esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Chronic Kidney Disease in Africa
## 2 Challenges of Estimating GFR in SSA
In a recent meta-analysis of 13 trials involving 90,409 participants (82.7% with diabetes and 17.3% without diabetes), allocation to an SGLT2 inhibitor reduced the risk of progressive kidney disease by 37% with no difference in benefits in patients with and without diabetes [30]. In the four CKD trials, risk reductions were similar, irrespective of primary CKD diagnosis. It is evident from the systematic reviews that the greatest benefit is in those with eGFR < 60 mL/min with albuminuria and/or underlying CV disease and most major guidelines recommend SGLT2 inhibitors for treatment and prevention of DKD [31]. However, the problem with all these major studies were that only 4-5% of patients entering these studies were identified as Black. In an analysis of the outcomes across geographic regions, there were consistent reductions in the composite endpoint of 50% reduction in eGFR, ESKD, death related to kidney failure, or CV cause. The reduction in hazard ratio ranged from 0.70 in Asia, 0.6 in Europe, 0.61 in Latin America, and 0.51 in North America [32]. There were no data from SSA, but the available data are reassuring that Africans are likely to benefit from SGLT2 inhibitors. In regard to GLP-1 receptor agonists, there has been no dedicated CKD study. However, eight CV outcomes trials included participants with an eGFR as low as 15 mL/min/1.73 m\({}^{2}\). In a meta-analysis of these studies, GLP-1 receptor agonists significantly reduced the risk for a composite CKD outcome, mainly driven by the reduction in albuminuria [33]. As a result, the consensus report by the American Diabetes Association (ADA) and Kidney Disease Improving Global Outcomes (KDIGO) only recommends GLP-1 receptors agonists for additional risk-based management and glycaemic control in patients with CKD already treated with metformin and SGLT2 inhibitors [34]. These very important advances in the treatment and prevention of DKD are difficult to translate into SSA. Currently, there are no generic formulations of these drugs, and they remain expensive, particularly the GLP-1 receptor agonists. It, therefore, remains unlikely that most people with DKD in SSA are likely to benefit from these drugs, due to affordability and availability.
## 3 Multi-Link Channel Access Schemes and Coexistence Issue  
For multi-link channel access, multiple operation options are considered. The usability of these options is related to the simultaneous transmission and reception (STR) capability of a device. In what follows, we first explain the STR capability, describe two basic multi-link channel access schemes, and present variants for enhancement along with the coexistence issue.
## 1 Introduction
Each of the described data is a function of some input parameters (the number of input parameters is determined on the basis of the type of the used strength criterion) and presents the \(P_{i}\) based on the all affecting parameters. For instance, in Figure 1e, \(P_{i}\) is a function of 17 independent input parameters. The challenge is to predict the \(P_{i}\) of the internal support system [7; 8; 9; 14]. The commonly known ways of \(P_{i}\) prediction employ different rock mass quality systems (e.g., rock mass rating, RMR; geological strength index, GSI, etc.) [15]. Some of these methods are also based on numerical codes, theoretical calculations, or coupled semi-analytical solutions [1; 4; 5; 7; 10; 11; 12; 16; 17; 18; 19; 20]. Most of the commercial numerical packages, usually, use different finite element/difference codes [21]. These packages first define the element types, the material properties, and the geometry, together with the boundary and loading conditions. Discrete element packages are, also, used to model a rock medium containing specific joint sets with pre-defined orientations. All the described approaches are used to derive the governing differential equations and to present the solutions of the developed system [5; 7; 12]. Hence, they first need to be well validated against rigorous, available solutions and case studies. In addition, some of the existing methods have drawbacks (e.g., some of the available numerical solutions do not consider the deterioration of the strength parameters or do not correctly take the plastic straining into consideration [1]). Also, they require an adequate knowledge and background about the fundamentals and the theory of the convergence-confinement method and numerical and mathematical techniques. In addition to requiring an expert for the analysis, it is usually a time-consuming process to obtain the GRC. There are also other complex characteristics in the original problem which can be taken into the account, for instance, the material softening. Considering the above, there is still a need to develop new methods for the reliable prediction of \(P_{i}\) in the elasto-plastic, strain-softening rock mass. In this regard, in this paper, the applicability of another new intelligent method of ANN modeling to predict the \(P_{i}\) of circular tunnels constructed in rock masses with different qualities is investigated. The remainder of the paper is organized as follows. Section 2 presents the description of the research problems with background information, data acquisition, and the principles of different types of ANNs. Section 3 is the performance evaluation and compares the performance of ANNs with that of the previously proposed EPR models. Section 4, summarizes the results of the analyses.
## 3 Angular Protection Methods
### Angular Protection: BS-ES Relative Distance Changes  
The following assumptions were made for the BS-ES relative distance changes:  
* The ES location is fixed while the distance between the BS and ES varies between 1 m and 20 km;
* There is a fixed 0\({}^{\circ}\) azimuth angle of the BS regarding the ES (\(\varphi\) = 0);
* The steering beam of the antenna array (\(\varphi_{s}\)) varies between \(-180^{\circ}\) and \(180^{\circ}\);
* The interference of the BS when the beam directs to all possible directions was evaluated;
* The beam sweep angle \(\varphi_{s}\), which has a frequency higher than the I/N interference threshold, should be considered for shut-off.  
Figure 6 presents the BS-ES relative change distance. In this case, the azimuth angle of the BS about the ES remains unchanged, whereas the distance between the ES and BS varies from 1 km to 20 km. The interference calculation process follows the structure shown in Figure 3b. Figure 7 illustrates the beam shut-off angle, concerning the BS-ES distance, using an ES antenna elevation distance of 0 to 20 km and a 4 \(\times\) 4 antenna array. The LOS and NLOS cases were separated to better illustrate the principle. The beam shut-off angle becomes smaller as the distance between the BS and ES increases. A longer distance implies a higher path loss and lower interference for all beam angles, which results in a need to shut off fewer beam angles. For NLOS, with the same BS-ES distance, the beam shut-off angle for the three cases is RMa > UMa > UMi. As for LOS, with the same BS-ES distance, the beam shut-off angle for the three cases is RMa <UMa < UMi.
# Analytical Framework of CP-Free Multiuser OFDM System for Coordinated Multi-Point at mmWave
## 1 Introduction
### Contribution and Organization  
The major concern of this paper lies in developing a novel framework for a CP-free multiuser OFDM system utilizing the CoMP-JT transmission method at mmWave. The main contributions of this paper are outlined below:  
* Proposed CHT with BD channel precoding to reduce multiuser interference in the CP-free multiuser OFDM system.
* Properly designed subcarrier mapping for out-of-band (OOB) reduction and input back-off (IBO)-aided high-power amplifier (HPA) for the peak-to-average power ratio (PAPR) reduction are also carried out in this work for cell-edge users at mmWave frequency.
* Conducted a numerical study to evaluate the BER, PAPR, and ergodic achievable rate performance of the proposed system. The proposed system also ratifies the effectiveness in terms of BER, PAPR, and ergodic achievable rate.  
The rest of this paper is organized as follows. In Section 2, the system model including network description, block diagram, and signal model are introduced. In Section 3, simulation results and discussions are described. A brief discussion on concluding remarks and future challenges in Section 4 ends the paper.
## 5 Discussion  
In recent times, the moon has emerged as a focal point of technological research and development, particularly in the realms of in situ resource utilization, human exploration and habitation, and its potential role as a staging point for human missions to Mars [87]. NASA's Artemis program is poised to establish a sustained presence on the lunar surface, ushering in an era of more extensive lunar exploration than ever before. This burgeoning lunar activity necessitates the development of advanced communication, navigation, and networking capabilities, and LUNANET stands as a prime example of meeting this demand. Notably, the United States, China, and India have already solidified their positions in lunar exploration, with several other nations, including the United Arab Emirates, declaring their intentions to embark on lunar missions in the near future. The moon, distinct from Earth, lacks an atmosphere, underscoring the importance of comprehending its surface and associated challenges. These missions serve as a source of national pride and demand substantial financial investments, emphasizing the significance of a thorough understanding of the lunar environment prior to their initiation. Moreover, lunar communication networks are not yet as established as their terrestrial counterparts. As noted in Section 2, researchers are actively engaged in the ongoing development of accurate propagation models. It is paramount to meticulously design and test the systems in such environments to ensure error-free operations, taking into account the limited optimization opportunities once the systems are deployed on the lunar surface. As elucidated in Section 3, lunar communication presents a multitude of challenges. Antennas, serving as pivotal components within the communication system, function as sensors for the entire network. Therefore, the stability of these antennas, both electrically and mechanically, is of the utmost importance. Conducting electromagnetic simulations is a fundamental step in this regard. As summarized in Section 4, lunar antennas possess many unique characteristics. Lightweight antennas with superior performance hold significant relevance in this context. Mission-specific antennas may necessitate additional coatings or special materials to withstand the extreme temperature conditions experienced on the lunar surface. The antennas discussed in the review article commonly serve dual roles, operating as both transmitters and receivers. This dual functionality is a prevalent feature in space communication systems, enabling bidirectional communication. It is vital to understand that the power capacity and data rate of antennas used in lunar missions vary significantly based on the specific mission's objectives and capabilities. Larger lunar rovers and assets, equipped with high-power and fast communication systems, may necessitate antennas capable of handling higher power levels and faster data rates. Conversely, smaller lunar assets and rovers, constrained by size and power limitations, may opt for lower-speed and lower-power antennas. The choice
# Combination of Robust Algorithm and Head-Tracking for a Feedforward Active Headrest
## 2 Design of the Optimal Filter Coefficients of Active Headrest with Traditional Algorithm
### Robust Algorithm for the Active Headrest  
The traditional algorithm derived in Section 2 only achieves the best noise control performance at both ears when the head is at a fixed position and rotation. The secondary path along with the position of ears changes with head rotations. If the optimal filter coefficient is obtained with Section 2, then several early-stage experiments show that the noise control at the left and right ears is severely degraded when the head rotates [20]. When the head rotates 90\({}^{\circ}\) toward the right, the secondary path from the loudspeakers to the left ear changes significantly; thus, the noise reduction at the left ear can be 7 dB less than that at the right ear.  
To release the degradation of noise control performance when the head rotates, the robust algorithms has been proposed. In the robust algorithm, the residual noises at the ears when the head rotates leftward 60\({}^{\circ}\), rightward 60\({}^{\circ}\) and has no rotation, are simultaneously added to the cost function. Thus, the error points are equally spaced to divide the total angular range through which the human head can rotate into six parts. Therefore, the cost function becomes  
\[J_{robust}=\sum_{i=1}^{3}E\left[e_{1i}^{2}(n)+e_{2i}^{2}(n)\right]. \tag{8}\]  
where \(e_{1i}(n)\) and \(e_{2i}(n)\) are the error signals at the two ears when the head rotates leftward 60\({}^{\circ}\), rightward 60\({}^{\circ}\), and has no rotation, respectively. Primary noise at the ears and the transfer functions of secondary path when the head rotates to different angles are obtained in advance. The optimal filter coefficients in the robust algorithm are derived as  
\[\mathbf{w}_{opt}=-\left(\sum_{i=1}^{3}\mathbf{R}_{i}\right)^{-1}\left(\sum_{i=1}^{3} \mathbf{p}_{i}\right), \tag{9}\]  
where \(\mathbf{R}_{i}\) and \(\mathbf{p}_{i}\) are the autocorrelation matrix as Equation (5) and the cross-correlation vector as Equation (6), respectively, when the head rotates to different angles.  
More error residual signals need to be controlled using the robust algorithm than the traditional algorithm. The active headrest with the robust algorithm achieves control performance when the head rotates to other angles based on the fact that the ANC achieves certain noise reduction in the surrounding space of the control target [15; 22].
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### UAV Real-Time Object Detection Evaluation
#### 4.7.4 Energy Consumption  
Energy consumption is an evaluation metric that could be easily overlooked, and as is evidenced by our review, just 6 studies (< 10%) reported energy consumption. Considering that detection missions are carried out on UAVs, the batteries of these UAVs have to provide energy for their own flight, the computing platform, and data transmission at the same time. However, the energy seems not easy to calculate. In [88], a data transmission energy consumption model, a UAV computing energy consumption model, and a UAV flight  
Figure 11: Detection speed interval distribution. The detection speed was divided into 9 categories, which are less than 1 FPS, 10 FPS, 20 FPS, 30 FPS, 40 FPS, 50 FPS, 60 FPS, 70 FPS, and more than 100 FPS. The figures outside the bar indicate the number of studies in that speed interval. The blue, red, and green bars represent the literature that used traditional methods, machine learning, and deep learning, respectively.  
energy consumption model were developed to estimate the total energy consumption over the whole real-time object detection task.
## 3 Greedy Scanning Data Collection Strategy
### Head Nodes Re-Election  
To achieve balanced energy consumption, the head node should be re-elected when its energy is below a certain threshold. Here we appoint \(E^{\prime}\) as the current energy of the head node, while \(E\) is the original energy when it is elected as head node. If the ratio of \(E^{\prime}\) to \(E\) is below a threshold \(Th\)  
Figure 12: The way of updating when the mobile sink moves along the column in one collecting period. **(a)** The mobile sink moves to the upside grid cell; **(b)** The head nodes of the marked grid cells are needed to be updated.  
Figure 13: The way of updating when the mobile sink moves into another column when goes on to the next collecting period. **(a)** The mobile sink moves to the left neighbor column to start a new collecting period; **(b)** The head nodes of the marked grid cells are needed to be updated.  
then the operation of re-election will be started. The current node informs other nodes in the same grid cell, and the latter compete for the role of head node with the new \(K\). After a new head node is elected, it broadcasts in its communication range. The nodes in the same grid could know which one is the current head node. The relation of grid cell side length \(a\) and node communication range \(R\) must satisfy \(a\leq\frac{\sqrt{5}R}{5}\) as described in Section 3.2.1, all the neighbor head nodes can receive this information. According to this broadcasting information, the neighbor head nodes are also informed about the change of head node in this grid. Then, these neighbor head nodes update their neighbor table, and the former head node transfers its \(DN\) and neighbor table to the current head node. In the re-election process, the node which is going to retire still has the duty of head node until the new elected node becomes a head node. That is to say, the old head node is still responsible for collecting and routing sensory data before the re-election process is completed.
## 3 Design and Analysis of the Birdcage RF coil
### Impedance Matching  
It can be observed in all cases of Figure 8 that the pattern of variations in the input impedance (at the dominant resonance mode as well as the higher order resonance modes) provided by the proposed analytical solution does perfectly coincide with the one which was obtained using conventional 2D circuit simulation technique. The numerical values of the input impedance \(Z_{in}\) at the desired resonance frequency is further used to design the impedance matching circuit which serves as an interface between the RF coil and receiver circuit. A conventional \"L-matching\" network with lumped (series) inductance and (parallel) capacitance can be used to accomplish this task. The input impedance \(Z_{in}\) serves as the load impedance which is used to obtain the required values of the lumped components of the impedance matching network connected between the desired segment (end-ring in this case) of the birdcage RF coil and the 50 ohm port of the receiver. The numerical values of the input impedance \(Z_{ER\_p}\) as obtained via proposed analytical solution along with the corresponding values of the lumped components of the matching circuit for all configurations of the designed 1.5 T and 3 T birdcage RF coils are provided in the Table 4.  
Figure 8: Input impedance obtained using proposed analytical solution and conventional 2D simulation for the different configurations of the birdcage RF coil: (**a**) 1.5T low pass, (**b**) 1.5 T high pass, (**c**) 1.5 T band pass, (**d**) 3 T low pass, (**e**) 3 T high pass, (**f**) 3 T band pass.  
The reflection coefficient S11 which is a core parameter to explains the impedance matching of an RF circuit can easily be determined with the help of input impedance by using the following Equation (23).  
\[\text{S11}=\frac{\left(Z_{ER_{p}}-50\right)}{\left(Z_{ER_{p}}+50\right)} \tag{23}\]  
A comparison between the reflection coefficients obtained by using the proposed analytical solution in the absence as well as presence of the impedance matching circuit for all configurations of the implemented 1.5 T and 3 T birdcage RF coils is provided in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MRI System** & **Coil Configuration** & \(Z_{ER,p}\) **(Ohm)** & **Cm (pF)** & **Lm (nH)** \\ \hline \multirow{3}{*}{1.5 T} & Low Pass & \(34.42-\text{j}10.86\) & \(33.7\) & \(85.6\) \\  & High Pass & \(439.88-\text{j}37.44\) & \(15.3\) & \(350\) \\ \cline{1-1}  & Band Pass & \(68.65-\text{j}29.33\) & \(10.8\) & \(98.2\) \\ \cline{1-1}  & Low Pass & \(131.74-\text{j}54.57\) & \(8.3\) & \(90\) \\ \cline{1-1}  & High Pass & \(13.23+\text{j}137.45\) & \(13.5\) & \(333\) \\ \cline{1-1}  & Band Pass & \(82.20+\text{j}130.45\) & \(16.3\) & \(136.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input impedance \(Z_{ER,p}\) of the designed birdcage RF coils at dominant resonance frequency and the corresponding lumped components of the matching circuit for 1.5 T and 3T MRI systems.  
Figure 9: _Cont._
# Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning
## 2 Methods  
### Study Design and Participants  
Data were exported from the PMRCardio database, a Microsoft Structured Query Language Server Management Studio (version 15.0.18330.0) that stores clinical data of patients hospitalised in cardiology wards at the Charlotte Maxeke Johannesburg Academic Hospital (CMJAH, Johannesburg, South Africa). The CMJAH is a quaternary-level state-owned institution equipped with a cardiac intensive care unit, a cardiac catheterisation and electro-physiology laboratory, and outpatient services. All acutely and chronically ill patients with cardiovascular diseases are hospitalised in cardiac wards. Their admission data are stored digitally in the PMRCardio database by registrans or residents rotating in the cardiology wards. Clinical parameters available in the dataset (features) include demographic data, clinical history and examination findings, laboratory indices, electrocardiogram (ECG), echocardiogram, angiography, and in-hospital all-cause mortality status.  
The tenth revision of the international classification of diseases (ICD-10) code was used to select patients diagnosed with heart failure. The following ICD-10 diagnoses were used to select patients included in the analysis: \"heart failure unspecified\" or \"congestive heart failure or \"left ventricular failure\", or \"dilated cardiomyopathy\", or \"ischaemic cardiomyopathy.\" Heart failure patients who are 18 years of age and older with a documented left ventricular ejection fraction (LVEF) hospitalised between October 2009 and December 2018 were included.  
Data exported from the database were merged into a single Microsoft Excel Sheet. All retrospective patient data used in the final analysis were further verified by comparing hard copies of medical records with electronic data. Patients were subsequently stratified into three groups based on the baseline LVEF: heart failure with reduced ejection fraction--HFrEF (LVEF < 40%), heart failure with midrange ejection fraction--HFmEF (LVEF = 40-49%), and heart failure with preserved ejection fraction--HFpEF (LVEF \(\geq\) 50%) [3].  
Admission biochemical results were obtained from the National Health Laboratory Service (NHLS, Johannesburg, South Africa). Approval to conduct the study was received from the University of the Witwatersrand Human Research Ethics Committee (clearance certificate number: M190515). Permission was also obtained from NHLS senior authorities.