esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis and Forecasting Process
### Introduction of the Dataset  
The dataset provided by a company composes the charging load data of a large charging station from April 2017 to June 2018. Given the complicated practical application, the dataset includes a number of data types, whereas only three featured data types are valuable for the forecasting model. These specific types are charging time, charging quantity and real-time electricity price and have been adopted as input respectively. The Liuyue station has many charging piles. Due to that the vehicle entering the charging station has a strong randomness in time, the selection of the charging pile is also random. The quality of the raw data is low, and it is therefore of importance for data pre-processing to the forecasting model.
# PAPR Reduction in UFMC for 5G Cellular Systems
## 2 The UFMC System Model  
The UFMC modulation format is a type of multi-carrier system, which converts high data rate signal into parallel lower rate streams. In contrast to OFDM, the UFMC system divides the entire band of \(N_{sc}\) sub-carriers into \(B\) sub-bands each contains \(N_{b}\) sub-carriers, where \(b=1,2,...,B\). Then, the data in each sub-band is processed by \(N\)-point IFFT and sub-band finite impulse response (FIR) filter, respectively. Next, the filtered signals in all sub-bands are added together and the time domain UFMC signal is generated. Figure 1 demonstrates the typical UFMC transceiver block diagram.
## 1 Introduction
[PERSON] assessed spatial variability in crop biomass in a 64-hectare wheat field using LiDAR technology [27]. Similarly, [PERSON] extracted 56 features using LiDAR technology to estimate forest above-ground biomass (AGB) and its components. They ranked and optimized these features using variable importance projection values derived from partial least squares regression [28]. [PERSON] compared direct and additive models for estimating tree biomass components using point cloud data, employing dynamic graph convolutional neural networks and octree convolutional networks [29]. [PERSON] proposed BioNet, a point cloud processing network for wheat biomass prediction, which incorporated attention-based fusion blocks for final biomass predictions [30]. [PERSON] developed a deep learning system to predict tree volume, AGB, and above-ground carbon stocks directly from LiDAR point cloud data, showing that an improved Minkowski convolutional neural network achieved the best regression performance [31]. [PERSON] introduced BioPM, an end-to-end prediction network that leveraged the upward growth characteristics of crops in wheat fields. Experimental results on public datasets demonstrated that BioPM outperformed both state-of-the-art non-deep learning methods and other deep learning approaches [32]. [PERSON] et al. proposed a point cloud regression method based on Minkowski convolutional neural networks, which significantly improved the accuracy of forest biomass prediction [33]. However, the existing methods for processing point cloud data often rely on basic geometric features, overlooking higher-order structures and multi-scale characteristics. This leads to a feature space that struggles to accurately reflect the mapping relationship between biomass and point cloud structures. Furthermore, biomass data inherently exhibits imbalance, and current deep learning methods have yet to effectively address the issue of features from minority samples being overlooked. To address the aforementioned challenges, this study proposed a point cloud-based deep contrastive regression method, Point Supervised Contrastive Deep Regression (PSCDR), which leverages the Chamfer distance metric to measure similarity between point clouds, fully utilizing their geometric properties. Additionally, a deep regression network, BMP_Net (BioMixerPoint_Net), incorporating neighbor point attention, was developed to enhance the capability of processing crop structural information. The objective is to provide a reference for rapid and accurate estimation of above-ground biomass and to enable dynamic monitoring.
## 3 Data Fusion  
Data fusion in BSNs is a procedure for processing data or information coming from multiple sensors with multi-level, multifaceted processing to make data more effective and meet users' needs better. BSNs produce a large amount of physiological data according to the application purpose. The way in which these data are manipulated is a fundamental issue faced by sensor node designers. Data fusion techniques combine data from multiple sensors and related information, which can achieve more accurate inferences compared to a single, independent sensor. It also can filter noise effectively, making predictions and inferences from monitored actions or phenomena [56]. At the same time, data fusion techniques can reduce data redundancy, and consequently reduce the load and energy consumption of BSNs, with the advantage of prolonging the network lifetime [57]. Not all data in BSNs needs to be fused, such as blood pressure, body temperature, and heart rate. Data of these types are measured by single sensors, and can reflect physiological performance directly. Other types of data which are measured from multiple sensor in BSNs, like heart waves, pulse waves, and data to identify people's motion, cannot be used directly. In these cases, data fusion techniques are necessary.  
There has been a lot of research in data fusion. Data fusion techniques were applied in the military field in early days, including automatic target recognition in the design of smart weapons, guidance of autonomous vehicles, remote sensors, and battlefield supervision. Later, they were applied in nonmilitary fields, such as robotics, automatic control of industrial manufacturing systems, and the development of smart buildings [58]. As a branch of WSNs, BSNs are also developing very fast in the fields of medicine [59], or activity recognition [60], in which data fusion techniques play an important role.
## 2 Materials and Methods
### SR-Micro-CT Analyses  
Synchrotron can generate X-rays at very high photon flux densities, with high brightness, small-angle beam divergence, a high level of polarization and coherence, with low emissivity, and with a possibility of monochromatization. This high flux allows for reaching very short total acquisition times of a few minutes compared to classical micro-CT. The high level of coherence enables exploitation of the wave nature of the X-rays to, for instance, perform phase-contrast CT--a technique that has been proven to dramatically increase the contrast of soft tissue while at the same time reducing the needed X-ray dose [29].  
Microtomography scans were performed at the \"Elettra SYRMEP\" synchrotron light source (Trieste, Italy) [30], using the white/pink beam micro-CT station in white beam configuration and, according to the dimension of the samples, projections were acquired over a total rotation of either 180\({}^{\circ}\) or 360\({}^{\circ}\) through steps of 0.2\({}^{\circ}\) (total of 900-1800 projections, respectively 2040 \(\times\) 2040-3870 \(\times\) 3870 pixels) with an average acquisition time of 100 ms per projection. The average beam energy was 22 keV and the sample to detector distance was set at 100 mm. Due to the different dimensions of the samples (length ranging from 2 mm to 3 cm), the specimens were studied at different resolution scales (with a pixel size from 2.44 \(\upmu\)m to 0.95 \(\upmu\)m). To obtain broad morphological data on both the internal anatomy and external morphology of the insects, the field of view was limited to parts of the insect (i.e., head, thorax, and abdomen) leading to multiple scans for each sample. Tomographic slices were not binned or reduced, and were reconstructed using the conventional filtered back-projection algorithm with the Shepp-Logan filter, and using the Syrmep Tomo Project (STP) software [31]. See Table 2 for an overview of the scan settings.
## 4 Optimization of Parasitic Motion
Moreover, the direction of the two revolute joints axes at the base should be parallel, while the remaining is perpendicular for both. Other example manipulators also have a similar result to that shown in Figure 5(a). Thus, the example can be representative. When the two spherical joints meet at one point, as shown in Figure 5(a), the design becomes complex. To avoid this problem, the radius of a limb perpendicular to the other two limbs can be adjusted to zero without loss of generality, as shown in Figure 5(b). Setting \(r_{p}=0\) for this particular limb does not violate the condition for \(v_{x}=v_{y}=0\). The numerical example is provided to show these manipulators have removed the parasitic motion in the required direction. Figures 7 and 8 are the task velocity for the optimal parasitic motion presented by taking the 3-RPS manipulator as a representative example. Figure 7 is the linear parasitic motion for the RPS manipulator shown in \[\delta v_{y} =f(r_{p},\xi_{2},\xi_{3},\alpha_{2},\alpha_{3},\psi,\theta,\phi, \omega_{x},\omega_{y})=\sum_{j=1}^{n}\sum_{k=1}^{m}v_{y}. \tag{35}\]0 The simulation result shows that the x-axis parasitic motion is zero in the entire rotational workspace. Similarly, the y-axis parasitic motion is eliminated when the condition for \(v_{y}=0\) is satisfied. Finally, the simulation for Figure 5(b) implies no parasitic motions in all directions. The result is shown in \[\delta v_{y} =f(r_{p},\xi_{2},\xi_{3},\alpha_{2},\alpha_{3},\psi,\theta,\phi, \omega_{x},\omega_{y})=\sum_{j=1}^{n}\sum_{k=1}^{m}v_{y}. \tag{35}\]1 Thus, the manipulator is capable of performing pure rotation about the center of the moving plate. The resulting motion is purely spherical but with a non-spherical spatial manipulator. It is known that the rotational motion about a fixed point has usually been achieved by a spherical manipulator. Achieving a pure independent rotational motion with non-spherical spatial manipulator can improve the problems that are existed in the spherical manipulators. \[\delta v_{y} =f(r_{p},\xi_{2},\xi_{3},\alpha_{2},\alpha_{3},\psi,\theta,\phi, \omega_{x},\omega_{y})=\sum_{j=1}^{n}\sum_{k=1}^{m}v_{y}. \tag{35}\]2 Optimal configuration for \(v_{x}=v_{y}=0\). During the optimization of the parasitic motion, two types of optimal structures are obtained, i.e., direction-specific optimal manipulator and manipulators with zero parasitic motion in all directions. Depending on the desired application, the direction-specific optimal manipulator can be chosen. For example, suppose the task strictly requires zero parasitic motion along the x-axis but loosens-up sensitivity for the y-parasitic motion. In that case, the result shown in Figure 5 can be used. Likewise, when the x-axis parasitic motion is not critical, the manipulator with the zero parasitic motion along the y-axis can be used.
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 2 Design of Proposed PIDD\({}^{\bullet}\) Controller
### Proposed PIDD* Controller  
The configuration of the proposed PIDD* will combine a FOPID controller and PIDD2 controller where the fractional calculus is only implemented on the second derivative term. The proposed design is to achieve a compromise between the robustness and superior performance of the controller to handle non-linear processes through the utilisation of non-integer differentiation while also reducing the increased complexity that the FOPIDD2 controller faced by only having five tuning parameters instead of seven.  
The PIDD* control action in Laplace's domain is given as,  
\[C(s)=\frac{U(s)}{E(s)}=\frac{U(s)}{R(s)-Y(s)}=K_{p}+\frac{K_{i}}{s}+K_{d1}s+K_{ d2}s^{\alpha}. \tag{9}\]  
In the above equation, \(K_{p}\), \(K_{i}\), \(K_{d1}\), and \(K_{d2}\) are the proportional, integral, and derivative constants. The order \(\alpha\) represents the non-integer differentiation parameter used in the controller. Figure 4 shows the block diagram of the proposed controller where the parameters can be tuned to control both linear and non-linear processes. A PID controller can be achieved when \(\alpha=0\) and PIDD2 controller can be achieved when \(\alpha=2\), allowing the proposed controller to control linear order and non-linear processes.  
It is important to note that the fractional-order operators can impact the controller performance, and reliable approximation methods are available to solve these fractional-order operators. The Oustaloop method and the Matsuda method are two commonly used approximation techniques. The Matsuda method involves obtaining a rational \(s^{\alpha}\) model through continued fraction expansion, which can create a transfer function of \(s^{\alpha}\) by fitting the original model at desired frequency points. The order of approximation, 'N', should be an even number to avoid an improper transfer function creation [45]. On the other hand, the Oustaloop method is the most widely used method for integer-order approximation of fractional-order operators. However, it is limited in fitting the expected frequency range in practical applications [46].
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Reliability Allocation Methodology Design
#### 4.2.1 Objective Function Selection  
The goal of mission reliability allocation is to minimize the cost of the ship electronic information system while satisfying the constraints. This means that in the design phase, it is necessary to find an optimal component reliability allocation scheme that minimizes the total cost of the system for a given reliability requirement. To achieve this goal, a mathematical model, often referred to as a cost model or cost function, is required.  
For each component in the ship electronic information system, let its cost be \(C_{i}\) and the corresponding cost coefficient be the following:  
\[k_{i}=\frac{C_{i}}{\sum_{i=1}^{n}C_{i}} \tag{7}\]  
where \(k_{i}\) is the cost coefficient of the \(i\)-th component, characterizing the component as a proportion of the total cost of the whole system; \(\sum_{i=1}^{n}C_{i}\) is the total system cost.  
Based on the understanding of the ship electronic information system, the cost function is defined as shown in Equation (8), taking into account conditions such as the importance and feasibility of the components, as follows.  
\[C_{i}(R_{i},f_{i},cr_{i},R_{i,\text{max}},k_{i})=k_{i}(1-f_{i})\ln\biggl{(} \frac{1}{R_{i,\text{max}}-R_{i}}\biggr{)}e^{\sigma_{i}} \tag{8}\]  
where \(f_{i}\) is the degree of technical feasibility, representing the degree of improvement in system reliability due to component improvement, the range of technical feasibility is defined as \([0.0-1.0]\); \(cr_{i}\) corresponds to the importance of the component, the importance of the role of the component in the system, including the degree of impact on the system performance, safety, stability, and other aspects. The range of importance is \([0.0-1.0]\); \(k_{i}\) is the cost coefficient of the \(i\)-th component.  
To ensure that the objective function of the ship electronic information system is to minimize the sum of system costs, the objective function formula is shown in Equation (9).  
\[\text{minC}=\sum_{i=1}^{n}C(R_{i}) \tag{9}\]Bringing the cost function Equation (8) into the objective function Equation (9), the objective function of the ship electronic information system can be obtained as presented in Equation (10).  
\[\text{min}C=\sum\limits_{i=1}^{n}C(R_{i})=\sum\limits_{i=1}^{n}k_{i}(1-f_{i}) \ln\biggl{(}\frac{1}{R_{i,\text{max}}-R_{i}}\biggr{)}e^{\sigma_{i}} \tag{10}\]
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Machine Learning for Drones  
Machine learning technology is a key technique that provides efficient solutions in wireless networks. The capability of this technology can be a key solution for mobility management issues of drones but deeper investigations are required. By offering training, this method provides continuous learning and improvement. The understanding of the drone's environmental impact will be enhanced with further research. This will enable unmanned aircraft systems to improve even further. Thus, this is a potential pathway to enable drones to become key connected components in future mobile networks.  
As an example, machine learning has been examined as a suitable technology for mobility prediction of drones, as investigated in [129]. Some research has been conducted in the literature to investigate the efficiency of machine learning for addressing mobility issues of connected drone networks [139; 142; 143]. However, to the best of our knowledge, sufficiently deep and numerous research works have yet to be conducted that can be considered to be comprehensive and efficient solutions for addressing the existing challenges. Thus, the work in this direction will be a key research area that needs to be examined in future research.
## 3 Fault Model and SH and GH Node Generation Mechanism  
Transient faults or soft errors are usually caused by high-energy protons, electromagnetic interference, or galactic cosmic rays, and they are considered one of the most daunting reliability challenges for microprocessors [22]. Transient faults can lead to random bit flips in hardware devices such as registers, and may further cause application crashes, hangs, and incorrect running results [25]. Although a soft error will not cause hardware damage or loss, the application errors caused by it may also lead to catastrophic failure [26]. At present, the closest approach to the actual situation is to conduct irradiation experiments using a particle accelerator. However, this method is expensive and difficult to schedule. Software-based simulation fault injection methods (such as LLFI) are more flexible and cost-effective and the effectiveness of LLFI has been demonstrated in [27]. Therefore, we use LLFI for the fault injection campaign in this paper. Based on LLVM, LLFI is able to perform static analysis and inject faults in selected locations in the program. At the same time, LLFI can inject faults into the routing programs by modifying the source or destination register values of the targeted instruction, potentially leading to the generation of SDC. This capability aligns with the requirements of this article. LLFI allows users to select the instructions to be injected in the configuration file, and its default injection option is all instructions that can be injected. In this paper, we use the default injection option.  
The main research object of this paper is the routing mechanism based on the Walker constellation architecture [28] and the time slot mechanism [29]. Through extensive research on SEU and SDC errors, we find that SEU may cause SDC errors in routing programs, and further cause SH and GH nodes in satellite networks, which will have a serious impact on the availability and reliability of satellite networks. Therefore, we conducted information collection and fault injection experiments on satellite routing programs at different phases. By analyzing the experimental data, we obtain the impact mode of SEU on routing programs, construct the fault model, and present the generation mechanism of SH and GH nodes.
## 4 Qualitative Findings and Discussion
### Key Design Challenges in Improving LSAD
#### 4.1.1 Stability  
All semi-rigid LSAD-using participants and some flexible LSAD-using participants reported varying degrees of slippage issues with LSAD during the activity. When slippage occurs, participants need to manually readjust the device by pulling it down. The shared expectation among all participants is that LSAD should demonstrate good stability to minimize the negative emotions associated with frequent adjustments during activities. For instance, Participant 7 mentioned: \"I think everyone who has worn it has experienced having to adjust it to the right position from time to time during activities [...], I think everyone should feel quite helpless\". Participants emphasized that improving device stability could potentially increase the utilization rate of LSAD during activities. In addition, for participants engaged in outdoor work, stability indirectly influenced the device's support effectiveness and their trust in the device.  
\"What I mean is, it would be great if it could be integrated with the pants because I need to lift my arms, squat [...], some larger range of motion. Wearing it while working is greatly affected, but I still need it. Can you understand? It always slips.\" (Participant 3)  
\"When I'm delivering or picking up goods, it slides with my clothes. Every time I finish, I must pull it down with my hands. It really affects my work a lot.\" (Participant 4)
## 2 Materials and Methods
### Overview of the Network
#### 2.2.2 Network Structure  
A sensor node is a device designed to detect specific physical attributes in its surrounding environment. It possesses the ability to process these collected data as needed and communicate them to other components within the network. Typically, it includes a portable, long-lasting power source, commonly in the form of a battery housed within its package. In our current setup, we introduce an energy harvesting module connected through a power management board. Figure 3 illustrates the fundamental layout of the proposed sensor node. We propose the utilization of a microcontroller unit equipped with peripheral control modules. These microcontroller processors can be of the 8-, 16-, or 32-bit variety in terms of their operational architecture. Some microcontrollers come with built-in BLE functionalities, while others do not include such capabilities. In cases where the microcontroller lacks integrated BLE functionalities, a separate standalone BLE module needs to be added. Our sensor node also integrates an external ultra-low-power timer module, specifically designed for operations demanding minimal power consumption. Lastly, the sensor node leverages the same power management system and energy storage module as used in [38] for consistency and efficiency.
## 3 Case Simulation and Result Analysis
### Simulation and Results Analysis
According to the simulation results, when the sonar transceiver combination is active working of second sonar and passive working of first and third sonars, the instantaneous detection probability and cumulative detection probability of the multi-sonar system are both the highest. The instantaneous detection probability and cumulative detection probability of the multi-sonar system under the other two forms of sonar transceiver combinations are almost the same. This is because the search routes of the first and third sonars are symmetrically distributed relative to the centerline of the task area and the disappearance position of the target. Therefore, regardless of who actively works, the final effect is the same for the first and third sonars.