esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bending Effect on Wearable Antennas  
Different substrate materials have an impact on how wearable antennas flex. Figure 6 illustrates how the application determines the placement of wearable antennas on the body. Geometric or physical antennas' orientation or form modification is called the 'bending of antennas of various positions'. Several factors can affect antenna behavior. When bent, antenna radiation patterns show energy dispersion in space during transmission or reception. Bent antennas can yield unexpected lobes and nulls. Bending helps wearable, flexible, or conformable antennas fit in tight areas or curves. Their structure makes these antennas bend-resistant. Most antenna designs allow for deliberate bending or reconfiguration to change the antenna's properties, such as pattern shaping or frequency tuning. When discussing bending antennas in different locations, a wide range of factors are considered, ranging from mechanical and physical features to electrical properties, including impedance, polarization, and emission patterns. Effective antenna design and deployment depend on an awareness of the substantial influence that any physical modifications to an antenna can have on its performance [76].  
Figure 6: Bending of antennas of various positions.
## 4 Computational Fluid Dynamics (CFD) Simulations
### Simulation Results of the Quadrotor  
The pressure distribution around the rotor tip with the typical rotor spacing of L/R = 2.2, L/R = 2.6, L/R = 3.2, and L/R = 3.6 are shown in Figure 9.  
Obviously, the pressure difference between the upper and lower surfaces of the rotor provides thrust for the rotors. Additionally, the pressure difference on the rotor surface at smaller rotor spacing L/R = 2.2 and L/R = 2.6 lead to a stronger aerodynamic interference between adjacent rotors. Especially, for L/R = 3.6, the pressure difference is larger than other rotor spacings, which obtains larger thrust. Moreover, the pressure difference decreased when rotor spacings is larger than L/R = 3.6, which may be due to the decreased interference between the rotors.  
Figure 10 shows the velocity distribution between adjacent rotors with different rotor spacing.  
Figure 8: Mesh distribution.  
Figure 9: Pressure distribution: (a) L/R = 2.2; (b) L/R = 2.6; (c) L/R = 3.2; (d) L/R = 3.6.  
As shown in the figure, the velocity is larger near the rotor surface, and there is strong aerodynamic interference between adjacent rotors at L/R = 2.2. The downwash flows attract and interact with each other, which may increase more power consumption. Moreover, the aerodynamic interference between downwash flows decreases gradually with the increase of rotor spacing, and the interference is much weaker at L/R = 3.6. Therefore, according to the analysis in Figures 4 and 5, it illustrates that appropriate rotor spacing with moderate interference can bring larger thrust or lower power consumption for the quadrotor.  
Figure 11 shows the velocity vector distribution of adjacent rotors with different rotor spacing.  
Figure 11: Cont.  
Figure 10: Velocity distribution: (**a**) L/R = 2.2; (**b**) L/R = 3.2; (**c**) L/R = 3.6.  
It can be seen that the vortex is formed under the oblique part of the blades, circling along the circumferential direction. With the increase of rotor spacing, the interaction between rotors is weakened and the vortices under the rotor gradually expand outwards, eventually forming a large vortex from the bottom to the top of rotor. Additionally, it is clear that the vortex is larger and more widely distributed at L/R = 3.6 and L/R = 4.0 compared with other rotor spacing, which may bring smaller power consumption for the quadrotor.  
Figure 12 shows the vorticity distribution of the quadrotor with different rotor spacing.  
It can be found that the vorticity interacts with each other at L/R = 2.2, which causes the vortex to be damaged, resulting in deformation. As the rotor spacing increases, vorticity interference is much weaker. For the rotor spacing at L/R = 3.6, the vortex maintains a complete shape without interaction, which may bring larger thrust and smaller power. However, if the rotor spacing is further extended, the extra power consumption may be introduced with longer rotor arm support.
## 4 Performance Evaluation and Comparison
### Evaluation Metrics
#### 4.1.3 Recall (Sensitivity or True Positive Rate)  
Recall, also known as sensitivity or true positive rate, offers an assessment of the model's capacity to accurately identify positive instances in contrast to the actual positive instances existing within the dataset. The ratio of true positive predictions to the total actual positive instances determines the recall value. Mathematically, recall is calculated as follows:  
\[Recall=TP/(TP+FN)\]
## 3 Construction of Fault Diagnosis Model  
### Optimization Principle  
Following the VMD, it is imperative that the essential information of the original vibration signal from the rolling bearing is preserved, and that the decomposed signal accurately represents its intrinsic characteristics. Failure to achieve this renders the decomposition process ineffective. The criteria for successful decomposition are twofold: (1) the decomposed signal must retain a substantial amount of the information contained in the original signal and (2) the fault characteristics should be more pronounced post-decomposition. Consequently, this study employs envelope entropy as a metric to quantify the effectiveness of the decomposition in accordance with these established criteria.  
To compute the envelope entropy, one must first derive the envelope of the signal using the Hilbert transform. Let the input signal be denoted as \(x\) (\(t\)), and the envelope \(env\) (\(t\)) can be determined through the following steps:  
Firstly, the analytic signal \(\hat{x}\) (\(t\)) is derived by applying the Hilbert transform to \(x\) (\(t\)):  
\[\hat{x}(t)=x(t)+j\cdot H\{x(t)\} \tag{26}\]  
Subsequently, the envelope \(env\) (\(t\)) of the signal is computed:  
\[env(t)=\mid\hat{x}(t)\mid=\sqrt{x(t)^{2}+H\{x(t)\}^{2}} \tag{27}\]To calculate the entropy value, the signal envelope is normalized to align with the characteristics of a probability distribution. The resulting normalized probability distribution, denoted as \(p\left(t\right)\), is as follows:  
\[p(t)=\frac{env(t)}{\sum_{i=1}^{N}env(t)} \tag{28}\]  
The envelope entropy is ultimately computed using the normalized envelope described above, and its expression is presented as follows:  
\[H=-\sum_{i=1}^{N}p(i)\text{log}(p(i)) \tag{29}\]  
Envelope entropy serves as a metric for assessing the complexity of a signal, where elevated entropy values signify increased complexity, and diminished entropy values correspond to simpler modulation patterns. Employing minimum envelope entropy as the optimization criterion for VMD guarantees the preservation of fundamental signal characteristics while yielding decomposed modes that are both concise and interpretable.  
In the context of optimizing LSTM networks, the minimum fitness value, denoted as \(Fitness=1-Accuracy\), serves as the objective function. By minimizing this fitness value, one directly maximizes the accuracy of the model, thereby improving the accuracy of classification diagnoses.
## 4 Measurement Results
### ML-Csp-Ovr  
Among the three ML models, ML_CSP_OVR performs the best for both intrasubject and intersubject classification. For intrasubject classification, as shown in Figure 29, participant 12 achieves an accuracy of 90.5%, with both sensitivity and precision exceeding 90%. The lowest accuracy is 69.5% for participant 1. For intersubject classification, the accuracy achieved is 78.08%, with precision and sensitivity exceeding 88% for two out of four classes (see Figure 30).  
**Intersubject classification**  
Figure 28: One corner of the ML Model 2 framework.  
Figure 29: (**a**) Accuracy of ML Model 1. (**b**) Precision and sensitivity of ML-CSP-OVR.  
**Intersubject classification (Leave One Out)**  
Given the strong performance in both intrasubject and intersubject classifications, the task is extended to include intersubject classification with leave-one-out. This is the most challenging task, as EEG signals are known to be subject-specific, differing from one participant to another. In this setup, participant 12 (P12) is excluded from the training set, meaning the machine learning model has not seen any data related to P12 during training. After training, P12 is used as the test set. As shown in Figure 31, the ML-CSP-OVR model performs similarly to the intersubject classification without leave-one-out, achieving an accuracy of 76.39%. However, the confusion matrix indicates that the model struggles to discriminate between the RL and RV classes. This difficulty may be due to the proximity of the two actions, which are executed almost simultaneously. The extracted slices for the RL and RV classes overlap, leading to similarities that make it challenging for the model to differentiate between the two classes.  
Figure 31: Precision and Sensitivity of ML-CSP-OVR for intersubject classification with leave-one-out (**top**). Result of the Confusion Matrix (**bottom**).  
Figure 30: Precision and Sensitivity of ML-CSP-OVR for intersubject classification.  
Classification with the GoogLeNet classifier without using the CSP-OVR method was conducted to compare the performance of the CSP-OVR method. The training and validation sets are made of scalograms, generated using only the continuous wavelet transform. Figure 32 shows the result of this classification, whose accuracy is 26.82%, which is very close to 25% (the probability of randomly guessing four classes).
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 5 Conclusions  
This study has demonstrated an approach to urban localisation using ray-traced data and LSSVMs. It demonstrates that the direct localisation approach provides better localisation accuracy compared to the process of NLOS identification and mitigation and then exploiting the traditional localisation algorithms like TDOA and TOA. Because the direct approach is essentially a single BS localisation scheme, it has been demonstrated that AOA errors can greatly affect the location accuracy. In such circumstances, multi BS schemes like TDOA may be able to handle errors in one BS better. Granularity and performance of the Direct method can be further controlled by the size of training data. A larger training data size improves the tuning parameters. Training can also be done per BS, with the tuning parameters stored and referenced per each BS. More BSs can be used to obtain estimates for an MS position, and if each estimate can be assigned some confidence value, the use of multiple BSs can improve accuracy.  
The sensitivity of the approach with mobile scatterers is not currently known and requires further study. There are possibilities to mitigate the effects of mobile scatterers by identifying these components through identifying Doppler shift, and eliminating them when comparing against the ray-tracing data. There is an analogy with radar processing in removing clutter, in this case. Further processing using similar techniques, to remove multipath components as a resulting of moving scatterers, will be the subject of further research. Also raising the height of the BSs may provide deterministic multipath components, if the main scatters are assumed to be traffic. Densification through deployment of numerous micro BSs on street lamp posts may actually mean the probability of getting a LOS component increases. Mobility of the MS can also be a function of how fast the snapshot of measurements are processed. Availability of enough processing resources should make periodic estimation possible. Further accuracy for mobility or tracking scenarios can be improved by hybrid data fusion with the MS's sensor data.  
Figure 10: TDOA vs. Direct method in a LOS environment.  
**Author Contributions:** [PERSON] conceptualized this research, conducted the data processing, wrote methodology, and revised the manuscript. [PERSON] gave comments and suggestions. [PERSON] was responsible for the original idea for the research and gave comments and suggestions. [PERSON] supported funding and administrated the research.  
**Funding:** This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training (CDT) in Communications (EP/I028153/1) and Roke Manor Research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
###### Abstract  
Smart energy has evolved over the years to include multiple domains integrated across multiple technology themes, such as electricity, smart grid, and logistics, linked through communication technology and processed in the cloud in a holistic way to deliver on global challenges. Advances in sensing, communication, and computation technologies have been made that enable better smart system implementations. In smart energy systems, sensing technologies have spanned multiple domains with newer techniques that are more accurate, have greater dynamic ranges, and are more reliable. Similarly, communication techniques have now evolved into very high-speed, flexible, and dynamic systems. Computation techniques have seen a quantum leap with greater integration, powerful computing engines, and versatile software stacks that are easily available and modifiable. Finally, the system integration has also seen advances in the form of management, automation, and analytics paradigms. Consequently, smart energy systems have witnessed a revolutionary transformation. The complexity has correspondingly grown exponentially. With regard to smart meters, the measurement component has to scale up to meet the demands of the evolved energy eco-system by relying on the advancements offered. The internet of things (IoT) is a key technology enabler in this scenario, and the smart meter is a key component. In recent years, metering technology has evolved in both complexity and functionality. Therefore, it must use the advances offered by IoT to deliver a new role. The internet of things (IoT) is a key technology enabler in this scenario and the smart meter a key component. In recent years, metering technology has evolved in both complexity and functionality. To deliver on its new role, it must use the advances offered by IoT. In this review, we analyze the smart meter as a combination of sensing, computing, and communication nodes for flexible and complex design paradigms. The components are, in turn, reviewed vis-a-vis the advances offered by IoT. The resultant gaps are reported for future design challenges in the conclusion. The identified gaps are the lack of usage of the full spectrum of the available technology and the lack of an inter-disciplinary approach to smart meter design.  
smart meters; smart energy; IoT; sensing; metrology; 5G +
Footnote †: journal: 16 February 2023
## 3 Results and Discussion
### Effect of Fractionation on Lignin Nanoparticle Synthesis
#### 3.3.2 Colloidal Properties  
Figure 3B shows the stability of a lignin nanoparticle surface charge of NP-F1 to NP-F3. There was no trend observed in the zeta potentials of nanoparticle surfaces between the fractions. The Zeta potential of NP-F1 was -30.6 mV. The potential was at a minimum with F2 (-34.0 mV) and a maximum with F3 (-29.8 mV). NP-F1 and NP-F2 showed under -30 mV and stable zeta potential.  
**4. Conclusions**  
In this study, fractionation of SL by sequential solvent extraction was carried out to obtain lignin fractions with remarkable molecular weight differences, and lignin nanoparticles were produced by nanoprecipitation with the fractions at three different concentrations. The average particle size and surface charge increased with increasing pre-dialysis concentration. The average particle size was very similar between NP-C2 and NP-F1, and increased with further fractionation. Generally, polymeric nanoparticle size increases with increasing molecular weight and amount. However, nanoparticle size increased by increasing SL concentration, and there was also a small change in particle size with increasing molecular weight. These results do not follow the general trend in polymeric nanoparticle formation since raw lignin (SL) and derived fractions have different internal linkage styles, functional group ratio, and amount compared to plastics or several synthetic biopolymers. Further studies are in progress to learn additional details regarding the chemical structures and diffusion coefficients that affect nanoparticle formation of the fractions. In future, these nanoparticles could be used as reinforcement, surfactant, and UV block, or in material delivery system such as pesticide, antioxidant, and anticancer carrier.  
**Author Contributions:** Conceptualization, J.W.C.; data curation, J.H.L.; methodology, J.H.L. and S.Y.P.; writing--original draft preparation, J.H.L.; writing--review and editing, I.-G.C. and J.W.C.; supervision, J.W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Basic Science Research Program (NRF-2019R1A2C2086328) of the National Research Foundation funded by the Ministry of Science and ICT.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Simulations and Results
### Simulation Results  
Five testing scenarios were used to benchmark the proposed RLCA against the LQI controller. Simulations evaluated each controller's performance individually for the UAV model being considered. The closed-loop system ensures continuous tracking of the UAV's longitudinal position reference, under every operating condition. In each case, an additive white Gaussian noise signal was introduced in the system's reference input to emulate the impact of sensor noise in real-time flight scenarios.  
**A.**: _Step reference tracking:_ This simulation case is used to evaluate the controller's ability to track step changes in altitude under nominal (disturbance-free) conditions. The UAV longitudinal model with nominal parameters is used. The test is performed by tasking the UAV to track a step reference trajectory of +1.0 deg. The variations in the pitch angle of the LQI controller and the RLCA are shown in Figure 9.  
Figure 9: Step reference tracking response of the UAV under nominal conditions.  
_Ramp reference tracking:_ This test case assesses the UAV's ability to follow continuously changing altitude as a result of cruise altitude adjustments for steady ascent (or descent) during long-range flights. A ramp trajectory closely mimics how a UAV's autopilot system adjusts altitude smoothly during the takeoff phase or the landing phase. Hence, this test is performed by tasking the UAV model to track the ramp reference trajectory of 4.0 deg. peak-to-peak amplitude, +3.0 deg. offset, and a frequency of 0.1 Hz to represent a gradual altitude climb and descent. The reference tracking accuracy (or lag) manifested by each controller is presented in Figure 10.  
_Impulsive disturbance suppression:_ This simulation is used to test the UAV's disturbance rejection ability against sudden external forces caused by wind gusts or mid-air collision with flying objects. The test is performed by introducing an external pulse of \(\pm\)2.0 V magnitude and 100 ms duration in the control input signal at t = 2.0 s and t = 3.5 s, respectively, to observe the system's transient response. This arrangement emulates the application of a short-duration impulsive force under steady flight conditions of the UAV. The time domain profiles of the UAV's pitch angle, under the influence of each controller, are shown in Figure 11.  
_Step disturbance rejection:_ This simulation case examines the UAV's response to sustained external disturbances, such as constant wind disturbance or a sudden change in elevator deflection. The test is performed by introducing an external step signal of +1.0 V magnitude and 100 ms duration in the control input signal at t = 2.0 s to observe the system's disturbance recovery response. This arrangement emulates the  
Figure 11: Step reference tracking response of the UAV under impulsive disturbances.  
Figure 10: Ramp reference tracking response of the UAV under nominal conditions.  
application of a constant external force under steady flight conditions of the UAV. The time domain profiles of the UAV's pitch angle, under the influence of each controller, are shown in Figure 12.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 3 Group Mapping Query Tree Algorithm  
### Manchester Coding  
Manchester coding is widely used for collision detection in tree-based algorithm [12; 13; 14]. In Manchester code, the value of a bit is defined by the change in level within a bit window. The negative transition means a bit '0', whereas the positive transition means a bit '1'. In an RFID system, if more than two tags transmit bits with different values, then the negative and positive transition will counteract. This result is impermissible in Manchester coding during data transmission. Therefore, Manchester code makes it possible to trace a collision to an individual bit and find where the collided bit is [15]. Supposing there are two tags, the principle of tracing collision bits with Manchester code is shown in Figure 1. The number of collision bits detected by the reader are 4 and 5. Manchester coding is also used in this paper to achieve the purpose of accurately detecting collision bits in the received data.  
Figure 1: The sketch map of tracing the collision bits with Manchester coding.