esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Robot Inclusivity of Buildings Based on Surface Unevenness
## 2 Evaluating Robot Inclusivity
### Determining the Robot Inclusivity Level (RIL)  
The RIL (Robot Inclusivity Level) of a particular location indicates how well a robot can traverse the area without encountering performance issues. Thus, the RIL is crucial for evaluating the suitability of a building environment for a robot's optimal performance. For example, a low value of \(U\) suggests fewer issues for robots' navigation. Therefore, a higher RIL-SU should be assigned to areas with a low \(U\), while areas with a high \(U\) should be assigned a lower RIL, as a high \(U\) can lead to navigation issues such as localization errors. Based on these observations, a rule-based framework is developed to categorize RIL-SU with respect to surface unevenness into three levels: High RIL, Moderate RIL, and Low RIL.  
Let \(U_{X,Y}\) be the surface unevenness value recorded for the location \((X,Y)\) of the environment. Surfaces that are primarily level and smooth are represented by environments with low values of unevenness, which are less than the threshold \(U^{L}\). This correlates with a 'High' RIL, as in (1), enabling robots to perform navigation tasks with minimal issues in that location.  
The importance of RIL-SU becomes more pronounced as unevenness values increase, falling between \(U^{L}\) and \(U^{U}\), indicating greater terrain irregularity and slight elevation changes. This presents navigational challenges for robots. To navigate these conditions successfully, robots with intermediate inclusiveness scores require advanced mapping and localization algorithms, sophisticated sensors, and versatile motion mechanisms (such as adjustable suspension systems), especially if the environment cannot be modified to mitigate these challenges. Therefore, a 'Moderate' RIL is assigned to these locations.  
A surface with high unevenness implies a high probability of failure when the robot traverses that location. Such excessive unevenness may be caused by cables on the floor and rough floor finishes, such as coarse gravel or uneven stone tiles used for the floor surface. This indicates that the unevenness exceeds the threshold \(U^{U}\), thus resulting in a 'Low' RIL being assigned to such surface conditions.  
\[\text{RIL}=\begin{cases}\text{High}&\text{If }U_{X,Y}<U^{L}\\ \text{Moderate}&\text{If }U^{L}<U_{X,Y}<U^{U}\\ \text{Low}&\text{If }U^{U}<U_{X,Y}\end{cases} \tag{1}\]The thresholds \(U^{L}\) and \(U^{U}\) are determined empirically by running a robot through several rounds under various unevenness conditions. The values defined for \(U^{L}\) and \(U^{U}\) are 0.09 and 2, respectively. These threshold levels should be adjusted according to the features of the robot being considered, such as the availability of vibration-suppressing mechanisms. The threshold levels defined in this paper are suitable for most mobile robots without vibration-suppressing mechanisms.
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 3 Experimental Program  
### Material Properties and Mix Design of ECC and LWSCC  
This study included three main mixtures: lightweight self-consolidating concrete (LWSCC), engineering cementitious composites with PVA or steel fibers (ECCP and ECCS). The design and proportions for these three main mixtures are summarized in Table 1. In the LWSCC mixture, slate lightweight coarse aggregate and fine aggregate were used. Their maximum aggregate sizes were 12.5 mm and 4.75 mm, respectively. The aggregates had specific gravities of 1.53 and 1.8 and water absorption rates of 7.1% and 10%, respectively. The LWSCC mixture had a density of 1727 kg/m\({}^{3}\), which classifies it as lightweight concrete according to the ACI [28] and CSA [29]. Details on the development of the LWSCC mixture are discussed elsewhere [9].  
Of the ECCP and ECCS mixtures, ordinary Portland cement type 1 (ASTM C150 [30]) constituted 45%, Class F fly ash (ASTM C618 [31]) constituted 35%, and Class N metakaolin (ASTM C618 [31]) constituted 20% and were used as binders. The ECC mixtures also included silica sand as a fine aggregate, with a maximum aggregate size of 400 \(\upmu\)m and a specific gravity of 2.65. The fibers used in the ECC mixtures were 8 mm PVA fibers and 35 mm hooked-end steel fibers (SFs). According to the manufacturer's specifications, the PVA fibers were 8 mm in length, had an aspect ratio of 210, a diameter of 38 \(\upmu\)m, a tensile strength of 1600 MPa, a Young's modulus of 40 GPa, and a density of 1300 kg/m\({}^{3}\). The SFs were 35 mm in length, had an aspect ratio of 65, a diameter of 0.55 mm, a tensile strength of 1050 MPa, and a density of 7850 kg/m\({}^{3}\). Metakaolin was used in this study to achieve an ECC compressive strength exceeding 70 MPa and to improve the viscosity of the cementitious paste, thereby enhancing the distribution of fibers [9].
## 3 Methodology and Algorithm Descriptions
### Research Design and Main Stages
#### 3.2.1 Stage 1: Problem Definition and Objective Setting  
The study began by identifying the performance trade-offs between the Apriori and FP-Growth algorithms in the context of association rule mining for e-commerce applications. The primary objectives were to evaluate and compare the two algorithms based on efficiency (execution time and memory usage), rule quality (support, confidence, and lift), and scalability under varying parameter constraints.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Blockchain in Real-Time Data Handling
In a real-time metaverse environment, where user interactions must be instantaneous to maintain immersion, these delays significantly degrade the user experience. The energy consumption of PoW-based blockchain networks also poses a significant challenge [262]. The computational power required to solve cryptographic puzzles and validate transactions in PoW networks like Bitcoin leads to substantial energy usage, which not only limits scalability but also raises concerns about the environmental sustainability of blockchain in a highly interactive metaverse environment. Although alternative consensus mechanisms like PoS reduce energy consumption, they still face trade-offs in decentralization, as staking-based models can lead to a concentration of power among users with significant holdings, undermining the distributed, trustless nature that blockchain aims to provide. Another limitation is the challenge of interoperability between different blockchain networks within the metaverse [263]. With a variety of blockchains supporting different applications and digital assets, ensuring seamless communication and transaction finalization across multiple blockchains becomes a major technical hurdle. Cross-chain interoperability is still in its infancy, and current solutions like bridges are often slow, susceptible to vulnerabilities, and require additional layers of trust, which defeats the purpose of decentralization. In a real-time metaverse scenario, where assets and data need to flow freely across platforms, these delays in cross-chain transactions introduce friction and reduce the fluidity of the virtual economy. These limitations suggest that blockchain, in its current form, is not equipped to handle the high-frequency, low-latency data exchanges essential for the real-time metaverse. For blockchain to become a viable infrastructure for the metaverse, it must evolve to address these performance bottlenecks [264]. Emerging solutions, such as Layer 2 protocols--which enable off-chain processing to reduce congestion on the main chain--and sharding, which divides the blockchain into smaller, parallel chains, offer potential avenues for improvement. However, these solutions are still in the development stage and have not undergone the kind of testing that a fully developed real-time metaverse would require. Until blockchain technology can meet the demands of speed, scalability, and efficiency required by these virtual environments, alternative technologies like centralized databases or hybrid blockchain systems may need to fill the gap in supporting real-time, interactive experiences.
# High-Performance Digital Devices Design by the ASMD-FSMD Technique for Implementation in FPGA
## 1 Introduction
As a result, several operations can be executed during one clock cycle. It leads to an increase in device performance, as compared to the traditional approach. The proposed approach is demonstrated by an implementation in reconfigurable FPGA of sequential multipliers. Experimental studies have shown that using the ASMD-FSMD technique allows for increasing the performance of sequential multipliers on average by a factor of 2 and, for some examples, by a factor of 3. The main contribution of this paper is the use of the ASMD-FSMD technique for designing high-performance digital devices. The novelty of the proposed technique is the execution of several operations with the same variable in one clock cycle, which leads to an increase in the performance of the device. The main goal of the paper is to justify the increase in the performance of digital devices using the ASMD-FSMD technique, as well as provide practical recommendations for the design of high-performance digital devices by the ASMD-FSMD technique. Summarizing the above, the originality and novelty of the proposed approach consist of the following key points:  
* The use of the ASMD-FSMD technique for designing high-performance digital devices;
* The execution of several operations with the same variable in one clock cycle;
* The justification for the increase in the performance of digital devices using the ASMD-FSMD technique;
* The verification of the advantages of the ASMD-FSMD technique through implementations of the multiplier;
* The identification and summarization of practical recommendations for the design of high-performance digital devices using the ASMD-FSMD technique. The paper is organized as follows. Section 2 presents the traditional approach to the design of sequential multipliers. Section 3 discusses the ASMD-FSMD technique and presents the main hypothesis of the work. Section 4 presents the results of the experimental studies. The conclusions in Section 5 give recommendations for the practical use of the ASMD-FSMD technique to increase the performance of digital devices, as well as indicate directions for further research of the ASMD-FSMD technique.
## 7 Conclusions and Future Work  
We have shown how to use the parallel functional language Eden to provide parallel versions of PSO algorithms by means of Eden skeletons. The programmer only has to provide an appropriate fitness function and adjust parameters, whereas all low-level details of the parallelization are done automatically by the skeleton and the underlying runtime system. Moreover, different parallel implementations of PSO can be provided to the user (in the paper we have shown four of them) so that he can select the one that better fits his necessities.  
It is worth pointing out that the source code used to develop the skeletons is highly reusable. First, any programmer can use it by just defining an appropriate fitness function, while the whole parallel machinery is done automatically by the provided skeleton. Second, it is simple to modify the skeleton to obtain alternative parallel versions of it. In particular, we have shown how to modify psoPAR to obtain a different version called psoPARh to deal with heterogeneous processors, and we have also shown how to modify such version to obtain another version psoPARhv where the number of iterations to be performed in each parallel step is variable. In both cases, it was only necessary to modify a couple of lines of the source code to obtain the alternative version, keeping unmodified the main part of the source code.  
Our experiments have shown that the programmer does not need much work to adjust a skeleton to his concrete problem, but achieved speedups are good enough. Note that we do not claim to obtain optimal speedups but reasonable speedups--at low programmer effort.  
In our previous work [31], we showed how to use Eden skeletons to deal with the parallelization of genetic algorithms. In fact, our aim is to provide a library of generic Haskell versions of the most common bioinspired metaheuristics, as well as a library containing the corresponding Eden skeletons for each metaheuristic. By doing so, we will simplify the task of using these metaheuristics in functional settings, and we will also simplify the task of improving their performance by means of parallelizing them. Moreover, as several metaheuristics will be provided in the same environment, the programmer will be able to check more easily what metaheuristics fits better for each concrete problem.  
In addition to extending our library to deal with other swarm metaheuristics (like Ant Colony Optimization or River Formation Dynamics), as future work we are particularly interested in studying hybrid systems combining different metaheuristics.
## 5 Experimental Verification
### Influence of Voltage Outer Loop Bandwidth on the Stability of GCI Systems  
With voltage feedforward control considered, GCI grid-connected currents controlled by two different domains under different voltage outer loop bandwidths are shown in Figure 13.  
From Figure 13a, it can be seen that when the voltage outer loop bandwidth \(BW_{\text{v}}=30\) Hz and 130 Hz, the GCI system controlled by PI in the \(dq\) domain maintains stability; however, when the bandwidth of the voltage outer loop \(BW_{\text{v}}=180\) Hz, the grid-connected current of the GCI system controlled by PI in the dq domain diverges significantly, leading to system instability. As shown in Figure 13b, the GCI system controlled by phase-domain PR remains stable at bandwidths of 30 Hz, 130 Hz, and 180 Hz.  
Figure 12: Grid-connected current waveform under different power grid strengths without considering voltage feedforward. (**a**) The dq-domain PI-controlled GCI system; (**b**) the phase-domain PR-controlled GCI system.  
With voltage feedforward control not considered, GCI grid-connected currents controlled by two different domains with varying voltage outer loop bandwidths are depicted in Figure 14.  
According to Figure 14, when the voltage outer loop bandwidth \(BW_{\rm v}=\) 30 Hz, 130 Hz, and 180 Hz, the GCI system controlled by PI control in the \(dq\) domain and PR control in the phase domain remains stable.  
Figure 14: _Cont._  
Figure 13: Grid-connected current waveform with different outer loop bandwidths considering voltage feedforward. (**a**) The dq-domain PI-controlled GCI system; (**b**) the phase-domain PR-controlled GCI system.
# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Use Cases of the TinyML
#### 2.7.4 Anomaly Detection  
The difference between the mass of the majority of points and the point value is the anomaly. In [132], a study is given to see if TinyML is appropriate for tasks connected to anomaly detection. The Arduino Nano 33 BLE sense unit makes use of a general artificial neural network, an automatic encoder, and a variable automatic encoder. To check for irregularities in the uneven spin drying cycle, the study makes use of a Kenmore top-load washing machine type. The findings reveal that the precision is 90% and the accuracy is 92%.
# Interclass Interference Suppression in Multi-Class Problems
## 3 Interclass Interference Suppression Methods
### One-Class Classifier Based Approach  
One-class classifier [53] only uses the information of a certain classifier, so there is no interclass interference problem. Therefore, methods based on one-class classifiers can completely eliminate the interclass interference in multi-classifiers.  
The typical example of a one-class classifier is the one-class support vector machine (SVM) [54]. One-class SVM uses a kernel function to map the original normal data to a high-dimensional space, where one-class SVM tries to find a hyperplane that enables the normal data to be as far from the origin as possible. Let the distance between the hyperplane and the origin be \(\rho\). Then, the samples whose distance from the origin is smaller than \(\rho\) is detected as abnormal samples [54,55]. If there are \(m\) features and \(N\) training samples in the training set, let \(x_{i}(i=1,\ldots,N)\) denote the training data, then one-class SVM can be denoted by the following optimization problem.  
\[\begin{split}\min&\frac{1}{2}\|w\|^{2}-\rho+\frac{1 }{vN}\sum\limits_{i=1}^{N}\xi_{i}\\ &\text{s.t.}(w\cdot\varphi(x_{i}))\geq\rho-\xi_{i}\\ &\xi_{i}\geq 0,i=1,\ldots,N\end{split} \tag{1}\]  
where \(\xi_{i}\) is the slack variable, \(v\in(0,1)\) is the error rate, \(\varphi(.)\) is a nonlinear mapping that is usually realized by a kernel function.  
Research studies have shown that a one-class classifier can only use the information of the target class when defining the classification boundary, unlike multi-classifiers that can use the information of other classes. Therefore, in general, the performance of a one-classclassifier is difficult to make comparable to that of multi-classifiers [56]. Thus, one-class classifier-based approaches are not very suitable to suppress the interclass inference, either.
## 2 Study Area and Datasets
### Datasets
#### 2.2.1 GNSS Vertical Displacements
We fit the GNSS time series with the following equation:  
\[\mathrm{y}(t)=A+Bt+Ccos(wt+\theta_{1})+Dcos(2 wt+\theta_{2})+\sum_{i=1}^{k}E_{ i}H(t-tE_{i})+\varepsilon \tag{2}\]  
where \(t\) represents the epoch with daily resolution; \(y\) represents GNSS vertical displacement; \(A\) represents the offset; \(B\) represents the linear trend; \(C,D,\theta_{1}\), and \(\theta_{2}\) represent the amplitudes and phases of annual and semi-annual components; \(E\) represents the step caused by earthquakes, equipment replacement, and so on, at the \(tE\) epoch; \(\varepsilon\) represents the residual; and \(H\) represents the Heaviside function, which is 0 and 1 when \((t-tE_{i})\) is negative and positive, respectively. The main steps in the GNSS time series can be found in the database on the NGL website ([[http://geodesy.unr.edu/NGLStationPages/steps.txt](http://geodesy.unr.edu/NGLStationPages/steps.txt)]([http://geodesy.unr.edu/NGLStationPages/steps.txt](http://geodesy.unr.edu/NGLStationPages/steps.txt)), accessed on 30 December 2022); potential jumps with a threshold of 1 cm in the GNSS time  
Figure 3: Flow chart of the GNSS vertical displacements time series data processing process. series are identified using change detection and time series analysis software, jumps upon spectrum and trend (JUST) [46]. The outliers in each GNSS time series are detected and omitted based on the median and interquartile range (IQR) statistics [47]. Within a sliding window centered on the GNSS displacement corresponding to each epoch, the median and IQR are calculated. When the difference between the residual and the median is 3 times larger than the IQR, it is automatically determined as an outlier. \[\mid v_{i}-median\left(v_{i-\frac{L}{2},i+\frac{L}{2}}\right)\mid>3\cdot IQR \left(v_{i-\frac{L}{2},i+\frac{L}{2}}\right) \tag{3}\]  
where \(v_{i}\) represents the \(i\)th residual in the GNSS time series, \(L\) represents the length of the sliding window, which is set to be 1 year, and the window gradually slides with each epoch as the center of the window. We first extract the residual term using Equation (2), and then remove the outliers using Equation (3) for each GNSS time series; all the outliers are omitted after several iterations. Finally, we remove the offset, linear trend, and steps in the GNSS time series. The resulting signals mainly reflect hydrological loading deformation, including the seasonal and residual terms in Equation (2). Figure 5 shows the GNSS vertical displacements time series at six selected stations; after the iterations mentioned above are performed, the steps are easily identified, and the outliers are effectively removed.
## 3 Results  
### Statistical Values of AGB  
Along the development of WOR phenology, Table 4 showed that the mean of AGB increased from 1444.00 to 8700.45 kg/hm\({}^{2}\), the standard deviation of AGB also increased from 1026.08 to 4480.54 kg/hm\({}^{2}\), and the Coef. of Variation also exhibited wide variation with a range of values from 71.05%-51.50%. The wide range of AGB statistical characteristics showed that the amount of nitrogen fertilizer significantly affected AGB, and the relatively discretized statistical characteristics may influence the accuracy of AGB estimation at different growth stages.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Growth Stages**} & **Minimum** & **Maximum** & **Mean** & **Standard** & **Variance** & **Coef. of** \\  & **(kg/hm\({}^{2}\))** & **(kg/hm\({}^{2}\))** & **(kg/hm\({}^{2}\))** & **Deviation** & & **Variation** \\ \hline Seeding stage (n = 156) & 179.00 & 3982.00 & 1444.00 & 1026.08 & 1052847.38 & 71.05 \\ Bolting stage (n = 27) & 527.00 & 5428.00 & 3052.85 & 1798.74 & 3235464.82 & 58.92 \\ Flowering stage (n = 51) & 674.00 & 10286.00 & 4589.64 & 2743.17 & 7524995.81 & 59.77 \\ Pod stage (n = 51) & 1445.63 & 17402.00 & 8700.45 & 4480.54 & 20075254.99 & 51.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The statistical characteristics of aboveground biomass (AGB) from 2014 to 2016.
###### Abstract  
Frailty poses a considerable risk to the performance of daily living activities, affecting the quality of life of older adults and increasing their risks of falling, disability, hospitalization, and mortality. Frailty is related to pathology with a state of negative energy balance, sarcopenia, diminished muscle strength, and low tolerance for exertion. Few intervention studies have been conducted on prefrail community-dwelling older adults. This study investigated the effects of whole-body vibration training on the biomarkers and health beliefs of prefrail community-dwelling older adults. We conducted a quasi-experimental research. Ninety participants were allocated to either an experimental or control group. This study followed the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) guidelines for quasi-experimental research design. The experimental group participated in a 12-week whole-body vibration training conducted three times per week, with 5-min continuous vibration at 12-Hz frequency and 3-mm amplitude each time. DVDs on resistance training and manuals were provided to the control group. The data were analysed through descriptive analysis, a chi-square test, an independent \(t\) test, a paired \(t\) test, and analysis of covariance. There were 42 people in the experimental group and 48 people in the control group. After the 12-week training and intervention, ANCOVA is used to analyze the experimental and control group's results. Experimental results in 30s chair stand test (\(F=98.85\), \(p<0.05\)), open-eye stand on dominant foot test (\(F=26.15\), \(p<0.05\)), two-minute-step test (\(F=25.89\), \(p<0.05\)), left-leg bone mineral density (\(F=14.71\), \(p<0.05\)), barriers of perceived frailty (\(F=39.81\), \(p<0.05\)) and cues to action (\(F=92.96\), \(p<0.05\)) in health beliefs are statistically significant compared to the control group's results. The 12-week whole-body vibration program employed in this study partly improved the biomarkers and health beliefs of prefrail community-dwelling older adults. We recommend whole-body vibration training as an exercise strategy, thereby improving community-dwelling older adults' biomarkers and health beliefs.  
frailty; elderly; biomarker; health belief; whole-body vibration +
Footnote †: journal: Applied Sci. 2021, 11, 3557. [[https://doi.org/10.3390/app1108357](https://doi.org/10.3390/app1108357)]([https://doi.org/10.3390/app1108357](https://doi.org/10.3390/app1108357))