esa-sceva/satcom-chunk-collection huggingface dataset:
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks  
While several requirements can be set for 5G, not all these requirements need to be fulfilled simultaneously and may vary in different underlying scenarios. For example, in video transmission, high data rate is required whereas latency and reliability can be relaxed. On the other hand, in autonomous cars, latency and reliability is necessary but data rate can be compromised slightly. A visual representation of the required features of 5G is shown in Figure 2. According to the literature, some of the engineering requirements for 5G are higher data rate, close to zero latency, device scalability, spectral efficiency and cost efficient.  
Close to zero latency and maximum throughput are key features of the next generation networks. The achievement of these features require efficient and reliable inter cells or intra cell hand-off (or handover). _Hand-off refers to the process of sharing the dynamics of mobile networks such as frequency, time slots, spreading code or combination of them without degradation of the quality of service._ Hence, hand-off management is critical for next-generation networks. Because of the core requirement of  
Figure 1: Evolution of mobile generation networks.  
next-generation networks such as ultra-densification, high mobility, accessing the multiple radio access technologies make the handoff management more complex. The next generation networks will be ultra dense comprising of the combination of microcells and macrocells and, thus, these will involve different types of hand-offs such as inter-macrocell handoffs, intra-macrocell handoffs and multi-RATs handoff, as shown in Figure 3.  
Recently, hand-off management has attracted the research community [8]. [PERSON] et al. [9] proposed an algorithm based on base stations (BSs) estimated load. In the algorithm proposed in [9], the hand-off task is shifted from highly loaded BSs to base stations with light load. Hence, this algorithm improves the quality of service. A user-centered multi-objective hand-off scheme has been presented in [10]. In this technique, the user calculates achievable data receiving rate and the blocking probability of each BSs prior to selection of new BSs for handoff. Through this mechanism, the achievable data receiving rate increases and the data blocking probability reduces. Other useful studies presented on hand-off include hand-off management in software-defined 5G networks, predictive hand-off mechanism, and fast and secure handover for 5G [11; 12; 13].  
In 5G networks, one of the aims is to get thousand times higher data rate than the existing cellular networks and close to zero latency. To achieve the goals of 5G, the following key technologies will be majorly used in 5G:  
* Ultra Densification
* Millimeter Wave
* Massive MIMO  
In the following text, we discuss these key technologies in more detail.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 4 Experiment Results and Discussions  
### Test Environment  
Experiments on the proposed system calibration method and image reconstruction method were carried out and are presented in this section. The test platform is the SS-SDES introduced in Section 2.1. A 406-nm laser is used to illuminate the optical surface at a particular angle with \(\theta_{i0}=135^{\circ}\) and \(\varphi_{i0}=90^{\circ}\). The setting value of the angular velocity of the rotation stage (ND110-65 FS-S204, NIKKI DENSO) is \(\omega_{0}=4\times 2\pi\) rad/s, and the translation velocity of the two-dimensional translation stage (custom made, positioning accuracy 3 \(\upmu\)m) is \(v_{0}=20\)\(\upmu\)m/s. Therefore, the actual size of each pixel is \(k_{0}=5\)\(\upmu\)m/pixel. The sampling frequency is \(f_{0}=10^{6}\) Hz.  
A standard plate fabricated through electron beam exposure (EBE) [23] and ion beam etching (IBE) [24] was employed as the test sample. It is a \(127\) mm \(\times 127\) mm square fused silica plate with a thickness of 2.54 mm. A group of standard defects with different sizes, such as scratches and digs, are etched on its surface. The minimum scratch width is 0.5 \(\upmu\)m, followed by 1, 2, 3,..., 40 \(\upmu\)m. Thus, the scratches can be used for the establishment of straightness constraints, and the dig pairs with known distances can be used for scale constraints. The system calibration and image reconstruction methods are executed on Matlab (R2011b), Windows 7 64-bit operating system (Intel Xeon E3-1535M v5 processor, 16 Gb DDR4 2133 MHz memory).  
It is worth mentioning that under these setting values, it takes nearly 70 min to scan the entire standard plate. The scanning speed depends on the translation velocity \(v_{0}\). In order to keep the pixel resolution \(k_{0}\) unchanged, when one increases the translation velocity, the rotation velocity must be increased accordingly. However, in our experiment, the rotation stage we used cannot provide a higher rotation velocity on the premise of stability, thus limiting the scanning efficiency. Nevertheless, this will not affect the verification of the proposed system calibration and image reconstruction methods. In the practical application of defect detections, the rotation velocity can be increased by replacing the rotation stage with a faster one, so as to improve the scanning speed.
## 3 Results
### Adsorption of Ultraviolet Radiation  
UV absorption profiles at different wavelengths. The percentage of UV light absorbance in FL membranes was between 51.56% and 82.45%. The FL70 membrane had the highest value. The absorbance activities of the LBM FC had absorbances between 29.43% and 52.01%. The lowest value was shown by the FC 30% mixture.  
Figure 5: FTIR spectra of different LBMs obtained (MA: _Aloe barbadensis Miller_ mucilage; MO: _Opuntia ficus-indica_ mucilage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wave Number (cm\({}^{-1}\))** & **Vibration Mode** & **Functional Groups** \\ \hline
3600 and 3200 cm\({}^{-1}\) & Stretching of the phenolic OH group & Specific phenols of aloe, including \\  & & flavonoids, flavonoids. \\ \hline
2900–2936 cm\({}^{-1}\) & C-H bond stretching of carbohydrates. & Oligosaccharides and polysaccharides. \\  & Flexion belonging to C-O-H, C-O-C bonds & \\ \hline
1608 cm\({}^{-1}\) & Plane bending C = C bonds of & Depending on the concentration and if it \\  & lignin molecules. & was LMB FC and FL. \\ \hline
1870–1540 cm\({}^{-1}\) & Ketones carbonyl group & Ketones with possible keto enolic balances. \\ \hline
650–1580 cm\({}^{-1}\) & Stretch = CO & Amino acids and polysaccharides. \\ \hline
700–650 cm\({}^{-1}\) & Acetyl groups & These grant biological activity \\ \hline
1185–1045 cm\({}^{-1}\) & Stretch (CO, ether bond) and & Stretching of glycosidic bonds (CO, \\  & the pyramid rings (CC) stretch & ether bond) and pyramid rings (CC) \\  & the pyramid rings (CC) stretch & attributed to polysaccharides that are part \\  & & of pectins and mucilage. \\ \hline
3298 cm\({}^{-1}\) & Stretching of the hydroxyl groups, -OH & Carboxyl acids, amino acids, alcohols, \\  & & and water molecules. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Infrared absorption bands for the different LBMs.  
LBMs with long fibrous material exhibited better UV light absorbance, indicating better material distribution compared to LBMs with FC material. It is observed that membranes with a higher content of fibrous material can prevent the passage of UV light towards the lux meter, which increases the absorbance intensity.  
The different membranes showed a concentration-response relationship since the UV absorbance increased gradually with increasing concentration of the fibrous material. The result of the one-way ANOVA test shows that there is no significant difference among different wavelengths, but in the conformation, they are significantly different at \(p\)-value (\(p>0.05\)), as is shown in Table 6.
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 4 Results and Discussion
### Vibration Signals with Induced Noise  
The vibration data may contain measurement errors and noise as a result of experimental measurements. The level of induced noise depends on the application and the used measuring device. In the vibration measurements, the additive noise followed a Gaussian distribution [43; 44]. To study the effect of the noise on the performance of the Detrended Fluctuation Analysis (DFA) and the proposed method, we considered an additive zero-mean white Gaussian noise with a variable standard deviation (\(\sigma\)) to simulate the different  
Figure 11: (**a**) Scaling exponent (\(\alpha_{1}\)) distributions of bearing vibration signals (1000 samples each) collected from faulted bearings. (**b**) ROC curve for the proposed method. (**c**) Accuracy vs. cutoff plot.  
Figure 10: Scaling exponents of bearing vibration signals (12,000 samples/s) at short- and long-time scales.  
levels of the noise. For each noise level, we calculated the signal-to-noise ratio (SNR) as the ratio of the vibration signal power to the power of the noise.  
For this paper, the scaling exponent (\(\alpha_{1}\)) was proposed as a new diagnostic parameter of the bearing health. So, we focused here on the effect of the induced noise on the estimated scaling exponent. At each SNR, we calculated the scaling exponents (\(\alpha_{1}\)) of the vibration signals (induced by noise) from bearings under the three health conditions (normal, minor fault, and severe fault). Subsequently, we calculated the mean relative errors in the scaling exponent versus the corresponding SNR, as shown in Figure 12. In normal bearings, the relative errors in the scaling exponent remained below 10% for all the SNRs higher than 9 dB. However, the faulted bearings had mean relative errors in the scaling exponent below 10% for SNR \(\geq\) 12 dB. From Figure 12, it is clear that the white Gaussian noise had a negligible effect on the estimated scaling exponents for SNR \(\geq\) 18 dB.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 3 Characterization of the Observables  
This section describes the methodology to characterize the GNSS and cellular observables. This methodology is aimed at reducing the complexity of the simulations, in order to ensure a clear reproducibility and to focus on the main impairments of each technology.
## 4 Sensitive Analysis of Rotor Parameters
### Response Surface Analysis  
The optimization model is built based on the Optimization toolkit in Ansys Workbench. Figure 9 summarizes the fitting accuracy of the Kriging surrogate model and 2D-FEM calculation results. The Kriging surrogate model is an unbiased estimation model that predicts the results of uncalculated test points on the basis of calculated test sample data. It can be seen that the predicted values are highly consistent with the calculated values. The response surface of rotor optimization parameters to motor performance is shown in Figures 10-12.  
Figure 10: Response surface of bridge thickness and stiffener thickness for motor performance. (**a**) Efficiency; (**b**) rotor core loss; (**c**) rotor stress; (**d**) line back-EMF.  
Figure 9: Fitted curve of the predicted and calculated values.  
Figure 10 presents the effects of the bridge thickness and stiffener thickness on the efficiency, rotor core loss, rotor stress, and back-EMF. The efficiency of the motor first increases and then decreases with the increase in the bridge and stiffener thicknesses. The rotor core loss generally decreases with the increase in the bridge thickness. It can be seen from Figure 10c,d that the rotor stress and line back-EMF all decrease obviously with the increase in the bridge and stiffener thicknesses.  
The influence of bridge thickness and rib width on line back-EMF and rotor stress is displayed in Figure 11. It is obvious that bridge thickness has a greater impact on  
Figure 11: Response surface of bridge thickness and rib width for motor performance. (**a**) Line back-EMF; (**b**) rotor stress.  
Figure 12: Response surface of PM thickness and PM width for motor performance. (**a**) Rotor core loss; (**b**) rotor stress; (**c**) back-EMF; (**d**) power factor.  
motor performance. Figure 12 illustrates the effects of PM thickness and width on motor performance. The rotor core loss generally decreases with the increase in PM thickness. With the increase in the number of PMs, the rotor stress, no-load back-EMF, and power factor all increase significantly.
## 1 Introduction
### Problem Statement  
The integration of power internet of things (PIoT) into smart grid systems has led to significant advancements in energy management and resource optimization. However, existing research processes and demand management schemes in smart grid systems lack the novel and efficient methods required to fully capitalize on the potential of (PIoT) technology.  
The following key issues will be addressed in this address:  
* [noitemsep,topsep=0 pt]
* Inadequate demand management schemes: Current demand management schemes in smart grid systems fail to provide efficient energy consumption control and optimization, leading to wasted resources and poor system performance.
* Limited use of PIoT capabilities: Existing research processes have not fully explored the capabilities of PIoT technology in the context of smart grid systems, which hinders the development of effective and innovative demand management solutions.
* Integration challenges: Integrating PIoT technology with smart grid systems poses numerous challenges, such as data security, privacy concerns, and interoperability issues, which need to be addressed to ensure seamless and efficient implementation.
* System reliability: Ensuring the reliability of the PIoT-based smart grid system is critical for maintaining a consistent power supply and minimizing the risk of blackouts or other disruptions.
* Demand-side management: Developing effective strategies for demand-side management is essential for balancing energy consumption and generation, reducing peak demand, and promoting energy efficiency.  
Figure 2: Commonly used IoT technologies [11].
## 5 Comparing Detection Results with the Related Works in AWID  
Table 7 shows the detection results of the related works in AWID compared with the proposed CTDI model. CTDI applies the three data enhancement approaches: feature selection (FS), data balancing (DB), and Auto Encoder (AE), and CTDI achieves the best results for detecting all three attacks. [PERSON] et al. [17] only detected impersonation attacks, so they could achieve the highest results. As we reproduce the results of its selected features against the three attacks, its results become much lower than those of CTDI. The author of AWID [15] adopted J48 as its best classifier with FS. Its results show that the baseline of AWID is lower than that of CTDI.  
[PERSON] et al. [18] adopted SVM as the classifier and applied FS, DB, and AE, and it shows the second-best results in Table 7. [PERSON] et al. [19] adopted Logical Regression as the classifier with AE, and its results are slightly lower than [PERSON] et al.'s method [18]. They both adopt machine learning-based classifiers with AE to achieve good detection results, but they are still worse than CTDI. The combination of reinforcement learning (RL) with AE [20] shows lower accuracy, but its F1 is relatively high. This shows RL is less effective in detecting normal behaviors in AWID. The semi-boosted tree with FS and DB [16] has lower results than those employing DNN or AE. In conclusion, the proposed CTDI shows the best detection result among the related works for the three types of attacks in AWID. This shows that the proposed CTDI, which chooses the selected features with high differences between normal and attack behaviors, is very effective for wireless attacks.
## 4 Experimental Evaluation
### Experimental Setup  
The exploration environment is a two-dimensional grid map with a random distribution of landmarks (number proportional to the map area) and obstacles (number proportional to the map edge length). It is assumed that the landmarks can be recognized once they enter the detection range of the robot. In addition, the horizontal view of the robot is set to 360 degrees, and the robot measurement range is set to 5 m. Assume the robot is able to rotate from \(-\)180 degrees to 180 degrees, and the noise in the simulation is Gaussian noise. The grid size is 2 m \(\times\) 2 m, and the initial occupancy probability of each grid is 0.5. In this setting, the density of landmarks in the map is 3 landmarks per 100 grids.  
Each time a new environment is reset, the position of landmarks and obstacles, and the initial position of the robot in the map are generated randomly. The robot has to avoid obstacles and landmarks while traveling to the target. First, we select the five points closest to the robot and the three points closest to each landmark from frontier waypoints as candidates. Then, the robot evaluates the candidates by our algorithm and selects the best one among them. Then the robot uses the A* algorithm to plan a collision-free path to achieve the goal. The exploration is finished while 90% of the area is checked by the robot. It is important to note that the models with obstacle avoidance and path planning module are closer to the actual situation.  
An example exploration environment is shown in Figure 6, in which the frontier waypoints serve as candidate target points. The robot continually changes the local target and navigates to the local destination while updating the observed environment information until covering the whole map. The size of the map is 20 m \(\times\) 20 m for training and 20 m \(\times\) 20 m, 30 m \(\times\) 30 m, and 40 m \(\times\) 40 m for testing.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### State Splitting Procedure
#### 2.4.1 State Splitting Procedure for Power Minimization  
Using the classic state encoding methods, exactly one orthogonal code is assigned to each internal state. This implies applying codes with a Hamming distance greater than one. It may lead to an increase in switching activity of the memory elements used for saving the codes of FSM states. Of course, it is difficult or even impossible to guarantee a Hamming distance equal to 1 for all codes. The splitting of the internal states is one of the solutions to this problem. This operation gives more chances to find the couple of codes with the Hamming distance equal to one. Therefore, this should lead also to the decrease in power dissipation in the FSM [7].  
Let \(X_{P}(a_{i})=\{z\in Z\colon\varphi(a_{j},z)=a_{i},a_{i}\in A,a_{j}\in A\}\) be the set of all input vectors, which cause the transitions to the state \(a_{i}\). Let \(X_{F}(a_{i})=\{z\in Z\colon\varphi(a_{i},z)=a_{k},a_{i}\in A,a_{k}\in A\}\) be the set of all input vectors, which trigger the transition from the state \(a_{i}\).  
For any state \(a_{i}\), \(\text{card}(X_{P}(a_{i}))>1\) can be split into two new states \(a_{i}^{(1)}\) and \(a_{i}^{(2)}\). After this procedure, the state \(a_{i}\) is substituted with states \(a_{i}^{(1)}\) and \(a_{i}^{(2)}\) such that we have the following:  
* Sets \(X_{F}\) for the new states are the same as the set for source state: \[X_{F}\Big{(}a_{i}^{(1)}\Big{)}=X_{F}\Big{(}a_{i}^{(2)}\Big{)}=X_{F}(a_{i}),\] (15)
* Set \(X_{P}\) of the source state \(a_{i}\) is split into two individual components: \[X_{P}\Big{(}a_{i}^{(1)}\Big{)}\cup X_{P}\Big{(}a_{i}^{(2)}\Big{)}=X_{P}(a_{i}),X_{P}\Big{(}a_{i}^{(1)}\Big{)}\cap X_{P}\Big{(}a_{i}^{(2)}\Big{)}=\varnothing.\] (16)  
The procedure of splitting the internal states of the FSM is reversible, hence the machine can return to its previous form by the merging of the states \(a_{i}^{(1)}\) and \(a_{i}^{(2)}\) intoone state \(a_{i}\). After splitting, the number internal states are greater for the final FSM, but the average number of the input vectors that cause the transitions to the state is lower. Additionally, it is more feasible to assign the codes with a smaller value of the Hamming distance, which causes the lower power consumption in the synthesized FSM.
# Study and Investigation on 5G Technology: A Systematic Review
## 2 Existing Surveys and Their Applicability
### Article Organization  
This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3, the preliminaries of 5G technology are presented. In Section 4, recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5, a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7, 5G technology based on above-stated challenges summarize in tabular form. Finally, Sections 8 and 9 conclude the study, which paves the path for future research.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Authors\& References** & **MIMO** & **NOMA** & **MmWave** & **5G IOT** & **5G ML** & \begin{tabular}{c} **Small** \\ **Cell** \\ \end{tabular} & \
\begin{tabular}{c} **Beamforming** \\ \end{tabular} &
\begin{tabular}{c} **MEC** \\ **Optimization** \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [7] & Yes & - & Yes & - & - & - & Yes & - & - \\ [PERSON] et al. [8] & Yes & - & Yes & - & - & - & - & - & - \\ [PERSON] and [PERSON] [9] & - & Yes & - & - & - & - & - & Yes & - \\ [PERSON] and [PERSON] [10] & - & Yes & - & - & - & - & - & - & Yes \\ [PERSON] et al. [11] & - & - & Yes & - & - & Yes & - & - & - \\ [PERSON] et al. [12] & - & - & Yes & - & - & - & - & - & Yes \\ [PERSON] et al. [13] & Yes & - & Yes & - & - & - & - & - & - \\ [PERSON] et al. [14] & Yes & Yes & - & Yes & - & - & - & - & - \\ [PERSON] et al. [15] & - & - & - & - & Yes & - & - & - & Yes \\ [PERSON] and jha [16] & - & - & - & - & - & Yes & - & - & Yes \\ [PERSON] et al. [17] & - & Yes & - & - & - & - & - & - & - \\ [PERSON] et al. [18] & - & - & - & - & - & - & Yes & - & - & - \\ [PERSON] et al. [19] & Yes & - & - & - & - & - & Yes & - & - \\ [PERSON] et al. [20] & Yes & - & Yes & - & - & Yes & Yes & - & - \\ [PERSON] [21] & Yes & - & - & - & - & - & Yes & - & - \\ [PERSON] et al. [22] & - & Yes & Yes & - & - & - & - & - & - \\ [PERSON] et al. [23] & - & - & - & - & - & - & - & & Yes & - \\ [PERSON] et al. [24] & - & - & - & - & - & - & - & & Yes & - \\ [PERSON] and [PERSON] [25] & Yes & - & - & - & - & Yes & - & Yes & - \\ Our Survey & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparative overview of existing surveys on different technologies of 5G networks.