esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Measurements  
#### 2.2.1 Motion Analysis System  
A six-infrared-camera motion analysis system (Qualisys AB, Goteborg, Sweden) was used to analyze three-dimensional motion, with a sampling rate of 100 Hz [14]. Reflective markers were attached to anatomical landmarks to create a lower extremity model. The Anatomical markers were placed on the anterior superior iliac spine, posterior superior iliac spine, thigh, lateral epicondyle of the femur, medial epicondyle of the femur, shank, lateral malleolus, medial malleolus, calcaneus, and 1 st, 2 nd, and 5 th metatarsal joints according to the CAST model [15]. Static trials were used to define the transformation between the marker arrays and the segment coordinate system.  
Visual 3D (C-motion, Inc., Germantown, MD, USA) was used to analyze the spatiotemporal (gait velocity, stride length, normalized stride length, GT time, and GT step length) and kinematic (range of motion (ROM) of the hip, knee, and ankle joints) parameters [16]. The trajectories of the reflective markers were filtered using a second-order low-pass Butterworth filter with a cut-off frequency of 6 Hz.
# Renal Acoustic Radiation Force Impulse Elastography in Hypertensive Nephroangiosclerosis Patients
###### Abstract  
**Objective:** Hypertensive nephroangiosclerosis (HN) represents the second most common cause of chronic kidney disease. Kidney damage secondary to high blood pressure favors the appearance of serum and urinary changes, but also imaging, highlighted by ultrasonography (B-mode, Doppler, Acoustic Radiation Force Impulse Elastography). Acoustic Radiation Force Impulse Elastography (ARFI) represents a new imagistic method which characterizes renal stiffness in the form of shear wave velocity (SWV). **Aim:** This study aims to investigate renal stiffness in HN patients, and to assess the correlations between it and urinary albumin/creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), and intrarenal resistive index (RRI). **Material and Methods:** This cross-sectional study was performed on a group of 80 HN patients and 50 healthy, sex and age-matched, as controls. UACR (urinary immunodubridimetry), eGFR (Jaffe method), RRI, and renal SWV (Siemens Acuson 2000) were determined in all patients and controls. Data were expressed as mean \(\pm\) standard deviation. Statistical analysis was done by means Pearson's test and t-Student test, \(p\) values of less than 0.05 were considered statistically significant. **Results:** UACR, eGFR, RRI and SWV showed statistically significant differences between the HN patients and controls (\(p<0.0001\)). In the hypertensive patients group, statistically significant correlations were observed between the SWV and UACR (\(r=-0.7633,p<0.00001\)), eGFR (\(r=0.7822,p=0.00001\)), and RRI (\(r=-0.7978,p=0.00001\)). **Conclusions:** Kidney sonelastography characterizes imagistically the existence of intrarenal lesions associated with essential hypertension, offering a new diagnosis method for these patients.  
acoustic radiation force impulse elastography; hypertensive nephroangiosclerosis; intracranial resistive index  
1
## 3 Results and Discussion  
### Unrestrained Concentric Dent-Crack Defects  
Figure 6 shows the Von Mises stress distribution at different stages of crack propagation through the wall of the specimen having a 60 mm \(\times\) 4.3 mm crack at the center of a 4% OD deep unrestrained dent formed at 0.3 P\({}_{\mathrm{y}}\) denting pressure. The crack does not propagate during dent formation but stresses at the tip are higher than the yield stress of the material (Figure 6b). When the internal pressure is applied, the dent rebounds to recover the circular pipe geometry before the crack begins to propagate through the wall as shown in Figure 6c. Fracture occurs with visible upward displacement of the internal surface of the pipe (Figure 6d,e) due to plasticization under sustained pressure, typical in specimens with deep cracks [24; 26].  
Figure 5: Load-displacement curve for application and release of indenter.  
Table 2 shows the effect of varying the defect parameters on the burst pressure of a pipe with unrestrained concentric dent-crack defect, considering crack depths of 1.7 mm (0.2 t) and 4.3 mm (0.5 t). Internal pressure (denting pressure) ranging from 0% to 80% of yield pressure, \(\mathrm{P_{y}}\) (\(\mathrm{P_{y}}=\) 12.67 MPa), was maintained in the pipe during dent formation as crack length and dent depths were varied.  
Denting the pipe at a pressure of 0.8\(\mathrm{P_{y}}\) (=10.13 MPa) caused cracks to propagate in the circumferential direction during formation of the unrestrained dent-crack because the total bending and membrane strains normal to the circumferential plane surpassed the threshold fracture strain in the process. The risk of circumferential propagation increased with dent depth and denting pressure. It was impossible to dent the pipe to 6% OD depth at 0.3 \(\mathrm{P_{y}}\) denting pressure with 200 mm long crack. Similarly, creating 6%OD deep dents at 0.5 Py denting pressure was generally problematic for all crack lengths because large strains are involved.  
Figure 6: Distribution of von Mises stress in pipe wall at various stages of propagation of a crack inside unrestrained dent: (**a**) Model of pipe with unrestrained concentric dent-crack; (**b**) Before applying pressure; (**c**) At start of crack propagation; (**d**) After through wall crack emerges inside pipe; (**e**) When crack has fully opened and fracture is complete.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Benefits to IoT Application Areas
#### Telemedicine  
Smart sensors are deployed for telemedicine purposes to provide remote healthcare and medical services to outpatients. Telemedicine is driven by the need to provide realtime access to vital signs such as temperature, blood pressure, oxygen levels, and electrocardiogram signals of patients. This may involve implanting sensors in patients so that signals obtained via these sensors are communicated to a remote server situated at the doctor's station. CR-LPWAN systems can be deployed to improve access to spectrum in telemedicine applications. Since vitals are critical datasets, it is essential to avoid errors and to guarantee high speed transmission. CR-LPWAN is well suited in such a case to provide low latency transmission in appropriate white spaces. Furthermore, in case of poor weather conditions causing increased error rates, CR allows LPWAN technologies to adapt modulation schemes to the appropriate band for improved performance.  
In [93], an infrastructure-based CRN was introduced for telemedicine, wherein CR base stations were deployed to sense for free bands and forward such sensed data to a remote healthcare station. Support for real-time periodic telemonitoring of network traffic was also introduced. A CR system was developed in [94] for e-health applications in a hospital environment. The proposed system aimed at protecting medical devices from harmful interference by adjusting device transmission power to reduce electromagnetic interference. CR has been widely applied in wireless body area networks (WBAN) to improve medical services. For example, in [95], a viable CR architecture for medical body area networks was proposed based on ultra wideband radio technology. Further discussion on the use of CR in medical IoT applications can be found in [28; 96; 97; 98]. Such classic examples indicate that CR in LPWAN is a promising venture for IoT applications.
## 4 Experimental Results and Performance Evaluations
### Analysis of Power Loss  
The high-frequency soft-switching power losses of the proposed inverter are extremely low. IGBT fall current and tail current losses are almost zero due to ZCS commutation. Additionally, \(L_{1}\) and snubber inductors \(L_{S1}\) and \(L_{S2}\) conduction losses are negligible due to litz wire usage. The IGBT conduction losses of the proposed high-frequency inverter are the main power losses. The IGBT voltage and current characteristics curves are used for conduction power losses calculations. Figure 10 shows the current and voltage characteristics of the IGBT and that its antiparallel diode has constant temperature conditions. Experimental results yielded the switching characteristics curves. Equations (13) and (14) represent the characteristic curves, which approximate quadratic polynomials in the low forward current area and linear functions in the high forward current area.  
\[\left\{\begin{array}{ll}v_{CE}=-0.0015\,{i_{\mathrm{C}}}^{2}+0.0616\,{i_{ \mathrm{C}}}+0.904&({i_{\mathrm{C}}}<15\mathrm{A})\\ v_{CE}=0.0185\,{i_{\mathrm{C}}}+1.223&({i_{\mathrm{C}}}\geq 15\mathrm{A})\end{array}\right. \tag{13}\]  
\[\left\{\begin{array}{ll}v_{F}=-0.00141\,{i_{\mathrm{F}}}^{2}+0.0477\,{i_{F}} +0.859&({i_{\mathrm{F}}}<12\mathrm{A})\\ v_{F}=0.0152\,{i_{\mathrm{F}}}+1.05&({i_{\mathrm{F}}}\geq 12\mathrm{A})\end{array}\right. \tag{14}\]  
The conduction power loss calculations of the semiconductor are introduced into the simulation algorithm by these equations. Using the above equations in a circuit simulation, the conduction power losses of the diodes and IGETs can be calculated. Additionally, using Equation (15) and the experimental results, other stray power losses of the reverse recovery losses of the diodes and switching power losses of the IGBT can be estimated separately from the inverter total power losses [29; 30]. The power losses analysis results are given in Figure 11.  
\[\left(\text{Total power losses}\right)-\left(\text{Conduction power loss}\right)= \left(\text{Other stray power loss}\right) \tag{15}\]  
In this result, it is obvious that the \(D_{\text{PDM}}\) ratio of this PDM control scheme increases proportionately with the power losses increase. When the printing mode \(D_{\text{PDM}}\) ratio reaches 1.0 (heaviest load), only 20% of the total power losses are stray power losses and 80% are conduction power losses. This results from the ZCS soft commutation display low power losses, as seen in Figure 11.  
Figure 10: Current and voltage characteristics of IGBT and diode. (**a**) IGBT for \(S_{1}\) and \(S_{2}\). (**b**) Diode for \(D_{S1}\) and \(D_{S2}\).
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 4 Analysis of Authentication Factors
### Emergence of Multi-Factor Authentication  
Although any number of factors discussed above may be used as an authentication factor, only a few unique and practical factors are generally used for authentication in the scheme of MFA (using several factors simultaneously). Some are, undoubtedly, more powerful and complicated than others. All of them, however, are more secure and protected than password-only authentication.  
MFA's purpose is to verify genuine users to safeguard sensitive information by offering a layered defense and making it more difficult for unauthorized persons to acquire access. The advantage of using MFA is that it provides a more secure approach to authenticating users. Any factor that has been compromised or exposed to a data breach is no longer usable; nevertheless, the system can continue to provide authentication services using the non-compromised authentication factors. To obtain access to a target system, attackers must overcome several obstacles [87].  
The concept of MFA started with the physical verification of the user's identity card and verifying the user's photo with the corresponding authenticated photo present on the officially issued identity card. For example, when a person enters a bank, they are asked to show a photo ID. The person is then identified and authenticated based on their biometric parameters. That is, the bank matches the face of the person with the face on the ID card. It is a form of facial recognition and can be analogous to today's biometric authentication method. To generalize, this combined effect of more than one authentication factor and their unified and simultaneous utilization for user verification is represented in Equation (2).  
\[F(x)=f1(x1)\cup f2(x2)\bigg{\{}\begin{array}{ll}1,&\text{if}\,f1(x1)\cup g1(y1 ),\text{TRUE}\\ 0,&\text{otherwise.}\end{array} \tag{2}\]  
\(F(x)\) is the Multi-factor System Authentication Function over user \(x\) with a resultant Boolean Value. \(f1(x1)\) is the First Authentication Factor Function with input value \(x1\); \(g1(y1)\) is the Second Authentication Factor Function with the input value \(y1\); and so on, for its applicability to third and subsequent factors for authentication.
## 3 Data
### Self-Calibrating Palmer Drought Severity Index (SCPDSI) Data  
SCPDSI is an improved version of PDSI [37], with the aim of making it more suitable for the drought evaluation on a global scale. Similar to PDSI, SCPDSI was calculated from the PPT, temperature and the parameters related to the soil/surface characteristics [38; 39]. In our study, the monthly \(0.5^{\circ}\times\) 0.5\({}^{\circ}\) global SCPDSI gridded data were derived from the Climate Research Unit at University of East Anglia, spanning the period from January 2003 to December 2020.
# An Uncertainty-Driven Proactive Self-Healing Model for
## 2 Preliminaries and Scenario Description
### Task Management  
The arrival of tasks in \(G_{i}\) is assumed to be governed by a Poisson process with rate \(\lambda\). Based on this assumption, we can easily obtain the number of tasks \(Z(t)\) that arrive during the interval \((0,t]\) that follows a Poisson distribution, described by the corresponding Probability Mass Function (PMF) [19]:  
\[P[Z(t)=k]=\frac{e^{-\lambda t}(\lambda t)^{k}}{k!},\quad k=0,1,2,... \tag{1}\]  
The inter-arrival time instances follow an exponential distribution with the respective Probability Density Function (PDF) given by the following equation [19].  
\[f_{1}(x;\lambda)=\lambda e^{-\lambda x},\quad x,\lambda>0 \tag{2}\]  
where \(\lambda\) represents the rate of arrivals. Under the same rationale, we consider that the time instances depicting the conclusion of the execution of tasks are governed by an exponential distribution with the following PDF [19]:  
\[f_{5}(x;\mu)=\mu e^{-\mu x},\quad x,\mu>0 \tag{3}\]  
where \(\mu\) depicts the service rate of \(G_{i}\). In the above-discussed PDFs, we adopt an index (I or S) to depict the distribution that corresponds to the arrival or the execution of tasks, respectively. Based on the previous assumptions, we can easily observe that the average inter-arrival time and the average service time is given by the following equations [19]:  
\[\Omega[\text{I}]=\frac{1}{\lambda} \tag{4}\]  
\[\Omega[\text{S}]=\frac{1}{\mu} \tag{5}\]  
Equation (6), represents the traffic intensity in an M/M/1/K system given by the ratio of the arrival rate, compared to the service rate. The following equation holds true [18,19]:  
\[\rho=\frac{\lambda}{\mu} \tag{6}\]  
With simple calculations, we can deliver the probability of having \(v\) tasks in \(G_{i}\)'s local queue, as the following equation expresses [19]:  
\[P_{v}=\begin{cases}\frac{(1-\rho)\rho^{v}}{1-\rho^{k+1}},\quad\rho\
eq 1,\quad 1 \leq v\leq K\\ \\ \frac{1}{K+1},\quad\rho=1,\quad 1\leq v\leq K\end{cases} \tag{7}\]  
If we assume that \(\theta\) tasks should be at most in the local queue, we can easily calculate the probability of overloading, i.e., \(P_{\delta}\), as follows [19]:  
\[P_{\delta}=\sum_{v=\theta}^{K}P_{v} \tag{8}\]  
\(\theta\) is the threshold for considering \(G_{i}\) as overloaded and it should start offloading the upcoming tasks to peer nodes. For instance, offloading can be performed through the adoption of various techniques such as [20, 21, 22, 23, 24]. With the proposed model, we are able to support \(G_{i}\) to maintain a free space for high-priority tasks, based on a proactive approach [25]. We have to notice that the processes upon which the offloading decisions and the selection of the appropriate peers to host the offloaded tasks take place lies beyond the scope of this paper.
# A Systematic Literature Review on Military Software Defined Networks
## 5 Additional Contributions
### Underwater Acoustic Networks Applications  
Underwater acoustic networking (UAN) is a technology that enables new SDN applications for both commercial and military use. This includes underwater surveillance and data collection of ocean characteristics. Normally, the acoustic channel for underwater communication is characterized by noise, multipath, delay and path loss, while SDN can enable a more intelligent routing mechanism in such environments. This is reflected by two UAN papers [146; 147] that adopt the principles from OpenFlow to underwater networks. The authors present two examples of SDN architectures in order to achieve a more flexible routing, based on OpenFlow principles in the UAN domain. [PERSON] et al. [148] also emphasized the need for low energy consuming sensors and presented an architecture of an underwater sensor network with hardware sensors running on a hardware chip with customized SDN software.
## 2 Materials and Methods
### Classification of LULC and Assessing Its Accuracy  
Land cover classes, including vegetation cover (mainly date palm plantation trees), built-up cover, bare lands, and sand dunes, were extracted from each individual Landsat image by employing a widely utilized maximum likelihood algorithm, which is a supervised classification method [38]. The (ENVI) version 5.1 software environment was utilized for visualizing and processing the images. The classification process was achieved by creating training samples (spectral signatures) according to [PERSON]'s classification Level-1 scheme [39], selected visually from the images. A minimum of six or seven training areas containing at least 150 pixels were selected from each LULC class. After this, the image classification was performed and then output maps were evaluated and exported to the ArcGIS 10.2 software program for further operations, which entailed the calculation of areas and thematic layers preparation.  
To validate the LULC map accuracy, we used the overall accuracy and kappa coefficient approaches. Initially, 22 validation points were collected randomly from each category. Thus, in total, 87 points were collected and overlaid on the Google Earth software's base map, where every LULC class was verified by superimposing the Google Earth satellite image, taken on May 2020. Second, a confusion matrix was built to assess the obtained LULC maps. Finally, previous studies have suggested the use of quantity disagreement and allocation disagreement to assess the classification accuracy [40; 41]. However, in this study, the overall accuracy, producer accuracy (PA), and use accuracy (UA), in addition to errors of omission and commission, were used as previously described [42] to assess the classification accuracy.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 2 Materials and Methods
### Nanomaterial Preparation
#### 2.3. Modification of SPCE with MoS\({}_{2}\)  
The screen-printed carbon electrode surface (SPCE) was modified with LA-MoS\({}_{2}\)nano-sheets by electrochemical deposition. The SPCE was covered with 50 \(\upmu\)L of aqueous solution prepared with 5 mg/mL of LA-MoS\({}_{2}\) nanosheets dispersed under sonication in 0.5 M LiClO\({}_{4}\) solution. Then the potential was swept from 0 to \(-\)0.95 V vs. Ag/AgCl at scan rate of 50 mV s\({}^{-1}\) for 10 cycles. After electrodeposition, the modified electrode was rinsed several times with deionized water and dried under a gentle flux of N\({}_{2}\).
## 4 Design Analysis
### Data Analysis
#### 4.4.2 Indexing File  
The file includes the timestamp where the actual data file has been created, along with the collection interval. Previous missions MySat-1 and E-st@r-II followed a similar data packet structure, where data retrieved was packet based; in other words, telemetry data was collected at certain intervals and stored in one packet. For instance, the packet size was 113 and 35 bytes long for E-st@r-II and MySat-1, respectively [5]. Another data structure was implemented by AAUSats, where the packet format is in two packet length, 25 or 86 bytes long and distinguished by a one byte hexadecimal indicating a long or short packet [6].  
The advantage of the data structure design is to overcome difficulties related to researching the data needed and mapping them to the actual collection time. The telemetry data is either transmitted as beacon or as requested by the ground station. The beacons include the CubeSat's call sign, a counter for the OBC reset, the OBC uptime, OBC flags, and one general housekeeping packet, having a total of 193 bytes.  
The data analysis considered the communication with a ground station located in Abu Dhabi, with minimum and average access time to simulate the minimum and average data budget to ensure the total generated data of 1.04 MB/day is received by the ground station. Table 5 shows a summary of the telemetry calculations with different UHF downlink rates. It is noticeable that the total generated data cannot be transmitted fully in these two cases. However, the data structure mentioned earlier allows data transmission based on priority and makes it convenient to request certain data from any interval.  
\begin{table}
\begin{tabular}{l l c c c} \hline  & **Power Consumption (mW)** & \begin{tabular}{c} **Normal Mode Duty** \\ **Cycle (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Target Pointing Mode** \\ **Duty Cycle (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Safe Mode Duty** \\ **Cycle (\%)** \\ \end{tabular} \\ \hline EPS & 165.00 & 100 & 100 & 100 \\ \hline OBC & 363.00 & 100 & 100 & 100 \\ \hline \multirow{6}{*}{ADCS} & Fine sun sensors & 87.120 & 100 & 100 & 0 \\ \cline{2-5}  & GPS antenna & 16.500 & 100 & 100 & 0 \\ \cline{2-5}  & GPS receiver & 145.20 & 100 & 100 & 0 \\ \cline{2-5}  & M6P & & & & \\ \cline{2-5}  & magnetorquers & 935.00 & 0 & 8.60 & 0 \\ \cline{2-5}  & (Rod) & & & & \\ \cline{2-5}  & X, Y-axis & 440.00 & 0 & 8.60 & 0 \\ \cline{2-5}  & magnetorquers & & & & \\ \cline{2-5}  & \(Z\)-axis & 429.00 & 0 & 8.60 & 0 \\ \cline{2-5}  & 4 reaction wheel & & & & \\ \cline{2-5}  & (steady) & 660.00 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{COM} & UHF TX & 5808.0 & 6.70 & 13.7 & 6.70 \\ \cline{2-5}  & UHF RX & 254.10 & 100 & 100 & 100 \\ \cline{2-5}  & S-band & 5500.0 & 0 & 12.4 & 0 \\ \hline Payload & & 6490.0 & 100 & 100 & 0 \\ \hline \multicolumn{5}{c}{Total Energy Consumption Per Orbit (\(mWh\))} & 13,209 & 15,072 & 2954.3 \\ \hline \end{tabular}
\end{table}
Table 4: Power consumption of Light-1 during different modes.