esa-sceva/satcom-chunk-collection huggingface dataset:
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 6 Conclusions  
In this paper, we have proposed an energy-aware and delay-minimising routing protocol for heterogeneous Wake-up Receiver-based WSNs. Due to the different participants in a cluster, sink and Fog Node have more capabilities in terms of transmission power and energy budget than the individual sensor nodes. Each sensor node is equipped with a WuRx. When a sink wants to get data from a given sensor, it sends the request to its corresponding Fog Node. This latter wakes up this destination node as well as the intermediate relays using a novel multicast addressing scheme.  
Experimental results have shown that our proposed approach gives better results than the two strategies we compared it with. The simultaneous waking up of the destination and the relay acting nodes thus offers the possibility of shortening delays and saving energy, since multicast eliminates the need to send several WuPts.  
In the future work, we will further increase the communication opportunities of the network participants. It is necessary to expand the strategy and the addressing mechanism in order to achieve scalability. Even considering an indoor scenario, the expansion of a cluster is a necessary task. We aim also to enable inter-cluster communications via Fog Nodes. This has the advantage that if direct communication between a Fog Node and the sink is not possible, communication can take place via another intermediate Fog Nodes. Furthermore, inter-cluster communication can be used to address clusters that cannot be reached directly from the sink in large-scaled networks. We plan also in our future work to take into account the case of Fog Node failure and propose a healing strategy that enables the simple nodes to carry out inter-cluster communication. These nodes should then either be able to wake up nodes of a neighbouring cluster or, if possible, directly address the neighbouring Fog Node.  
[PERSON] contributed by way of conceptualisation, methodology, original draft writing, visualisation, editing, and funding acquisition. [PERSON] contributed by way of supervision, writing of sections, reviewing, editing, and visualisation. [PERSON] contributed by way of resources, reviewing, and editing. F.D. contributed by way of supervision, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Union Social Fund grant number K-7531.20/544-13 and financially supported by Leipzig University of Applied Sciences by funds of Sachsisches Staatsministerium fur Wissenschaft, Kultur und Tourismus.  
We acknowledge support by the Open Access Publication Funds of the Leipzig University of Applied Sciences.  
The authors declare no conflict of interest.
# An Experimental Tuning Approach of Fractional Order Controllers in the Frequency Domain
## 5 Conclusions  
The paper presents a tuning procedure for optimal fractional-order controllers based on the frequency domain response. The need of a process model is completely eliminated, making the method easily applicable to complex processes where a higher order model is needed. By eliminating the need of knowing the mathematical representation of the process' dynamics, the tuning procedure eliminates modeling errors.  
Most experimental methods are based on measuring both the amplitude and the time shift of the output signal. However, for the chosen process, the presented work has the major advantage of measuring only the output magnitude, completely excluding time shift measurements.  
The chosen process for the method validation is a smart beam equipped with piezoelectric actuators. Two fractional-order controllers are tuned with the purpose of diminishing the effect of disturbances on the beam. A fractional-order PD controller and a more complex, fractional-order PID are determined through constrained optimization routines. The tuning process consists first in determining the process frequency response magnitude as some key frequencies based on experimental tests. Next, the closed loop frequency response magnitude and phase values are imposed at these key frequencies, according to some performance specifications. Using these closed loop frequency response magnitude and phase values, the constrained optimization routine is used to estimate the fractional-order controller parameters. The tuning effort is the same for any type of fractional-order controller chosen. The difference between tuning different controllers is the number of chosen points from the frequency diagram as optimization constraints.  
The experimental results prove that the proposed method can be successfully used to tune fractional-order controllers in the absence of a process model.  
Conceptualization, I.B., S.F., O.P., E.D., and C.M.; methodology, I.B. and C.M.; software, S.F.; validation, I.B. and S.F.; formal analysis, E.D. and C.M.; investigation, E.. and C.M.; resources, S.F. and O.P.; data curation, I.B. and S.F.; writing--original draft preparation, I.B.; writing--review and editing, C.M. and E.D.; visualization, O.P.; supervision, C.M.; project administration, C.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI-UEFISCDI, project number PN-III-P1-1.1-TE-2016-1396, TE 65/2018.  
The authors declare no conflicts 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.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 2 Literature Review
### User Interaction Data and Evaluation Metrics for AI Models  
Collecting user interaction data is essential for the advancement and fine-tuning of AI NPCs, non-player characters, and recommendation systems. Assessing the performance of AI models using resilient measures is crucial to guarantee their efficacy and dependability. This topic examines the significance of user interaction data in the training of AI models and the metrics employed to assess their effectiveness.  
Gathering user interaction data of superior quality enables AI models to acquire knowledge about user behavior and preferences, resulting in suggestions that are more tailored and precise. ArtWhisperer, an online game, was specifically created to examine the interactions between humans and AI. It offers significant data that help in comprehending user methods and enhancing the ability to control AI [20]. Furthermore, the assessment of user happiness in voice-based interactions between humans and AI, using emotional metrics, emphasizes the significance of user feedback in improving AI systems [21].  
Assessing AI models entails the utilization of many metrics, including precision, recall, F1 score, and more specialized measures such as the Area Under the Curve (AUC). These indicators offer a thorough perspective on the model's efficacy in generating precise forecasts and suggestions. For instance, a machine learning model specifically created to forecast the quality of interactions in conversational AI assistants guarantees that enhancements to SLU, Spoken Language Understanding models, enhance the overall performance [22].  
Integrating user interaction data with rigorous evaluation measures might result in substantial enhancements in AI models. The utilization of rough set theory and the fuzzy analytic hierarchy process aids in the creation of an evaluation index system for AI design based on user experience. This method assists in identifying crucial elements that impact user happiness and subsequently optimizing AI interfaces [23].
## 2 Background  
The selection of stepsize parameters in MC simulations is generally performed by trial and error, based on shorter runs. Given that the simulation efficiency is not sensitive to small changes in these parameters, such a procedure is adequate for systems with a few types of degrees of freedom sampled, e.g., for neat liquids or solutions involving small solutes. For systems containing a large number of degrees of freedom whose stepsize parameters have to be tuned separately (e.g., a polypeptide with a large number of different types of torsion angles), the trial and error approach breaks down since it requires much longer trial runs and it is practically impossible to consider the effect of the change in the stepsize parameter of one type of degrees of freedom on the acceptance rate of a different type. As a result, such simulations are generally run with poorly tuned stepsize parameters.  
A procedure for automated tuning of stepsize parameters has already been introduced [11]. In that work the simulation was partitioned into blocks where acceptance rate statistics are gathered on the basis of which the stepsize parameters are modified to move toward the target acceptance rate. That work also emphasized the important point that the ensemble generated during tuning will deviate from the Boltzmann distribution. Thus, unless strict adherence to the Boltzmann distribution is not important (e.g., for simulated annealing), tuning should _precede_ data gathering, even though continuing the tuning may improve the sampling efficiency as the stepsize parameters adapt to new conformations of the system. This tuning algorithm has been implemented into the CHARMM program for simulation of macromolecules [12] and tested in detail on the alanine dipeptide in vacuum [8].  
Our recent experience with the automatic tuning of the chemical potential parameter called \(B\) in grand-canonical ensemble simulations to a target density gave us the motivation to develop the tuning technique presented here. For the tuning of \(B\), three algorithms were tested: (1) change \(B\) based on the average densities over two successive blocks; (2) change \(B\) based on the fluctuation of the number of molecules \(N\) calculated in successive blocks; and (3) change \(B\) continually as dictated by the PIC technique. While the first two techniques even failed to converge on larger systems the PIC technique always worked and outperformed the other two by several orders of magnitude. The reason for this is that in order for the block-average based methods to work the block averages should be converged enough (otherwise the change made moved \(B\) in the wrong direction), but that unduly lengthens the calculation. This result thus suggests that the tuning based on simulation blocks is significantly less efficient than tuning by the PIC technique.
## 6 Simulation Verification and Analysis
### The Analysis of Transient Oscillation Suppression Mechanism with Different VSG Control Strategies
When \(D_{\omega}\) = 0, TVSG control demonstrates substantial oscillations in the output current, accompanied by large transient currents that may trigger overcurrent protection, potentially causing system instability. When \(D_{\omega}\) = 20, including steady-state damping, TVSG control effectively mitigates output current oscillations under grid disturbances. However, it results in an obvious steady-state deviation in active power during grid frequency disturbances, which significantly impacts the stability of the grid-connected system. The TDP-VSG control, incorporating transient damping, exhibits robust transient stability under grid disturbances. Nevertheless, power overshoot issues lead to pronounced current oscillations during grid frequency disturbances. In contrast, the proposed APFBC-VSG control exhibits minimal transient oscillations and avoids significant overshoot in the output grid current during given power disturbances and grid frequency disturbances. The system promptly returns to a steady state following disturbances, demonstrating that the proposed APFBC-VSG control effectively mitigates current oscillations and overshoot, thus offering stable power support for large-scale renewable energy integration.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 2 Review of Related Literature
### Blockchain in WBAN/Iot  
The BSN energy efficient and secure scheme is presented in [41] heterogeneous sensor networks. All biosensors in the network use a certificateless cryptography scheme to solve security and privacy issues. A signcryption (online or offline) method is considered to reduce the difficulty in sensors. If patient data are known, then an online process is implemented, whereas the offline process is executed if the prior knowledge is unavailable.  
For secure patient transactions, hybrid blockchain technology is involved. More energy consumption and high computations are required while processing in the online phase.  
A new storage model was studied in [42] to store the collected data using blockchain technology in a WBAN. In the blockchain, a designated verifier-based sequential aggregate signature scheme is presented to guarantee that the medical records can be viewed by the corresponding WBAN patient, i.e., privacy is protected to unauthorized patients. The main concern of this paper is to reduce the energy consumption for data computations and transmission. Likewise, the main novelty of this paper is to minimize the storage space of the block by generating the signature. Data storage in blockchain improves the storage space, but security is not stronger. Blockchain technology is focused on the eHealthcare management system to provide the interoperable WBAN [43]. This system follows the IEEE 802.15.6 specification, and the proposed system has provided the advantages of a high security protection level, low hardware utilization, and stable performance. This is well suited for a large-scale environment, but emergency packets are delayed more. A secure cloud server-based health data auditing [44] is implemented in blockchain technology. For this purpose, a third-party auditor (TPA) is created to play the role of public auditing in which the integrity of the outsourced medical packets is verified periodically. In this paper, a decentralized security framework is added, which is integrated into the data auditing element for security provisioning and is referred to as the Ethereum blockchain technique. In addition to TPA, the private key generator is deployed to generate the public and private keys. Each health record is maintained in TPA and is transferred into Ethereum. It is not suitable for resource-limited WBANs, and its performance is high when small sensors are deployed.  
Table 1 illustrates the overall research gaps in the WBAN-IoT that are identified from the in-depth literature in terms of _QoS_, energy wastage, and security, which are solved by the proposed solution, and the gaps are filled with the best solutions for _QoS_ achievement in WBAN-IoT.
## 1 Introduction
### Effective Proposed Approaches for Classifying Multiple PD Sources
#### 1.3.2 Time-Frequency Features  
Time-frequency techniques derive features from PD signals to capture both temporal and spectral characteristics, addressing their non-stationary nature.
# Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product
## 3 Results
### Simulation Results  
In this subsection, we describe the results of the numerical simulations. Figure 3 shows the results in terms of the number of clusters and ranks for \(\sigma_{ij}^{(s)}=0.1\)\((s=1,2,3;\ i\
eq j)\), for which we assume that the true difference is relatively small. Therefore, it is difficult to recover the true clustering structure. The vertical axes represent the results in terms of ARIs. Here, the methods are compared in terms of the medians and interquartile ranges (IQRs) of the ARIs. In Figure 3, the results of the second and third methods, which utilize only the difference and not the inner product, indicate lower performance than that of the proposed method. As the results in Figure 3 show, the model that utilizes the inner product tends to outperform models that focus only on the differences. In particular, for \(d\geq k\), the results of the proposed method tend to be superior to those of the other methods.  
The results for \(\sigma_{ij}^{(s)}=0.2\ (s=1,2,3;\ i\
eq j)\) are shown in Figure 4. The tendency of these results is similar to those for \(\sigma_{ij}^{(s)}=0.1\ (s=1,2,3;\ i\
eq j)\), whereas the results corresponding to \(\sigma_{ij}^{(s)}=0.2\) tend to indicate higher performance than those corresponding to \(\sigma_{ij}^{(s)}=0.1\).  
From the overall results, we note two specific observations. First, the number of clusters is relatively large and performance tends to be relatively low, irrespective of the type of method; this tendency has been reported in [35]. Second, all methods depend on the selection of hyperparameters. Based on the simulation results, the tuning parameters should be set to \(d\leq k\).
## 3 Proposed System
### The Incremental BoW Model  
In most hierarchical BoW approaches, a common method to build the vocabulary tree is to extract a rich set of features from some training images and perform k-medians clustering using \(k\)-means++ seeding [28]. In our proposed approach, we employ the BIRCH algorithm and update the vocabulary tree in a rapid and incremental manner.  
BIRCH is a hierarchical clustering algorithm that is particularly suitable for large datasets, as it can efficiently cluster data points by incrementally processing the data objects and building a CF tree. The CF is a data structure used in BIRCH, which contains three attributes: \(N\) (the number of data objects in the cluster represented by the CF), \(LS\) (the linear sum of the data objects in the cluster represented by the CF), and \(SS\) (the squared sum of the data objects in the cluster represented by the CF). The CF structure has the nice property that its attributes satisfy a linear relationship, i.e., the attributes of the CF are the sum of the attributes of its children.  
In the vocabulary CF tree, the internal nodes represent clusters and the leaves represent words of the vocabulary. Each node and leaf contains a CF structure, where \(N\) represents the sum of the number of image blocks of features in the CF, \(LS\) represents the sum of the product of the feature vectors and the number of image blocks, and \(SS\) is not used (since all feature vectors are unit vectors). When a new image region feature is extracted, it is added to the vocabulary CF tree by selecting the intermediate node that minimizes the distance at each level from root to leaf. If the addition of a new feature would cause a leaf to exceed the maximum radius of its hypersphere, the leaf is split into two leaves. Similarly, if the increase in the number of leaves would cause a node to exceed its maximum capacity, the node is split into two child nodes. Finally, all CF structures on the path are updated.  
In addition to the vocabulary tree, an inverse index is also maintained and updated, which stores a list of images where each word in the vocabulary has appeared. By using the inverse index, we can speed up the generation of loop closure hypotheses, as we only need to compare images that have at least one word in common with the query image.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion
### Sensitivity to Noise
#### 4.2.2 Gaussian Random Noise  
In this case, Noise() is a function which follows a Gaussian distribution with a mean of 0 and standard deviation of 1. Since the Gaussian noise is not mathematically bounded, the amplitude of the noise has been assumed to be equal to two times the standard deviation (which is a bound for 95.4% of the possible values). Figure 14 represents the functions \(P_{E}(\mathrm{t})\) and \(P_{\mathrm{C}}(\mathrm{t})\) obtained using Equations (32) and (33) with different noise levels.  
Table 4 shows how adding a gaussian noise to \(P_{E}(\mathrm{t})\) and \(P_{\mathrm{C}}(\mathrm{t})\) impacts into the calculated power depending on the method.  
The results of this analysis show a degradation of the accuracy of the result when noise increases. When the noise level is 15%, the average power obtained is 114.140 mW (which corresponds to an error of 3.2%) and 126.584 mW (which corresponds to an error of \(-\)7.3%) using methods 1 and 2, respectively. In general, method 1 tends to underestimate the average power, whereas method 2 tends to overestimate it. As observed with uniformly distributed noise, method 1 has shown better robustness when noise increases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Average Power (mW) for Different Levels of Noise**} \\  & \multicolumn{6}{c}{**(Gaussian Noise)**} \\ \cline{2-7}  & **0\%** & **5\%** & **10\%** & **15\%** & **20\%** & **25\%** \\ \hline Reference method & 118.05 & 117.924 & 118.086 & 117.418 & 118.265 & 117.952 \\ Instantaneous power (method 1) & 115.983 & 115.817 & 115.665 & 115.065 & 115.022 & 114.140 \\ One-period average (method 2) & 116.691 & 117.288 & 119.136 & 121.281 & 124.101 & 126.584 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results obtained with different levels of noise (Gaussian noise).  
Figure 14: AC component of \(P_{E}(k)\) and \(P_{\mathrm{C}}(k)\), filtered and non-filtered, with different noise levels (gaussian noise).
## 3 Results and Discussion
### Viscosity Assay  
The viscosity of the formulations was determined 24 h after preparation and after 28 days of storage at 20-22 \({}^{\circ}\)C. It was found that its value varies depending on the ointment base used, the method of introducing the active substance into the base, as well as the set shear rate (Table 5). As the shear rate increased, the viscosity of the tested formulations decreased.  
Formulations prepared on a lipophilic absorption base (Eucerin Ointment I) were characterised by higher viscosity compared to formulations on a hydrophilic base (Pentravan(r)), on average 1.25 times (\(p<0.05\)) for suspension-type ointments and on average 1.5 times (\(p<0.01\)) for emulsion-type ointments. Higher viscosity values had formulations to which benzoaine was introduced in solid form, after levigation (suspension systems), while emulsion systems showed lower viscosity. All prepared formulations showed thixotropy, which is a desirable phenomenon in the case of semi-solid forms and allows an easy and pleasant application to the skin. The thixotropy present in Pentravan(r) was also confirmed in a study by [PERSON] _et al._ [35]. No significant changes in the viscosity of the tested formulations were observed after 28 days of storage at 20-22 \({}^{\circ}\)C, confirming their rheological stability.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Formulation Code**} & \multicolumn{2}{c}{**Shear Rate (mPa-s)**} \\ \cline{2-3}  & **500 s\({}^{-1}\)** & **900 s\({}^{-1}\)** \\ \hline P-6 & 1289.03 \(\pm\) 31.54 & 868.76 \(\pm\) 16.60 \\ \hline P-E-30 & 1009.14 \(\pm\) 34.06 & 547.29 \(\pm\) 17.40 \\ \hline E-24 & 1511.49 \(\pm\) 98.94 & 1157.05 \(\pm\) 13.83 \\ \hline E-E-36 & 1294.89 \(\pm\) 64.88 & 946.36 \(\pm\) 35.50 \\ \hline \end{tabular}
\end{table}
Table 5: Viscosity parameters for four benzoaine semi-solid ointments.