esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 2 Market and Trends of Memory Technologies
### General Context and Evolution of Nonvolatile Memories
#### 2.2.3 Limitations of Computing Systems  
The memory hierarchy of computing systems is organized in tiers, with CPU at the top for computing, followed by SRAM, DRAM, storage memory, and HDD/Tape at the bottom of the architecture. Each memory block of the system has greater capacity than the preceding but slower latency. Thus, there is a cost vs. performance tradeoff in memory subsystem, performance increasing as it moves closer to the processors. Then capacity increases by roughly one order in each subsequent level [31]. It is becoming more accepted by the community that the standard memory architecture (organized in a cache/memory/storage hierarchy) is no longer able to address the bottlenecks in current computing systems [32; 33].  
First, there is a latency gap between DRAM and NAND [32], leading to high energy consumption and long latency to move data within the system [34]. DRAM latency will be sustained and throughput will improve. On the other hand, NAND will maintain or improve performance with cost leadership. Thus, there is space for a new memory to enter the hierarchy, fill this gap between DRAM and NAND, and improve computing system performance [33]. This new class of memory is known as storage class memory (SCM). As it may be too difficult for one memory to fill the gap, two types of storage class memories can be distinguished: memory mapped and storage mapped [11]. Memory mapped SCM should combine read/write and endurance performances close to DRAM, with improved retention time (without being a true nonvolatile memory) and lower cost. Storage mapped SCM should be nonvolatile, cost much less than DRAM, and have improved performances (speed and endurance) with respect to Flash memories. In particular, the 3 DXpoint using PCRAM fits well between NAND and DRAM in the computing memory hierarchy [31].  
Moreover, memory performance and speed growth have not kept pace with that of processors (memory latency remains almost constant), leading to a performance gap known as the \"memory wall\" [35; 36]. This performance gap between processor and memory is widening with technology scaling [37]. In von Neumann systems, separation of computing and memory becomes critical for performance and efficiency when data movement becomes prominent, as is the case in current system. Memory access energy costs 1-3 orders of magnitude higher than computing [38], and consumes approximately 1000 times the energy of a complex addition [36]. Currently, data movement between the main memory and conventional computation units is a major contributor to the total system energy consumption in consumer devices. For instance, it was reported that data movement accounts for 62.7% of the total energy consumed by Google consumer workloads [39]. This is why new non von Neumann architectures were proposed and developed to exploit locality and near-memory computing in order to reduce the cost and energy of data movement.
## 5 Implementation: Deep-learning in IoMT Edge Devices
### Deploying DNNs on IoMT Edge Devices  
In recent years, cloud computing and IoT techniques have grown rapidly, proving benefits in different aspects of human life. Based on a prediction, the number of IoT devices will approach 21 billion by 2025, and the market is reaching USD 112 Billion by then (IoT Analytics Research 2018; investingshr). At the same time, these devices generate a massive amount of data that should be analyzed locally, which demands highly efficient data processing platforms and techniques. DNNs can address the process of the produced data. However, they come with extensive computation and storage requirements beyond the capability of current embedded devices to execute them within a limited power budget. As an example, VGG-16 [87] as a popular deep convolutional neural network, consists of 138 million parameters, requiring 500 MB memory space, and involves 15.5 million floating-point operations per second (FLOPs) [88]. Such a requirement is beyond the capability of IoT devices.  
Considering the above facts, it is highly challenging to deploy DNNs on resource-constraint embedded devices, and extensive effort is needed to enable such deployment. Among these approaches are pruning [89], quantization [90], knowledge distillation [91], parameter sharing [92], and compression [93] to reduce the computational complexity of  
Figure 8: An example of Deep Neural Networks with input, output, and hidden layers.  
DNNs. The impact of these approaches should be measured on the underlying device where DNNs are going to be deployed as different approaches show varying impacts on different platforms. After observing the system parameters (such as throughput, latency, and power), algorithms should be refined and tuned to improve these parameters. Figure 9 shows a schema for co-optimization of DNNs and the hardware platforms.
Multiple Error Correction in Redundant Residue Number Systems: A Modified Modular Projection Method with Maximum Likelihood Decoding  
[PERSON]  
1,2,6,7,8,9,10,11  
[PERSON]  
1, [PERSON]  
3,  
costs and network traffic. The transition from replication to erasure codes and RDD is complicated by the need to update the hardware-software base and recode the entire volume of stored data. Nevertheless, there are examples of successful adoption of these technologies by Facebook [5] and Microsoft Azure [3].  
This paper proposes a new approach to error correction codes based on a redundant residue number system (RRNS) [6; 7]. Along with the Reed-Solomon codes [8], the Rabin dispersal algorithm [9], etc., RRNSs provide the functionality implemented by erasure codes and the functionality implemented by RDD algorithms. In addition, an important property of RNSs is the ability to perform arithmetic operations over encoded data. This property can be used in modern distributed data processing systems [10; 11; 12] and encrypted data processing schemes [13]. Therefore, RRNSs are a unique and versatile tool for a wide range of applications.  
There are two main strategies for correcting errors in RRNSs. The first one involves syndrome decoding [14; 15; 16]: erroneous parts of the data are determined by comparing a special numerical syndrome calculated on the obtained data with the reference values. The second strategy is to restore error-free data based on the obtained code [17; 18; 19] by calculating the error magnitude and correcting the code during the decoding process. The authors [19] suggested a maximum likelihood decoding (MLD) approach to simplify the corrective decoding method by reducing the enumeration of values during decoding.  
This paper proposes a modified method for corrective data decoding in RRNSs using MLD. Unlike the decoding method [19], this approach involves an alternative version of the Chinese Remainder Theorem (CRT) [20; 21; 22] for more efficient data restoration due to parallel execution of decoding and error correction. The CRT with fractions allows dividing the decoding process into two independent parts: determining the weighted characteristic of a number (a relative estimate of its magnitude) and decoding itself. The weighted characteristic can be directly used for error correction, and data decoding can be performed in parallel. The proposed modified modular projection method with MLD speeds up the procedure of error correction and number restoration in the weighted number system compared to the original method [19]. At the same time, it consumes many more hardware resources.  
This paper is organized as follows: In Section 2, the existing research in the field of error correction in RRNSs is surveyed. A detailed introduction to RNSs, including the notations and preliminaries used below, is given in Section 3. Additionally, this section briefly describes MLD [19]. Section 4 presents the proposed method and analyzes its complexity. Section 5 is devoted to the hardware implementation of the proposed method, comparing it with the existing methods. The outcomes of this paper are summarized in Section 6.
# Total Outer-Independent Domination Number: Bounds and Algorithms
## 2 Bounds and Exact Results for \(\gamma_{t}^{oi}(G)\)
### Bounds
The independent domination number of \(G\), represented by \(i(G)\), is the smallest size of any independent dominating set in \(G\). Detailed information can be found in the survey [31]. Proposition 3: _Let \(G\) be a nontrivial graph of order \(n\) and let \(S\) be a \(\gamma_{t}^{ai}(G)\)-set. The following statements hold._  
(i): _If_ \(V(G)\setminus S\) _is a maximal independent set of_ \(G\)_, then_ \(\gamma_{t}^{ai}(G)\leq n-i(G)\)_._  
(ii): _If_ \(V(G)\setminus S\) _is not a maximal independent set of_ \(G\)_, then_ \(\gamma_{t}^{ai}(G)\geq n-\alpha(G)+1\)_._  
Proof. : First, we proceed to prove (i). Suppose that \(V(G)\setminus S\) is a maximal independent set. Please note that any vertex of \(S\) has a neighbor in \(V(G)\setminus S\). Hence, \(V(G)\setminus S\) is an independent dominating set of \(G\). Thus, \(i(G)\leq|V(G)\setminus S|=n-\gamma_{t}^{ai}(G)\), as desired. Now, we proceed to prove (ii). Suppose that \(V(G)\setminus S\) is not a maximal independent set of \(G\). Therefore, there exists \(v\in S\) such that \(N(v)\cap(V(G)\setminus S)=\emptyset\). Hence, \((V(G)\setminus S)\cup\{v\}\) is an independent set of \(G\). Thus, \(\alpha(G)\geq|(V(G)\setminus S)\cup\{v\}|=n-\gamma_{t}^{ai}(G)+1\), as desired. Now, we present the exact results for several well-known graph families, as proposed in [12; 13]. These results provide valuable insights into the behavior of \(\gamma_{t}^{ai}(G)\) for specific graph structures. Furthermore, in the next subsection, we propose an ILP formulation to find a \(\gamma_{t}^{ai}(G)\)-set. **Proposition 4**. : _The following statements hold for any integer \(n\geq 3\)._  
1. \(\gamma_{t}^{ai}(K_{n})=n-1\)_._
2. \(\gamma_{t}^{ai}(C_{n})=\lceil 2n/3\rceil\)_._
3. \(\gamma_{t}^{ai}(P_{n})=\lfloor 2n/3\rfloor\)_._
4. \(\gamma_{t}^{ai}(W_{n})=\lfloor n/2\rfloor+1\)_._
5. _If_ \(r\) _is a positive integer, then_ \(\gamma_{t}^{ai}(K_{r,n-r})=\min\{r,n-r\}+1\)_._  
These results contribute to a better understanding of the total outer-independent domination number in various graph families, including complete graphs, cycles, paths, and wheel graphs.
# Estimating Flood Inundation in Urban Areas Using a Scenario Generation Method and Inundation Graphs
## 2 Methodology  
### Model for Application  
The model used for this research is the HDM-2D model, which is an unsteady two-dimensional depth-averaged flow model using the Petrov-Galerkin stabilization approach. The model uses the depth-averaged mass and momentum equations, as shown in Equations (1) and (2) [21; 22].  
\[\frac{\partial h}{\partial t}+h\frac{\partial u_{j}}{\partial x_{j}}+u_{j} \frac{\partial h}{\partial x_{j}}=0 \tag{1}\]\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}=-g \frac{\partial(H+h)}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\Bigg{(}v_ {T}\frac{\partial u_{i}}{\partial x_{j}}\Bigg{)}-gn^{2}\frac{u_{i}\sqrt{\mu_{j} \mu_{j}}}{\mu^{4/3}} \tag{2}\]  
where \(t\) is time; \(u_{1},~{}u_{2}\) are the vertically averaged velocity components corresponding to the \(x\), \(y\) directions, respectively; \(g\) is the acceleration of gravity; \(H\) is bottom elevation and \(h\) is flow depth, \(n\) is Manning's roughness coefficient, and \(v_{T}\) is kinematic eddy viscosity. The above-shown equations are written using the indicial notation and the [PERSON] summation convention for repeated indices. Through solving equations that use principles of mass and momentum conservation, the model utilizes boundary conditions of discharge and water surface elevation to calculate the water velocity and depth outputs for the constructed grids. This enables the model to estimate hydraulic parameters within complex topographical settings. The model has shown accuracy in modeling and its assessments in later research, with continuous validation and practical implementation showing a mass conservation error of less than 0.7% [23]. Also, it was applied to the confluence of the primary and tributary channels [24] and was employed for application with a pollutant dispersion model [25]. Furthermore, the model was applied to a dam break problem and showed the ability to simulate subcritical and supercritical transition, with some limitations showing discrepancies immediately after the break [26; 27]. Also, some inaccurate performance was observed at the joint area of the main and tributary channel when applied to a confluent channel due to the three-dimensional flow structure in the joint area [21]. Recent applications have been associated with inland flooding and river inundation, using a wet/dry handling technique using a flux-blocking scheme also known as the thin film method [28; 29; 30]. Therefore, this model was applied to this study for scenario application due to its capabilities.
## 6 Weather Data  
Once the roof model was verified and validated, its thermal behavior was studied under the outdoor environment of different cities. Data from four representative towns in Mexico with warm weather were used. Table 1 shows the four major cities with their corresponding type of weather. The weather data used for the simulations were filtered from files provided by the National Meteorological Service-National Commission of Water (Servicio Meteorologico Nacional-Comision Nacional del Agua (SMN-CONAGUA)), the public body responsible for providing information on the state of the weather that prevails or affects the territory of this country. The SMN-CONAGUA obtains the weather data files from weather stations situated in each city. The data files contained recorded several variables recorded every ten minutes for a whole year. Still, only solar radiation, wind speed, and air temperature for the simulations were used, as shown in the Results Section. The thermal behavior of the roofs was analyzed for the week with the highest temperatures of the year 2018.
## 2 Experiment Setup  
### Rotor System  
The experiment was conducted on a model-scale rigid-rotor system having a two-bladed rotor, as shown in Figure 1, in the helicopter of the Key Laboratory of Ministry of Education in Beihang University. The model-scale rigid-rotor system tested in this investigation consists of two 0.5 m diameter bladed rotors. Each blade is untwisted and has a rectangular planform with a uniform chord length of \(c=0.025\) m, with a blade root cutout of 0.049 m (about 20% rotor radius). Additionally, the rotor blades have NACA0012 symmetrical airfoil sections and are of all-aluminum construction. The detailed rotor properties are shown in Table 1. The rotor drive mechanism consists of a hub mounted on a rotating drive shaft. The rotors are powered by an 5 Kw electric motor through a belt transmission that has a 4:1 transmission ratio, which can be seen in Figure 2. The rotor is equipped with a Hall-effect speed sensor to indicate the rotor speed. During the tests, the rotor speed was set to \(800\,\sim 1200\) r/min. The rotor blade hubs are hingeless and rigid, and the collective pitch angles are set at \(3\,\sim 14^{\circ}\). The collective pitch angles are all from the prior experimental databases for actual rigid-rotor helicopter flight in the laboratory [43; 44].
## 5 Conclusions  
In this paper, we present a novel smoothing algorithm generating a three-dimensional continuous curvature path from a sequence of waypoints. Our algorithm is composed of two stages: one is a reference path generation by the 3D Dubins path, and the other is a reference path approximation by several cubic Bezier curves.  
Figure 19: Horizontal and vertical plan views.  
Figure 20: Curvature.  
For the 3D Dubins reference path, we propose a novel method to extend a 2D Dubins path to a 3D path by applying a simple constraint that the pose is a unit vector pointing to the next waypoint to the pose of each waypoint. Our method is straightforward for use in waypoint guidance problems. Its advantages include preserving the same methodology and complexity of the 2D Dubins path design.  
Since the curvature of the resulting 3D Dubins reference path is discontinuous at the transition point between a straight line and a circular arc, we propose to divide the circular arc into several parts and approximate each piece using a Bezier curve to avoid curvature discontinuity. This method also overcomes the limitation on the size of the turning angle of the Bezier curve and allows all waypoints to pass through. We also provide a criterion to select a required number of Bezier curves to strictly meet the maximum curvature constraint.  
The numerical simulations are conducted to evaluate the proposed path-smoothing algorithm. In the simulations, two different sequences of waypoints are considered. The results show that the proposed algorithm produces a continuous curvature path that passes through all waypoints with a slight increase in path length compared with the Dubins reference path.  
Our path-smoothing algorithm can be used for path following applications in many areas, such as aerospace and robotics, as it generates a 3D parametric path that simultaneously satisfies curvature continuity and maximum curvature requirements. However, since the proposed algorithm is tested only in simulation and not in a real environment, future work will be to perform experiments in a real environment.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1F1A1063780).  
Not applicable.  
The author declares no conflict of interest.
## 4 The Proposed 3-Tier AKA Mutual Authentication and Key Agreement (AKA) Protocol
### Registration Phase  
In the registration phase, an entity registers with the 5G service provider. This entity can be an edge device, fog node, or cloud data center. Figures 3 and 4 show the registration protocol of the edge device \(\delta_{d}\). The following steps describe the details of this protocol. The registration protocols for the fog nodes and the cloud data centers should follow the same protocol steps.  
**Step 1:**: The edge device starts the session by sending a hello message, including the device's ID, i.e., \(\delta_{d}\) ID, to register with the 5G service provider \(u_{v}\), as shown in line 1 of Figure 4.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## Abbreviations  
Renewable Energy Source (RES), Greenhouse Gas (GHG), International Energy Agency (IEA), IoT (Internet of Things), Total irradiance (\(G_{i}\)), Ambient Temperature (\(T_{amb}\)), Module Temperature (T\({}_{mod}\)), Wind Speed (WS), Wind Direction (WD), Humidity (H), UV Radiations (Uv), Angle of Tilt (\(A_{T}\)), Output Voltage (\(V_{A}\)), Output Current (\(I_{A}\)), Output Power (\(P_{A}\)), Output Energy (E), Duty Cycle (DC), Grid Voltage (\(V_{U}\)), Current To Utility-grid (\(I_{TU}\)), Power To Utility-grid (\(P_{TU}\)), Power From Utility-grid (\(P_{FU}\)), Utility Grid Impedance (Z), Load Voltage (\(V_{I}\)), Load Current (\(I_{L}\)), Load Power (\(P_{L}\)), Battery State (\(B_{S}\)), Battery Current (\(I_{B}\)), Battery Voltage (\(V_{B}\)), Battery Temperature (\(T_{B}\)), Controller Current (\(I_{C}\)), Controller Voltage (\(V_{C}\)), Controller Temperature (\(T_{C}\)), Network Time Protocol (NTP), Direct Current (DC), Alternating Current (AC), Message Queuing Telemetry Transport (MQTT), Amazon Web Services (AWS),Simple Notification Service (SNS), Artificial Intelligence (AI), Artificial Neural Network (ANN), Feed-Forward Neural Network (FFNN).
## 3 The Proposed Approach
### Cause-Effect Graph
#### 3.1.2 Graphical Symbols in the Cause-Effect Graph  
1. Basic graphic symbols 1. Identical An identical symbol describes a relationship in which the result appears if and only if the cause appears. Figure 2a provides an example of an identical symbol. If C\({}_{1}\) occurs, then E\({}_{1}\) occurs; if C\({}_{1}\) does not occur, then E\({}_{1}\) does not occur; 2. Not A not symbol describes a relationship in which the result appears if and only if the cause does not appear. Figure 2b provides an example of a not symbol. If C\({}_{1}\) occurs, E\({}_{1}\) does not occur; if C\({}_{1}\) does not occur, E\({}_{1}\) occurs; 3. An or symbol describes a relationship in which the result appears if and only if at least one of the related causes appears. Figure 2c provides an example of an or symbol. If one of C\({}_{1}\) and C\({}_{2}\) occurs, then E\({}_{1}\) occurs; if neither C\({}_{1}\) nor C\({}_{2}\) occurs, then E\({}_{1}\) does not occur; 4. And An and symbol describes a relationship in which the result appears if and only if all related causes appear. Figure 2d provides an example of an and symbol E\({}_{1}\) occurs only if both C\({}_{1}\) and C\({}_{2}\) occur; if one of C\({}_{1}\) and C\({}_{2}\) does not occur, E\({}_{1}\) does not occur. 2. Constraint relationship graphic symbols To represent the constraints in a cause-effect graph, some symbols are used to identify them. Constraint relationship graphs are either between causes (input conditions) or effects (output results). Four main constraints are considered in terms of causes, as follows: 1. E (Mutually Exclusive) As shown in Figure 3a, an E constraint symbol connects the two causes a and b, as a and b do not occur at the same time. In other words, at most, one of them occurs at any time; 2. I (Inclusive) Figure 3b provides an example of an I constraint symbol, where at least one of the three causes (a, b, and c) appears; 3. O (Only) Figure 3c shows an example of an O constraint symbol, where one and only one of the two causes (a and b) must occur; 4. R (Required) Figure 3d provides an example of an R constraint symbol, where b must appear when a appears.  
Figure 2: Basic graphical symbols in a cause–effect graph: (**a**) identical; (**b**) not; (**c**) or; (**d**) and.  
One constraint is considered in terms of the results as follows:  
1. M (Mask) As shown in Figure 3e, an M constraint symbol exists between the two results a and b if b does not appear when a appears and b may or may not appear when a does not appear.  
The difference between a mutually exclusive constraint and an only one is that a mutually exclusive constraint allows for not making a choice, and if a choice is required, then, at most, one choice is chosen. An only constraint requires that one and only one must be chosen. The main difference between them in a computer program is that there is no default option for a mutually exclusive constraint, but there is a default option for the only constraint.