esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Generation of 3D Models of the Studied Columns and Finite Element (FE) Analysis
### FE Analysis Using ABAQUS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & \(\mathbf{\lambda_{\text{I}}}\) & \begin{tabular}{c} **P\({}_{\text{u-EXP}}\)** \\ **(kN)** \\ \end{tabular} & \
\begin{tabular}{c} \(\upmu\)**(P\({}_{\text{u-rand}}\))** \\ **(kN)** \\ \end{tabular} &
\begin{tabular}{c} \(\upmu\)**(P\({}_{\text{u-EXP}}\)** \\ \end{tabular} & **COV(P\({}_{\text{u-rand}}\))** & \(|\mathbf{\varepsilon_{\text{max}}}|\) \\ \hline SHS2L300 & 0.84 & 175.7 & 177.8 & 0.985 & 0.086 & 0.043 \\ RHS1L3000 & 0.88 & 513.5 & 454.7 & 0.980 & 0.073 & 0.077 \\ \hline C5L200 & 0.95 & 370.1 & 387.5 & 1.103 & 0.036 & 0.065 \\ C6L200 & 1.47 & 404.1 & 413.2 & 0.931 & 0.175 & 0.171 \\ C6L550 & 1.41 & 353.2 & 388.1 & 1.026 & 0.212 & 0.175 \\ C5L900R & 0.84 & 336.0 & 326.0 & 1.007 & 0.055 & 0.029 \\ C6L900 & 1.32 & 333.5 & 345.2 & 0.988 & 0.139 & 0.098 \\ C6L1200 & 1.20 & 284.5 & 300.7 & 1.142 & 0.108 & 0.185 \\ C6L1550 & 1.02 & 230.0 & 249.2 & 1.008 & 0.095 & 0.102 \\ \hline RHS 120 \(\times\) 80 \(\times\) 3-SC2 & 0.82 & 441.0 & 434.2 & 1.072 & 0.093 & 0.086 \\ RHS 120 \(\times\) 80 \(\times\) 3-1077 & 0.79 & 463.0 & 432.1 & 1.018 & 0.109 & 0.075 \\ RHS 120 \(\times\) 80 \(\times\) 3-1577 & 0.79 & 382.0 & 401.5 & 0.973 & 0.045 & 0.058 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results and predicted results for the studied columns. COV, coefficients of variation. For all the studied columns, \(\mu(\text{P}_{\text{u-rand}})/\text{P}_{\text{u-EXP}}\) and COV (\(\text{P}_{\text{u-rand}}\)) versus \(\lambda_{\text{l}}\) are plotted in Figure 9(a),b, respectively. A plot of \(|\epsilon_{\text{max}}|\) against \(\lambda_{\text{l}}\) is shown in Figure 9(c). It is observed that the value of \(\mu(\text{P}_{\text{u-rand}})/\text{P}_{\text{u-EXP}}\) ranges between 0.931 and 1.103 for all the columns, except the column with \(\lambda_{\text{l}}=1.20\). Compared with \(\text{P}_{\text{u-EXP}}\), the value of \(\mu(\text{P}_{\text{u-rand}})\) for most columns with relatively lower cross-sectional slenderness (\(\lambda_{\text{l}}<1.0\)) is overestimated, while the value of \(\mu(\text{P}_{\text{u-rand}})\) for columns with higher cross-sectional slenderness seems to be underestimated. For the column with \(\lambda_{\text{l}}=1.20\), the value of \(\mu(\text{P}_{\text{u-rand}})/\text{P}_{\text{u-EXP}}\) is 1.142. It indicates that most predicted results (from the 50 models with random \(\omega\)) significantly overestimate the experimental result. This may be because the value of the actual maximum localized imperfection for this column, which is not reported in the work of [28], is relatively larger compared with the modelled \(\omega\), whose maximum value is \(\min\{0.008b,\,0.5\}\). Figure 10: Predicted results against cross-sectional slenderness. (**a**) \(\mu(\text{P}_{\text{u-rand}})/\text{P}_{\text{u-EXP}}\) versus \(\lambda_{\text{l}}\); (**b**) coefficients of variation (COV) (\(\text{P}_{\text{u-rand}}\)) versus \(\lambda_{\text{l}}\); (**c**) \(|\epsilon_{\text{max}}|\) versus \(\lambda_{\text{l}}\).
## 2 Materials and Methods
### Biomass Characterisation
#### 2.4.2 SEM-EDS Analysis  
In addition to the EA-IRMS, it was decided to include a morphological characterisation of the algal biofilm adhering to the discs with a double objective. Firstly, to determine whether there were any significant morphological differences observed over the cultivation days as well as between the biomass grown with urban wastewater and that grown with the standard culture medium. Secondly, microscopic observations were necessary to check for traces of possible contamination by competing organisms, identified by the possible presence of fungal hyphae or organisms morphologically distinct from the algal population. To this end, a part of the collected samples was assigned for SEM-EDS analysis.  
In order to prepare the samples for analysis on each sampling day, aiming to preserve as much as possible the stratification acquired by the biofilm during its growth, the discs were laid flat on Petri dishes and then fixed in place with small adhesive strips. They were then frozen at \(-80\)\({}^{\circ}\)C and lyophilised.  
Finally, the biomass discs were gently detached from the polycarbonate membranes and mounted on aluminium specimen stubs with double-sided tape and coated with a layer of gold with a Q150R ES Sputter Coater (Quorum Technologies, Lewes, UK) and observedvia Scanning Electron Microscopy (SEM) using a Zeiss EVO 15 HD VPSEM (Oberkochen, Germany) operating at 12 kV accelerating voltage to record images, coupled with an Oxford Instruments Microanalysis Unit (Abingdon, UK) with Xmax 80 EDS detector for microanalyses. Standard details used for EDS calibrations are reported in [30].  
Analysis of NH\({}_{4}\)+, NO\({}_{3}\)\({}^{-}\) and PO\({}_{4}\)\({}^{3-}\) Content in W e A  
In addition to biomass samples for EA-IRMS and SEM-EDS analyses, samples of the culture media from Exp3 were collected over time to investigate how the levels of specific ionic species in the growth media varied over the course of 15 days, in particular ammonium, nitrate and phosphate. For this purpose, 50 mL of supernatant was collected every 2-3 days and analysed using a portable spectrophotometer (model: DR1900, Hach Lange S.r.l., Milan, Italy) and the appropriate kits from the same manufacturer, certified to perform colorimetric tests in cuvettes. Specifically, the Hach LCK303 and LCK304 kits, based on the indophenol blue method, were used for ammonium; the Hach LCK339 kit, based on the 4-nitro-2,6-dimethylphenol reaction product, was used for nitrate; and the Hach LCK348 and LCK350 kits, based on the phosphomolybdate blue method, were used for phosphate. Trial analyses were carried out before the tests to determine the dilution factor to be applied to the samples in order to fall within the measuring ranges of the kits.
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 1 Introduction
### Overview of the Proposed Method  
In this body of work, we have alluded to the concept of measuring heart size in order to determine a heart's optimal operating conditions. This work did not involve placing any active electrical components into the human body at any point in our process. The current state of the heart was monitored by inserting two flexible spherical receiver coils on either side of the ventricular chambers and positioning a transceiver coil directly above the chest, which was required to observe the changes in the reflection coefficient in the external coil at the resonant frequency. In comparison to existing works, this work not only provides a viable solution for measuring the size of the ventricular chambers by providing the distance between the coils implanted on both sides of the heart, but it also solves the problem of involving electronic circuitry in the human body.  
The process that was used for the experimental part will also be used for the real-time application of our method. Whereas, the coils will be made of flexible PCB, rather than the copper coils that were used for the experiments, because the flexible PCB is very light and also provides better comfort to the patient. In addition, a nanoVNA will be attached to the transceiver or the external coil during the practical application of our system so that the system as a whole is quite compact and it will be convenient to measure the changes in the receiver coils.  
The remaining sections in the paper are arranged in the following categories: In Section 2, the methodology and optimization procedure of the proposed inductive coupling system are discussed. In Section 3, the simulated results obtained using a heterogeneous phantom with varying distances between the implantable coils are described. This section also discusses the effects of frequency on animal and human tissue. The results of the fabrication and measurements are reported in Section 4 and, finally, the work is concluded in Section 5.
# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
###### Abstract  
Although the main purpose of conventional geographical positioning systems (GPSs) is to determine either the fastest path or the shortest distance to a destination, this function may not be enough for electric vehicles (EVs). This is simply because the fastest/shortest path may consume relatively higher energy when compared to other paths depending on the nature, speed limit, and topography of the road. This means that the driving range of the EV per charge decreases dramatically. This paper aims to develop a new algorithm and model dedicated for EV GPS which not only selects shortest/fastest routes, but also focuses on the most energy efficient route. This is achieved by considering many factors including aerodynamics, wind speed, topology of roads, with a clear objective of reducing the energy consumed from the battery. A MATLAB Simulink model is developed and validated with real-life case studies to ensure the results are realistic and accurate.  
electric vehicles; optimum route planning; energy management; energy consumption calculation; wind speed; electric vehicle model +
Footnote †: journal: Applied Sciences  
Article
## 4 Discussion
[48] aimed to determine the inter- and intra-observer alignment reliability before and after TKA using LLRs. Observers of differing experience levels (including an orthopaedic consultant, a senior orthopaedic registrar, a junior orthopaedic registrar, and a medical student) performed measurements for the inter-observer reliability. All the measurements were conducted on a DICOM viewer InteleViewer(tm) (Intelerad Medical Systems Incorporated, Montreal, QC, Canada). The authors observed excellent interobserver ICCs for all pre-operative readings of the mechanical axes on the LLRs with an ICC of >0.9 at all experience levels. [PERSON] et al. [49] determined the inter- and intra-observer reliability of four angular parameters (including the mHKA) for the coronal plane on digital radiographs. Four orthopaedic surgeons specialising in lower-limb arthroplasty blindly measured the angles on 42 radiographic studies twice, with a minimum of 2 weeks between measurements, using an I-sight(tm) (Philips, Eindhoven, the Netherlands). The ICC for the mHKA inter-observer reliability was 0.99 (95% CI 0.97-0.99) and greater than 0.9 for the intra-observer reliability for the four raters. This article was subject to methodological criticism by [PERSON] and [PERSON] [50], who argued that the conclusion reached by [PERSON] et al. [49] may have been misleading due to the inappropriate use of statistical tests. In a study evaluating the accuracy of high tibial osteotomy planning, [PERSON] et al. [30] repeated measurements ten times with one of the observers. They obtained an intra-observer ICC of 0.996 (95% CI 0.989-0.999). The authors did not analyse the interobserver variability. [PERSON] et al. [28] evaluated the intra- and inter-observer reliability of aHKA measurements on LLRs and compared them with the aHKA measurements on CTs. Three observers with different levels of seniority (a senior surgeon, a surgical fellow, and an orthopaedic trainee) performed the measurements in three separate sessions, each one week apart, using a hospital PACS (Centricity Enterprise Web, version 3.0, GE Medical Systems, FL, USA). An excellent intra-observer correlation was found for the aHKA (Pearson Product Moment (r) = 0.92, range of 0.87-0.95). The interobserver correlation was also excellent for the aHKA (r = 0.91). Our research used the test-retest method rather than the parallel forms method. Each error distribution was comparable to that obtained with a two-sample \(t\)-test, which increased the statistical robustness. We could not compare our results with other published studies in numerical terms because they did not provide basic numerical measures to quantify the agreements between sets of measures (for instance, the mean bias error, standard deviation, mean absolute error, or root mean square error). An intraclass correlation coefficient only determines the average agreement between measures, and this requires contextualisation. The variability in a sample influences the reliability coefficient of a test. A test will tend to have a higher reliability coefficient when its variability is greater (Spearman-Brown prophecy formula).
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Packets Forwarding Scheme (UIWPF)
#### 3.4.3 Reliable Packets Forwarding Process
In Equation (34f), the \(\rho_{b}(\rho\,\iota(\iota)_{\iota\iota\iota\iota})\) and \(\mathcal{Q}_{\iota}\iota\) are, the initial packet loss probability and the level of variation in the length of the \(\iota\)th virtual queue can be determined using Equation (34g) and Equation (34h), respectively. The expression \(\sum_{j=1}^{n}\mathcal{L}\big{(}\mathcal{Q}_{j}\big{)}/\mathcal{Q}_{\iota}\) indicates the used queue space of a forwarding candidate in the network. The \(\beta_{1},\beta_{2},\mathcal{Q}_{j},\mathcal{Q}_{\ell}\) are, the constant depends on the variation in the length of the \(\iota\)th queue, the queue capacity constant defined by the user, the number of packets in the \(\mathcal{J}\) the queue and maximum queue length \(\ell\) of a forwarding node in the network. The \(\mathcal{N}(\mathcal{L})^{2}\) and \(\iota\mathcal{Q}_{\iota}\) in Equation (34g) are, the number of node's neighbors and queue length of \(\iota\)he forwarding node in the network. In Equation (4a), the \(\mathcal{Q}_{\iota}^{n\,e\,w}\) and \(\mathcal{Q}_{\iota}^{\mathcal{O}\ell}\) is the \(\iota\)he queue length in the \(\iota\)he flow in the present and previous calculation in the network. The Equation (34f) is basically used as a congestion indicator at each hop along a routing path from the source towards the destination in the network. The congestion indicator values vary between 0 and 1 for each node in the network. The constraints in (34h) and (34i) indicate that the congestion indicator values always must be less than 1, which means that the queue length is suitably managed when the senders sending the packets. On the contrary, if the congestion indicator value is equal to 1 it means that the queue length of a forwarder is not suitably managed and continue receiving packets from the neighbors will lead to packet loss due to buffer overflow. Thus, before reaching the defined threshold, the priority value of the congestion is decreasing at each forwarding node, which prevents the packets from being dropped. Constraints in (34i) indicate the expected delay from relay node \(\mathcal{L}\) towards the \(\mathcal{S}_{\iota\iota\iota\iota}\) is computed by the average remaining hop counts \(\mathcal{A}_{\rho_{g}}()_{re\ell\,a\,g}\) along a routing path in the network. All above constraints guaranteed that the data path loop does exist along a routing path during the packets transmission in both upward and downward directions in the network. Constraints in (34k) show that the back off time to acquire the channel \(\big{(}\ell_{\delta e\,c\,c\,\ell\,e\,f}\big{)}\) for finding an appropriate forwarding relay node \(\big{(}\mathcal{F}_{re\ell\,a\,g}\big{)}\) and acknowledgement time \((\ell_{ac\,d})\) for sending a data packet must be less than the defined threshold.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
###### Abstract  
The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system's mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.  
performance evaluation; grey-target decision; tracking mode; satellite attitude control system Article  
1  
[PERSON], [PERSON], [PERSON], [PERSON] Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode. _Appl. Sci._**2024**, _14_, 2867. [[https://doi.org/10.3390/app14072867](https://doi.org/10.3390/app14072867)]([https://doi.org/10.3390/app14072867](https://doi.org/10.3390/app14072867))
## 3 Motivation of Proposing the Optimization Framework  
While previous optimization efforts on hardware have been mentioned, the limited energy supply, low processing ability, low data rate, and short communication range inevitably affect the performance behavior of the whole network and also limit their further potential utilizations. The solving of these hardware-based issues to improve performance quality takes some time, such that it is difficult to satisfy rapidly evolving applications and requirements. In comparison, efforts on non-hardware (software) aspects (for instance the development of energy-efficient and robust protocols, the design of specific communication strategies or the exploration of configurations on existing protocols) can provide a quick alternative for performance optimization on given scenarios in terms of various metrics (Section 4.2).  
The widely accepted methods of evaluating the above solutions before real deployment can be done through software modeling (theoretical model and simulation) or real-world testbed experiments. The latter are very time-consuming, with repeated experiments and large amounts of raw data required for subsequent evaluation, while the use of simulations/theoretical models can provide a more efficient way. When compared with simulations, theoretical models are faster to evaluate but are also sometimes idealized and inaccurate for realistic conditions. Therefore, simulation is considered to be a more suitable trade-off between accuracy and efficiency. Nevertheless, in order to get a satisfactory performance for the given scenario, the selection of the optimal WSN infrastructure, out of hundreds of potential solutions, is still required before real deployment. Thus, the use of full and exhaustive simulations to explore all possible solutions is also regarded as a very time-consuming and inefficient method.  
Thus, how to effectively and accurately achieve an optimized simulation result from a large solution space is a challenge. As a fast and efficient search engine, the use of GA gives a solution. However, almost all GA-based optimization applications in wireless sensor networks mentioned in Section 2.2 are based on theoretical and analytical models. In addition to the issues of inaccuracy for realistic situations, such models always require expert knowledge in related domains as well as strong mathematical background. Therefore, it prevents many non-expert researchers and some ordinary users, without much knowledge about sensor networks, from quickly accessing the optimization process. Thus, in order to reduce time spent on evaluating all possible solutions and avoiding expert knowledge based analysis process. A generic optimization framework is proposed and will be described in detail in the following sections.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
###### Abstract  
This paper offers a review of different types of Error Correction Scheme (ECS) used in communication systems in general, which is followed by a summary of the IEEE standard for Wireless Body Area Network (WBAN). The possible types of channels and network models for WBAN are presented that are crucial to the design and implementation of ECS. Following that, a literature review on the proposed ECSs for WBAN is conducted based on different aspects. One aspect of the review is to examine what type of parameters are considered during the research work. The second aspect of the review is to analyse how the reliability is measured and whether the research works consider the different types of reliability and delay requirement for different data types or not. The review indicates that the current literatures do not utilize the constraints that are faced by WBAN nodes during ECS design. Subsequently, we put forward future research challenges and opportunities on ECS design and the implementation for WBAN when considering computational complexity and the energy-constrained nature of nodes.  
automatic repeat request (ARQ); error correction; hybrid automatic repeat request (HARQ); wireless body area network (WBAN) +
Footnote †: journal: Sensor and Actuator Networks
# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 1 Introduction
* First of all, a formula for correlation coefficient of IFSs is defined. * Secondly, using the above correlation coefficient, an \(\alpha\)-relation (for a preassigned value of \(\alpha\)) and an equivalence relation [49; 50; 51] are generated to generate two approximations. * Finally, a classification-based hybrid algorithm (IFRSCAD) consisting of both IFS and RS is proposed to generate the certain and possible fuzzy rules. Furthermore, the proposed algorithm (IFRSCAD) is implemented using Matlab with two well-known datasets: KDDCUP'99 Network Anomaly Detection dataset [52] and Kinsune Network Attack dataset [53]. The classification results are compared with other classification-based methods, namely, [PERSON] et al. [17], [PERSON] et al. [35], deep-RBF network [16], Bayes network [15], and decision tree [14]. It is found that the proposed algorithm is comparatively more efficient than others with respect to true positive rates and false positive rates. The time-complexity of the IFRSCAD is also compared with a well-known clustering-based algorithm MCAD [30] and is found to be comparatively efficient. This paper is formatted in the following ways. The recent advances in this field are described in Section 2. The problem definition is given in Section 3. The algorithm and the flowchart explaining the system are given in Section 4. The time-complexity analysis is presented in Section 5. The experimental study and outcomes are presented in Section 6, and, lastly, the conclusions, limitations, and future directions of work are given in Section 7.