esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 2 Overview and Problems of RPL
### Introduction of RPL  
RPL [3; 7], standardized by IETF, is a source routing protocol. It can support widespread applications such as NAN (Neighbor Area Networks) and HAN (Home Area Networks) in smart grids [8; 9], wireless sensor networks, etc. As shown in Figure 1, the RPL network can be constructed and maintained by DODAG (Destination-Oriented Directed Acyclic Graph, DODAG), which is a directed acyclic graph that each edge is oriented toward and terminating at root. DODAG can effectively prevent routing loop problems, and can be established according to ICMPv6 control messages and objective function (OF) OF 14 [10; 11; 12]. ICMPv6 control messages used in RPL and their corresponding functions are listed in Table 1.  
Figure 1: RPL Network.  
OF, another important tool in RPL, is used to define the rules for network topology construction and routing decisions in LLNs, such as \"the minimum ETX\". The main rules of RPL nodes defined by OF are as follows:  
1. How to get and update routing metrics information;
2. How to calculate the rank of node (the individual position of the node relative to root in DODAG);
3. How to choose the node's preferred parent (the next hop node).  
Furthermore, based on diverse application scenario requirements, various OFs can be designed, such as OF0 [13], ETXOF [14], MRHOF (Minimum Rank with Hysteresis OF) [15], etc.
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 3 Conventional Frequency-Domain ANC Algorithms  
The conventional frequency-domain ANC algorithms in [24; 25; 26; 27; 28; 37; 39] can be understood as direct frequency-domain implementations of the FxLMS algorithm. Since the FDAF algorithm is a special  
Figure 1: FxLMS Algorithm.  
case of PBFDAF, we only present the PBFDAF in this paper. The corresponding method using the conventional filtered-x scheme is referred to as the frequency-domain partitioned block filtered-x LMS (FPBFxLMS) algorithm. The method using the modified filtered-x scheme is referred to as the frequency-domain partitioned block modified filtered-x LMS (FPBMFxLMS) algorithm.
## 7 Conclusions
\begin{tabular}{l l} \(HMAC()\) & a hash function for computing MAC \\ \(H_{1}\) & one message that are not inspected by the SN \\ \(HN\) & the HN \\ \(HRES^{*}\) & a hashing response from \(RES^{*}\) \\ \(HXRES^{*}\) & a hashing expected response from \(XRES^{*}\) \\ \(ICB\) & an initial counter block \\ \(IK\) & an integrity key \\ \(K\) & a long-term key between the UE and the HN \\ \(K_{AMF}\) & a key between the UE and the access and mobility management function \\ \(K_{AUSF}\) & a key derived from \(CK\) and \(IK\) \\ \(\mathcal{K}_{P}\) & the key set of the penetrator \\ \(K_{SEAF}\) & a key derived from \(K_{AUSF}\) \\ \(K_{SN,HN}\) & the session key between the SN and the HN \\ \(KDF()\) & a key derivation function \\ \(MAC\) & a MAC of the HN \\ \(MAC_{ARPF}\) & a MAC of the ARPF \\ \(MACf\) & the \"MAC failure\" indication \\ \(MAC_{SEAF}\) & a MAC of the SEAF \\ \(MAC_{SN}\) & a MAC of the SN \\ \(MAC_{UE}\) & a MAC of the UE \\ \(MAC_{UE,2}\) & another MAC of the UE \\ \(MK\) & a MAC key \\ \(ngKS1\) & identifying the \(K_{AMF}\) and the partial native security context \\ \(PED\) & the generation and verification of a public key encryption and decryption \\ \(RED\) & the generation and verification of a public key encryption and decryption \\ \(RAND\) & an unpredictable challenge of the HN \\ \(RAND_{SN}\), \(RAND_{SEAF}\) & two unpredictable challenges of the SEAF \\ \(RAND_{UE}\) & an unpredictable challenge of the UE \\ \(RAND^{\prime}UE\) & a challenge calculated based on \(RAND_{UE}\) and \(RAND_{SEAF}\) \\ \(RES\) & a response \\ \(RES^{*}\) & a response from \(RES\) \\ \(Result\) & the authentication result \\ \(SEAF_{ID}\) & the identity of the SEAF \\ \(SHA256()\) & a hash function \\ \(SN\) & the SN \\ \(SNN\) & the serving network name of the SN \\ \(SQN\) & a fresh sequence number generated by the HN \\ \(SQN_{UE}\) & the highest sequence number the USIM has accepted \\ \(SUCI\) & a SUCI of the UE \\ \(SUPI\) & a SUPI of the UE \\ \(Syncf\) & the \"Synchronization failure\" indication \\ \(T_{AUSF}\) & the current time of the AUSF \\ \(T_{SN}\) & a timestamp generated by the SEAF \\ \(T_{UDM}\) & the current time of the UDM \\ \(UE\) & the UE \\ \(x\) & an ephemeral private key of the UE for Diffie-Hellman exchange \\ \(x\cdot G\) & an ephemeral public key of the UE for Diffie-Hellman exchange \\ \(XOR\) & the generation and verification of an XOR value \\ \(XMAC\) & a MAC locally computed by the UE \\ \(XRES\) & an expected response \\ \(XRES^{*}\) & an expected response from \(XRES\) \\ \(y\) & an ephemeral private key of the HN for Diffie-Hellman exchange \\ \(y\cdot G\) & an ephemeral public key of the HN for Diffie-Hellman exchange \\ \(\Delta t_{AUSF}\) & the time difference set by the AUSF \\ \(\Delta t_{UDM}\) & the time difference set by the UDM \\ \(||\) & a concatenation \\ \end{tabular}
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 1 Introduction
[PERSON] proposed a modular robot calibration method considering joint stiffness to improve its positioning accuracy [20]. The literature review shows that most of the current research work is either focused on traditional industrial robots or aimed at hardware improvements. As a new type of lightweight robot, it is difficult to improve the positioning accuracy of cooperative robots from the aspect of hardware. So far, there are few studies available on the positioning error modelling of lifting cooperative robots, and most of them are based on DH parameters or joint stiffness to correct the positioning error. In addition, a laser locator is used to assist the experiment, which has a high cost and low universality. Each robot needs to be recalibrated, which is inconvenient and inefficient to improve the accuracy of cooperative robots used in different scenarios. Thus, a universal and economical method to improve the positioning accuracy of cooperative robots is needed. As a result, this paper presents a method for stiffness modeling and error compensation for a 6-DOF cooperative robotic manipulator. This method can solve the problem of the complex stiffness model, in which it is difficult to establish joint stiffness and joint link stiffness based on the ANSYS finite element analysis method. Moreover, it can avoid costly and complex calibration methods, such as the use of a laser tracker or excessive calibration times. Firstly, the forward and inverse kinematics models for the manipulator were built to obtain the relationship between joint angles and the end position. A stiffness model that consists of the joint stiffness and joint link stiffness was built to determine the mapping model of joint angles and the spatial stiffness of the end of the manipulator. The end motion error caused by the insufficient stiffness was estimated and compensated via simulation and experimental studies. Figure 1 shows a schematic of the proposed method in this paper.
## 3 Fault-Tolerant Output Regulation Scheme
#### Actuator Fault Estimation
If the UI observer is designed to be fast enough, the estimated fault converges towards the real one \(\hat{f}_{a}(t)\to f_{a}(t)\), and the system  
\[\dot{x}(t)=Ax+B(Kx(t)-\hat{f}_{a}(t)+f_{a}(t)), \tag{18}\]  
becomes \(\dot{x}(t)=(A+BK)x(t)\) and thus \(\lim_{t\to\infty}x(t)=0\). Let us summarize the conditions for solving the fault-tolerant output regulation problem for unmodeled signals. The output of the nonlinear system (1) tracks an unknown desired signal \(y_{ref}(t)\) in (5) despite the presence of actuator faults \(f_{a}(t)\) in a neighborhood of the origin \(x=0\) if there exists a control law (2) with gains \(K\), \(\Pi\) and \(\Gamma\) and the signal \(\hat{f}_{a}(t)\); as a consequence, the steady-state values are \(x_{ss}=\Pi w\) and \(u_{ss}=\Gamma w\). As mentioned before, \(K\) is computed from LMIs (9) and (10); \(\Pi\) and \(\Gamma\) are obtained from the HGO observer and the solution of the Equation (7), and finally, \(\hat{f}_{a}(t)\) from the UI observer (13). This algorithm is summarized in Figure 1.  
**Remark 1**. : _Notice that if output regulation takes place, then \(x_{ss}=\Pi w\) and \(u_{ss}=\Gamma w\), the steady-state of the input \(u(t)\) holds when the \(f_{a}(t)\) is incipient, that is, it does not appear abruptly and it might vanish in time [29]._
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.7 End-to-End Orchestration of 6G 3D Wireless Networks  
According to [26], end-to-end orchestration is crucial in next-generation telecom networks due to the heightened expectations for low latency and seamless connectivity among multiple service providers. Low latency is a primary feature of 5G and is anticipated to become even more significant among the various endpoints associated with different service providers in 6G. The seamless connectivity in the 6G scenario requires an autonomous and fully automated process. The transparency and traceability of such processes are necessary for fine-grained access policies. In this case, through the use of an integrated interoperability architecture, blockchain can meet essential requirements and provide ideal tracing capabilities.
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 7 Discussion
### Surface Nets Algorithms  
The general concept of the surface nets algorithm is based on the approach of the marching cubes algorithm, whereby cubes are constructed from layered, structured data. The method is therefore preferably used in the field of medical imaging, which provides structured data for evaluation. In addition to medical imaging, the method is also used in computer geometry and offers a simple and effective procedure for the determination of isocontours or isosurfaces. By smoothing the surfaces, the terracing effect can also be significantly reduced. However, the method does not always produce the most accurate or precise surface meshes, especially for complex geometries or strong curvatures. It can also be observed that the quality of the surface nets algorithms is only evaluated by means of visual inspection, while a comparison with the original marching cubes algorithm by [PERSON] and [PERSON] is only carried out in one case.
# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 3 Methodology
### Data Collection  
With the aim of investigating the development history and the future trends of the event knowledge graph in the last decade, we collected the documents on the Web of Science (WoS) that were published between 1 January 2012 and 31 December 2022. To perform a broad search, the filter was set to (Topic = (event graph *) OR (events graph)) for all literature. The language was selected as English and the content was set to full records and citations, with a total of 510 unique documents returned. Among all documents, 49.4% are papers, 45.2% are conference proceedings, 3.1% are online publications, 1.4% are conference abstracts, and 0.9% are book chapters. As conference abstracts and book chapters took a small proportion and were not significant in the following network analysis, we cleaned, de-weighted, and filtered the data and finally obtained 412 valid documents. Furthermore, we counted the number of publications per year to obtain an overview of how the research popularity of the event knowledge graph has changed over the past 10 years. Figure 4 shows a steady increase year by year, reflecting that the event knowledge graph has attracted more and more attention from scholars in recent years.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results  
In the system model considered in this paper, a UAV is operating within the range of 2 gNBs in a 5G-RAN network. One gNB is associated with the UAV and the other one is the interfering gNB. The UAV sends its measured SINR to the gNB, which is then served as the input to the DQL algorithm. The SINR received by the UAV served in gNB \(\mathcal{L}\) at time \(t\) is computed as follows  
\[\delta^{\ell}[t]=\frac{P_{\text{TX},\ell}[t]|c_{\ell,\ell}^{*}[t]h_{\ell}[t]|^{ 2}}{\sigma_{n}^{2}+\sum_{b\
eq\ell}P_{\text{TX},b}[t]|c_{\ell,b}^{*}[t]h_{b}[t ]|^{2}} \tag{16}\]  
This is the received SINR that will be optimised by the proposed DQL, which is located in the cloud, linked to the RAN. The output of the DQL is then fed back to the gNB. The gNB then sends these commands back to the UAV, which optimises the SINR and mitigates interference.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.3 Telegram Bot Integration  
The Telegram Bot API allows the system to send automated messages to a predefined chat ID whenever a fall event is detected. This integration involves the following steps:  
* _Bot Setup_: A Telegram bot is created and configured with a unique API token. This token is used to authenticate and send messages on behalf of the bot.
* _Chat ID Configuration_: The predefined chat ID, which corresponds to the recipient (caregiver or family member), is configured within the system. This ensures that alerts are directed to the correct individual or group.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
###### Abstract  
In this review, methane absorption characteristics mainly in the near-infrared region and typical types of currently available semiconductor lasers are described. Wavelength modulation spectroscopy (WMS), frequency modulation spectroscopy (FMS), and two-tone frequency modulation spectroscopy (TTFS), as major techniques in modulation spectroscopy, are presented in combination with the application of methane detection.  
methane; tunable diode laser; wavelength modulation spectroscopy; frequency modulation spectroscopy; two-tone frequency modulation spectroscopy +
Footnote †: journal: Applied
## 3 System Design
### Mathematical Modeling of the Power System
#### 3.2.2 Wind Turbine  
The wind turbine designed and assembled by Taiyuan University of Technology was employed in this study. The key specifications of the wind turbine are presented in Table 4.  
Ten wind turbines can be used to form a 100 KW wind farm. Different types of wind turbines output different power based on their power curve characteristics. Through a comprehensive literature review, a model used to describe the performance is proposed as follows [19].  
\[\left\{\begin{array}{l}P_{wind}=P_{1}+P_{2}+P_{3}\\ P_{1}=\sum P_{R}t(v/v_{R})^{3}(v_{c}\leq v\leq v_{R})\\ P_{2}=P_{R}\sum t(v_{R}\leq v\leq v_{F})\\ P_{3}=0(v<v_{c}\text{ and }v>v_{R})\end{array}\right. \tag{6}\]  
where \(P_{wind}\) is the output of wind power (Wh/day), which consists of \(P_{1}\), \(P_{2}\) and \(P_{3}\); \(V_{c}\) is the cut-in wind speed (2.5 m/s); \(V_{R}\) is the rated wind speed (10 m/s); \(V_{F}\) is the cut-off wind speed (45 m/s); \(V\) is the wind speed; \(P_{R}\) is the rated electrical power (10 KW), which is average energy at the wind speed of 10 m/s for one minute; \(t\) is the time (hours).
## 3 Finite Element Model of a 6 \(\times\) K31 WS + FC Wire Rope
### Material Properties of the Wire  
The material parameters are critical to establishing an accurate finite element model of the wire rope. There are four kinds of specimens, corresponding to four different diameters (1.5 mm, 1.7 mm, 1.9 mm, and 2.7 mm) of wires in a 6 \(\times\) K31 WS + FC wire rope. Each type of specimen contains 15 identical wires with lengths of 200 mm. Note that the steel wires in a 6 \(\times\) K31 WS + FC wire rope are made of the same material. The wires have been straightened carefully by special equipment in advance without introducing any damage. Here, a schematic illustration of the tensile experiment is shown in Figure 5a. The equipment used is an Instron 68 FM-300 Floor Model Universal Testing Machine, the maximum speed and force capacity of which is 560 mm/min and 300 kN, respectively. Then, experiments are conducted according to GB/T228.1-2021 [29] to acquire the material parameters of the wires. To enhance the credibility of the experiment result, each type of wire has been tested 15 times. The experimental result of a typical wire sample is shown in Figure 5b.  
After data processing, we discover that the experiment results (strain-stress curves) of the same type of wire are in good consistency. Therefore, an arbitrary strain-stress curve of each type of wire is selected as a representative, as depicted in Figure 6. In Figure 6, the strain at a fracture of \(R\) = [0.75 mm, 0.85 mm, 0.95 mm, and 1.35 mm] is 0.336, 0.185, 0.323, and 0.0446, respectively.  
Based on the experiment results, the Poisson's ratio \(\mu\) and elastic modulus \(E\) of each type of wire are obtained, and they are all approximately 0.29 and 196, GPa respectively, and the deviations are less than 5%. This indicates that \(\mu\) and \(E\) are insensitive to the variation in wire radius. Therefore, we can safely assume that \(\mu\) and \(E\) of each type of wire are the same, i.e., \(\mu\) = 0.29 and \(E\) = 196 GPa. The material parameters of the fiber core are obtained according to the reference [30], and all the material parameters obtained are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Radius of Wire Specim (mm)** & **Density (g/cm\({}^{3}\))** & **Tensile Load (kN)** & **Tensile Strength (MPa)** & **Poisson’s Ratio** & **Elastic Module (MPa)** \\ \hline
0.75 & & 3.62 & \(2.03\times 10^{3}\) & & \\
0.85 & & 5.29 & \(2.05\times 10^{3}\) & & \\
0.95 & 7.85 & 5.93 & \(2.09\times 10^{3}\) & 0.29 & \(1.96\times 10^{5}\) \\
1.35 & & 10.02 & \(2.13\times 10^{5}\) & & \\ Rope core & 1.15 & \(\backslash\) & \(\backslash\) & 0.3 & \(8\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters of the wire specimens.  
Figure 5: Illustrations of the tensile experiments. (**a**) equipment of tensile experiments (**b**) result of a typical wire sample.
## 5 Experiment and Results Analysis
### Training Environment and Parameter Configuration  
Experimental environment: Windows 10 64-bit operating system, the processor is Intel (R) Core (TM) i7-9700 CPU @ 3.00 GHz. Model training and testing were conducted using the PyTorch deep learning framework in the Python 3.8 environment.  
The configuration of parameters during model training significantly impacts the model's performance. Proper parameter settings can accelerate the convergence of the model and improve prediction accuracy. The parameter settings during the training process are shown in Table 3.  
In the model training stage, the dataset is divided into a training set and a test set in an 8:2 ratio, and then transformed into feature tensors. After disrupting the order, the processed data are entered into the model for training.
## 3 Results
### Effects of Surgery on the Metabolome of CRC Patients  
The mPLS analysis was performed to assess intraindividual variations between t0 and t1 in the metabolomic fingerprints of CRC patients. The results obtained show significant differential clustering, with optimal separation of the two timepoints using each type of NMR spectra acquired, namely CPMG, NOESY, and DIFFUSION (Figure 2). All models classify t0 and t1 samples with an accuracy in the range 82-85%, and the best results were obtained using NOESY spectra. These data indicate that both low molecular weight metabolites and high molecular weight macromolecules (i.e., lipoproteins, proteins) contribute to the discrimination.  
From univariate analysis emerges that after surgery there is a significant increase of pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2 (Figure 3). Moreover, after surgery we observed a significant decrement of acetone, 3-hydroxybutyrate, LDL-Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio (Figure 3). Furthermore, several lipoprotein-related subfractions were shown to be significantly altered between t0 and t1 (Figure S1). These data point to a relevant rearrangement of the metabolic pathways related to lipoproteins, ketone bodies, and energy metabolism.  
Figure 2: Score plots of the first two components of the mPLS models calculated using each of the three typologies of NMR spectra acquired: CPMG; NOESY; diffusion-edited. Discrimination accuracy of each model is reported. Each dot represents an NMR spectrum; dots are colored as follows: t0—orange, t1—turquoise. The first component mainly describes the differences between t0 and t1. The second component mainly reports the within-subject variation.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 1 Introduction
[PERSON] [23] explored the direct acid or alkali leaching of fly ash and found that the chemical properties of raw fly ash are highly stable, achieving a leaching rate of only around 15% at best. However, further investigation into other factors revealed that wet mixing with additives can effectively enhance both the roasting efficiency and leaching efficiency of fly ash under the same additive conditions. [PERSON] [24] investigated the leaching kinetics of fly ash under various conditions by enhancing the roasting and leaching process with microwave assistance. Among these experiments, studies on leaching times of less than 30 min are also one of the directions for further experimental exploration in this work. [PERSON] et al. [25] used NaCl for fly ash roasting and investigated the influencing factors of acid leaching conditions, [PERSON] et al. [26] adopts a novel approach which uses a low-temperature ammonium fluoride activation method to assist leaching, achieving a lithium leaching rate of over 90%. The unique properties of ammonium fluoride have significantly reduced energy consumption during the roasting stage. [PERSON] et al. [27] utilizes a combined room temperature Na\({}_{2}\)S\({}_{2}\)O\({}_{7}\) mechanochemical activation and Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\) pressurized leaching technique to recover aluminum and lithium from coal fly ash, obtaining an aluminum leaching rate of 95.58% and a lithium leaching rate of 71.59%. In the stage of extractinglithium from the liquid phase into a final product, [PERSON] [28] examines the effect of the solvent extraction on recovering lithium from hydrochloric acid leachate. [PERSON] et al. [29] synthesizes an adsorption resin to extract lithium from an alkaline solution which has a low lithium concentration, which overcomes adsorbent material loss due to dissolution in an alkaline environment. [PERSON] et al. [30] develops a manganese dioxide lithium ion-sieve which achieves an exceptional lithium adsorption rate of 100% and a desorption rate of 100%. These studies are crucial for designing efficient extraction methods and optimizing the strategies for utilizing fly ash, which ultimately enhances its economic and environmental value. This study aims to analyze the method of roasting extraction of lithium, highlighting the influential parameters and the mechanisms for the extraction of lithium from those fly ash samples generated in power plants in Guizhou Province, China. An innovative ultrasonic pre-treatment method was applied to improve the lithium extraction. The effects of the various factors were compared between the new method and the traditional roasting method. The fly ash samples involved in this study were characterized using X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and X-ray fluorescence (XRF) to analyze the reaction mechanisms and characteristics. The findings tested in this study reveal considerable potential for enhancing our understanding of the process of extracting lithium from fly ash.