esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Chronic ankle instability (CAI) is characterized by impaired ankle joint position sense (JPS) and compromised balance. Despite previous studies investigating the correlation between ankle JPS and balance in individuals with CAI, the potential mediating effect of pain in this relationship remains unclear. Understanding the role of pain as a mediator between ankle JPS and balance could provide valuable insights into the complex interplay among these variables in individuals with CAI. Therefore, further research is needed to elucidate the mediating effect of pain and its implications in assessing and managing ankle JPS and balance deficits in individuals with CAI. This cross-sectional study aimed to investigate the mediating role of pain in the association between ankle JPS and balance in individuals with unilateral CAI. Methods: Fifty-five individuals diagnosed with CAI participated in this study. Ankle JPS was assessed using a digital inclinometer, whereas the balance was measured using a computerized dynamic posturography device. Results: Participants with CAI showed impaired ankle JPS in the affected leg compared to that in the asymptomatic leg (\(p<0.001\)). Ankle JPS errors were greater in both dorsiflexion and plantarflexion directions in the CAI. Balance was compromised in the CAI leg (\(p<0.001\)). Moderate correlations (\(p<0.001\), \(r=0.31\) to \(0.48\)) were found between the balance variables. Pain significantly mediated the ankle JPS-balance relationship in the CAI (\(p<0.05\), Sobel test). The findings suggest that individuals with CAI exhibit impaired ankle JPS and compromised balance. Pain plays a mediating role in the association between ankle JPS and balance in individuals with CAI. These results highlight the importance of considering pain as a potential mediator when assessing and treating balance issues in individuals with CAI. Healthcare professionals should incorporate assessments of ankle JPS and pain into the management of interventions that address these factors and improve balance and functional ability.  
chronic ankle instability; ankle joint position sense; balance; pain; mediation analysis; proprioception; postural stability +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Equations and Analytical Methods
#### 2.4.1 Power Required and Power Available  
In the aviation industry, the power needed for maintaining a constant airspeed and altitude is calculated based on the drag force produced by the airframe and the airflow. For the prototype used in this study, the friction between the roller and the ground was supposed to be the main drag force, according to a theory used in a study on mobile robotic dynamics with friction [35]. Therefore, the static friction between the steel roller ball and ground surface is of a great concern, since most frictional loss is associated with static friction. Once the vehicle starts to move, the static friction shifts to dynamic friction, which mainly depends on the frictional performance of the roller balls.  
The static friction between the steel roller ball and the wooden surface was calculated using the following equation [35].  
\[F_{r}=\mu.N \tag{1}\]  
where \(F_{r}\) is the resistive force of friction, \(\mu\) is the coefficient of friction for the two surfaces at zero velocity, and \(N\) is the perpendicular force pushing the two objects together. In the study, \(N\) is the weight of the prototype (Table 1). After the vehicle was set in motion, the friction coefficient \(\mu\) was the Coulomb friction at non-zero velocities (Table 1) [35].  
In this study, calculation of the driving direction and the sum of forces is not required as conventional control methods of holonomic vehicles [13], because the holonomic movement was realized by only one force, a simple control model.  
The Power of the motor can be obtained by knowing the current and voltage of the DC motor (Table 1).  
\[P=V.I \tag{2}\]  
where \(P\) is the power, \(V\) is the potential difference, and \(I\) is the current.
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
## 4 Numerical Investigation
### Material and Fracture Modelling  
As the tension coupon specimen is in a uniaxial stress state prior to necking, the true stress-strain response can be easily defined using the following conventional equations:  
\[\sigma_{true}=\sigma_{Eng}\big{(}1+\varepsilon_{Eng}\big{)} \tag{4}\]  
\[\varepsilon_{true}=\ln\big{(}1+\varepsilon_{Eng}\big{)} \tag{5}\]  
where \(\sigma_{true}\) is the true stress, \(\varepsilon_{true}\) is the true strain, \(\sigma_{Eng}\) is the engineering stress, and \(\varepsilon_{Eng}\) is the engineering strain.  
However, after necking, such equations are no longer valid owing to strain localization and triaxial stress states. The Swift law was used to extrapolate the post-necking true stress-strain relationship, which can be expressed as follows:  
\[\sigma_{true}=A(\varepsilon_{P}+B)^{n} \tag{6}\]  
where \(A\) and \(B\) are the material parameters and \(\varepsilon_{p}\) is the plastic strain.  
For the lean duplex stainless steel used in the present work, \(A=1451\), \(B=0.1101\), and \(n=0.4437\), which resulted from curve fitting of the measured stress-strain data prior to necking.  
In some cases, FE modeling with the conventional plasticity model has been found to overestimate the ultimate load of the steel connections as it does not reflect material softening [24; 25]. In the present work, finite element analysis incorporating fracture simulation was performed to achieve more accurate results. Table 3 lists the damage parameters calibrated from the results of standard tension coupons using procedures proposed by [PERSON] and [PERSON] [26]. The Rice and Tracey [27] model built in ABAQUS [22] was used to set the damage initiation criterion, in which fracture strain \(\varepsilon_{f}\) at damage onset is set as a function of the stress triaxiality \(\eta\). The damage evolution law was defined as a nonlinear function of the effective displacement.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 5 Operational Requirements and Radar Specifications  
With the preliminary discourse on performance metrics covered, it is appropriate to deliberate on the operational requirements of a radar altimeter intended for UASs. In this context, the existing aviation standards serve as a good reference. In active use for the past half-century, RTCA DO-155, the minimum operational performance standard for low-range radar altimeters, is considered the gold standard [59]. Likewise, EUROCAE ED-30 is a similar standard with minor modifications [60]. Furthermore, the specifications of contemporary radar altimeters developed for the aviation industry serve as a good reference to compare performance metrics [61]. However, these systems generally operate in the 4.2-4.4 GHz band. More recently, radar altimeters employing FMCW in the mmWave band with a high update rate and dynamic range designed specifically for small- to medium-scale drones have emerged [39]. To this end, there is no such reference specifically dedicated to radar altimeters intended for use on UASs. It is therefore not possible to have a one-to-one mapping of every single requirement listed in the referred standards. This article strives to adapt the existing MOPSs for radar altimeters used in commercial aviation for applicability to UASs. The requirements of reliability and the environmental stress screening of avionics for commercial and military aviation is a separate domain that is more related to the product aspect than the technological aspect. With this understanding, the discussion on performance metrics has been kept to the maximum and minimum measurable altitude, the measurement accuracy, and the update rate.
## 3 The Limitations of Seluge's Merkle Tree  
Referring to Equation (1) defined in Section 2, in Seluge, to identify a value of \(M\), we have to fill the equation with the default values of \(N=48\), \(|H(.)|=8\) and \(payload\ size=72\). Authors of Seluge have used a truncated 64-bit SHA-1, which is more vulnerable to collision attacks compared to 160-bit SHA-1. However using Seluge's default values, the value of \(M\) will be equal to eight (k = 3).  
Unfortunately, for situations where security must be at the highest possible level (e.g., battlefields, healthcare systems), using 64-bit SHA-1 is not recommended. Furthermore, as shown below, Seluge cannot afford to use 160-bit SHA-1, because no value of integer number \((K)\) will satisfy Equation (1) when \(|H(.)|=20(160\ bit)\). This statement remains true, even if we use a maximum payload size instead of Seluge's default value of 72:  
\[\frac{(48*20)}{2^{K}}+K*20\
ot\leq 102\]  
The only solution for this problem is to decrease Seluge's default value of the number of packets per page from \(N=48\) to a possible maximum of \(N=17\), which then will result in the \(K\) to satisfy Equation (1) with a value of four, consequently creating a Merkle hash tree with \(2^{4}=16\) leaf nodes.  
This value for \(N\) is an awkward selection, because it will highly degrade the performance of Deluge as the underlying dissemination protocol.  
Our findings show that this Merkle hash tree is not highly secure and efficient enough to be used in situations where security is critical, and therefore, usage of this tree-based mechanism is not suggested in mission-critical situations like military zones or generally in networks that are related to human safety. However, the way that Seltage has employed this method still provides a reasonable security level compared to other similar mechanisms [16,25] for situations where security does not need to be very strict.  
Later, in Section 8, we will show how much overhead this structure of the Merkle tree incurs in the dissemination of the code updates in Seltage.
# A Wideband Metal-Only Patch Antenna for CubeSat
###### Abstract  
This article presents a compact wideband high gain patch antenna for CubeSat. The proposed metal-only antenna mainly consists of an upper patch, a folded ramp-shaped patch and sharing pins connecting the antenna with the ground plane. By adjusting the lengths and widths of two arms of the upper F-shaped patch, a second resonant frequency is generated, and hence, the \(-10\) dB bandwidth is increased. Moreover, the effect of arms' lengths and widths on reflection coefficients, operating frequency and bandwidth is presented. To validate the design and the simulation results, a prototype metal-only patch antenna was fabricated and tested in a Chamber. A good agreement between the simulated and measured results is achieved. The measured results show that the fabricated prototype achieves a \(-10\) dB bandwidth of 44.9% (1.6-2.7 GHz), a small reflection coefficient of \(-24.4\) dB and a high efficiency, i.e., 85% at 2.45 GHz. The radiation performance of the proposed antenna is measured, showing a peak realized gain of 8.5 dBi with cross polarization level less than \(-20\) dB at 2.45 GHz and a 3 dB gain bandwidth of 61.22%.  
CubeSat; antenna; patch antenna; reflection coefficient; gain; bandwidth; polarization 2021, 10, 50. [[https://doi.org/10.3390/electronics10010050](https://doi.org/10.3390/electronics10010050)]([https://doi.org/10.3390/electronics10010050](https://doi.org/10.3390/electronics10010050))
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
###### Abstract  
Respiratory motion serves as a major challenge during treatment of lung cancer patients using radiotherapy. In this work, an image-based method is presented to extract a respiratory signal directly from Cone Beam CT (CBCT) projections. A dense optical-flow method is used to acquire motion vectors between successive projections in each dataset, followed by the extraction of the dominant motion pattern by application of linear kernel Principal Component Analysis (PCA). The effectiveness of the method was tested on three patient datasets and the extracted breathing signal was compared to a ground-truth signal. The average phase shift was observed to be 1.936 \(\pm\) 0.734 for patient 1, 1.185 \(\pm\) 0.781 for patient 2 and 1.537 \(\pm\) 0.93 for patient 3. Moreover, a 4D CBCT image was reconstructed, considering the respiratory signal extracted, using the proposed method, and compared to that reconstructed considering the ground-truth respiratory signal. Results showed that a minimal difference was found between the image reconstructed using the proposed method and the ground-truth in terms of clarity, motion artifacts and edge sharpness.  
Keywords:lung cancer; image-guided radiotherapy; respiratory motion; cone beam CT; PCA; optical flow +
Footnote †: journal:
###### Abstract  
Satellite communication is a promising transmission technique to implement 5G and beyond networks. Attenuation due to rain begins at a frequency of 10 GHz in temperate regions. However, some research indicates that such attenuation effects start from 5-7 GHz, especially in tropical regions. Therefore, modeling rain attenuation is significant for propagating electromagnetic waves to achieve the required quality of service. In this survey, different slant link rain attenuation prediction models have been examined, classified, and analyzed, and various features like improvements, drawbacks, and particular aspects of these models have been tabulated. This survey provides various techniques for obtaining input data sets, including rain height, efficient trajectory length measurement techniques, and rainfall rate conversion procedures. No survey of the Earth-space link models for rain attenuation is available to the best of our knowledge. In this study, 23 rain attenuation models have been investigated. For easy readability and conciseness, the details of each model have not been included. The comparative analysis will assist in propagation modeling and planning the link budget of slant links.  
Earth-space link; attenuation measurement; microwave propagation; rain fading; rain attenuation 202113965. [[https://doi.org/10.3390/rs13101965](https://doi.org/10.3390/rs13101965)]([https://doi.org/10.3390/rs13101965](https://doi.org/10.3390/rs13101965))  
13 May 2021
# On the Energy Performance of Iridium Satellite IoT Technology
## 5 Evaluation  
On the basis of the models provided in the previous section, this section evaluates the average current consumption and the lifetime of the considered Iridium IoT device as well as the corresponding energy cost per delivered bit. The section is organized into three subsections, which focus on each of the aforementioned performance parameters, respectively.  
We performed the evaluation for a wide range of \(T\) values. We also considered different values for \(p\), which allowed us to capture ideal and non-ideal conditions (note that, in absence of application-triggered retries, we experimentally observed a value of \(p\approx 0.1\)). We assumed \(T_{Retry}=3\min\).
## 2 Datasets and Methodology  
### Satellite Precipitation Data  
To perform the evaluation of the 3 h precipitation forecast over the sea, observational data were obtained from the IMERG Final Run (version 06B) at a horizontal resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) (about 10 \(\times\) 10 km\({}^{2}\)) and a temporal resolution of 30 min from the Goddard Earth Sciences Data and Information Services Center (GES DISC) website. We used the complete calibrated precipitation field from the post-real-time final run product, which is the satellite-gauge combination available \(\sim\)3.5 months after the observation month. To calculate it, first the precipitation estimates are computed using the Goddard Profiling Algorithm (GPROF2017) from the various satellite passive microwave sensors comprising the GPM constellation. These are then intercalibrated to the Combined Ku Radar-Radiometer Algorithm (CORRA) product, forward- and backward-morphed, and combined with microwave precipitation-calibrated GEO-IR fields, to be finally adjusted with seasonal Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) surface precipitation data to provide the global half-hourly dataset [71].
## 6 Mathematical Representations
### Half-Car Suspension
#### 6.2.1 Passive Car Suspension  
The half-car model used in this study is shown in Figure 5, the MR damper's parameters are listed in Table 3, whereas the vehicle parameters are tabulated in Table 4.  
The equations that describe the model in Figure 5 are given by:  
\[m_{s_{1}}\dot{x}_{1}=-k_{T}(x_{1}-w_{r})-b_{T}(\dot{x}_{1}-\dot{w }_{r})+k_{1}(x_{3}-x_{1})+b_{1}(\dot{x}_{3}-\dot{x}_{1}) \tag{4}\] \[m_{s_{2}}\dot{x}_{2}=-k_{T}(x_{2}-w_{f})-b_{T}(\dot{x}_{2}-\dot{w }_{f})+k_{2}(x_{4}-x_{2})+b_{2}(\dot{x}_{4}-\dot{x}_{2})\] (5) \[m_{c}\ddot{x}_{3}=-k_{1}(x_{3}-x_{1})-b_{1}(\dot{x}_{3}-\dot{x}_{ 1})-k_{2}(x_{4}-x_{2})+b_{2}(\dot{x}_{4}-\dot{x}_{2})-Bm_{c}\ddot{\theta}\] (6) \[m_{c}\dot{x}_{4}=-k_{1}(x_{3}-x_{1})-b_{1}(\dot{x}_{3}-\dot{x}_{ 1})-k_{2}(x_{4}-x_{2})+b_{2}(\dot{x}_{4}-\dot{x}_{2})+Am_{c}\ddot{\theta}\] (7) \[I\ddot{\theta}=A[k_{1}(x_{3}-x_{1})+b_{1}(\dot{x}_{3}-\dot{x}_{ 1})]-B[k_{2}(x_{4}-x_{2})+b_{2}(\dot{x}_{4}-\dot{x}_{2})] \tag{8}\]  
Figure 5: Half-car passive suspension scheme.  
\[x_{c}=\frac{Ax_{3}+Bx_{4}}{L} \tag{9}\] \[\theta=\frac{x_{4}-x_{3}}{L} \tag{10}\]  
where:  
\(m_{s_{1}}\) = mass of the rear suspension (kg);  
\(m_{s_{2}}\) = mass of the front suspension (kg);  
\(m_{c}\) = mass of vehicle body (kg);  
\(k_{1}\) = Stiffness coefficient of the rear suspension (N/m);  
\(k_{2}\) = Stiffness coefficient of the front suspension (N/m);  
\(k_{T}\) = Stiffness coefficient of the front wheel (N/m);  
\(b_{1}\) = damping coefficient of the rear damper (Ns/m);  
\(b_{2}\) = damping coefficient of the front damper (Ns/m);  
\(b_{T}\) = damping coefficient of the tire (Ns/m);  
\(I\) = moment of inertia of the mass of vehicle body (kg m\({}^{2}\));  
\(A\) = distance from the center of mass to vehicle rear axle;  
\(B\) = distance from the center of mass to the vehicle front axle;  
\(L\) = distance between the front and rear axles;  
\(x_{1}\) = vertical displacement of the rear suspension (m);  
\(x_{2}\) = vertical displacement of the front suspension (m);  
\(x_{3}\) = vertical displacement of the rear of the vehicle (m);  
\(x_{4}\) = vertical displacement of the front of the vehicle (m);  
\(w_{r}\) = Rear wheel vertical displacement input (m);  
\(w_{f}\) = Front wheel vertical displacement input (m).  
The employed magnetorheological damper's parameters are listed in Table 3.  
The vehicle's parameters are listed in Table 4.
# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 3 Experiments and Results
### Underwater Defect Detection Based on YOLOv8s-UEC  
The specific detection results on the test set are shown in Figure 15, which shows that the proposed network effectively improved the detection of underwater defects in general and had better robustness. Specifically, the improvement in detection performance was divided into three aspects: (1) the detection effect for small objects such as underwater cracks was improved, (2) the prediction boxes for various types of underwater defects were more reasonable, (3) the proposed network had a higher confidence when the same object is detected.  
In addition, the real detection effect of the network was visualized using the ground truth as a benchmark, where the prediction boxes for correctly detected defective samples was marked in green, those for incorrectly detected non-defective samples were marked in blue, and those for missed defective samples were marked in red. Figure 15a-h presents pairs of images showing cracks in clear water without light, clear water with light, muddy water without light, and muddy water with light scenes, respectively, which shows that with the improvement of the network, the missed and incorrectly detected samples of crack defects in different scenes gradually disappeared, which proves that the effectiveness of the proposed network for detecting small objects such as underwater cracks was gradually improved. In Figure 15i,j the detection effect of the proposed network on the three types of defects is improved, while the output prediction boxes are closer to the ground truth and more reasonable in distribution. In Figure 15k,l, the proposed network has higher confidence than the original network in detecting the same defects, which proves that the proposed network has a higher credibility in judging the defects. Taken together, the results show the proposed network can accurately detect defects of different categories and sizes in different underwater scenes, which shows that YOLOv8s-UEC has high applicability and credibility in the underwater defect recognition task.  
Figure 14: Precision–recall curve of different networks.  
Figure 15: _Cont._  
Figure 15: Underwater defect detection based on YOLOv8s and the proposed YOLOv8s-UEC network: (**a**–**h**) small-size crack detection in different environments; (**i**,**j**) more reasonable prediction boxes; (**k**,**l**) higher confidence.
## 5 Brillouin Optical Correlation Domain Reflectometry
### Basic BOCDR System  
Figure 31 shows a basic setup for BOCDR [9]. A laser diode is used for the light source, and the lightwave is divided into two ways. One is launched to the sensing fiber (fiber under test: FUT) as a pump light, which causes spontaneous Brillouin scattering (SpBS). The backscattered Stokes SpBS is mixed at a fiber coupler with the reference wave from the same LD. A photo diode (PD) receives the interference between the Stokes and the reference wave to output the beat note between the two lightwaves. The intermediate frequency is around 11 GHz, whose spectrum shape is measured and displayed with an electrical spectrum analyzer (ESA). For the position-selective measurement, the LD lightwave frequency is modulated with the direct FM nature of the LD.  
In Figure 31, the Stokes wave backscattered at point A has the same phase FM as that of the reference wave, or the two waves that are under the highest correlation. Therefore, the beat note between the Stokes caused at point A and the reference wave shows a Lorentz-shaped spectrum with a width of around 30 MHz. However, the Stokes waves that are caused at non-correlation points, such as point B in Figure 31, shows the broadened spectrum shape, or the background spectrum shaped like a mountain. The BOCDR output spectrum, which is measured directly by the ESA, is almost the same as that measured in the BOCDA system [9; 81].  
The performance improvement schemes that have been proposed and developed for the BOCDA systems can also be applicable to the BOCDR systems. Several examples of the BOCDR systems with the performance improvement schemes are shown in the following section.