esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
In this study, the computational fluid dynamics (CFD) method is used to investigate the effect of inlet-turbulence intensity on the friction coefficient for the zero-pressure gradient (ZPG) flow, and for this purpose, conservation equations, namely continuity, momentum, and turbulence model equations, are solved under specified boundary conditions using ANSYS CFX commercial code. If the standard deviation is divided by the mean velocity (\(\overline{u}\)), the turbulence intensity, Tu, is obtained:  
\[Tu=\frac{u^{\prime}}{\overline{u}}\times 100\;\% \tag{5}\]  
where the velocity fluctuations, \(u^{\prime}\), are in the main direction of the flow. The turbulence-length scale is considered as the sum of the integral length scale and the micro scale. Std. k-\(\epsilon\), RNG k-\(\epsilon\), std. k-\(\omega\), SST k-\(\omega\), and Gamma-Theta (GT) have been used and compared. The Gamma-Theta turbulence model is a turbulence model that solves two separate transport equations for the transition criterion and \"intermittency\" based on the Reynolds number defined according to the momentum thickness, thus solving the transition region much more precisely. The empirical correlation it uses is designed to capture the bypass transition even at low-inlet-turbulence intensities [36]. It is used with the SST turbulence model in ANSYS CFX. Since all turbulence models are solved using their standard forms and recommended turbulence coefficients in the program, detailed information about the turbulence models and coefficients used can be found in the work of [PERSON] [37] and in the modeling manual of the program (ANSYS CFX R13) [38]. ANSYS CFX includes a powerful pre- and post-processing interface and an advanced finite volume solver. ANSYS CFX solver has proven its reliability in terms of the results it has provided, both in the industry and in the literature. Stable results can be obtained during the solution thanks to its coupled solver.
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 6 Stages of the Quantification of QoE in CIAs
### Input Stage
Currently, it is challenging to use this source of information, as most network traffic is encrypted. Other proposals used active polling by generating synthetic network traffic directed at infrastructure elements and evaluating the responses of the target devices. Active polling allows for deriving metrics, such as response times or the status of the client or cloud server, from response codes. User feedback is a direct source of input information related to QoE in CIAs. Some proposals in the literature collected user feedback through forms or surveys, allowing users to express their opinions when the study exposed them to different boundary conditions. Users rated their experience on a predefined scale or in their own words. The most frequent scales used were the ACR or MOS, as mentioned above. Other scales include the Standard Deviation of Opinion Scores (SOS) [99] and the Net Promoter Score (NPS) [100]. The SOS is a measure that states that in properly conducted QoE evaluation tests, user ratings differ very little, as all users experience the same boundary conditions. The NPS, on the other hand, assesses not only user satisfaction but also user loyalty. The NPS determines how likely a person is to recommend a brand, company, product, or service to another person.
_applied_  
_sciences_  
_Article_  
**Comparative Analysis of Lithium-Ion and Lead-Acid as Electrical Energy Storage Systems in a Grid-Tied**  
**Microgrid Application**  
**[PERSON] \({}^{\star}\)\({}^{\copyright}\), [PERSON] \({}^{\copyright}\) and [PERSON]**  
Department of Electrical Engineering, Tshwane University of Technology, eMalahleni 1034, Mpumalanga, South Africa  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract** Microgrids (MGs) are a valuable substitute for traditional generators. They can supply inexhaustible, sustainable, constant, and efficient energy with minimized losses and curtail network congestion. Nevertheless, the optimum contribution of renewable energy resource (RER)-based generators in an MG is prohibited by its variable attribute. It cannot be effectively deployed due to its application's power quality and stability issues. Therefore, an energy storage system is employed to alleviate the variability of RERs by stabilizing the power demand against irregular generation. Electrical energy storage systems (EESSs) are regarded as one of the most beneficial methods for storing dependable energy supply while integrating RERs into the utility grid. Conventionally, lead-acid (LA) batteries are the most frequently utilized electrochemical storage system for grid-stationed implementations thus far. However, due to their low life cycle and low efficiency, another contending technology known as lithium-ion (Li-ion) is utilized. This research presents a feasibility study approach using ETAP software 20.6 to analyze the performance of LA and Li-ion batteries under permissible charging constraints. The design of an optimal model is a grid-connected microgrid system consisting of a PV energy source and dynamic load encompassed by Li-ion and LA batteries. Finally, the comparative study led to significant conclusions regarding the specific attributes of both battery technologies analyzed through the operation, revealing that Li-ion is a more conducive energy storage system than LA.  
**Keywords:** electrical energy storage system (EESS); lead-acid (LA); lithium-ion (Li-ion); microgrid (MG); renewable energy resource (RER)
## 3 Correction Method for Antenna Pattern Errors  
Based on the analysis of antenna pattern errors presented in the previous section, the impact of these errors on the visibility function can be categorized into two types: multiplicative errors and additive errors.  
Antenna pattern differences and antenna pattern measurement errors can be uniformly expressed in the form of amplitude error and phase error:  
\[F^{\sigma\prime}_{n}(\xi,\eta)=F^{id}_{n}(\xi,\eta)(1+\Delta F_{n}(\xi,\eta)) e^{j\Delta\varphi_{F}(\xi,\eta)} \tag{18}\]  
where \(F^{\sigma\prime}_{n}(\xi,\eta)\) is the measured antenna pattern. \(F^{id}_{n}(\xi,\eta)\) is an ideal antenna pattern for all elements in the array. \(\Delta F_{n}(\xi,\eta)\) and \(\Delta\varphi_{F}(\xi,\eta)\) is the amplitude error and phase error. These errors arise from the process of antenna fabrication, assembly, and measurement. As a result, the antenna patterns of each element are inconsistent, and the measured antenna pattern on the ground is different from the actual antenna pattern in orbit. Antenna pattern differences and antenna pattern measurement errors are multiplicative errors and scale with the BT of the target. As indicated by Equation (1), \(T_{B}(\xi,\eta)\) is the amplification factor.  
The errors introduced by the backlobe and cross-polar antenna pattern can be uniformly expressed as additive errors to the visibility function:  
\[V^{\sigma\prime}_{kj}=V^{id}_{kj}+\Delta V^{F}_{kj} \tag{19}\]  
\(\Delta V^{F}_{kj}\) is the contribution of the backlobe pattern and the cross-polar pattern to the visibility function.  
The in-orbit calibration of FPASR mainly involves injecting correlated and uncorrelated noise signals into the receiver channels. It relies on the response to a known signal to correct for various errors, mainly errors in the receiver channels. However, it is important to note that this correction mechanism does not include the antenna; consequently, the errors related to the antenna pattern still cannot be solved by noisy signal injection. Multiplicative errors and additive errors are difficult to correct by a single correction method, so a two-step correction method is required to correct the antenna pattern errors. Initially, corrections are applied to backlobe contributions and cross-polar contributions utilizing an artificial BT distribution. Subsequently, differential processing of the visibility function is conducted to mitigate the amplification factor attributed to antenna pattern errors. The flowchart illustrating this correction process for antenna pattern errors is presented in Figure 1.
# Additively Manufactured Polymers for Electronic Components
## 3 Polymers Used for AM in Electronic Applications
### Extrinsically Conductive Polymers (ECPs)  
The electrical behaviour of ECPs can be described by what is called the percolation threshold, which defines the particular relationship between electrical conductivity and filler content (Figure 6) [52].  
An evident increase in the material's electrical conductivity occurs at a specific volume fraction of filler, known as the percolation concentration. This concentration is directly related to the type of filler used and its dispersion within the matrix. When the filler concentration is below this threshold, the material acts like an insulator, exhibiting an electrical conductivity similar to that of the material without any filler added. By increasing the filler content, a rapid increase in the electrical conductivity of the material is observed. This is due to the formation of a three-dimensional network between the filler particles, which ensures electron transfer. It depends not only on the particles' dimensions but also on the shearing rate used in the dispersion procedure. The main fillers used for polymer nanocomposites are metallic particles [62; 63], ceramic particles [64; 65; 66; 67], carbon allotropes [68; 69; 70], carbon fibres [71], and their hybrid combinations [72; 73; 74; 75; 76; 77; 78; 79; 80]. The interaction between fillers and matrices also has a direct effect on other characteristics of the material, like mechanical ones. Carbon black is one of the main fillers used to impart electrical conductivity to polymeric matrices, but it is responsible for lowering the elongation at break. Hence, the filler loading must be low enough to maintain the good mechanical properties of the material. Percolation concentrations of carbon black and CNT composites can be respectively below 0.5 wt% and 0.1 wt% [81]. CNTs and graphene are both advanced carbon-based materials that possess unique properties, making them preferable in electronic applications compared to other fillers, such as carbon black. CNTs and graphene are ideal for mechanical reinforcement in nanocomposites thanks to their exceptional mechanical strength and stiffness, with a tensile strength several times greater than steel. Moreover, they  
Figure 6: Percolation mechanism in extrinsically conductive polymers [60]. Reprinted with permission from ref. [61].  
are excellent electrical conductors, suitable for electronic applications like transistors [82], sensors [83; 84; 85; 86], and other devices [60; 87; 88; 89; 90].
It enables simulation of the behaviour of particulate terrain, such as soil or ice, by discrete element methods, while using FEM for accurate modelling of a tyre as a continuous body [20; 21]. [PERSON] et al. [21] proposed a coupled approach for the investigation of tyre-sand interaction, and [PERSON] et al. [2] and [PERSON] et al. [22] proposed a Lagrangian meshfree particles method for large deformation and failure flows of geomaterial with the development of an elastic-plastic soil constitutive model. Many studies have established tyre models using FEM to predict tyre-road contact stress or to evaluate tyre performance. The truck radial tyre model developed in [23] and the 205/55R16 tyre model developed in [18] are examples. The main challenge generating 3D FEM meshes is the description of the complex pattern of a tyre tread composed of many grooves and blocks. An effective mesh generation procedure for 3D automobile tyres was proposed in [24]. When a vehicle is braking, the front wheels are subjected to more vertical force than the rear wheels. Therefore, different friction forces are applied to the front and rear wheels, which affects the braking efficiency of the vehicle. A tyre can be classified by the curvature of the tyre contact zone geometry in three different shapes [25]: underdeflected, ordinary deflected and overdeflected tyre shapes; an underdeflected tyre shape occurs when the value of the tyre's internal pressure is abnormally high, an ordinary deflected tyre shape occurs with a correct internal tyre pressure, guaranteeing a maximal surface area for the wheel tyre-road contact zone, and an overdeflected tyre shape can occur due to insufficient tyre pressure or overloading by additional external force. This study extends our previous work [26] and concentrates on modelling the distribution of the friction coefficient throughout the tyre tread. The overall aim is to determine the friction coefficient distribution in the tyre-road contact zone by calculating the normal, tangential longitudinal and lateral contact forces for the laterally deflected tyre with the road surface for different internal tyre pressures and external loads, focusing on the front wheels of a braking car. The article has four sections: Introduction, The tyre-road model, Results and discussion and Conclusions, where model section has in addition the subsections Problem description, Tyre-road geometry, Mechanical properties, Tyre material model, Contact interaction asymmetry and Tyre-road finite element method.
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 3 Proposed Search Algorithm
### The BSS-VQ Search Algorithm
Moreover, a quantity \(P_{hit}(m\;|\;bss_{k})\), \(1\leq m\leq CSize\), is defined as the \(m\) ranked probability that a codeword hits the best-matched codeword in subspace \(bss_{k}\) for sorting purposes. For example, \(P_{hit}(m|bss_{k})|_{m=1}=\max_{\mathbf{c}_{i}}\{P_{hit}(\mathbf{c}_{i}|bss_{k })\}\) denotes the highest hit probability in \(bss_{k}\). As can be seen, the lookup table in each subspace gives the ranked hit probability in descending order and the corresponding codeword. \begin{table}
\begin{tabular}{c c} \hline \hline \(j\)th-Order & Mean \\ \hline
0 & 15.3816 \\
1 & 19.0062 \\
2 & 15.4689 \\
3 & 21.3921 \\
4 & 26.8766 \\
5 & 28.1561 \\
6 & 28.0969 \\
7 & 21.6403 \\
8 & 16.3302 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dichotomy position for each dimension in the codebook CB1. In the encoding procedure of BSS-VQ, the cumulative probability \(P_{cum}(M\mid bss_{k})\) is firstly defined as the sum of the top \(M\)\(P_{hit}(m\mid bss_{k})\) in \(bss_{k}\), namely  
\[P_{cum}(M|bss_{k})=\sum_{m=1}^{M}P_{hit}(m|bss_{k}),\;1\leq M\leq CSize. \tag{11}\]  
Subsequently, given a threshold of quantization accuracy (\(TQA\)), a quantity \(M_{k}(TQA)\) refers to the minimum value of \(M\) that meets the condition \(P_{cum}(M\mid bss_{k})\geq TQA\) in \(bss_{k}\), namely  
\[M_{k}(TQA)=\underset{M}{\text{argmin}}\{M:P_{cum}(M|bss_{k})\geq TQA\},\;1\leq M \leq CSize,\;0\leq k<BSize. \tag{12}\]  
Finally, a BSS-VQ encoding procedure is described below as Algorithm 1:  
```
Step 1. Given a \(TQA\), \(M_{k}(TQA)\) satisfying (12) is found directly in the lookup table in \(bss_{k}\). Step 2. Referencing Table 2 and by means of (9) and (10), an input vector is assigned to a subspace \(bss_{k}\) in an efficient manner. Step 3. A full search for the best-matched codeword is performed among the top \(M_{k}(TQA)\) sorted codewords in \(bss_{k}\), and then the index of the found codeword is output. Step 4. Repeat Steps 2 and 3 until all the input vectors are encoded. ```  
**Algorithm 1** Encoding procedure of BSS-VQ  
In short, Table 2, the first lookup table, is prebuilt by performing (8). Subsequently, the second lookup table regarding \(P_{hit}(m\mid bss_{k})\) and the corresponding codeword is built for each subspace by following the training mechanism. Accordingly, the VQ encoding can be completed using Algorithm 1.
# Driver Identification and Detection of Drowsiness while Driving
## 4 System Architecture  
The system architecture features a client-server scheme with a single server and two clients, as depicted in Figure 3. Initially, the program was tested on a laptop without the client-server scheme, running solely within a single file.  
There is the Jetson Nano client located inside the car with a camera focusing on the driver. The main program runs on this client, opening the camera and sending captured images of the driver in the car to the server or processing the image in case of a loss of coverage. The server houses the part of the program that analyzes the image data received from the Jetson Nano client. As for driver identification, features that allow for the recognition of a person are extracted and compared to the characteristics in the database containing all authorized drivers to start the vehicle. If the ID of the person received from the client is in the Server database, he/she is authorized; otherwise, it would be considered an attempted theft, and the user will not be authorized to start the car. Note that the Server database does not contain all the user's data, only the IDs. In this way, the privacy of drivers cannot be compromised in the case of drowsiness detection. For drowsiness detection, typical signs of sleepiness are determined by identifying the person's face and eyes, relying on the height of the eyes to determine if they are closed or open. When both eyes are closed for more than a certain period (greater than a simple blink to avoid false positives), the driver's state is classified as asleep. Otherwise, the driver's condition remains awake. If the Server (or the Jetson Nano client in case of loss of coverage) detects a drowsiness state, i.e., the driver is asleep, an alarm is triggered to awaken them.  
In addition to this, the control client is responsible for receiving performance key indicators from the Server, including the user number inside the vehicle and the driver's state. Regarding identification, this will have a value of 0 if the user is authorized to start the car and 1 if they are not authorized. In the case that the person is not authorized, the user number is 404. Finally, the driver's state has a value of 0 if they are awake and 1 if they are asleep.  
The entire project architecture is implemented using 5G networks. For this purpose, two 5G modems were used, each connected to one client, one to the Jetson Nano and the other to the control client. The use of 5G networks has the advantage of offering an ultra-fast connection speed, which enables instant data transfer, facilitating the download and real-time streaming of high-quality multimedia content, such as 4K or even 8K videos. Additionally, the low latency of 5G ensures an instant response in online applications and services, which are crucial aspects of the project that was carried out.  
Figure 3: Client-server scheme.
# A Survey on Satellite Communication System Security
## 4 Attacks in Satellite Communication Systems
### Integrity Threats
To successfully execute a nulling attack, the spoofer must be aware of its spatial relation to the victim receiver, as this knowledge is crucial for the precise delivery of the nulling signals. However, this type of attack is very difficult to successfully execute in the real world and remains largely theoretical. Additionally, there are still no known instances of its practical application [56].
## 1 Introduction
Specifically, these deficiencies included: (i) an assumption that a nominal model of the process under control is available; (ii) the process under control does not experience unmeasured disturbances during operation; (iii) the dynamics of the process under control are assumed time-invariant with known parameters and (iv) packet losses are not experienced between the controller and actuator nodes. In particular, the contributions of this paper are to extend the work of [14] to develop and test an adaptive compensator for feedback packet losses to help overcome limitations (i)-(iii), and to also introduce a predictive buffering mechanism to help overcome limitation (iv). In addition, we also describe a simple automatic controller tuning method, which uses the plant parameter estimates obtained from the adaptive compensator to calculate a robust Proportional Integral Derivative (PID)-like pole-placement controller. The overall method is applied to a servomotor example using a prototype Hardware-In-The-Loop (HIL) test facility to illustrate its effectiveness. The results obtained are contrasted with a buffering scheme using a frequency domain robust control design technique to compensate for packet losses, and shown to compare favorably. The compensator and buffering mechanism are based around a recursively updated Controlled Auto Regressive Integrated Moving Average (CARIMA) process model. The model provides predictions of the process state which include the effects of unmeasured disturbances, which are used to reconstruct estimates of a sequence of one or more missing data samples using a time history of successfully received process data. The model is also employed to recursively calculate future control actions (either using our suggested adaptive control law, or for any arbitrary causal control law) to enable the buffering technique. Under the assumption that a suitably rich time history of data has been available, the estimates will be  
Figure 1: Wireless sensor/actuator network (WSAN)-based Control System. optimal in the least squares sense, and the future calculated control moves will be (open-loop) optimal. Together, this affords the system a great deal of resilience against packet losses. The remainder of the article is organized as follows. Section 2 reviews some previous work in this area, while Section 3 states some assumptions. Section 4 presents the main descriptions of the proposed adaptive compensator for sensor packets, while Section 5 presents details of the proposed predictive buffering mechanism for control packets and the adaptive controller design. A prototype HIL test facility to test an implementation of the proposed techniques in a small WSAN is described in Section 6, and a case study and experimental results are discussed in this Section. After a short discussion, the paper is concluded in Section 7.
## 6 Research Areas
### Human Factors  
Humans and robots collect and process information differently. While humans can understand abstract and unstructured data easily, robots need structured instructions. On the other hand, robots can process and execute instructions more accurately than humans without tiring. Human-robot interactions deal with understanding the intent of the human operator and breaking it down into simple instructions that can be executed by the robot. At the same time, it also deals with presenting the raw data collected by robots in a form that makes sense to humans. Human-robot collaboration (HRC) can utilize the strengths of both agents [258].  
Augmented, virtual, and mixed reality are excellent tools for presenting site information in a human-friendly format. [PERSON] et al. [259] discovered that annotating the visual data captured by a UAV helps human operators perform inspection tasks by reducing their workload and decreasing the likelihood of missing an error in the work. [PERSON] et al. [164] augmented the live visual feed from a UAV with the BIM. [PERSON] et al. [175], on the other hand, augmented the as-planned BIM model with a reconstructed 3D model of the building created from UAV images in an augmented reality environment. Both of these methods used and converted information captured by robots into a more intuitive format for human evaluation. However, using augmented reality with BIM is challenged by the problem of alignment, as consistently keeping the BIM aligned with the reality in a moving UAV is a challenging task [164].  
Even though many studies are working on making robots more and more autonomous, full or partial human intervention is still required for the safe and efficient use of robots [260]. Robotics, being a fairly new technology, requires proper training for the operators [115; 260]. Virtual reality is found to be an excellent test-bed for prototyping new human-robot interactions [261; 262]. It provides a safe environment to train human operators without the risk of harm to the site, the robot, or any human [260].