esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Implementation of the App  
Figure 23 shows the high-level implementation design of the mobile app which distinguishes between two user types: \"general public\" and \"professionals\". The ability to do accurate borehole measurement, analysis and interpretation is the main difference between these two user categories. Users can see all the data collection fields available. Users who have registered as professionals will be able to capture all fields, but basic public users will only be able to submit data by going through a selection list that must be filled.  
The mobile phone app was tested using Kimbasseke available boreholes data that added up to 39, analysis was performed, and the map was generated illustrating the location of each borehole. Professionals, on the other hand, can export the submitted data in a preferable format such as CSV, Excel, KML, shapefile and file Geodatabase among others. The exported data from the laptop can be processed to conduct further analysis, data stored for future research and investigations. Figures 25 and 26 below shows a list of collected data and the generated map achieved in the app.  
Figure 22: Groundwater production map.  
With regards to the app interface features, Figure 23(a)-f below shows the running procedure of the app from a smart/mobile phone.  
Figure 23: App Implementation.  
Figure 24: (a–f) Running procedure of the developed app on a mobile phone. (Source: Authors).  
Furthermore, the professional interface also has statistical features such as bar chart and pie chart that summarise the collection process of data: for example, which country used the app recently, or which questions were answered the most, etc. The app includes a feature that allows users to verify each other's information. With registrations, each user is given a user rating and a one-star rating. The star rating of a particular user rises because of many verifications.  
Figure 26: Map generated using qGis (Professional interface). (Source: Authors).  
Figure 25: User Interface. (Source: Authors).  
The challenge with a borehole is that special equipment is required for measurement. The public, however, can obtain the GPS locations, take a photo and answer some basic questions in the form of a selection list or comment. Furthermore, this aligns public awareness framework, engaging population into decision-making, educating them of environmental issues and implementing them into the engineering world. Moreover, this will promote successful citizen science (see Figure 27); as such, together we can overcome global groundwater challenges by monitoring them regularly, by contributing positively to the future management of water resources, as well as land management. Finally, such information will empower citizens, allowing them to address local districts and stakeholders, and from there, further analysis/decision might be taken.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 2 Related Works  
Since tracking the MPP can effectively optimize PV power generation, a considerable amount of studies have been published on MPPT methods. For example, various MPPT methods can, in general, be divided into P&O [22; 23; 24], incremental conductance (IC) [25; 26; 27], mountain climbing [28; 29; 30], FL [31], GA [27], and ANN [32].  
To illustrate, [PERSON] et al. [33] proposed an IC algorithm with an integral regulator to control the duty cycle of the boost converter. The method they offered successfully provided a stable MPP and could track changing atmospheric conditions more quickly. [PERSON] et al. [34] reduced the step size of the traditional FL method and increased the control rules to reduce search loss and MPP oscillation and improve control stability. These methods have many achievements, but their limitations are apparent, such as requiring a large number of sensors, thus leading to high complexity and cost.  
Therefore, interest has been generated in ANN technology. The ability of ANNs to predict unknown parameters inspired its application in MPP tracking. NNs can be trained offline for nonlinear mapping and then can be used effectively in an online environment [35]. To overcome the inherent shortcomings of complex ANN operations, researchers have made many optimizations, e.g., [PERSON] et al. [36] introduced MPPT in the case of load changes, and the NN can still provide the correct duty cycle to optimize the PV system. [PERSON], [PERSON] et al. [37] comparatively indicated that ANN methods have smaller steady-state errors but a higher training demand. [PERSON] et al. [38] proposed a new P&O-based NN method, which improves the tracking accuracy, response time and overshoot of MPP. [PERSON] et al. [39] proposed a single-sensor NN method that can effectively track the MPP, thereby reducing the cost and complexity of the PV MPP controller. However, the selection of network parameters in these methods is too subjective and unclear, making it fluctuate in training and challenging to converge.  
In summary, whilst a considerable body of research has been carried out on MPPT, much less fits the discrete-time and rapid-response characteristics. In addition, it seems to be a common problem that existing studies focus on mathematical modeling but neglect the fully online process. When controlling according to the above methods, three challenges in terms of notable oscillation for MPP, insufficient accuracy for tracking, and slow convergence for controlling, are increasingly apparent.  
Particularly driven by diverse and emerging energy demands, conventional fuels are being renovated to renewable energy for sustainability. Hence, it is an urgent need to study and improve the tracking and controlling strategies to achieve high utilization of solar power, thus effectively promoting and rationally guiding the development of the vehicle industry.
# Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control
## 3 Validation Experiment  
To verify and validate the proposed method, this study conducted an experiment to capture real human gait as the inputs for the simulation and controller. To capture the human subjects' gaits, a motion capturing system, NDI Optotrak Certus, was used. A motion capture system can be divided into several types, including acoustic type, optical type, electromagnetic type, and inertial type [22; 23]. This study used the NDI Optotrak Certus optical 3D motion measurement system. The optical 3D motion capture system has several advantages: (1) it is easy to use and is able to capture a large range of activities without cable and mechanical restrictions; (2) it has a high sampling rate, which can meet the needs of animation, sports, and medical motion measurements; (3) its markers are cheap and convenient to expand. The motion capture system is shown in Figure 4a and it has a precisely calibrated coordinate system and measurement space, as shown in Figure 4b.  
Figure 4: Gait capture system (**a**) and its coordinate system and measurement space (**b**).  
Figure 3: The simulation block diagram for the AILC-based gait tracking.  
The position sensor is composed of three CCD (charge coupled device) lenses, which can capture the infrared light emitted by the markers and measure their precise positions. The system control unit (SCU) is a processing device that controls the position sensor and the attached Strobers and streams data to the host computer. Markers are infrared light emitting diodes that are tracked by the position sensor when they are activated by the Strober. The Strober is a device controlled by, and connected to, the SCU, responsible for activating and deactivating markers.  
Before the experiment, the physiological data of the subjects, such as gender, quality, height, bone length, and pelvic width were reported. Five markers were attached to the pelvic, hip, knee, ankle, and toe, respectively. Markers were indexed with \(i(i=1,\,2,\ldots,5)\), as shown in Figure 5 (left). Then, the subjects were allowed to walk around the room for a while to adapt to the test environment. During the experiment, the gait on the flat ground was collected. The angles between the markers were recorded. The scene during the experiment is shown in Figure 5 (right). The data collection time of each trial was 15 s.  
Figure 6 is the flow chart of the experiment, including gait measurement and trajectory tracking modules. The captured gait information was transported into MATLAB/Simulink in real-time for the trajectory tracking simulation.  
Figure 5: The layout diagram of markers (**left**) and gait capture scene (**right**).  
Figure 6: Flow chart of gait measurement and exoskeleton trajectory tracking.
# Cell Therapy in the Treatment of Coronary Heart Disease
## 4 Pluripotent Stem Cells for Cardiac Cell Therapy
### Induced Pluripotent Stem Cells  
In 2006, [PERSON] and [PERSON] demonstrated the ability to reprogram differentiated somatic cells into induced pluripotent stem cells (iPSCs) [118]. This groundbreaking discovery provides a solution to the problems that arise when using ESCs. First, there is no need for the use of human embryos, and second, there are no problems with rejection and histocompatibility. Cells formed after differentiating modified iPSCs can be used for patient-specific therapy. Directed differentiation of induced pluripotent stem cells (iPSCs) into cardiomyocytes may be achieved through BMP, TGFb/activin/NODAL, Wnt, and FGF signaling pathways [119]. Although iPSCs-derived cardiomyocytes have an immature and fetal-like phenotype, they exhibit a combination of atrial, ventricular, and nodal-like electrophysiological properties. Differentiation protocols utilizing a blend of chemicals and growth factors have resulted in the creation of subtype-specific cardiomyocytes [120] (Figure 2).  
The resulting cells can be used to restore the depleted contractile components of the myocardium [121,122]. Upon differentiation, cardiomyocytes exhibit contractility and excitability in response to sympathetic and parasympathetic nervous system signals; however, there is currently no optimal approach for the production and transplantation of fully functional cardiac tissue capable of generating synchronized contractions and rhythmic activity from in vitro cultured cells [123].  
Some studies have demonstrated improvements in cardiac function and a reduction in fibrosis formation following implantation of iPSCs-derived cardiovascular progenitor cells or cardiomyocytes in acute and subacute stages of MI in rodent models [124, 125, 126, 127]. The effectiveness of these cells in improving cardiac function, revascularization, and/or remusularization of infarcted hearts is further confirmed in non-human primates [128, 129, 130] and pigs [131, 132]. The safety of early clinical trials using human iPSCs-derived cardiomyocytes in patients with severe ischemic cardiomyopathy has been reported [117, 133].
# Simulation Study on Ground Vibration Reduction Measures of the Elevated Subway Line
## 2 Numerical Model
### Vibration Source Model
The finite element model of the wheelset is shown in Figure 3a. The cell type is a 6-surface solid cell, and the number of cells is 30,524. The frequency range of the modal calculation of the wheelset is 0-5000 Hz. The rails are established according to the parameters of the UIC54 rail, and in order to improve the computational efficiency, the rail is regarded as a flexible body only in the bridge section of the multi-body system, and the rail outside of the bridge is regarded as a rigid body. The rail flexible body finite element model is shown in Figure 3b, and the cell type is a 6-surface solid cell. The frequency range of the rail modal calculation is 0-5000 Hz. The flexible body finite element model of the U-type bridge is shown in Figure 3c. The cell type is a 6-surface solid cell, and the number of cells is 54,997. The length of the U-type bridge is 35 m, and it is for two-line traveling. The pier flexible body finite element model is shown in Figure 3d, the cell type is a 6-surface solid cell, and the number of cells is 14,219. Flexible bodies are connected to each other using force elements. The rails are connected to the U-bridge using force elements to simulate fasteners. The pier is connected to the U-bridge using force elements to simulate bridge bearings. The pier is solidly connected to the earth, and together they form a multi-flexible body system. The model schematic is shown in Figure 4.  
Figure 4: Schematic diagram of model connection. Figure 3: Flexible body components: (a) wheelset; (b) rail; (c) U-type bridge; (d) pier.
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 5 Results and Discussion
### Time Measurements and Quality of Reconstructions
In summary, for non-parallel implementations on CPU, the periodic and conventional DRT have a similar performance to FMT and were much faster than PPFRT. Again, if a forward and backward pair is needed, the PPFRT will be preferable to the FMT, which only excels in the backward path. When we port multi-scale methods to the Halide [30] parallel language, -denoted by DRTpar, CDRTpar and PDRTpar-, then our proposed methods achieve a result even better than FMT. Notice that FMT cannot be further optimised, as it uses the FFTW [31] library underneath, and it has already minimized the angular sampling density (\(3N/2\) vs. \(4N\)). Regarding the behaviour between our proposed methods and the conventional DRT, in these parallel versions, both periodic and central DRT reduce even more than the 1:2 factor suggested by the number of operations. We think this could be a combined effect of the minor memory footprint and a better coherence of memory accesses. Encouraged by these results, we will propose, in a later section, to take the central DRT to a mobile phone, in order to test whether we can support video acquisition rates, 30 fps. That implementation will be written in OpenCL language and run on the GPU of the mobile phone. In Figure 15, we show how the multi-scale DRT transforms behave when executed on a PC's GPU, again, programmed with Halide. The CPU versions reported are those parallelised, and they were already faster than Fourier-based Radon transforms on CPU. Now, porting them to GPU starts to bring benefits for sizes larger than 1024, as, less than that, the dominant times come from data movement and synchronizing between CPU and GPU. Basically, on a powerful GPU, we could sustain, with the proposed methods, 30 fps acquisition rates, even on 4K video streams. As a final note, regarding the backward transforms, the periodic DRT results are completely analogous to those of conventional DRT, in terms of quality. Central DRT has no inverse. For a more detailed assessment on how conventional DRT, and, hence,  
Figure 14: Log–log representation of execution times (in milliseconds) for different algorithms and sizes of input on the same CPU platform. DRT stands for conventional DRT, whilst CDRT and PDRT are our proposed central and periodic DRT. FMT stands for fast Mojette transform. PPFRT stands for pseudo-polar Fourier-based Radon transform; _par_ stands for parallel versions. periodic DRT, compares with the Fourier-based transform and the Mojette transform, see [PERSON] et al. (2018).
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 6 Evaluation of Authentication Accuracy
### Accuracy-Related Parameters  
As for the authentication accuracy, we refer to the error rate as the false acceptation rate and false rejection rate. After the setup is fixed, we consider that there are two related system parameters: the number of the total provers \(M\) and the threshold \(h\). We consider parameter \(M\) to be the maximum number of authentication trials that enables an authentication system to operate without producing false errors. \(M\) is the number of devices that can be used in the system. The error rate is likely to be increased along the increase in \(M\).
## 4 Discussion
### Node Transmitting Power  
As shown in Figure 4, the initial power of the transmitting node was set to zero. With an increase in the number of iterations to the third iteration, the transmission powers of nodes \(S_{1}\) and \(S_{3}\) stabilized at fixed values of 8.9 W and 7.2 W, respectively. This is because node \(S_{1}\) is located in the central frequency band, and its Nash equilibrium value of transmission power exceeds \(P_{lim1}\) due to the magnitude of its channel state information parameters. However, to protect marine mammals, the transmission power of \(S_{1}\) can only be limited to the bio-friendly power upper limit \(P_{lim1}\). Similarly, node \(S_{3}\) is positioned in a non-central frequency band, and its Nash equilibrium value of transmission power exceeds \(P_{lim2}\), causing its transmission power value to be limited to \(P_{lim2}\).  
The transmission power of node \(S_{2}\) stabilized at 8.9W between the third and ninth iterations. This is due to Formula (22) in this paper's algorithm, where when \(t<t_{1}\), the computed power (P\({}_{k}\)) reaches a Nash equilibrium point, denoted as P\({}_{1}\). When \(t\geq t_{1}\), the value of \(P_{k}\) continues to increase until it exceeds \(P_{lim1}\). However, node \(S_{2}\) is situated in the central frequency band where marine mammals localize. To avoid disrupting the normal life of marine mammals, the value of \(P_{1}\) must be lower than the restricted power \(P_{lim1}\). Consequently, the transmission power of node \(S_{2}\) stabilizes at the size of \(P_{lim1}\), which is 8.9 W. As the number of iterations increases to the tenth iteration, due to energy depletion and changes in channel state information, the value of the transmission power of \(P_{k}\) gradually decreases below \(P_{lim1}\). At this point, the variable expression for \(P_{k}\), denoted as \(P_{1}\), remains the same. Similarly, when \(t<t_{2}\), the value of \(P_{k}\) obtained from Formula (22) at that time is abbreviated as \(P_{2}\). The transmission power of node \(S_{4}\) stabilizes at the value of \(P_{lim2}\), which is 7.2 W when \(t\geq t_{2}\). As the number of iterations increases to the eighth iteration, the transmission power of node \(S_{4}\) starts to decrease and stabilizes at the Nash equilibrium value \(P_{2}\).  
Figure 4: Convergence curve of node transmitting power.  
Figure 3: Mammal and sensor node distribution.
## 2 Materials and Methods
### Design of Experiments and Statistical Analysis  
For the selection of the lignin isolation variables and their ranges, we first performed an initial screening with different reagent combinations (dioxane: acetic acid, GVL acetic: acid and GVL: dioxane all catalyzed by different HCl doses) under various temperatures and time intervals with 10% solids loading. These combinations then were performed and their lignin yield, G units' values, and molecular weights were analyzed accordingly. Keeping in view the lignin quality with highest G units, a combination of dioxane: acetic acid and HCl was chosen. For the selection of the ranges of lignin isolation variables, we selected the center point values as 10% solids loading, 48 h of time, and 10 mL of catalyst dose based on our preliminary trials and their ranges were defined around this center point. These three factors (time, solids loading, catalyst dose), specific to the lignin isolation, play a fundamental role [33]. Solids loading is important in a sense that high solids loading is recommended for the desired economic operation of the biobased refineries [34]. The catalyst dose and time is also crucial as they play an important role of the catalyst mass transport into the biomass cell walls [35]. Talking specifically for the case of the lignin isolation, room temperature is important as it somehow mimics the classical proto lignin isolation process i.e., milled wood lignin [36].  
After selecting these variables and their ranges, to further sort out the optimum experimental conditions, response surface methodology (RSM) was used with lignin yield as response (\(\Upsilon\)). Three variables solids loading (\(\chi_{1}\)), catalyst dose (\(\chi_{2}\)), and time (\(\chi_{3}\)) were optimized and were tested in their ranges of 5-15% wt. for solids loading, 5-15 mL for HCl concentrations, and 24-72 h for time. Among from the experimental designs a Box-Behnken Design (BBD) with three center points was chosen to design the experiments. Data analysis was done with the help of Design Expert software (Version 8.0, State-Ease, Inc., USA). Lignin yield was calculated on the basis of initial weigh of lignin in pine saw dust.
# SMS: A Secure Healthcare Model for Smart Cities
## 2 Need for Transition from Cloud to Edge in a Smart City Ecosystem
### Security Vulnerabilities in Mobile Edge Computing  
Use of edge computing helps to realize the full potential of IoT in the best possible way. With edge computing, data processing and analysis tasks are brought much closer to the data source. Some major security vulnerabilities and challenge areas with edge computing are [10; 11; 17; 18; 19; 20]:  
**Insecure IoT Devices**: The efficiency and reliability of edge computing are highly dependent on the \"security\" and \"privacy\" features of the smart devices. The IoT devices and the data generated by them are always at a high risk. Any compromised device might lead to a bad decision. Most of the IoT device manufacturers are mainly concerned about the working of the device and its functionality. There are hardly any inherent security features included within the IoT devices as it is of the least concern [10; 11; 17; 18; 19; 20].  
**Identification of Usable Data**: Although processing data at the device level helps to improve the latency and response time, it might lead to incomplete data at the main information repository. Due to the limited processing and storage capabilities at the edge, there are chances that an important portion of the data may be of greater usage if left out [10; 11; 17; 18; 19; 20; 21].  
**Lack of Unified Security Standards**: The different IoT devices are mostly of different make and follow their specific syntax, semantics, and operating standards. Up until now, there is no unified security standard available that is universally applicable across all categories of IoT devices. Furthermore, the weakly coupled edge devices on the network are vulnerable to physical and cyber-attacks, making privacy preservation a difficult task [10; 11; 17; 18; 19; 20; 21].  
**Distributed Denial of Service (DDoS) Attacks**: DDOS attacks constitute the most widely reported security threats to mobile edge computing. The attackers flood the devices with data packets through compromised devices, which then deny the services to the legitimate users. In addition, a compromised edge device may restrict the normal services by targeting the edge server with a DOS attack [10; 11; 17; 18; 19; 20; 21].  
**Side Channel Attacks**: The user's general data and information patterns can be accessed and collected overtime. This information may not be sensitive and private but, over time, it can be used to profile the user and get ahold of the user's private data by linking it through side channels [10; 11; 17; 18; 19; 20; 21].  
**Malware Attacks**: The entire IoT system can be corrupted with malware through a compromised edge device. Further, a compromised edge device can be used to bypass the whole authentication and authorization process to obtain access of the edge server [10; 11; 17; 18; 19; 20; 21].