esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Factor Analysis of Elevator Brake Failure Based on DEMATEL-ISM
## 2 Identification of Different Risk Factors for Elevator Brake Failure  
### Identification of Equipment Failure Risk Factors  
FMEA is an analysis method for finding faults from a reliability point of view. It involves analyzing the occurrence of the failure of each component unit in the equipment and evaluating the impact of these failures on the overall equipment [21]. The process typically follows a structured approach of'structure-function-failure-risk', assessing the function of each component based on the equipment's design. Then, the risk is assessed qualitatively based on three indicators: Severity, Occurrence and Detection. The primary advantage is that the equipment structure is decomposed to comprehensively consider all possible failure modes and influencing factors in a form that does not miss or repeat, which helps to identify potential problems. However, FMEA has limitations, including (1) It is subject to subjective judgment and relies on qualitative rather than quantitative analysis of problems; (2) It is suitable for single-factor failure analysis and cannot be applied to failure analysis under the interaction of multiple factors; (3) It does not account for personnel-related impacts. To address these shortcomings, the equipment-level failure risk factors for elevator brakes were determined by avoiding the shortcomings of FMEA and taking advantage of its non-missing or non-repeating advantages, as shown in Table 1 (All the tables in this study were created using Microsoft Office LTSC Standard 2021 software).  
\begin{table}
\begin{tabular}{c c} \hline
**Failure Risk Factors** & **Potential Forms of Failure** \\ \hline \multirow{3}{*}{Brake Springs} & Abnormal stiffness drop \\  & Abnormal deformation \\  & Fracture \\ \hline \multirow{6}{*}{Gate Tile Assemblies} & Poor heat resistance of friction discs \\  & Pollution or damage of friction discs \\  & Inadequate fit \\  & Abnormal deformation of the gate tile assembly \\  & Damage, fracture of gate tile assembly \\ \hline \multirow{6}{*}{Rotors} & Surface contamination or damage \\  & Inadequate fit \\  & Abnormal deformation \\  & Damage, breakage \\ \hline Spring-loaded Rod & Spring-loaded Rod failure \\ \hline Brake Arm & Abnormal deformation \\  & Damage, breakage \\ \hline \multirow{6}{*}{Gate Valve Pins} & Poor movement of the gate tile assembly \\  & Abnormal deformation \\  & Damage, breakage \\ \hline \multirow{6}{*}{Brake Arm Pin} & Poor brake arm movement \\  & Abnormal deformation \\  & Damage, breakage \\ \hline \multirow{2}{*}{Saddle} & Abnormal deformation \\  & Damage, breakage \\ \hline \multirow{6}{*}{Solenoid Assembly} & Long electromagnetic force release time or incomplete elimination \\  & Cannot overcome the spring force \\ \cline{1-1}  & Solenoid Assembly \\ \cline{1-1}  & Solenoid coil burns out \\ \cline{1-1}  & Scuffing \\ \cline{1-1}  & Jamming of the plunger assembly movement \\ \hline \end{tabular}
\end{table}
Table 1: Table of risk factors for failure of elevator brake equipment.
# CFD Assessment of Car Park Ventilation System in Case of Fire Event
## 2 Materials and Methods
### Data Collection
#### 2.1.2 Legislation  
The legislation widely adopted for car park ventilation in the studied region includes the approved documents F and B, issued by the Ministry of Housing, Communities, and Local Government of the UK. Approved document F [24] establishes air quality and ventilation standards for various types of buildings under normal conditions. For enclosed areas, like car parks, it specifies that the average concentration of harmful components, particularly CO, should not exceed 30 ppm over an eight-hour period. Additionally, the maximum concentration in exits, entrances, and ramps where cars are in motion or parked with running engines should not exceed 90 ppm. The document recommends 6 air changes per hour for the entire car park and 10 air changes per hour for exits, entrances, and ramps.  
Approved document B [25] addresses fire safety standards. It stipulates that the fire load must be defined, and if the design is satisfactory for a specific floor, the potential spread of fire to other levels should be minimal. In the event of a fire, the ventilation system should be capable of providing 10 air changes per hour. Moreover, the system should be designed in two equal parts, each capable of functioning independently or simultaneously.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 6 Application to a Drone Cloud with Ring Topology
### Complexity Analysis of Proposed Algorithm  
The complexity of the proposed QRNG-based Frequency Hopping Spread Spectrum (FHSS) algorithm is analyzed in terms of the random number generation, frequency hopping, and synchronization.  
* Random Number Generation: The QRNG generates a random number \(R_{o}\) per hop, where each generation is effectively \(O(1)\) due to dedicated QRNG hardware.
* Frequency Selection: For each frequency hop, a lookup is performed to select \(f_{o}\) from \(F_{\text{avail}}\). Assuming the set \(F_{\text{avail}}\) has size \(|F|\), selecting a frequency \(f_{o}\) involves a mapping operation, making the complexity \(O(\log|F|)\) for each hop due to binary search or direct indexing in practice.
* Synchronization and Resynchronization: Each drone periodically synchronizes its hopping sequence with the other drones in the network. Assuming \(N\) drones and each drone communicates with two neighboring drones in a ring topology, the synchronization process is \(O(N)\).  
Given the above, the total complexity for frequency hopping across \(T\) time intervals in a network of \(N\) drones is approximately:  
\[O(T\cdot(1+\log|F|)+N)=O(T\log|F|+N)\]  
This represents a relatively low complexity, making the QRNG-based FHSS efficient for real-time operation even in dense drone networks.
## 1 Introduction
[10] introduced a semi-active suspension-switching control method using road profile estimation, enabling multi-controller switching via disturbance assessment and roughness classification. [PERSON] et al. [11] developed an innovative control approach grounded in sliding-mode theory, employing a parallel adaptive clone selection algorithm to fine-tune controller settings. This method effectively enhances passenger comfort and vehicle stability across diverse road surfaces for hydropneumatic suspension systems.Wang [PERSON] et al. [12] employed a learning vector quantization neural network to classify road surfaces and then integrated a particle swarm optimization approach to fine-tune hydropneumatic suspension settings. By implementing a fuzzy-logic-based control system for real-time suspension adjustments, their method successfully minimized vertical oscillations in wheeled loaders across various operational scenarios. Although significant strides have been made, implementing intelligent algorithms to fine-tune control strategies, detect road conditions in real time, and automate suspension adjustments continues to present considerable hurdles. This is particularly true in underground mining settings, where practical research and application still lag. This paper proposes a multi-mode hydropneumatic suspension control method based on road surface grade identification, aiming to dynamically adjust the hydropneumatic suspension system through real-time road condition identification. This approach enhances the vehicle's ground adaptability and ride comfort in the complex tunnels of underground mines. This paper is organized as follows: Section 2 outlines the system framework and models the hydropneumatic suspension, vehicle dynamics, and road profiles. Section 3 presents the road surface grade identification method, providing the basis for active hydropneumatic suspension adjustment. Section 4 introduces a hydropneumatic suspension control method utilizing road gradient recognition across various modes, designs a fuzzy-logic-based sliding-mode controller, and optimizes the controller's parameters using the Hiking Optimization Algorithm (HOA). Section 5 examines the controller's impact on suspension parameters in varied driving scenarios. Section 6 summarizes the findings.
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 1 Introduction  
According to Cisco [1], video will comprise 82% of all global Internet traffic by 2022. Such a high percentage implies a need for high-quality encoders. To achieve the best compression results, an encoder must be adapted to the context it is used in, which can be attained by constructing a completely new encoder that matches a specific use-case. For example, in a first-person view (FPV) drone piloting system, input from the user affects the direction of movement of the drone. Such information could be used by the encoder for efficient motion estimation. Encoder adaptation and better--but still inefficient--encoding can also be attained more simply and cheaply. Popular implementations of the video coding standards MPEG-4 Advanced Video Coding (AVC), also known as H.264, and High-Efficiency Video Coding (HEVC), also known as H.265, are x264 and x265, respectively. These include a set of encoding parameters that can be tweaked to optimize the compression efficiency. This is the approach we consider throughout this paper, where our goal was to find the set providing the best quality/bitrate ratio.  
Some of the parameters with the greatest impact on encoder operation are Group of Pictures (GOP) size, Quantization Parameter (QP), and target bitrate. GOP size defines the distance (in frames) between two intracoded frames. QP is used to define how strongly the transformation coefficients will be quantized, which is usually described by two parameters: QP min and QP max. QP min sets the minimum quantizer scale and, similarly, QP max sets the maximum quantizer scale. While encoding a video, the encoder will attempt to generate a video with the given target bitrate. Note that all three of these parameters are correlated, especially QP and target bitrate. Therefore, the target bitrate is often not achieved, so we consider the actual bitrate.  
The best way to evaluate video quality is through surveying a group of people and obtaining a subjective quality assessment [2]; unfortunately, this method is not scalable and requires a lot of time and resources and, so, we evaluated video quality using four objective image/video quality measures. The four measures are Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Video Multi-method Assessment Fusion (VMAF), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Later sections describe these four measures in greater detail.
## 5 Discussion and Policy Implications
### Policy Implications  
Identifying the EQI changes and the spatial heterogeneity of driving factors provided scientific information about key factors for effective management. Therefore, we provide the following suggestions to improve quality of life and to achieve sustainable development in Henan Province:  
1. Optimize the industrial development pattern. The GDP and population density of socio-economic elements are the main factors affecting the ecosystem quality in Henan Province. Henan Province should optimize the layout of agriculture, industry and services according to the physical conditions of different natural domains. In particular, the transformation of high-energy consumption industries to green and energy-conserving industries should be actively promoted. Thus, ecological and environmental protection industries should be widely supported, and the use range of clean energy such as solar energy should be expanded;
2. Optimize the pattern of land space development and protection. Cites in the central and northern plain areas experience lower ecosystem quality due to population agglomeration, the continuous expansion of construction land and high levels of urbanization. Henan Province must consider the adverse impact of expanding construction land into arable and forest land areas and improve land use efficiency. A red line for farmland protection should be delineated to develop high-quality arable land; moreover, the dynamic monitoring of arable land should be strengthened in order to limit the further expansion of urban construction land in plain areas. New methods for the expansion of urban green areas and the construction of urban artificial ecosystems must be explored. Moreover, to improve the level of natural environment protection and management, construction projects in hills and mountainous areas should be developed in a way that prevents the destruction of natural resources caused by the exploitation of mineral resources and the development of tourism;3. Improve the quality of forest ecosystem. Henan Province should focus on the ecosystem development of Taihang Mountain and Hill Area, the Western Henan Mountain and Hill Area, the Nanyang Basin Area, and the Tongbai Mountain and Hill Area. More attention should be paid to the ecological restoration of mines in these areas. Firstly, the layout of reserve forest bases must be planned to nurture young and middle-aged forests, cultivate a stable forest ecosystem, and enhance the capacity of forest carbon sequestration. Secondly, degraded forests must be restored scientifically, and the quality and function of shelter forests should be improved. Thirdly, the protection of natural forests and the construction of sand control forests should be carried out in the Yellow River Plain Area and the Huai River Plain Area. In additions, it is necessary to consolidate and strengthen the implementation of forestry policies and safeguard measures in the plain area according to local conditions, in order to prevent forestry projects from reversing any improvements in ecological environment quality due to lack of supervision.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Windowing Based Methods
#### 3.2.1 Jain's Method  
Like the concept of Bartlett-Hanning window, a new window consisting of a Hamming window and a Blackman is proposed in [125], which is given by  
\[w(n)= \lambda\Bigg{(}0.54-0.46\cos\frac{2\pi n}{N-1}\Bigg{)} \tag{42}\] \[+(1-\lambda)\Bigg{(}0.42-0.5\cos\frac{2\pi n}{N-1}+0.8\cos\frac{ 4\pi n}{N-1}\Bigg{)},\]  
where \(0\leq|n|\leq(N-1)/2\).  
Note that the formula in (42) becomes a Hamming window if \(\lambda=1\) and a Blackman window if \(\lambda=0\). Figure 2 shows the frequency responses of Hamming window, Blackman window and this new window, where the variable \(\lambda\) is evaluated as 0.0625 to ensure superior performance than using Blackman window or Hamming window alone, and the length of the filters is \(N=128\). Compared with Blackman and Hamming windows, the new window has the best first side-lobe level, the best maximum side-lobe level and the best spectral efficiency.
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 3 Collaborative Scheme with Global Navigation Satellite System (GNSS) and RFID Location
### System Overview  
This scheme employs one mobile RFID reader and one GNSS receiver to determine the RFID tag location. The target object (such as the light pole shown in Figure 1) is equipped with one RFID tag to obtain the position. The hardware is shown in Figure 1. Figure 1a is the GNSS receiver that measures the position of the RFID reader. Figure 1b denotes the range-based RFID reader. Figure 1c is the RFID tag.  
One of the requirements in public infrastructure asset inspection and management is to obtain the exact coordinates of objects, and then its location is projected on a street view or map for inspection. In our system, we achieve such a goal through the following steps.  
1. The RFID reader measures the distance (\(d_{i}\)) between the reader and the RFID tag attached to the target object when the RFID reader is moving, where i denotes the i-th location in RFID reader trajectory.
2. The RFID reader position (\(p_{i}\)) is measured by the GNSS receiver, which is expressed as (\(x_{i},y_{i}\)) for the coordinate values.
3. When the RFID reader is moving, two tuples (\(p_{i}\), \(d_{i}\)) are ready. When three or more tuples are prepared, the exact location of the target tag can be estimated according to the method shown in the following section.  
Figure 1: The hardware of the location system. (**a**) Global Navigation Satellite System (GNSS) receiver; (**b**) radio frequency identification (RFID) reader; (**c**) RFID tags.  
Figure 2 illustrates the snapshot of the RFID tag location process. At time \(t_{1}\), the RFID reader gets the distance \(d_{1}\) and its position \(p_{1}\), then the tuple is \((t_{1},p_{1},d_{1})\). When the reader is moving, the other tuples are obtained, such as \((t_{2},p_{2},d_{2})\) and \((t_{3},p_{3},d_{3})\). Finally, the coordinates of the tag are calculated based on these tuples.  
There are two phases to locate the target object: the reader location and the tag position computation. In our scheme, the reader location is achieved by the GNSS receiver mounted on the reader. Although the GNSS receiver is of low accuracy and the GNSS signal is weak in high building-density areas, the GNSS error is a systematic error, which can be computed with many methods or mitigated by some augmented methods such as ground enhancement signal networks or satellite enhancement networks [33; 34]. Therefore, in our scheme, GNSS devices are adopted as the reader locations, which largely reduces the total cost. Tag position computation is introduced in the following section.
### Challenges in Solar Power Integration  
Integrating solar power into UAVs presents several challenges. The efficiency of photovoltaic cells, the variability of solar energy, and the added weight of solar panels and energy storage systems impact UAV performance. For example, the efficiency of photovoltaic cells can vary significantly under different weather conditions. On cloudy days, these cells might not generate as much electricity as on sunny days. Additionally, batteries and other storage devices must be lightweight enough to allow the UAV to fly efficiently, but they also need to store enough energy to power the UAV for extended periods. To address these challenges, engineers have developed lightweight, high-efficiency photovoltaic cells and aerodynamically optimized designs to enhance energy efficiency [9; 14; 15; 18; 29; 30].
# Off-Line Optimization Based Active Control of Torsional Oscillation for Electric Vehicle Drivetrain
###### Abstract  
As there is no clutch or hydraulic torque converter in electric vehicles to buffer and absorb torsional vibrations. Oscillation will occur in electric vehicle drivetrains when drivers tip in/out or are shifting. In order to improve vehicle response to transients, reduce vehicle jerk and reduce wear of drivetrain parts, torque step changes should be avoided. This article mainly focuses on drivetrain oscillations caused by torque interruption for shifting in a Motor-Transmission Integrated System. It takes advantage of the motor responsiveness, an optimal active control method is presented to reduce oscillations by adjusting motor torque output dynamically. A rear-wheel-drive electric vehicle with a two gear automated manual transmission is considered to set up dynamic differential equations based on [PERSON]'s law of motion. By linearization of the affine system, a joint genetic algorithm and linear quadratic regulator method is applied to calculate the real optimal motor torque. In order to improve immediacy of the control system, time consuming optimization process of parameters is completed off-line. The active control system is tested in AMEsim(r) and limitation of motor external characteristics are considered. The results demonstrate that, compared with the open-loop system, the proposed algorithm can reduce motion oscillation to a satisfied extent when unloading torque for shifting.  
active control; torsional oscillation; electric vehicle drivetrain
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Result Categories
#### 6.3.1 Energy Savings  
In Figure 17, we plotted the scaled energy consumption for an operator in three different scenarios over 24 h. It presents the energy savings obtained by (_i_) activating sleep modes, (_ii_) optimizing resource utilization, and (_iii_) by sharing hardware resources with other operators. In the simplest case, activating symbol sleep provided up to two percent of energy savings over 24 h compared to the scenario with no sleep modes and network optimization. Enabling deeper sleep modes could increase these gains by up to seven percent. Deep sleep modes were most beneficial during the off-peak hours (4-7 h) as they provided up to 15-18 percent of energy savings as most sites had very few or no connected users.  
In [22], we presented the benefits of AI-assisted network optimization. In this work, by modifying the AI architecture and the input features of the agents, we were able to achieve more instances where both KPIs were fulfilled. The average energy savings obtained by combining network optimization and sleep modes were around 20 percent over 24 h compared to having only symbol sleep. Finally, the lowest curve (_TowerCo EC per MNO_) shows the average energy consumption per operator if they were to use shared hardware. By sharing resources, operators could achieve a reduction of up to 70 percent in energy consumption as they shared both the active- and idle-mode energy consumption.  
Figure 17: Energy consumption per MNO in different scenarios over 24 h. (NO—network optimization and EC—energy consumption. Note: The values are scaled with respect to the highest initial energy consumption with no sleep modes).  
During the active period, the energy consumption was distributed amongst the MNOs according to Algorithm 1 outlined in Section 4.5, while the idle-mode energy consumption was divided amongst all the participating MNOs. The savings could be as high as \(20/13\) percent of the AAU/RRU energy consumption at full load.  
The operators' requirements play an important role as they not only determine the outcome of the AI-driven network optimization, but also the outcome category both for the operator and the TowerCo service provider. We present this analysis in the following section.