esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Data Set  
The Institutional Review Board (IRB) at the University of Michigan approved this study (IRB#HUM00069035). Full polysomnography (PSG) data was collected for 96 patients at the University of Michigan Sleep Disorders Center. For each patient, polysomnography consisted of electroencephalography (EEG), electrooculography (EOG), submental and tibial electromyography (EMG), electrocardiography (ECG), two piezoelectric belts for recording plethysmography (PPG), coronasal airflow sensor (FlowTH), air pressure transducer (NPRE), digital micro-phone and pulse oximeter.  
The oronasal airflow sensor used in this study is a thermocouple-based one from Respironics Model: Pro-Tech-P1273 (Philips Healthcare, Eindhoven, The Netherlands). Clinical annotations for respiratory events were carried out by expert clinicians from the Sleep Disorders Center at the University of Michigan (Ann Arbor, MI) and according to recommendations of the AASM [11]. Apneic events in the data set are either obstructive (OSA), central (CSA), or mixed (MSA). The data set spans different apnea severity levels as reflected by the apnea-hypopnea index (AHI) computed over night for patients in the study. 10 are non/minimal sleep apnea patients (AHI \(<\) 5), 36 are mild sleep apnea patients (5 \(\leq\) AHI \(<\) 15), 27 are moderate sleep apnea patients and 23 are severe sleep apnea patients (AHI \(\geq\) 30). Table 1 provides distribution for the numbers and types of the apneic events per each class of the patient severity levels.  
The oronasal airflow signals of 66 patients (with different apnea severity levels) were used for training the proposed modeling framework. The developed framework was then tested on 30 patients (distinct from the training data) composed of 5 none/minimal apnea, 5 mild, 5 moderate, and 15 severe sleep apnea patients.
## 2 Mid-Infrared Optical Parametric Oscillation Laser (OPO)
### LiNbO\({}_{3}\), PPLN, MgO-Doped PPLN Optical Parametric Oscillator
 } & \multirow{4}{*}{25\(\times\) 3 \(\times\) 1 mm\({}^{3}\)} & \multirow{4}{*}{1.937 \(\mu\)m} & \multirow{4}{*}{1.937 \(\mu\)m} & \multirow{4}{*}{1.937 \(\mu\)m} \\  & & & & Output wavelength 3.85 \(\mu\)m \\  & & & & Output wavelength 3.85 \(\mu\)m \\  & & & & Optical efficiency 17.9\% \\  & & & & Output wavelength 3.35 \(\mu\)m \\  & & & & Output wavelength 3.35 \(\mu\)m \\  & & & & Output wavelength 3.35 \(\mu\)m \\  & & & & Output wavelength 3.35 \(\mu\)m \\  & & & & Output wavelength 3.35 \(\mu\)m \\  & & 5\% mol & Nd: YVO\({}_{4}\) & Repetition rate 1 MHz \\  & & & & Output wavelength 3.8 \(\mu\)m \\  & & & & Output wavelength 3.8 \(\mu\)m \\  & & & & Output wavelength 3.8 \(\mu\)m \\  & & & & Output wavelength 3.2 \(\mu\)m \\  & & & & Average power 1.72 W \\  & & & & Optical efficiency 7.17\% \\  & & & & Output wavelength 3.5 \(\mu\)m \\  & & & & Average power 1.39 W \\  & & & & Output wavelength 3.8 \(\mu\)m \\
2020 & \multirow{4}{*}{ \begin{tabular}{c} Xinjiang Normal \\ University \\ \end{tabular}  } & \multirow{4}{*}{40 \(\times\) 10 \(\times\) 2 mm\({}^{3}\)} & \multirow{4}{*}{1.064 \(\mu\)m} & \multirow{4}{*}{1.064 \(\mu\)m} & \multirow{4}{*}{1.064 \(\mu\)m} \\  & & & & Output wavelength 3.8 \(\mu\)m \\  & & 5\% mol & Nd: YAG & Average power 1.075 W \\  & & & & Optical efficiency 10.2\% \\  & & & & Output wavelength 3.87 \(\mu\)m \\
2020 & \multirow{4}{*}{ \begin{tabular}{c} Shandong \\ University \\ \end{tabular}  } & \multirow{4}{*}{25 \(\times\) 3 \(\times\) 1 mm\({}^{3}\)} & \multirow{4}{*}{1.937 \(\mu\)m} & \multirow{4}{*}{1.937 \(\mu\)m} & \multirow{4}{*}{1.937 \(\mu\)m} \\  & & & & Output wavelength 4.1 \(\mu\)m \\
2020 & \multirow{4}{*}{ \begin{tabular}{c} Shandong \\ University \\ \end{tabular}  } & \multirow{4}{*}{5\% mol} & \multirow{4}{*}{Tm: YAP} & \multirow{4}{*}{1.064 \(\mu\)m} \\  & & & & Output wavelength 3.64 \(\mu\)m \\
2020 & \multirow{4}{*}{ \begin{tabular}{c} Xinjiang Normal \\ University \\ \end{tabular}  } & \multirow{4}{*}{40 \(\times\) 10 \(\times\) 2 mm\({}^{3}\)} & \multirow{4}{*}{1.064 \(\mu\)m} & \multirow{4}{*}{1.064 \(\mu\)m} \\  & & & & Output wavelength 3.26 \(\mu\)m \\
2020 & \multirow{4}{*}{ \begin{tabular}{c} Xinjiang Normal \\ University \\ \end{tabular}  } & \multirow{4}{*}{50 \(\times\) 8.2 \(\times\) 1 mm\({}^{3}\)} & \multirow{4}{*}{1.064 \(\mu\)m} \\  & & & & Optical efficiency 10.2\% \\
2020 It can be seen from the table that the output power, wavelength and conversion efficiencies of periodically poled crystals have been improved substantially from LiNbO\({}_{3}\) to MgO-doped PPLN.
# Fault-Tolerant Control of Multi-Joint Robot Based on Fractional-Order Sliding Mode
## 3 Fractional-Order Sliding Mode Controller Design  
Before introducing the design process of fractional-order sliding mode control law, the basic notation of fractional calculus is given below.  
\[D_{t}^{\alpha}=\begin{cases}\frac{d^{\alpha}}{dt^{\alpha}}&,\alpha>0\\ 1&,\alpha=0\\ \int\limits_{0}^{\tau}{(d\tau)^{-\alpha}}&,\alpha<0\end{cases} \tag{1}\]  
In the above formula, \(D_{t}^{\alpha}\) is called an arbitrary-order differential operator. There is no unified solution for the value of \(\alpha\). The three most representative solutions are given by [PERSON] (GL), [PERSON] (RL), and [PERSON] [40]. From a control and signal processing perspective, [PERSON] expresses fractional calculus by summing terms, so it is intuitive to implement using digital signal controllers. When [PERSON] defines fractional calculus where the Laplace transform is performed, the initial value of n fractional derivatives of the function at the initial moment needs to be known. For the actual system, limited by the still vague concept of fractional calculus, it is very difficult to obtain the initial value of n fractional derivatives at zero time. The Caputo fractional operation scheme of order only needs to know the initial value of the integer derivative, and the integer derivative has a clear physical meaning, so it is widely used in practice and its expression is as follows.  
**Definition  
fractional calculus can achieve a more complete description of the historical process of the model.  
When designing a controller using fractional-order sliding mode theory, it must be ensured that the system is asymptotically stable when it approaches the sliding mode surface. Usually, Lemma 1 is used to judge whether the system is stable or not.  
**Lemma 1** ([42]).: _Assuming that the state \(x=0\) is the equilibrium point of the fractional non-autonomous system, the fractional differentiation of the system state is written as_  
\[D_{t}{}^{\alpha}x(t)=f(x,t) \tag{4}\]  
The function \(f(x,t)\) in the above formula needs to satisfy the Lipschitz condition.  
Supposing that there is a Lyapunov function \(V(t,x(t))\) such that the following formula holds  
\[\left\{\begin{array}{l}a_{1}\|x\|\leq V(t,x)\leq a_{2}\|x\|\\ \dot{V}(t,x)\leq-a_{3}\|x\|\end{array}\right. \tag{5}\]  
In Equation (5) \(a_{1}\), \(a_{2}\), and \(a_{3}\) are all coefficients greater than zero; the system described by Formula (4) is said to be asymptotically stable. Usually, considering that the coefficients \(a_{1},a_{2},a_{3}\) are difficult to obtain, Lemma 2 is often used to judge whether the origin is asymptotically stable.  
**Lemma 2** ([42]).: _For a given continuous function \(\dot{x}=f(x)\), \(f(0)=0\) and \(x\in R\), if there is a continuous positive definite function \(V:R^{n}\to R\), \(a\in R^{+}\), \(\beta\in(0,1)\) and there is a neighborhood \(U_{0}\subseteq R^{n}\) of the origin such that the following formula holds_  
\[\dot{V}(x)+aV^{\beta}(x)\leq 0,x\in U_{0}/\{0\} \tag{6}\]  
Then the origin is an equilibrium point, where the system states can be reached in a finite time.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Overview of RRD+ Mechanism
#### 3.1.2 Link-Quality Monitoring  
Due to unpredictable path attenuations, RSSI values might vary even when neither node moves. In order to take this phenomenon into account, we introduce two RSSI thresholds: Safety Threshold and Hysteresis Threshold, where the Safety Threshold is larger than the Hysteresis Threshold, as shown in Figure 1. We used a dotted line for Safety Threshold, and a dashed line for Hysteresis Threshold. Note that, due to the nature of wireless-signal propagation, in reality both RSSI thresholds and transmission range are most likely to look like a cloud and in our simulation model we used a probabilistic propagation model to take into account coverage-zone instability.  
Figure 1: Node P is the parent node of nodes A, B, and C. A is in the Safety zone of node P. B is in the Hysteresis zone of node P. C is in the Danger zone of node P.  
When New RSSI is higher than or equal to Safety Threshold, the node is considered to be in the Safety zone of its parent node, and it has good link quality with it; this is the case of node A in Figure 1. When New RSSI is smaller than Safety Threshold but higher than Hysteresis Threshold, which is the case of node B in Figure 1, we need to first detect movement direction and then consider whether to stop using the link or not. In order to reduce coverage-zone variation influence, we add a hysteresis value to Old RSSI when comparing it to New RSSI. When New RSSI is smaller than Hysteresis Threshold, which is the case of node C in Figure 1, only New RSSI and Old RSSI are used to estimate direction without using hysteresis.
# Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging
## 2 Simulation Method
### Artifacts Simulation
Pleural breaks, also known as pleural irregularities or pleural abnormalities, appear as disruptions in the smooth pleural line. It was achieved by changing the pleural reflectance parameter in the function of distance to the axis of the probe standard application point (landmark). Figure 5 shows this simulated artifact. 5. Lastly, pleural sliding refers to the dynamic movement of the visceral and parietal pleura during respiration. As mentioned before, it was created using the UV maps of the animated lungs model and calculating pseudorandom values on that basis. The sliding is then achieved by modifying the intensity of the outer part of the pleural line according to the random values. The attenuation phenomenon is also applied during ray marching, according to the traversed tissue attenuation values. Furthermore, pleural fluid was implemented using a custom 3D object and appropriate material parameters. In addition to the mentioned artifacts that are highly important during lung ultrasound examinations, it was crucial for educational purposes to simulate the presence of consolidation. Consolidations are regions of lung tissues filled with liquid instead of air. It ensues from the accumulation of inflammatory cellular exudate within the alveoli and  
Figure 4: B-lines example: (**a**) real examination; (**b**) the LUS simulation. Figure 5: Pleural break example: (**a**) real examination [18]; (**b**) the LUS simulation. contiguous ducts. The simulation of the image of consolidation was achieved using a predefined mesh object created in 3D software (3D Studio Max, Autodesk) and positioned in a virtual patient interior. Material attributes are added to the consolidation object to increase attenuation and induce more reflectance and then rendered in the final image (Figure 6). Finally, image noise and radial blur are applied to the image. By simulating these artifacts within the Lung Ultrasound Simulator, users can develop a comprehensive understanding of their appearance, characteristics, and clinical significance. This simulation enables medical professionals and students to gain practical experience in identifying and interpreting these artifacts, enhancing their diagnostic capabilities and preparing them for real-world lung ultrasound examinations. Due to their lesser importance and the complexity involved in their implementation, other artifacts have not been incorporated into the system at this stage.
## 2 Materials and Methods
### Biological Tests
#### 2.7.3 Scratch Test  
_In vitro_ scratch test is based on the observation of a gap (i.e., the \"scratch\") on a confluent monolayer of cells until the contact between cells is established again [62; 63]. Images are taken immediately after the creation of the scratch and at regular intervals to investigate cell migration. At the end, images collected at each time point are compared to ascertain the rate of cells migration.  
Mouse embryonic fibroblast cells (NIH 3T3) were seeded in 24-multi-well and incubated at 37 \({}^{\circ}\)C to allow the creation of a confluent monolayer. Cells were cultured in Dulbecco's modified Eagle's medium (DMEM) containing L-glutamine 2 mM, 1 mM sodium pyruvate, 10 mg/mL penicillin/streptomycin, and 10% (_v/v_) FBS (fetal bovine serum, Invitrogen, Thermo Fisher Scientific, Rodano, Italy). The type of cells and the size of the multi-well influence the number of cells required to create a confluent monolayer. To produce the scratch, by scraping the confluent monolayer of cells, a 200-\(\upmu\)L pipet tip was used. Then, cells were immediately washed with 500 \(\upmu\)L of PBS and in each well 500 \(\upmu\)L of medium (DMEM) and 500 \(\upmu\)L of the eluates of electrospun fibers (PCL, PCL_4555, PCL_BG10 and PCL_BGZn) were added. Eluates were prepared by soaking samples (3 cm\({}^{2}\)/mL surface of sample/liquid volume) in DMEM and incubated at 37 \({}^{\circ}\)C for 72 h. Then, samples' eluates were filtered before their usage with cells. To acquire the same area of the scratch during image acquisition, a visible mark was created. Images were taken immediately after the creation of the scratch on confluent monolayer (i.e., time 0) before incubating the multi-well at 37 \({}^{\circ}\)C until the next time points. Images were acquired at three time points: at time 0, time 1 (24 h after the scratch), and time 2 (48 h after the scratch). For each image,ImageJ (NIH, Bethesda, MD, USA) [54] was used to measure the area of the gap between the edges of the scratch. The cell migration rate was calculated as follows according to [64]:  
\[\text{{Cell migration rate}}\left(\%\right)=\frac{A0-A1}{A0}\times 100 \tag{1}\]  
where \(A0\) is the area of the gap at time 0 and the \(A1\) is the area of the gap at time X where in this case X is either 24 h or 48 h.
## 4 Evaluation
### Curve following  
We evaluated the VFGL using a figure-eight shape path following. The reference path was generated by two tangent circles. The radii of the two circles were 250 m. The reference velocity and altitude were 25 m/s and 100 m. The desired roll angle and velocity were limited within \([-45^{\circ},45^{\circ}]\) and [18 m/s, 30 m/s], respectively. The parameters of the guidance law in the HIL simulation were \(k_{v}=0.1\) and\(c_{v}=2\sqrt{k_{v}}\).  
The trajectory of the UAV in this scenario is shown in Figure 12. The reference heading rate switches between 0.1 rad/s and \(-0.1\) rad/s at the tangent point of the reference \"8\" path. The result shows that the UAV followed the reference path well; however, the reference curvature varies. The cross-track error in the HIL simulation of curve following is shown in Figure 13. The maximum of the cross-track error after t = 20 s is 3.97 m, which emerges shortly after the first switch. The cross-track error at the second switch is within 1.95 m. These errors were mainly produced by the model error between a real fixed-wing UAV PT60 and the simplified UAV model expressed by (1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Parameters** \\ \hline
1 & \(k_{v}=0.1\), \(c_{v}=\sqrt{k_{v}}\) \\
2 & \(k_{v}=0.1\), \(c_{v}=2\sqrt{k_{v}}\) \\
3 & \(k_{v}=1\), \(c_{v}=2\sqrt{k_{v}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for straight-line following.  
Figure 11: The cross-track error in the HIL simulation of straight-line following.  
C. Path following with obstacle avoidance  
The VFGL was used to avoid the obstacles during path following. The reference curve is the \"8\" shape path in the previous scenario. Considering a circle obstacle located at the tangent point, the radius of the obstacle is 50 m. The desired roll angle and velocity are limited within [\(-30^{\circ},30^{\circ}\)] and [18 m/s, 30 m/s], respectively. The parameters of the guidance law are given by  
\[k_{v}=0.1,c_{v}=2\sqrt{k_{v}},k_{rep}=1,R_{d}=100,d_{s}=10 \tag{22}\]  
The result is shown in Figure 14. First, the UAV converges to the reference curve and begins to fly along the reference path. Once the obstacle is detected, the UAV is repelled by the virtual repulsive force to move around the obstacle. After that, the UAV is pulled back to the reference path by the virtual spring force and the virtual centripetal force. During the process, there is no overshoot or oscillation; the UAV avoids the obstacle smoothly. Using the VFGL, a fixed-wing UAV can avoid obstacles on the reference curve during path following.  
The HIL simulation results demonstrate that the VFGL was effective in path following and obstacle avoidance with a fixed-wing UAV. The UAV could accurately follow a predefined line or curve. When an obstacle was detected in the reference path, the UAV could smoothly avoid the obstacle; after that, it converged back to the reference.  
Figure 12: UAV trajectory in the HIL simulation of curve following.  
Figure 13: The cross-track error in the HIL simulation of curve following.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Training Details  
The ODGNet is trained on an NVIDIA GPU RTX 3080 with 10 GB memory. The computer system is ubuntu 18.04 with Cuda 11.1. The deep learning framework is PyTorch 2.0 with python 3.8. The optimizer uses Ranger with the initial learning rate set to 0.0005 and the batch size set to 8. A total of 50 epochs are trained on the Cornell grasp dataset and each epoch contains 1000 batches. A proportion of 90% of the dataset is set as the training set and the remaining 10% is used as the test set. For the training set, 90% is used for training and 10% is used for validation.
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 2 Principle of BOTDR  
Brillouin optical time domain reflectometry can be used as BOTDR technology; the wavelength of Brillouin scattering depends on the temperature and strain of optical fiber sensor. Its working principle is that a beam light of the specific frequency is injected into the fiber, and the light inside the fiber interacts with the elastic sound waves to produce Brillouin backscattered light. Temperature and stress variation of the fiber affect the wavelength of Brillouin scattering. Through the measurement of Brillouin frequency shift, the influence of external factors on the fiber can be obtained by using its relationship with strain and temperature [14; 15].  
There is a linear relationship between the frequency shift of Brillouin scattered light and the strain or temperature of the optical fiber; the strain and temperature of the object can be monitored by BOTDR technology. Equation (1) gives the relationship between Brillouin frequency shift with the strain and temperature [16; 17; 18; 19]:  
\[\Delta v_{B}(\Delta T,\Delta\epsilon)=v_{B}(\epsilon,T)-v_{B}(0,T_{0})=C_{T} \cdot(T-T_{0})+C_{\epsilon}\cdot(\epsilon-\epsilon_{0}) \tag{1}\]  
Among them:  
\(\Delta v_{B}(\Delta T,\Delta\epsilon)\)--the amount of change in Brillouin frequency shift.  
\(v_{B}(\epsilon,T)\)--the Brillouin frequency shift corresponding to the ambient temperature T and the strain \(\epsilon\).  
\(v_{B}(0,T_{0})\)--the Brillouin frequency shift corresponding to the initial temperature T\({}_{0}\) and the strain is 0.  
\(C_{T}\)--the temperature coefficient, \(C_{T}=\frac{\partial v_{B}(T)}{\partial_{T}}\)  
\(C_{\epsilon}\)--the strain coefficient, \(C_{T}=\frac{\partial v_{B}(\epsilon)}{\partial_{\epsilon}}\)  
\(T_{0}\)--the initial temperature.  
\(\epsilon\)--the actual amount strain of optical fiber.  
\(\epsilon_{0}\)--the initial strain of optical fiber.  
According to Equation (1), it can be seen that the strain and temperature caused by the external forces and temperature can be obtained through distributed real-time monitoring on the measured object [20; 21].
## 2 Materials and Methods
### Comparing RAP and iAPAR Model Results  
To compare RAP predictions to ANHP predicted by iAPAR, we fit a linear model including any significant predictors from the previous analysis and excluding any random effects. To compare our predictions against the ANHP ground data, we calculated several model metrics. Mean absolute error (MAE; absolute value of observed minus predicted value) was used to model error. We used MAE percent to put the overall error into perspective (MAE %; the percentage of the mean absolute error relative to the mean of the response variables). We calculated mean absolute percent error (MAPE; mean of the absolute values of observed minus predicted divided by the observed values) to evaluate the percent error that is balanced across the dataset values. We used mean percent error (MPE; the mean of the observed value minus the predicted value divided by the observed value) to obtain a sense of bias. We used R\({}^{2}\) (explained variance = 1 - residual sum of squares/by the total sum of squares, coefficient of determination) to evaluate the overall model fit and \(r\) (Pearson correlation coefficient) to understand the relative fit within the data set. We calculated these metrics for ecosites separately and then recalculated the metrics for all ecosites and grazing management regimes.
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
## 3 Results and Discussion
### Polymer Flood-Oil Recovery and Pressure Response for Tertiary Mode Polymer Flood  
#### 3.3.1 Polymer Viscosity Half the Oil Viscosity in Complex-Wet Model  
As described earlier, a polymer (1000 ppm) with half the viscosity of the oil (9.58 mPa-s) was injected in the tertiary mode to observe additional oil recovery. The micromodel with complex wettability was used for polymer flooding. First, the bump-rate injection at a rate five times higher than that of the brine flood was performed, to eliminate any possible capillary end effects. A minor amount of recovered oil and increased pressure response for the bump rate can be seen in Figure 19. The polymer flood results for the tertiary mode are also shown. Figure 19 shows that no additional oil was produced by polymer injection in either experiments. One reason for this could be that the polymer followed the pre-flushed path of the brine and could not contribute additional recovery, due to lower aqueous viscosity compared to the oil. Furthermore, the pressure of the polymer flood was lower compared to the bump rate in both experiments, as shown in Figure 19. This analysis supports the assertion that the lower pressure difference for polymer flooding was the reason for no additional recovered oil.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 3 Type of Sensor Used to Detect PD Signals
### Acoustic Sensor  
The acoustic method employs an extremely sensitive sensor made of a specialized piezoelectric film, depicted in Figure 9a, and set up as shown in Figure 9b. This technique is used to measure partial discharges (PD) in high-voltage equipment like power transformers and high-voltage cables. When functioning at low resonant frequencies, the piezoelectric sensor film adopts a disc-shaped crystalline structure, facilitating straightforward calculation of its resonances. As explained in reference [112], this precise arrangement makes it simple to determine resonances.  
Figure 7: There are three types of electrical sensors: (**a**) a UHF sensor attached to a GIS grounding bar; (**b**) an oil valve sensor for the reactor of a power transformer (left side) for use with a DN 50 gate valve and (right side) for a DN 80 gate valve; (**c**) an inductive UHF sensor for the termination of a power cable [109].