost, and the performance of GNSS-R soil moisture retrieval is analyzed in details.
## 3 Results
### Dynamic Mechanical Test Results
#### 3.2.2 Effects of Mesh Density on the Hysteresis Behavior of WMD  
Figure 11 illustrates the effect of mesh density on the damping characteristics of a WMD isolator subjected to a sinusoidal load with an amplitude of 0.5 mm and a frequency of 1 Hz.  
As shown in Figure 11, the symmetric hysteresis loops slightly spin counterclockwise as the mesh density increases, suggesting that the dynamic stiffness of the WMD is improved to some extent. The dynamic stiffness and loss factors are determined using Equations (2) and (6) and then plotted as a function of mesh density, as shown in Table 6 and Figure 11, respectively. As the mesh density increases, the dynamic stiffness of NiTi WMD increases, whereas the loss factor decreases. The increased mesh density results in more compacted wire structures and multiplied dry friction joints in unit volume. This not only makes it difficult for the WMD to activate the martensite transformation but also limits the motion of friction joints. Therefore, both the phase transformation damping and the frictional dissipation of NiTi WMD decrease with its increased mesh density, resulting in a lower loss factor.
## 3 Fabrication and Measurement of the Single-Patch Multi-Beam Antenna  
The designed and fabricated structure of the antenna is shown in Figure 9. The substrate of the antenna is FR-4 (\(\varepsilon_{r}\) = 4.3, height = 1 mm). The operating frequency of the antenna is 2.4 GHz. The total size of the antenna is 55 mm \(\times\) 55 mm \(\times\) 1 mm. Coaxial probe feeding is used to excite the four ports. The measurement of return loss is conducted by using Agilent (Santa Clara, CA, USA)'s MS4644B Vector Network Analyzer. In addition, the radiation pattern is measured in the anechoic chamber. The return loss and radiation pattern of the single-patch multi-beam antenna are shown in Figures 10 and 11, respectively.  
Figure 8: The radiation pattern of the final antenna with different excited ports: (**a**) single excitation and (**b**) dual excitation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Excitation Port** & **Beam Peak Direction** & **Beamwidth** & **Gain** \\ \hline
1 & 135\({}^{\circ}\) & 190\({}^{\circ}\) & 2.68 dB \\
2 & 225\({}^{\circ}\) & 190\({}^{\circ}\) & 2.68 dB \\
3 & 315\({}^{\circ}\) & 190\({}^{\circ}\) & 2.68 dB \\
4 & 45\({}^{\circ}\) & 190\({}^{\circ}\) & 2.68 dB \\
1 and 2 & 180\({}^{\circ}\) &Since the antenna structure is symmetrical, the measured results of port 1 are shown for single feeding, while the results of ports 1 and 2 are shown for dual feeding. The return loss of a single-patch multi-beam antenna is well matched with simulation and measurement for both single and dual feeding at 2.4 GHz. The measured gains of the final antenna at 2.4 GHz were 2.64 dB and 2.9 dB in single and dual feeding, respectively.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### Network Topology  
Typically, a number of sensor nodes and one coordinator are organized to constitute a WBAN. In a single WBAN, there is only one coordinator which is either directly connected to all the nodes through one-hop star topology (as shown in Figure 13) or through two-hop extended star topology (which is often called as Restricted Tree Topology as shown in Figure 14) to exchange the frames via a relay node. It should be noted that the two-hop extended star topology is restricted in Medical Implant Communication Service (MICS) band. The maximum numbers of nodes in a body area network are limited to '_mMaxBANSize_' which varies for different applications.  
Figure 11: Layout of Access Phase in a Non-Beacon Superframe Boundary Mode [9].  
Figure 14: Network Topology (Two-Hop Extended Star) [9].  
Figure 12: Non-Beacon With Out Superframe Mode [9].  
Figure 13: Network Topology (One-Hop Star) [9].
## 4 Experimental Results
### Experimental Results
PCA-L2 was executed by entering vectors reshaped from the diminished input image and measuring Euclidean distance. The PCA dimension was 20. Table 7 shows the accuracies achieved by PCA-L2 using time-frequency representations on MIT-BIH-arrhythmia-ECG. PCANet was executed with 4 as the patch size, 4 filters, 4 as the block size, and 0.5 as the block overlap ratio. Table 8 shows the accuracies achieved by PCANet using time-frequency representations on MIT-BIH-arrhythmia-ECG. Table 9 presents the accuracies achieved by the different models based on CNN using time-frequency representations on MIT-BIH-arrhythmia-ECG. The best accuracies achieved by the time-frequency representations on MIT-BIH-arrhythmia-ECG were 48.91% for DenseNet-201 in MFCC, 63.04% for DenseNet-201 in spectrogram, 84.78% for DenseNet-201 in log spectrogram, 38.04% for DenseNet-201 in mel spectrogram, and 75.00% for DenseNet-201 in scalogram. The log spectrogram input to the DenseNet-201 model achieved the best accuracy for the MIT-BIH-arrhythmia-ECG dataset. There were some cases of learning failure because the size of the dataset was too small. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Type** & **Model** & **Test Accuracy** \\ \hline MFCC & & 4.35 \\ \hline Spectrogram & PCA-L2 & 1.09 \\ \hline Log spectrogram & & 2.17 \\ \hline Mel spectrogram & & 3.26 \\ \hline Scalogram & & 1.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracies achieved by PCA-L2 using time-frequency representations on MIT (Massachusetts Institute of Technology)-BIH (Beth Israel Hospital)-arrhythmia-ECG. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Type** & **Model** & **Accuracy** \\ \cline{2-3}  & & **Training** & **Test** \\ \hline MFCC & & 100 & 78.26 \\ \hline Spectrogram & & 100 & 89.13 \\ \hline Log spectrogram & & 100 & 86.96 \\ \hline Mel spectrogram & & 100 & 82.61 \\ \hline Scalogram & & 100 & 86.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracies achieved by PCANet using time-frequency representations on MIT-BIH-arrhythmia-ECG. The mean accuracies of the different time-frequency representations applied to PTB-ECG were 98.56% for MFCC, 97.84% for spectrogram, 95.83% for log spectrogram, 94.72% for mel spectrogram, and 97.34% for scalogram. The mean accuracies of the different time-frequency representations applied to the CU-ECG were 91.53% for MFCC, 91.34% for spectrogram, 89.06% for log spectrogram, 87.58% for mel spectrogram, and 92.25% for scalogram. The mean accuracies of PTB-ECG and CU-ECG corresponding to the time-frequency representations were 95.04% for MFCC, 94.59% for spectrogram, 92.45% for log spectrogram, 91.15% for mel spectrogram, and 94.79% for scalogram. The MFCC accuracies were 0.45%, 2.60%, 3.90%, and 0.25% higher than the spectrogram, log spectrogram, mel spectrogram, and scalogram accuracies, respectively. Figure 15 shows a comparison between the accuracies of the different time-frequency representations.
## 3 Results and Discussion
### Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS) Production  
ROS production was higher (_p_ < 0.05) in _C. area_-treated groups compared with controls (Figure 6). T2 fish treated with 50 mg/kg dose of methanolic extract of _C. area_ showed the highest ROS production at all post-treatment time points, followed by T1 and T3. Similarly, RNS production also showed an increasing trend during the post-treatment period (Figure 7). Increased superoxide anion production in blood leukocytes has been observed in _L. rohita_ fed with food containing 0.5% _Achynanthes_[44].  
Figure 4: Ceruloplasmin activity under different treatments administered with various levels of _C. area_ extract in _L. rohita_ during post treatment days (values are mean \(\pm\) SE). Mean values with different superscript within a column for a parameter are significantly different (_p_ < 0.05).  
Figure 5: Serum antiprotease activity under different treatments administered with various levels of _C. area_ extract in _L. rohita_ during post treatment days (values are mean \(\pm\) SE). Mean values with different superscript within a column for a parameter are significantly different (_p_ < 0.05).  
Respiratory burst, reflected by enhanced oxidation levels of phagocytes after stimulation with foreign agents, is regarded as an important indicator of non-specific immune mechanisms in fish [53]. In vitro treatment of rainbow trout with glycyrrhizin extracted from _Glycyrrhizia glabra_ has been reported to increase macrophage levels. This stimulatory effect may be the result of the proliferative responses of leukocytes [54]. In Ergosan-injected rainbow trout, immunostimulatory effects had been suggested due to an increase in respiratory burst activity, the degree of phagocytosis and interleukin expression [55]. The reduction in NBT by intracellular superoxide radicals (produced by leukocytes) can be used to measure respiratory burst activity. It has been reported that treatment with low concentrations of immunostimulants yields higher respiratory burst activity of phagocytes in fish, while higher doses of immunostimulants yield lower NBT values, suppressing defensive mechanisms [56].
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 4 Ultrasonic Fading Modeling for UAC
### \(\kappa\)-\(\mu\) Shadowed Distribution  
The suitability of the \(\kappa\)-\(\mu\) shadowed distribution to explain signal fading in UAC was first studied in [20]. It has an underlying physical model, provides a unification of popular fading models and has good analytical properties (closed forms for the probability density function and other relevant functions are available). The main parameters that define this distribution are:  
* \(m\)-parameter: All of the main components exhibit a common shadowing fluctuation that is represented by a Nakagami-\(m\) random variable with shaping parameter \(m\).
* \(\mu\)-parameter: This represents the number of clusters. Although the number of clusters should be a natural positive number, the parameter \(\mu\) is allowed to take any non-negative real value, which leads to a more general and flexible distribution.
* \(\kappa\)-parameter: This represents the ratio between the total power of the main components and the total power of the scattered waves. The main components are also called dominant components, but in the case of \(\kappa\) less than one, the term dominant may be confusing.  
An empirical validation of this model was conducted in [20], where data from the TREX04 experiment were used to fit the model (that experiment was conducted by the Naval Research Laboratory, in the coast of New Jersey in April 2004, see details in [5]). The measurements were taken in a frequency range below 20 kHz. In the next section, the set of new measurements conducted by the authors is used to confirm the suitability of the \(\kappa\)-\(\mu\) shadowed distribution to model the fading also in ultrasonic UAC.
###### Abstract  
Transmissions are extensively employed in mechanical gearboxes when power conversion is required. Being able to provide specific maintenance is a crucial factor for both economics and reliability. However, although periodic transmission maintenance increases the systems' longevity, it cannot prevent or predict sporadic major failures. In this context, structural health monitoring (SHM) represents a possible solution. Identifying variations of a specific measurable signal and correlating them with the type of damage or its location and severity may help assess the component condition and establish the need for maintenance operation. However, the collection of sufficient experimental examples for damage identification may be not convenient for big gearboxes, for which destructive experiments are too expensive, thus paving the way to model-based approaches, based on a numerical estimation of damage-related features. In this work, an SHM approach was developed based on signals from numerical simulations. To validate the approach with experimental measurements, a back-to-back test rig was used as a reference. Several types and severities of damages were simulated with an innovative hybrid analytical-numerical approach that allowed a significant reduction of the computational effort. The vibrational spectra that characterized the different damage conditions were processed through artificial neural networks (ANN) trained with numerical data and used to predict the presence, location, and severity of the damage.  
gears; SHM; FEM; pitting; surface fatigue Article  
A Model-Based SHM Strategy for Gears--Development of a Hybrid FEM-Analytical Approach to Investigate the Effects of Surface Fatigue on the Vibrational Spectra of a Back-to-Back Test Rig  
1 Free University of Bolzano/Bozen, Faculty of Science and Technology, Piazza Universita 1, 39100 Bolzano, Italy 1  
Footnote 2: email: [EMAIL_ADDRESS]; Tel.: +39-0471-017748