esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Oil Quality and Aroma Characteristics Evaluation
#### 3.5.2 Analysis of Aroma Content in Rapeseed Oil  
The aroma characteristics in the commercial refined rapeseed oil, high-acid rapeseed oil, and enzymatically deacidified rapeseed oil were identified based on the gas chromatography-mass spectrometry coupled (GC-MS, headspace solid-phase microextraction (HS-SPME) and NIST 17 database [70]. As shown in Table 2, a total of 38 aroma-active compounds were identified in three different rapeseed oils including aldehydes (5), sulfur-containing compounds (3), nitriles (9), heterocyclic compounds (8), acids (4), alcohols (4), ketones (2), and phenols (3). Specifically, 32 types of aroma-active compounds in commercial refined oil were detected with a total content of 47.1 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, in which nitrile compounds of 19.4 \(\mathrm{\SIUnitSymbolMicro g}\)/kg and heterocyclic compounds of 10.6 \(\mathrm{\SIUnitSymbolMicro g}\)/kg were the top two volatile compounds that occupied more than 63.7% of the total aroma contents, leading to the mixing aroma characteristics containing pickled vegetable, spicy, pungent, and roasted odors [71; 72; 73; 74]. Compared to the high-acid oil with the main volatile compounds of nitriles (16.4 \(\mathrm{\SIUnitSymbolMicro g}\)/kg) and aldehydes (5.2 \(\mathrm{\SIUnitSymbolMicro g}\)/kg), the enzymatic deacidification by CALB@MCM-41-C8 led to higher aldehydes compounds of 37.7 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, thus altering the picked vegetable, spicy, and pungent aromas into fatty, bitter almond aromas. The increased aldehyde content in deacidified rapeseed oil could be related to the higher reaction temperature of 65 \(\mathrm{\SIUnitSymbolMicro C}\) compared to the room temperature, as well as light exposure due to enzymatic reactions [75; 76]. Overall changes were observed in volatile compound changes, thus validating the discernible impacts of different refining methods such as enzymatic deacidification on rapeseed oil's odors. Figure 5: (**a**) Composition of lipid composition in rapeseed oil before and after enzymatic deacidification using CALB@MCM-41-C\({}_{8}\); (**b**) antioxidant stability evaluation for commercial refined rapeseed oil and high-acid and deacidified rapeseed oils based on their oxidation induction time and peroxide values.
## 3 Results and Discussion
### Sensory Evaluation Results  
From the point of view of the consumers, beverages based on by-products should be, visually and in textural terms, as homogeneous as milk-based products [53; 54]. In fact, the most important indicators of the quality of a fermented product are its sensory features, and the most important sensory characteristics of fermented products that are decisive for consumers are taste and aroma [55; 56]. Sensory evaluation scores of the fermented and control samples are summarized in Table 5. The initial smell was described by panelists as \"bitter\", \"irritating\" or \"pungent\", characteristic of mustard. In fact, _C. sativa_ seeds contain  
Figure 2: The changes in G\({}^{\prime}\) (storage modulus) and G\({}^{\prime}\) (loss modulus). sulfur-containing compounds called glucosinolates. These compounds cause the pungent flavor associated with mustards and they are present to a greater or lesser extent in other members of the _Brassicaceae_ family [57; 58]. After fermentation, the panelists indicated that the taste and smell changed significantly to pleasant and slightly acidic (\(p<0.05\)), which can be linked with the production of lactic acid (Table 1). On day 14 the highest scores for smell and taste were noted; however, scores decreased on subsequent days because they were considered too intense (\(p<0.05\)). Presumably, the disappearance of bitter flavor notes as a result of fermentation may be linked to the degradation of glucosinolates by LAB, but this requires more in-depth research. In fact, the ability of various LAB strains to cause glucosinolates' degradation and conversion into bioactive functional compounds that exert antioxidative, antininflammatory and anticarcinogenic effects was reported [59; 60]. Changes in color were also noticeable to the panelists (\(p<0.05\)); these results are consistent with the \(\Delta\)E values (Table 2). It was indicated that the product had an attractive pale yellow color; however, it began to darken slightly during storage. An increase in consistency and mouth feel scores was noticed (\(p<0.05\)). However, on day 28 the panelists indicated a slightly lower rating than on days 14 and 21 due to an increase in viscosity and difficulty in sticking to the palate (\"too vicious\"). Moreover, the panelists indicated that on day 1, the cohesiveness of the beverages was too low. To sum up, it can be stated that the overall acceptability shifted from unacceptable (3.1 \(\pm\) 0.19 and 2.6 \(\pm\) 0.10 for non-fermented CPC-15% and CPC-20%, respectively) to acceptable by consumers (4.0 \(\pm\) 0.01 for sample CPC-15% on day 1, and 4.4 \(\pm\) 0.31 for sample CPC-20% on day 5).
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 4 Experimental Results
### Experimental Environment  
Figure 14 shows a diagram of the experimental equipment configuration. This experiment was carried out off the coast of the Korea Maritime and Ocean University. Table 3 shows parameters for sea trials. The range between the transmitter and receiver was 400 m and the sea depth was approximately 10 m. The transmitter was at a depth of 2 m, whereas the receiver was at a depth of 4 m. A transmitter, the ITC-3013 [29], offers highly efficient broadband transmission with a hemispherical beam pattern.  
Figure 12: Comparison of the measured Doppler factor.  
Figure 13: (**a**) Doppler error, (**b**) Histogram of the estimated Doppler error.  
We used a directional transmitter because we wanted to reduce the effects of multipaths in shallow water. At approximately 400 m, the receiver was a Teledyne Reson TC4032 [30]. Figure 15a shows the measured channel impulse response. A linear plot of the impulse-response function, Figure 15b, shows the delay-Doppler spread function. An assessment of the channel environment is required to interpret the received signals. As a result, the characteristics of the underwater channel were estimated prior to the experiment. A 128-ms LFM (linear frequency modulation) pulse train with a 2-kHz bandwidth was used and it was repeated 200 times.  
Figure 14: (**a**) Layout diagram of experimental area, (**b**) Experimental setup.  
Figure 15: (**a**) Measured channel impulse response. (**upper panel**) A linear plot of the impulse-response function, (**b**) Delay-Doppler spread function.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Sampling frequency & 192 kHz \\ Carrier frequency & 12.5 kHz \\ Data rate & 10–60 bps \\ Bandwidth & 1.92 kHz \\ Range between projector and hydrophone & 400 m \\ Projector depth & 2 m \\ Hydrophone depth & 4 m \\ Projector & ICT-3013 \\ Hydrophone & Teledyne Reson TC4032 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of sea trials.
# Research on Defect Detection of Bare Film in Landfills Based on a Temperature Spectrum Model
## 4 HDPE Film Weld Defect Detection Test  
### Improvement of Test Device  
Considering the concealment of the defect location at the welding seam, the water flow cooling method cannot identify the edge of the weld contour, which affects the final repair process. Thus, for the defect of the welding seam, the experimental setup needs to be improved. The infrared thermal imaging system device used to detect defects at the welding seam of the HDPE film is shown in Figure 9. The device is composed of two 500 W halogen lamp heating sources, an infrared thermal imager, and a terminal computer. The HDPE film material was heated by a heating source, and an infrared image of the material was collected by a Fluke Tis10 9 Hz thermal imager. The heating source and the infrared  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time/min** & \begin{tabular}{c} **Defect Areas Average** \\ **Temperature/\({}^{\circ}\)C** \\ \end{tabular} & \
\begin{tabular}{c} **Complete Area** \\ **Average** \\ **Temperature/\({}^{\circ}\)C** \\ \end{tabular} &
\begin{tabular}{c} **Temperature** \\ **Difference/\({}^{\circ}\)C** \\ \end{tabular} \\ \hline
0 & 32.3 & 31.7 & 0.6 \\
10 & 31.4 & 33.4 & \(-\)2 \\
11 & 31.7 & 34.1 & \(-\)2.4 \\
12 & 32.3 & 34.9 & \(-\)2.6 \\
13 & 32.8 & 35.4 & \(-\)2.6 \\
20 & 40.8 & 42.7 & \(-\)1.9 \\
25 & 45.5 & 46.5 & \(-\)1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature difference data at different times in the 0.20 cm HDPE film defective–complete area.  
Figure 8: HDPE film of 0.20 cm defective–complete area temperature change curve.  
Figure 7: HDPE film of 0.15 cm defective–complete area temperature change curve.  
thermal imager are fixed at a certain distance from the HDPE film, while ensuring that the infrared thermal imager is perpendicular to the surface of the HDPE film. This setup not only enables sufficient heating of the material but also facilitates full-coverage image acquisition by the infrared thermal imager. Improving the GIF is used to reduce the small noise interference of the image and retain the defect contour [22; 23]. Combined with the edge contour extraction technique, important information about the defective area of the rendered image is extracted to establish an infrared thermal imaging system for defect detection of HDPE film.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 4 Grounding Configurations and Optimization Techniques
### Hybrid Grounding Systems  
Hybrid grounding combines vertical rods and horizontal conductors for improved performance [20]. The combined resistance is given by the following:  
\[R_{h}=\frac{R_{v}R_{h}}{R_{v}+R_{h}} \tag{19}\]  
where \(R_{v}\) = resistance of vertical electrodes, and \(R_{h}\) = resistance of horizontal electrodes.  
Chemical electrodes can further reduce resistance by ionizing the surrounding soil, ensuring long-term stability. Figure 4 illustrates a hybrid grounding system layout.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline
**Enhancement Material** & **Reduction in Resistance (\%)** & **Voltage Reduction (\%)** & **Current Withstand Increase (\%)** \\ \hline Bentonite & 40\% & 30\% & 35\% \\ \hline Conductive Concrete & 60\% & 45\% & 50\% \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of ground resistance with enhancing materials.
# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
###### Abstract  
The acoustic emission (AE) technique was applied to monitor the pitting corrosion of carbon steel in NaHCO\({}_{3}\) + NaCl solutions. The open circuit potential (OCP) measurement and corrosion morphology in-situ capturing using an optical microscope were conducted during AE monitoring. The corrosion micromorphology was characterized with a scanning electron microscope (SEM). The propagation behavior and AE features of natural pitting on carbon steel were investigated. After completion of the signal processing, including pre-treatment, shape preserving interpolation, and denoising, for raw AE waveforms, three types of AE signals were classified in the correlation diagrams of the new waveform parameters. Finally, a 2D pattern recognition method was established to calculate the similarity of different continuous AE graphics, which is quite effective to distinguish the localized corrosion from uniform corrosion.  
carbon steel; pitting corrosion; acoustic emission; wavelets; pattern recognition Article
# Quasi-Static Load Sharing Characteristics of a Planetary Fear Set with Planet Journal Bearings
###### Abstract  
The quasi-static load sharing characteristics of a planetary gear set with planet journal bearings are numerically investigated. The application of journal bearings instead of rolling bearings in planetary gear sets is an alternative to increase the rotation speed. Therefore, an effective analysis method is needed to estimate the dynamic performance of a planetary gear set with planet journal bearings. Here, an available load sharing factor computation method is developed considering the effect of the variable journal bearing stiffness. Results of an experiment are used to validate the effectiveness of the computation method. Furthermore, the load sharing characteristics of a planetary gear set with planet journal bearings are influenced by bearing parameters and operating conditions significantly due to the changing bearing stiffness of journal bearings. The effects on the load sharing factor and the bearing stiffness are studied in detail simultaneously based on the proposed analysis method. The load sharing factor of a planetary gear set with planet journal bearings has a similar variation with the bearing support stiffness under the same load. These researches provide a theoretical basis for the application of journal bearings in planetary gear sets.  
planetary gear; journal bearing; load sharing; stiffness; high speed Article
This study first explores the characters of the radiometric errors of BNU-1 images and then tries to correct these errors, using a relative sensor normalization method. The corrected images are finally evaluated by comparing them with the Landsat@LI images and the field observations.
# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 2 Mathematical Model of Cooperative Search
### UAV Movement Model  
In order to conduct research on swarm behaviors conveniently, the following assumptions about UAV swarm are made:  
Figure 1: The division of the mission area.  
* The agents of UAV swarm are assumed as the mass points with the maximum velocity.
* There is no leader in the UAV swarm. The monomers decide the next moving directions and velocities separately according to the information from the neighbors and the nearby environmental grids.
* The detection and communication range of UAV is limited.  
The assumptions about the environments are as follows:  
* When the monomers reach the boundaries of the mission area, the velocities are reversed.
* The monomers cannot collide with other monomers.  
According to the above assumptions, the kinematic model of UAV with the maximum velocity is modeled. The UAV is controlled by a simple proportional controller. The inner-loop controller updates the objective velocity \(\overset{\rightarrow}{mvel}\), and the outer-loop controller minimizes the position norm \(\|\overset{\rightarrow}{P}-\overset{\rightarrow}{P_{sp}}\|\), where \(\overset{\rightarrow}{P_{sp}}\) is the objective position, and \(\overset{\rightarrow}{P}\) is the current position of UAV agent. In the emergent searching process, the agents can communicate with their neighbors to calculate an accurate direction and velocity of each agent.
## 5 Lattice Structures in Medical Devices and Applications
### Topological Design of Cellular Scaffolds in Spinal and Other Orthopedic Implants
Achieving the right balance between permeability, stiffness, and bone growth will continue to be the key focus in the development of next-generation orthopedic implants. Synthetic HA with a stoichiometric Ca/P molar ratio of 1.67 has been formulated to imitate the main inorganic component of the bone ECM--bone apatite. Nonetheless, bone apatite is non-stoichiometric with a Ca/P molar ratio of less than 1.67 and called calcium-deficient carbonated hydroxyapatite. Moreover, bone apatite includes various trace elements, such as Mg, Zn, and Sr, that replace some of the lattice's components, resulting in bone apatite  
Figure 5: Shows comparison of smaller pore size (**A**) vs. larger pore size (**B**). with beneficial impurities present in small quantities. The process of ion substitution occurs when an ion that has been introduced into a crystal lattice structure replaces an atom or ion that was already present in the lattice. This can happen when the introduced ion is of a similar size and charge as the ion replaces, allowing it to fit into the lattice without disrupting its overall structure. Doping involves attaching an extra ion to the crystal lattice's surface structure, which does not incorporate into the lattice itself, nor modify the unit cell parameters. This is unlike the case when a larger or smaller ion is directly substituted within the crystal lattice, which results in changes to the unit cell parameters. Determining whether substitution or doping has occurred is crucial, and analytical techniques such as X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) can confirm the mechanism that has taken place.
## 5 Experiments
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MOSI**} & \multicolumn{2}{c}{**MOSEI**} \\ \cline{2-5}  & **Acc\({}_{2}\uparrow\)** & **Precision** & **Recall** & **F1\(\uparrow\)** \\ \hline HCL & \(85.12\pm 0.28\) & \(84.32\pm 0.29\) & \(86.14\pm 0.33\) & \(86.34\pm 0.28\) \\ \hline \multicolumn{5}{c}{20\% data missing} \\ \hline CL & \(85.08\pm 0.19\) & \(83.25\pm 0.43\) & \(85.72\pm 0.51\) & \(85.16\pm 0.37\) \\ w/o CL & \(80.15\pm 0.22\) & \(80.97\pm 0.49\) & \(83.91\pm 0.27\) & \(84.18\pm 0.31\) \\ \hline \multicolumn{5}{c}{40\% data missing} \\ \hline CL & \(84.65\pm 0.14\) & \(83.23\pm 0.26\) & \(81.37\pm 0.44\) & \(82.27\pm 0.53\) \\ w/o CL & \(75.58\pm 0.13\) & \(74.62\pm 0.54\) & \(75.14\pm 0.28\) & \(72.91\pm 0.37\) \\ \hline \multicolumn{5}{c}{60\% data missing} \\ \hline CL & \(73.89\pm 0.34\) & \(69.68\pm 0.55\) & \(70.28\pm 0.22\) & \(70.93\pm 0.41\) \\ w/o CL & \(59.97\pm 0.26\) & \(53.52\pm 0.31\) & \(56.37\pm 0.21\) & \(57.29\pm 0.48\) \\ \hline \multicolumn{5}{c}{80\% data missing} \\ \hline CL & \(62.18\pm 0.29\) & \(61.38\pm 0.21\) & \(66.31\pm 0.49\) & \(60.41\pm 0.39\) \\ w/o CL & \(48.29\pm 0.47\) & \(43.97\pm 0.22\) & \(50.26\pm 0.39\) & \(51.38\pm 0.31\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of contrastive learning on two benchmarks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Acc\({}_{3}\uparrow\)** & **Precision** & **Recall** & **F1\(\uparrow\)** \\ \hline
**Ours** & \(73.24\pm 0.32\) & \(72.53\pm 0.74\) & \(70.31\pm 0.58\) & \(74.01\pm 0.93\) \\ \hline \multicolumn{5}{c}{Importance of modality} \\ \hline w/o Text & \(53.41\pm 0.42\) & \(52.69\pm 0.78\) & \(49.27\pm 0.21\) & \(41.37\pm 0.45\) \\ w/o Emoji & \(69.21\pm 0.09\) & \(68.47\pm 0.37\) & \(67.94\pm 0.46\) & \(66.25\pm 0.84\) \\ w/o Meme & \(69.58\pm 0.37\) & \(67.98\pm 0.69\) & \(70.11\pm 0.83\) & \(70.29\pm 0.50\) \\ w/o Inner Text & \(70.52\pm 0.73\) & \(71.29\pm 0.05\) & \(68.43\pm 0.28\) & \(67.19\pm 0.16\) \\ \hline \multicolumn{5}{c}{Importance of fine-grained modal annotation} \\ \hline w/o T-Label & \(71.47\pm 0.28\) & \(72.38\pm 0.73\) & \(69.83\pm 0.57\) & \(71.25\pm 0.49\) \\ w/o E-Label & \(71.76\pm 0.78\) & \(73.58\pm 0.27\) & \(71.04\pm 0.34\) & \(72.34\pm 0.18\) \\ w/o M-Label & \(72.09\pm 0.31\) & \(70.71\pm 0.03\) & \(71.59\pm 0.42\) & \(72.21\pm 0.25\) \\ w/o I-Label & \(71.94\pm 0.98\) & \(72.41\pm 0.85\) & \(69.73\pm 0.59\) & \(71.18\pm 0.51\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of ablation studies on SM-MSD.
# Combinatorial Generation Algorithms for Some Lattice Paths Using the Method Based on AND/OR Trees +
## 3 Results
### Combinatorial Generation Algorithms for Dyck Paths  
#### 3.2.1 Combinatorial Set  
A Dyck \(n\)-path is a lattice path in the plane which begins at \((0,0)\), ends at \((n,n)\), consists of steps \((0,1)\) and \((1,0)\), and never rises above the diagonal \(y=x\)[21]. The set of Dyck \(n\)-paths is a subset of the North-East lattice paths beginning at \((0,0)\) and ending at \((n,n)\).  
Figure 4 shows all possible \(5\) variants of the considered Dyck paths for \(n=3\).  
The total number of Dyck \(n\)-paths is defined by the Catalan number \(C_{n}\) (the sequence \(A000108\) in OEIS [20]):  
\[C_{n}=\frac{1}{n+1}\binom{2n}{n}. \tag{3}\]  
The value of \(C_{n}\) can also be calculated using the following recurrence that belongs to the required algebra \(\{\mathbb{N},+,\times,R\}\):  
\[C_{n}=\sum_{i=0}^{n-1}C_{i}C_{n-1-i},\quad C_{0}=1. \tag{4}\]  
In addition, the sequence of values of \(C_{n}\) is defined by the generating function  
\[\sum_{n\geq 0}C_{n}x^{n}=\frac{1-\sqrt{1-4x}}{2x}.\]  
Figure 4: All Dyck paths beginning at \((0,0)\) and ending at \((3,3)\).
## 4 Data Analysis and Findings
### Hypotheses Testing Measurements  
Significance is benchmarked at a maximum 5% error allowance so that a hypothesis may be accepted. However, it should be noted that the rejection of any hypothesis should not be viewed negatively, but rather objectively informative with alternative explanations to the hypothesised phenomenon. The Significance and Hypotheses Testing results are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Hypotheses** & _t_**-Values** & _p_**-Values** & **Results** \\ \hline
**H1** & User Competency -> Behavioural Intention to Use & 3.393 & ** & 0.001 & Accepted \\ \hline
**H2** & Knowledge Acquisition -> Behavioural Intention to Use & 2.318 & * & 0.021 & Accepted \\ \hline
**H3** & Organisational Support -> Perceived Ease of Use & 2.14 & * & 0.032 & Accepted \\ \hline
**H4** & Technical Training -> Perceived Ease of Use & 1.648 & & 0.099 & Rejected \\ \hline
**H5** & Process Training -> Perceived Ease of Use & 0.531 & & 0.596 & Rejected \\ \hline
**H6** & Technical Training -> User Competency & 2.34 & * & 0.019 & Accepted \\ \hline
**H7** & Process Training -> User Competency & 0.588 & & 0.557 & Rejected \\ \hline
**H8** & User Competency -> Perceived Ease of Use & 2.715 & ** & 0.007 & Accepted \\ \hline
**H9** & User Competency -> Perceived Usefulness & 6.038 & ** & 0 & Accepted \\ \hline
**H10** & Lean -> Perceived Ease of Use & 2.136 & * & 0.033 & Accepted \\ \hline \hline \end{tabular}
\end{table}
Table 6: Significance and hypotheses testing results.  
Figure 3: Coefficient of Determination results from SmartPLS 3.0.
## 3 Simulation
### Results of Robot Walking
#### 3.2.2 Stability Indices and CP  
Figure 5 shows the results for the stability indices and the CP of the robot walking, where the shadow areas indicate the right stance state. As shown in Figure 5a, the COG and ZMP have a phase difference between them, and CP seems distant from the COG and ZMP as a result of being out of cycle. To show the specific details of the data with time, Figure 5b,c shows the stability indices and the CP on the \(x\)-axis and in \(y\)-axis, respectively. In Figure 5b, high values on the \(x\)-axis denote the forward direction of walking. As shown in Figure 5b, the value of CP overlaps with the value of COG, while it has an overshooting period in each step, which is caused by the fast crossing velocity. Additionally, the values of COG and CP are always higher than the value of ZMP, because the upper body is leaning forward in the robot's posture. In Figure 5c, positive values in the \(y\)-axis indicate the left side of walking, and negative values indicate the right side of walking. As shown in Figure 5c, the COG is drawn inside the ZMP trajectory. This means that the COG well follows the BOS, including the ZMP; in other words, the stability is consistently guaranteed. However, the trace of CP is located outside of the COG and the ZMP, because the walking robot was designed based on the cart model, and the body moves forward without moving up and down, which causes more rotation in limb joints. The values regarding to the CP are predominantly affected by the rotation angles of the hip and ankle in the sagittal and transverse plane, according to the designed robot modelling and its motion control in various environments.  
In summary of simulation, the \(x_{CP}\) resembles the \(x_{COG}\) in the sagittal plane excepting for the sharp rising period of value. This sharp increasing period is caused by the radical changeable speed of swing limb. In the transverse plane, the \(y_{CP}\) is different from the \(y_{COG}\), since it is affected by the altered speed and trace of swing limb and the changeable rotation in the hip joints.