esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
## 3 Bootstrap Methodology and Its Improvement
### Modified Exponential Sample Empirical Function  
For long-life devices such as electronic products, the failure rate rarely increases due to fatigue or wear and tear. Therefore, the tail of the cumulative distribution function of failure can be approximated by the exponential function, with a mean equal to the sample mean [19; 20; 21]. In this study, the exponential distribution function is utilized to fit the samples and correct the empirical distribution function \(F_{n}(x)\). The exponential distribution function generally has a good fitting property, which can better estimate the unobserved data points and reduce the influence of random errors on the results. The steps involved are as follows.  
1. A linear empirical distribution function is introduced for each segment before \(n-u\) samples, where \(n\) is the total number of samples, and \(u\) is the number of tail samples.
2. The samples after \(n-u\) are fitted using an exponential distribution with the same mean as the original sample. Considering integer values below five for \(u\) results in a smaller variance in the right tail fit [22]. The modified empirical distribution function for the samples is \[F_{n}(x)=\left\{\begin{array}{ll}0&x<x_{(1)}\\ \frac{i}{n}+\frac{x-x_{(i)}}{n\big{(}x_{(i)}-x_{(i)}\big{)}}&x_{(i)}\leq x\leq x _{(i+1)}\\ &i=1,\ldots,n-u+1\\ 1-\frac{u}{n}\exp\Bigl{(}-\frac{x-x_{(u-u)}}{\theta}\Bigr{)}&x_{(n-u)}\leq x \end{array}\right.,\] (6) where \(v=\frac{1}{u}\biggl{[}\frac{x_{(n-u)}}{2}+\sum\limits_{i=n-u+1}^{n}\Bigl{(}x_ {(i)}-x_{(n-u)}\Bigr{)}\biggr{]}\). The simulation-based method for generating random samples that obey the modified empirical distribution function \(F_{n}(x)\) involves the following steps: 1. Uniformly distributed pseudo-random numbers \(\gamma\) in the interval [0, 1] are generated; 2. If \(\eta>1-\frac{u}{n}\), then \(x_{\rm F}=x_{(n-u)}-v\ln\bigl{[}(1-\gamma)\frac{n}{u}\bigr{]}\) is the desired random number; otherwise, go to step (3); 3. Let \(\beta=(n-1)\gamma\), and \(i=[\beta]+1\); then, \[x_{\rm F}=x_{(i)}+(\beta-i+1)\Bigl{(}x_{(i+1)}-x_{(i)}\Bigr{)}\] (7) is the desired random number.  
Improved Bootstrap Data Expansion Methodology Based on RBF Neural Network and Reliability Assessment
## 3 Results
### Efficiency Analysis  
The term efficiency refers to the ratio between useful input and useful output. In a broader sense, it is the ratio between environmental impacts and useful output. In either case, a definition of the useful output is required. In LCA, this is called the functional unit.  
The evaluation of usefulness exceeds a physical analysis. Thus, methods such as PIOT, SFA, and MFA can identify the flows without referring to useful output. Methods that are originally intended for analyzing processes or networks, such as energy analysis, ENA, and exergy analysis, do not necessarily comprise a definition of useful output either. Exergy analysis is conceivable without this definition but usually refers to the exergy of the product to determine the exergetic efficiency.  
Some processes or networks do not produce a clear, useful output, as is the case for the analysis of cooling systems. In these cases, methods that are primarily intended for products are more difficult to apply. For example, [PERSON] et al. [5] (p. 140) define the cooling of 1 kg of water from 35 to 28 \({}^{\circ}\)C as the functional unit. This specific functional unit is hardly comparable to other studies using another functional unit definition. Moreover, the ambient temperature and humidity are significant factors to consider. Therefore, previous studies have described different functional units of cooling systems. So far, no universal comparability has been able to be achieved.
## 5 Conclusions  
The main objective of this study was to perform a dosimetric evaluation of Lutathera(r) and compare the results between GATE and Planet(r) Dose. The findings show that the absorbed doses calculated using either MC simulations with GATE or the commercially available Planet(r) Dose software are acceptable. Regarding Planet(r), the bi-exponential fitting of the TACs is superior to the tri-exponential fitting when four time points are used.  
Overall, both toolkits can be used for the dosimetry of PRRT with Lutathera(r), as they provide optimal and patient-specific results.  
Data curation, L.-A.Z., T.K. and P.F.K.; Dose calculation, K.C., P.P., G.C.K. and I.S.; CT scan evaluation and interpretation, T.K., P.F.K. and G.C.K.; Formal analysis, K.C., P.F.K., A.M., P.P. and G.C.K.; Funding acquisition, G.C.K.; Investigation, I.S., T.N., K.C., L.-A.Z., P.F.K., A.M., P.P. and G.C.K.; Methodology, I.S., T.N., K.C., P.F.K., A.M., P.P. and G.C.K.; Project administration, G.C.K.; Resources, I.S., L.-A.Z., T.K., A.M. and G.C.K.; Software, T.N. and P.P.; Supervision, P.P. and G.C.K.; Validation, I.S., K.C., P.P. and G.C.K.; Visualization, I.S. and P.F.K.; Writing--original draft, I.S., K.C., P.F.K. and A.M.; Writing--review and editing, P.P. and G.C.K. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by: (a) European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 872735. The results published in this study reflect only the author's view and the Research Executive Agency (REA) and the European Commission is not responsible for any use that may be made of the information it contains, (b)the European Regional Development Fund (ERDF), Greek General Secretariat for Research and Innovation, Operational Programme \"Competitiveness, Entrepreneurship and Innovation\" (EPAnEK), under the frame of ERA PerMed (project POPEYE T11 EPA4-00055), and (c) the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \"2 nd call for H.F.R.I. research projects to support faculty members & researchers\" (project number: 2692).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Cancer Hospital of Thessaloniki \"Theagenio\" (17201/31-10-2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.  
The authors declare no conflict of interest.
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
## 2 Materials and Methods
### The FOPI Controller in the Frequency Domain  
The general form of FOPI controller using fractional operator can be expressed as:  
\[u(t)=K_{c}e(t)+K_{I}D_{t}^{-\lambda}e(t),(\lambda>0) \tag{6}\]  
where \(K_{c}\) and \(K_{I}\) represent the proportional and integral terms respectively; \(\lambda\) is the fractional order of the integration.  
Using Laplace transform Equation (2), Equation (6) can be rewritten in the form of transfer function:  
\[G_{c}(s)=K_{c}+\frac{K_{I}}{s^{\lambda}} \tag{7}\]  
In special case \(\lambda=1\), Equation (7) becomes the normal PI controller (integer order). Therefore, the FOPI controller has an additional tuning parameter (\(\lambda\)) compared to conventional one. The advantages of fractional-order are indisputable, but it also leads to difficulties in the tuning procedure. In this work, a design approach in frequency domain firstly proposed in [18] is used to derive the analytical tuning rules of the FOPI.  
The FOPI controller is converted to the frequency domain by substituting \(s=j\omega\) into (7):  
\[G_{c}(j\omega)=K_{c}+\frac{K_{I}}{\left(j\omega\right)^{\lambda}} \tag{8}\]  
The fractional power in Equation (8) can be expressed as follows:  
\[(j\omega)^{\lambda}=\omega^{\lambda}j^{\lambda}=\omega^{\lambda}\left[e^{i[ \frac{\pi}{2}+2n\gamma]}\right]^{\lambda}=\omega^{\lambda}\left[e^{i[\frac{\pi }{2}\lambda+2n\lambda\pi]}\right] \tag{9}\]  
where \(n=0,\pm\frac{1}{\lambda},\pm\frac{2}{\lambda},\ldots,\pm\frac{m}{\lambda}\). Finally, it can be rewritten in the following equation:  
\[\left(j\omega\right)^{\lambda}=\omega^{\lambda}(\cos\gamma_{I}+j\sin\gamma_{ I}),\;\gamma_{I}=\frac{\pi\lambda}{2} \tag{10}\]  
Substituting (10) into (8) and transforming into a complex equation, the FOPI controller in the frequency domain is obtained as Equation (11):  
\[G_{c}(j\omega)=\left(K_{c}+\frac{K_{I}\cos\gamma_{I}}{\omega^{\lambda}}\right) -j\frac{\left(K_{I}\sin\gamma_{I}\right)}{\omega^{\lambda}} \tag{11}\]
## 4 Discussion
Other physiological recordings like Electroencephalogram (EEG) and Functional Near Infra-Red Spectroscopy (\(t\)-NIRS) can also be incorporated into this experimental setup to compare the efficiency of different modalities in the detection and classification of body imbalance, as well as to enhance the feedback mechanisms in the process of rehabilitation, physical therapy, sports medicine, and patient recovery assessments. Research of this kind has a lot of potential to be translated into clinical and social applications. From the analysis of the GRF and EMG data, clinicians can proactively treat patients who have impaired balance control. It can be valuable for designing targeted rehabilitation protocols regarding balance and fall prevention in elderly subjects. It is possible to use the classification of the intensities of imbalance to indicate the approaches that can be used in rehabilitation. For instance, patients with high-intensity imbalance can be trained more intensively in terms of strength and stability, whereas patients with medium or low levels of imbalance should be trained less intensively. Its application can be highly beneficial for patients and help physical therapy to be as effective as possible in terms of recovery. The recording of GRF and EMG signals can give clear readings throughout the rehabilitation process of a patient. Health care professionals can monitor the change in balance performance, either in the short or long term, depending on real-time data of muscle activation patterns and ground reaction forces to modify treatment strategies. Likewise, in sports, balance and stability play a major role in a competitor's performance. GRF and EMG data enable athletes to diagnose motor behaviors, determine their areas of weakness, and plan strategies for improvement. Applications of these kinds of data can go a long way in helping athletes increase their capability and, at the same time, reduce the prevalence of such injuries. Balance analysis using GRF and EMG can help inform ergonomic improvements in the workplace. Employers can increase worker stability and reduce the risk of musculoskeletal injuries by identifying tasks with high imbalance intensities and modifying work settings or implementing training programs. Data from GRF and EMG investigations can help to shape public health campaigns aimed at enhancing balance and mobility in varied populations. For example, community programs aimed at increasing physical activity in older individuals can use the data from these studies to construct effective balance training exercises.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 5 Performance Simulation and Results Analysis
### System Parameter Settings  
In our simulations, the simulation uses the G3-PLC parameters of the NB PLC standard based on ITU-T G9903, and the physical layer of the G3-PLC protocol uses OFDM modulation to provide robustness against potentially harsh channel conditions. the parameters of polar codes and the OFDM system and their settings in the simulations are shown in Table 1.
###### Abstract  
With increasing of the size of spatial truss structures, the beam component will be subjected to the overall motion with large deformation. Based on the local frame approach and the geometrically exact beam theory, a beam finite element, which can effectively reduce the rotational nonlinearity and is appropriate for finite motion and deformation issues, is developed. Dynamic equations are derived in the Lie group framework. To obtain the symmetric Jacobian matrix of internal forces, the linearization operation is conducted based on the previously converged configuration. The iteration matrix corresponding to the rotational parameters, including the Jacobian matrix of inertial and internal forces in the initial configuration, can be maintained in the simulation, which drastically improves the computational efficiency. Based on the Lagrangian multiplier method, the constraint equation and its Jacobian matrix of sliding joint are derived. Furthermore, the isogeometric analysis (IGA) based on the non-uniform rational B-splines (NURBS) basis functions, is adopted to interpolate the displacement and rotation fields separately. Finally, three dynamic numerical examples including a deployment dynamic analysis of spatial truss structure are conducted to verify the availability and the applicability of the proposed formulation.  
keywords: geometrically exact beam; local frame; isogeometric analysis; sliding joint; dynamic analysis +
Footnote †: journal: Applied Mechanics and Physics Letters A
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 4 Experimental Demonstration of Decomposed Optical Architecture
### Physical Performance of Decomposed Prototype  
Due to the broadcast and selective structure of NOS, only one of \(N\) optical gates is set to ON status during sending optical payloads to the corresponding destination hardware cube. This procedure requires the rest of the optical SOA gates to keep any optical signal from passing NOS. Otherwise, these leaked optical signals may lead to the signal interference after optical signal coupling at the output port of NOS. Therefore, in order to evaluate the implemented prototype and quantify the performance degradation due to optical signal interference, the optical power difference of SOA-based optical gates switching between ON and OFF status (ON/OFF ratio) is assessed in this part. It is depicted in Figure 4a that the SOA-based optical gate obtains a larger power difference when switching between ON and OFF status at higher SOA operation currents (better performance). When increasing the SOA operation current higher than 30 mA, the power difference of SOA-based optical gates is larger than 60 dB. The power spectra of optical signal passing through the SOA-based optical gates are shown in Figure 4b with ON and OFF status, respectively, and SOA operation current is configured as 60 mA in the assessment. With an ON/OFF ratio of higher than 60 dB, the SOA gates can block almost all the optical signals at OFF status, avoiding extra noise at output channel. Meanwhile, the target optical signal is amplified though ON SOA gate (output channel), guaranteeing a minimal signal interference across channels. Benefiting from the operation features of SOA, we can set the ON/OFF status of SOA gates within nanoseconds and achieve a rapid packet switching with good signal quality.  
Figure 4: (a) Optical signal power difference with ON and OFF SOA status; (b) Power spectra of optical signal passing through SOA based optical gates.  
Figure 3: Experimental setup consisting of four hardware cubes and one NOS.  
To quantify the physical performance and validate the feasibility of the decomposed computing architecture with decomposed hardware, the BER of the implemented computing prototype is assessed with a SOA operation current of 60 mA, as shown in Figure 5. The Xilinx IBERT module [31] is deployed in all the four hardware cubes for calculating the BER of the computing prototype. BER is recorded when processor cubes send instructions to memory cubes. The commercially available plug-in SFP+ transceivers are applied in the assessment. The end-to-end BER without NOS (B-to-B) is measured as a criterion in the evaluation. It is shown in Figure 5 that the implemented computing prototype can perform the non-error packet transmission with 0.5 dB power compensation under a BER of \(1\times 10^{-9}\) and operation current of 60 mA. This demonstrates that the implemented computing prototype can provide a feasible physical interconnection among decomposed hardware cubes
## 3 Results
### Introduction of Online Personalisation and Adaptive Modelling Algorithm  
Most of the available thermal sensation and comfort predictive models (e.g., [30; 31; 32; 33; 34; 35; 36; 37]) are static models. That is, they predict the average vote of a large group of people based on, for example, the 7-point thermal sensation scale instead of individual thermal comfort, and they only describe the overall thermal sensation/comfort of multiple occupants in a shared thermal environment. To overcome the disadvantages of static models, adaptive thermal comfort models aim to provide insights and opportunities to personalise the thermal comfort prediction of individuals [38]. The idea behind adaptive models is that occupants and individuals are no longer regarded as passive recipients of the thermal environment, but rather, they play an active role in creating their own thermal preferences [39]. The suggested linear regression model, represented by (3), in the present paper is considered as a global model, also called an offline model [40; 41], for an adaptive personalised model to assess and predict individual thermal comfort under a cyclist's helmet.  
Figure 11 depicts the proposed steps for retuning and personalising the offline regression model (3). The suggested personalised adaptive tuning algorithm consists of the main components shown in Figure 11.  
Figure 11: Schematic representation of the proposed online personalisation algorithm to predict thermal comfort under the helmet. The retuning and personalisation algorithm is based on data streaming obtained from the developed SmartHelmet prototype and the cyclist’s personal vote of thermal comfort acquired from the developed SmartHelmet App. The streamed data is fed, together with the developed offline model, to an online parameter estimation algorithm based on a recursive least-squares (RLS) algorithm.
## 1 Introduction  
The recent evolution in information technology, such as 5G, Internet of Things, and big data [1; 2; 3], is promoting the growing and dynamic demands on bandwidth and capacity of optical data transmission. With the limited bandwidth provided by electrical devices, exploiting polarization multiplexing and higher-order modulation formats, for instance, 16 QAM and 64 QAM can increase the spectrum efficiency of transmission systems [4; 5; 6]. However, with higher-order modulation formats the system performance has less tolerance against carrier phase estimation error, and it is difficult to achieve fast convergence during compensating polarization misalignment. The conventional algorithm for carrier phase noise is the blind phase search (BPS) algorithm, which has the disadvantage of having high complexity [7; 8]. Some efforts have been made to decrease the computational complexity [9; 10].  
Regarding polarization tracking, the traditional algorithms are the constant modulus algorithm (CMA) and its variants [11; 12; 13]. The CMA algorithm is capable of tracking polarization rotation of up to 1 \(\mathrm{Mrad}/\mathrm{s}\)[14], which is not enough for meeting the requirements imposed by dynamic or burst optical detection scenarios.  
For coherent optical communication system, both polarization tracking and carrier phase recovery are essential. The algorithms that were mentioned above such as CMA+BPS can be used for polarization tracking and carrier phase recovery. While we also have another choice, the extended Kalman filter (EKF) has been demonstrated in coherent optical transmission systems [15; 16; 17; 18; 19]. This is a joint scheme and can be used for polarization tracking and carrier phase recovery simultaneously, the overall computational complexity is lower than CMA+BPS. However, it is commonly known that the tuning parameters' Q value is significant for determining EKF performance. The higher laser linewidth, the larger Q value should be configured theoretically. The same rule exists in the case of tracking polarization rotation. Therefore, an adaptive Kalman filter (AKF) scheme is expected, in which Q value can change along with the conditions in an intelligent manner [20]. The convergence speed should be improved further in the case of high-order modulation format to meet the requirement of the next-generation dynamic optical transmission.  
Based on our previous work [21], we make some improvements to AKF and investigate it via numerical simulation and experimental demonstrations. Firstly, the model of the adaptive Kalman filtering assisted by the pilot symbols is described, in which the Q value can be updated under dynamic network conditions. With the detailed simulation and experimental demonstration, the dependence of the scheme performance on pilot symbols parameters and operating conditions, including format ratio and phase noise, the polarization rotation frequency drift rate is investigated comprehensively. Finally, the convergence improvement and excellent tolerance against initial Q value are verified in the proposed scheme. Computational complexity analysis is carried out at the end.
## 5 Conclusions  
To summarize, six different metrics of mutual coupling of simultaneously registered signals representing blood pressure and pulse interval dynamics have been considered in this study. Stress test response patterns represented by the reaction of the recorded signals to the external input by tilting the body into the upright position have been analyzed. While nearly _all_ studied metrics significantly differed between patients and healthy subjects, only a few of them appeared informative for the differential diagnostics of patients with autonomic disorders of different etiology and severity. Besides the widely used BRS index, also Sync and TDS mutual information metrics representing the temporal fractions of the analyzed signal records exhibiting rather synchronous dynamics exhibited significant differences. While BRS characterizes the _intensity_ of the pulse intervals response to the blood pressure changes during observed feedback responses, both Sync and TDS likely indicate how often such responses are _activated_ in the first place. Our results indicate, that in most cases, BRS is typically _reciprocal_ to both Sync and TDS suggesting that _low intensity_ of the feedback responses characterized by low BRS is rather compensated by their _more frequent activation_ indicated by higher Sync and TDS. A notable exception can be observed in diabetes patients with autonomic neuropathy, where a likely breakdown of this compensation could be observed.  
Conceptualization, M.I.B. and O.V.M.; formal analysis, N.S.P.; investigation, S.A.P. and M.I.B.; project administration, O.A.M. and M.I.B.; resources, O.V.M.; software, N.S.P.; supervision, M.I.B.; writing--original draft, M.I.B.; writing--review and editing, O.A.M., O.V.M. and M.I.B. All authors have read and agreed to the published version of the manuscript.  
We would like to acknowledge the financial support of this work by the Ministry of Science and Education of the Russian Federation in the framework of the basic state assignment No. 2.5475.2017/6.7.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.