esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Individual Networks  
Our results demonstrate that the rigid subnetwork could correct rigid transformations that could not be corrected by the affine subnetwork alone. Using a rigid-affine network yielded better registration results than using an affine subnetwork without a preceeding rigid subnetwork (Table 4).  
The results for Dice coefficient and level of image folding were mixed when comparing our approach to a benchmark network. For the first NIDDK dataset, the Dice coefficient of the benchmark and the proposed network were similar. However, upon further visual evaluation, the proposed multistage network showed a higher overlap of all corresponding structures. For the second NIDDK dataset, the spatial alignment between the benchmark and the proposed network was similar, indicating comparable performance. Both models also achieved a comparable performance (similar Dice coefficients) on the external kidney dataset. However, for the augmented external kidney dataset and the M\({}^{2}\)OLIE liver dataset, the proposed multistage network significantly outperformed the benchmark and yielded significantly (\(p<0.00001\) and \(p=0.00152\)) higher Dice coefficients. For the first and second NIDDK dataset, our proposed multistage networks produced registration results with significantly less image folding compared to the benchmark, resulting in medically plausible transformations. For the external kidney dataset, the augmented external kidney dataset and the M\({}^{2}\)OLIE liver dataset, the proposed network and the benchmark yielded similar levels of image folding. These results show that the proposed multistage network is promising for improving the registration of certain datasets while competing with the benchmark for other datasets.  
The level of image folding is low for all registration approaches used. It should also be noted that the number of Jacobian determinants \(\leq\)0 is a global metric. Spatial information, i.e., where image folding occurs, is not represented in this metric. For the neural networks and the baseline, the majority of image folding occurred in areas that are not relevant for the clinical analysis, e.g., in the image background.  
The visual evaluation of the deformation fields demonstrated that the deformation within the individual organs and structures was smooth. The deformation fields of the baseline contained large areas in gray. Only in the area of the segmentations or in their surroundings colors are they visible in the deformation field. This is due to the fact that the segmentation of the fixed and moving image was used to determine the structure of interest for the baseline. Outside of these structures, deformations were generated that were identical in all three spatial directions.
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Al-Based Approaches to Gait Analysis
#### 1.4.2 Previous Gait Studies Using Fractal Parameters  
Only a handful of papers were found in the literature addressing the problem of analyzing and classifying gait using fractal parameters. [PERSON] et al. proposed a fractal algorithm that calculates a number (in the range of 1-2) characterizing the geometry of a one-dimensional signal equivalent to a gait pattern. Once the algorithm was calibrated, the results were accurate, reproducible, and independent of the amplitude of the signal (though only of the dynamics of change in the analyzed waveform). It could be used to measure an individual's gait while walking on flat terrain, walking uphill, and climbing stairs. It should be noted, however, that the authors intended to use the developed algorithm to compare its results with data obtained from pulse sensors, electrocardiograms (ECG), and other sources [41]. No further studies appear to have been conducted by the above authors in this area. [PERSON] et al. showed that in patients with Parkinson's disease, acceleration signalschange in a more complex pattern with ageing and disease progression, and that their fractal dimensions are higher than those observed in healthy subjects [42]. [PERSON] et al. have proposed a gait pattern classification system which uses a decision tree and an algorithm for estimating gait distance using information on the duration of phases in the gait cycle. This has been applied to both horizontal and stair walking, and an accuracy of 95.00-98.87% was achieved for stair walking [43]. [PERSON] et al. analyzed the influence of run length and various parameters used in fractal time series analyses of step intervals. They showed that a careful selection of fractal analysis methods and parameters is required [44]. [PERSON] et al. proposed a wearable sensor system (sensory shoes) that can be used to study human gait dynamics using a support vector machine and fractal analysis. An accuracy of 93.57% was obtained, showing its significant potential in gait identification [45].  
Fractal parameters have also been used to analyze the influence of various behaviors, including gait, on EEG changes, as well as to analyze the non-linear properties of surface electromyography (sEMG), not only for the analysis of muscle activity, but also for the control of prostheses and/or other devices [46].
## 2 (Com)2 INet: Communication and Computation Integrated Network Architecture
### System Architecture
\begin{bmatrix}\{NID_{11},NID_{12},\ldots,NID_{1m_{1}}\}_{CID_{1}}\\ \{NID_{21},NID_{22},\ldots,NID_{2m_{2}}\}_{CID_{2}}\\ \vdots\\ \{NID_{R},NID_{R2},\ldots,NID_{Rm_{R}}\}_{CID_{R}}\end{bmatrix} \triangleq \Phi\left( \begin{bmatrix}FID_{1},FBD_{1}\\ FID_{2},FBD_{2}\\ \ldots\\ FID_{R},FBD_{R}\end{bmatrix} , \begin{bmatrix}NID_{1},NBD_{1}\\ NID_{2},NBD_{2}\\ \ldots\\ NID_{R},NBD_{R}\end{bmatrix} \right)\\ \{NID_{R},NID_{R}\}\end{bmatrix} \tag{2}\]where \(\{NID_{i1},NID_{2},\ldots,NID_{im_{i}}\}\) represents the set of network components served by the \(i\)-th computing service, \(NID_{ij}\) represents the \(j\)-th network component serving the \(i\)-th computing service, \(m_{1}-m_{R}\) represents the number of network components served by the \(R\) computing services, and \(\Phi(\cdot)\) is the behavior clustering mapping function. The core of this computing paradigm lies in establishing the relationships among computing services, functional groups, and network components. To achieve resource scheduling and service guarantees, the following steps are implemented. (1) Service-Group Mapping: based on customized task requirements and fine-grained heterogeneous resources from both communication and computation perspectives, functional modules such as inter-domain scheduling, intra-domain scheduling, and QoS/QoE guarantees are constructed. These modules match the most optimal functions to different computing services, enabling dynamic \"service-group\" mapping. (2) Group-Component Mapping: leveraging the obtained [FID,FBD] and [NID,NBD] information, heterogeneous resource scheduling strategies are generated. The corresponding network components are then clustered to execute network configuration files issued by the mapping adaption layer. This process completes service/network-level configuration and function deployment, thereby realizing \"group-component\" mapping and ensuring the execution of computing services. (Com)2 INet architecture decouples computing service layer from converged network layer vertically through the mapping adaptation layer, realizing the coordinated scheduling and intelligent integration of the service level and network level. On the one hand, (Com)2 INet architecture is conducive to network providing users with customizable computing services, and on the other hand, it is also conducive to providing users with virtual private networks to satisfy the requirements of related computing services in terms of reliability, security, and scalability. Furthermore, (Com)2 INet architecture horizontally decouples the control plane and the operation plane. The upper application plane communicates with the control plane through the northbound interface, and the underlying data plane communicates with the control plane through the southbound interface (e.g., P4 Runtime). The control plane uniformly schedules heterogeneous network resources, translates decisions into configuration files, and sends them to the data plane to complete computing services.
## 5 Other Interesting Applications  
It is also remarkable that DES can be used in many other applications of a very different nature, which shows the great versatility of this type of mixture. For instance, some molecular analytical methods are based on the use of biological samples that have been previously preserved. DESs have been used successfully for the stabilization of different kinds of samples prior to their analysis without interfering with the methods [142]. NADES can also improve the analytical method for the analysis of different drugs (aspirin, atorvastatin, metformin, metoprolol), as shown recently by [PERSON] et al. (2020) [143].  
Additionally, some authors have demonstrated the usefulness of DESs as antibacterial activity for the improvement of different candidate materials for medical purposes [144]. [PERSON] et al. (2020) described the potential use of NADES with collagen in a practical application combining wound healing by collagen peptides and the antibacterial properties of the NADES [57].  
More recently, even NADESs have been prepared to be used as nano-carriers for the encapsulation of anticancer drugs to prevent and treat breast cancer [145]. In addition, DESs can act as therapeutic agents by themselves. [PERSON] et al. (2015) showed that different DESs have been toxic to some cancer cell lines such as human prostate cancer cell line (PC3), human malignant melanoma cell line (A375), human liver hepatocellular cell line (HepG2) and human colon adenocarcinoma cell line (HT29), human breast cancer cell line (MCF-7), human oral keratinocyte cell line (OKF6) cells, and carcinoma-derived human oral keratinocyte cells (H413). Besides, toxicity in normal cells was lower than the cancerous ones [146]. Although certain types of DESs have been used as effective drug deliverers or even have medical or pharmaceutical activities, it is recognized that further investigation is needed prior to clinical use [41].  
Metals and metal salts present high solubilities and electrical conductivities in DESs, so they are good candidates for the extraction/recycling of metals in solution. In addition, DESs can be used in industrial separation processes [147]. The study carried out by [PERSON] et al. showed that the ChCl:levulinic acid DES had the highest distribution coefficient for separating ethanol from heptane [148]. The application of DES as media for the extractive desulfurization of fuels (e.g., SO\({}_{2}\) sequestration) is also growing in importance. In this area, DESs have also demonstrated some interesting properties in improvements in carbon dioxide (CO\({}_{2}\)) capture [138].  
On the other hand, DESs have been used in genomics, incorporating them in the study of genome and the nucleic acids (DNA/RNA) of organisms. DNA in DES dissolution showed novel behaviors of this nucleic acid [149].
## 2 Materials and Methods
### Optical Mocap  
The camera of a smartphone (Lenovo Group Limited, Hong Kong, China) was used to record 2D angles of the participant. The camera, which could record pictures as well as videos (at 30 Hz), was positioned on the left-hand side of the participant at a fixed distance, with the lens parallel to the sagittal plane of the participant. The images and videos were analyzed for joint angles using an open-source image-processing algorithm (Detectoron, Facebook AI Research, Facebook Inc., Cambridge, MA, USA) to identify the human in frame, the major joints of the skeleton, and the lines between the joints. The joint centers were defined as (Figure 1a): ankle joint as the _lateral malleolus_, knee joint as the patella, hip joint as the _greater trochanter_, shoulder joint as the _acromion_, elbow joint as the _lateral epicondyle_, and wrist joint as the _lunate bone_. Based on the joint coordinates, flexion/extension angles were determined (Table 1). Joint angles of the right-hand side of the participants were provided by the algorithm, but were excluded from analyses to avoid unreliable data induced by parallax errors.  
The projected frontal area of the participants was captured using an infrared depth-sensing camera (Intel(r) RealSense(tm) Depth Camera D415, Intel Corporation, Mountain View, CA, USA) with a sampling frequency of 3 Hz placed in front of the cyclist (Figure 2). Furthermore, an average of ten iterations was calculated to eliminate the influence of noise and different pedal position [29].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Anthropometric Data (cm)**} & \multicolumn{1}{c}{**2D Joint Angles**} & \multicolumn{1}{c}{**3D Joint Angles**} \\  & & **Optical Mocap (\({}^{\circ}\))** & **Inertial Mocap (\({}^{\circ}\))** \\ \hline Body Height & Shoulder Breadth (acromion) & Left Knee & Left Knee \\ Chest Circumference & Hip Breadth (standing) & Left Elbow & Left Shoulder \\ Under-Bust Circumference & Chest Circumference (scye) & Left Hip & Left Elbow \\ Waist Circumference (minimum) & Hip Circumference & Back & Right Knee \\ Waist Circumference (trousers) & Arm Circumference (scye) & Head & Right Shoulder \\ Neck Circumference (shirt) & Shoulder Breadth (bideltoid) & Left Shoulder & Right Elbow \\ Neck Circumference (tight, hull) & Hip Breadth (sitting) & Neck (2D) \\ Lower Arm Circumference (mid, Hull) & Upper Arm Length & Left Hip \\ Biceps Circumference Hull & Lower Arm Length & Right Hip \\ Spine-Shoulder Length & Stemum to Femur Length & Pelvis \\ Arm Length & Upper Leg Length & Left wrist \\ Back Length (shirt) & Lower Leg Length & Right wrist \\ Torso Length (shirt) & Body Mass (kg) & Chest \\ Age (years) & Gender (m/f) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of participant data that was recorded from measurements and optical and inertial motion capture.
## 4 Methodology
### Types of Experiments  
For each setup in Table 4, two experiments were performed:  
* the **free-input-layer** experiment, in which all the LeNet model vertices were free to be swapped or moved; and
* the **locked-input-layer** experiment, in which the LeNet input layer vertices were initially assigned to the same device and, then, they were locked, i.e., the input layer vertices could not be swapped or moved during the whole algorithm.  
The free-input-layer experiments allow all the vertices to freely move from one partition to the others, including the input layer vertices. These experiments represent situations in which the device that produces the input data is not able to process any part of the neural network and, thus, must send its data to nearby devices. In this case, we would have to add more communication to send the input data (the LeNet input layer) from the device that contains these data to the devices chosen by the approaches in this work. However, as the increased amount of transferred data involved in sending the input data to nearby devices is fixed, it does not need to be shown here. On the other hand, the locked-input-layer experiments represent situations in which the device that produces the input data can also perform some processing, therefore, no additional cost is involved in this case.  
Nine partitioning approaches were employed for each experiment listed in this subsection (for each setup and free and locked inputs). These approaches are explained in the next subsections and the corresponding visual partitionings are shown for the approaches that cause it to be necessary. It is worth noting that these visual partitionings are not considered the results of this paper and are shown here for clarification of the approaches.
## 4 Challenges and Future Directions
### Computing Power  
The diverse IoUT ecosystem comprises various devices with different computing capabilities. One of the major reasons for the integration of blockchain with the IoUT is due to the security it offers. The consensus mechanism in blockchain enables no third party involvements, thereby improving the security to a great extent. However, the blockchain-IoUT integration requires a high computational cost in order to make the PoW consensus algorithm work. This requires the miners to use computing systems with very high computational power. As most of the IoUT devices have low computational power, consensus algorithms such as PoW may be difficult to deploy. Hence, lightweight mechanisms such as the one proposed by [73] are required. Another possible solution would be to include devices with high computational power in the IoUT ecosystem.
# Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
###### Abstract  
Studying critical currents, critical temperatures, and critical fields carries substantial importance in the field of superconductivity. In this work, we study critical currents in the current-voltage characteristics of a diluted-square lattice on an Nb film. Our measurements are based on a commercially available Physical Properties Measurement System, which may prove time consuming and costly for repeated measurements for a wide range of parameters. We therefore propose a technique based on artificial neural networks to facilitate extrapolation of these curves for unforeseen values of temperature and magnetic fields. We demonstrate that our proposed algorithm predicts the curves with an immaculate precision and minimal overhead, which may as well be adopted for prediction in other types of regular and diluted lattices. In addition, we present a detailed comparison between three artificial neural networks architectures with respect to their prediction efficiency, computation time, and number of iterations to converge to an optimal solution.  
Keywords:superconducting film; diluted square lattice; Shapiro steps; prediction; artificial neural networks +
Footnote †: journal: Applied Sciences
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Sensor Location  
Wearable sensors eliminate the location limitation set by external sensors, but they yield another complication. Selecting locations on the subject's body to mount the sensors is a difficult decision, especially when the number of sensors available is limited. It is extremely important to decide optimal locations since the performance of the system and the data obtained depends on it. Studies using wearable sensors vary greatly in terms of body locations selected to mount sensors, however, the most common areas are the sternum, waist, lumbar, lower back, and different upper or lower extremity location such as wrists, thighs, ankle, heels, and feet. The selection of sensor locations will depend on the gait, balance, and RoM parameters to be measured. For static balance assessment the most common locations are lumbar, waist, and/or holding the device in the chest/sternum due to the capability of measuring trunk sway at these locations (Table 3). The most reliable step count comes from insole pressure sensor since it can detect the pressure applied to the sensor once a subject is performing a step [26; 42; 43; 71]. Studies that use IMUs for gait analysis tend to mount the devices on locations below the knee joint, such as feet, ankles and shanks, due to the high movements involved in those areas when a person is walking (Table 3).
## 5 Evaluation of OWTG Loadings under OC4 Phase I Load Cases
### Load Case 3.4a  
Similarly, in Load Case 3.4a, the OWTG is only investigated under the load of stochastic wind flow. In addition, the regulation of blade pitch angle is taken into consideration in this case and the initial conditions are set as: rotor speed of 9 rpm and blade pitch angle of 0\({}^{\circ}\). The analysis results of flapwise and edgewise shear force agree very well with the results by other OC4 research teams, as shown in Figure 14a,b respectively. The PDF of pitching moment at the blade root is shown in Figure 14c. The curve discrepancies are originated from the rate and magnitude of pitching action, namely, the variations of these curves are dependent on whether the rated rotor speed (12.1 rpm) is achieved or not. In this case, the employed wind flow data for every research team is the same. In other words, the simulation error between individual teams mainly comes from the used OWTG models. For example, the variation of these curves is highly determined by the position of blade mass center. There is an interesting issue reported by Rise DTU [29]. Since the PDF of wind flow data under different time step interval is the same, the precision of simulation results will not be affected by time step interval setting.  
Figure 14: Analysis Results for Load Case 3.4a of OC4 Phase I. (**a**) PDF- flapwise shear force at blade root; (**b**) PDF- edgewise shear force at blade root; (**c**) PDF- pitching moment at blade root.
## 3 Results
### Effect of the Ionic Wind  
Horizontal air velocity(u) was modified by varying the voltage applied to the fan. The vertical component (\(v\)) of the air velocity was measured, just below the point where the sample is placed, by using a hot wire anemometer and a metal mesh instead of the sample. By varying both the fan speed and the applied potential, measurements were taken. Experimental values are represented by the blue dots in Figure 12.  
With the fan switched off, or with a low speed, the experimental values of \(v\) follow the equation proposed by [PERSON] (Equation (5)). By merging Equation (5) with Equation (6), it is possible to obtain Equation (8), which has been used to fit the experimental data:  
\[v=\alpha\sqrt{V(V-V_{0})} \tag{8}\]  
where \(\alpha\), the pre-square root factor, considers all the constants, and it depends on the electrode geometry, the ion mobility, and the electrode gap. \(V_{0}\) is the corona onset voltage.  
It is possible to observe how the component of the vertical speed depends on the intensity of the potential applied to the discharge electrode, but also on the speed of the fan. Values obtained from the data fitting procedure are reported in Table 8. Two trends are observable: a slight increase in the value of \(\alpha\) and \(V_{0}\), the corona onset voltage, with increasing the horizontal air velocity. The latter one, in particular, increases by about 2 kV for an increase of the airspeed of 0.4 m/s.  
When the horizontal air velocity exceeds the 0.42 m/s, the transition between laminar and turbulent flow takes place. For flow over a flat plate, it is reported that transition occurs when Reynolds number should exceed 50,000. In the present case, the transition occurs earlier, at 11,200, because of the superficial irregularities presented by the metal grid. Similar behavior is expected to be present with the ACC, because of its high surface roughness. This aspect is also observed experimentally by the experimental points at a lower potential than the corona onset voltage (\(V<V_{0}\)),  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**D Band**} & \multicolumn{4}{c}{**G Band**} & \multirow{2}{*}{**I\({}_{\text{D}}\)/I\({}_{\text{G}}\)**} \\  & **Peak Center** & & & & & & \\ \hline
0 kV & 1345 & 199 & 1.58 & 1601 & 81 & 1.15 & 3.40 \\
7 kV & 1348 & 184 & 1.60 & 1611 & 75 & 1.17 & 3.32 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the peak fitting with Lorentzian function.  
Figure 11: Energy dispersive microanalysis (EDX) results for the ACC before and after the treatment.  
which possess a higher vertical velocity, _a priori_ the ionic wind formation, due to the turbulence eddy. In Figure 13 are reported the experimental data, at u = 1.80 m/s, and, on the background, the fit at u = 0, to highlight this difference.