esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Object Grasp Technique for Placement of Objects in Virtual Reality
## 1 Introduction  
Virtual Reality (VR) is used for training in medical emergency situations [1], mechanical assembly [2, 3] and military preparation [4] due to its low cost, rapid setup time, safety and capability to repeat and track users' tasks. These tasks involve a series of actions to be performed, for example, walking around an area while moving objects from one place to another. VR apps usually implement object grasping by attaching a single object to the controller. This is sufficient for many actions, such as grasping an apple, but does not allow users to grasp multiple objects and release them in a controlled manner. For instance, a nurse placing surgical equipment on a table or an operator sorting shelves will grab multiple objects on each hand. We believe that various VR applications that lack the capability of multi-object grasp could benefit from it. For example, virtual stores [5] and physical rehabilitation applications [6] may present a more realistic and transferable experience with multi-object grasp methods. For example, when visiting a clothes shop, it is common to grab multiple clothes to test various sizes, or in the supermarket to pick up multiple zygorts of different flavours. In rehabilitation, there are several tasks that include grabbing multiple items such as playing with cards or moving specifically shaped tokens.  
In this paper, we design, implement and evaluate an interaction technique for grasping multiple objects in VR and to release them in a controlled manner. The technique is designed to be similar to the real action of grasping and releasing multiple objects so that the practisedskills could be transferred to real life. Thus, ray-casting and bounding-box techniques were discarded, since they are not inspired in the real world. Moreover, fully physical simulations were not considered since they are impractical for grasping multiple objects.  
Three techniques were compared: the use of the hands in real life, a traditional single-object grasp technique and the new multi-object grasp technique. The aim is to understand if using the multi-object grasp technique in VR provides any benefit and if the users are comfortable using it.  
Section 2 covers previous techniques for grasping objects. Section 3 explains the implemented single-object grasp and multi-object VR techniques as well as the procedure that was followed for selecting some of the options within the design space. Then, a user study (Section 4) was performed to compare single-object and multi-object grasping in Virtual Reality and in the real-life on a task involving moving and placing coloured tokens. In Section 5, the obtained measurements and their statistical analysis are presented. These results show that the multi-object grasp technique, although seen as the most complex one, leads to significantly less travelled distance and physical effort. Lastly, in Section 6, high-level insights are highlighted as well as the limitations and future work.
## 3 The Proposed Method
### Accuracy Evaluation  
The MCMC-powered PRE method gives the estimation results of the mechanical parameters as probability density distributions. Therefore, it is necessary to invent a new index for accuracy evaluation. Suppose a normalized estimate of a mechanical parameter is given as \(a\). \(\mathrm{E}[a]\) denotes the average of the estimated value \(a\). If its correct value is \(a_{0}\), the error can be evaluated by \(\varepsilon=\sqrt{\left(\mathrm{E}[a]-a_{o}\right)^{2}}\). Thus, the accuracy evaluation index \(S_{A}\) can be given by the following formula:  
\[S_{A}=1-\sqrt{\left(\mathrm{E}[a]-a_{o}\right)^{2}} \tag{24}\]
## 2 Materials and Methods
### Experimental Design and Statistical Analysis  
In the present study, response surface methodology (RSM) was used. Using the Box-Behnken design (BBD), the influence of four independent variables--HM (ratio of solvent to fresh fruit) (X\({}_{1}\)), Duration (X\({}_{2}\)), Temperature (X\({}_{3}\)), and Extractant (X\({}_{4}\))--on different physicochemical parameters of peaches was investigated. The coded and actual levels of independent variables used in the RSM design are listed in Table 1. The dependent variables in this study are TPC, TFC, FRAP, CUPRAC, DPPH, ABTS, \(\upalpha\)-glucosidase, and AChE. They are denoted from Y1 to Y8, respectively. A second-order polynomial equation was used to express these dependent variables as a function of the independent variables as follows:  
\[Y_{j}\,=\,\,\beta_{0}+\sum\
olimits_{i\,=\,0}^{k}\beta_{i}X_{i}+\sum\
olimits _{i\,=\,1}^{k}\beta_{ii}X_{i}^{2}+\sum\
olimits_{i>0}^{k}\beta_{ij}X_{i}X_{j}+E \tag{3}\]  
where \(Y_{j}\) (\(j\) = 1, 2, \(\ldots\), 8) represents the responses to be modeled; \(\beta_{0}\) is the constant coefficient; \(\beta_{i}\) is the coefficient of linear effect; \(\beta_{ij}\) is the coefficient of interaction effect; \(\beta_{ii}\) is the coefficient of squared effect; \(k\) is the number of variables; and \(X_{i}\) and \(X_{j}\) define the independent variables (Hydro module (HM) (ratio) (\(X_{1}\)), Duration (\(X_{2}\)), Temperature (\(X_{3}\)), and Extractant (\(X_{4}\))). The statistical significance of the coefficients was verified using the Student's \(t\)-test (\(\upalpha\) = 0.05), goodness-of-fit was established as the determination coefficient (R\({}^{2}\)), and the model consistency by the Fisher F test (\(\upalpha\) = 0.05).  
The experimental design and statistical analysis were performed using Design-Expert software (Version 13.0.5.0, State-Ease Inc., Minneapolis, MN, USA). The complete experimental design consisted of 29 experimental runs, taken in random order. Center Points per Block were established at 5 to be able to estimate the pure error sum of squares.
# Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings
## 4 Conclusions  
The chemical characterization of coated liposomes represents a very important step in the development of new drug delivery systems. In the present work, zwitterionic liposomes coated with two polysaccharides: chitosan and hyaluronic acid, which are widely used for biomedical applications, were synthesized and characterized.  
The average size of vesicles being increased after coating with CH and HA, together with the \(\zeta\)-potential, suggested a proper coating procedure. The \(\zeta\)-potential of DOPC/DOPE-CH suggested the better physical stability of these preparations, indicating a lower probability of aggregation of liposomes versus time.  
The coated liposomes were analyzed by surface infrared (ATR-FTIR) spectroscopy, nuclear magnetic resonance (NMR) and time-of-flight secondary ion mass spectrometry (ToF-SIMS).  
The NMR spectra showed the presence of two polymers on the surface of the liposomes. In fact, the proton signals of both liposomes showed broad unresolved peaks, which are typical of aggregates. The comparison of these vesicles suggest that CH coats the vesicles more efficiently.  
The analysis of both the infrared and secondary ion mass spectra confirmed the presence of chitosan on the surface of the liposomes. In particular, the ToF-SIMS mass spectra, which investigates the very surface layer of systems (up to a maximum depth of 3-5 nm), provided crucial information on the coating processes.  
Conceptualization, C.B., G.T., A.D. and C.R.; methodology, C.B., G.T., M.C. and A.M.; software, G.L., L.C., V.V. and C.B.; formal analysis, C.B., G.L., M.C. and A.C.; investigation, G.T., M.C. and C.B.; data curation, G.T., C.B. and G.L.; writing--original draft preparation, G.T., M.C., G.L. and C.B.; writing--review and editing, G.T., C.B., G.L., A.C., M.C., L.C., V.V., A.D., C.R. and A.M.; supervision G.T., C.B. and C.R.; project administration, A.M., A.C., A.D. and C.R.; funding acquisition C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Mechanical Behaviors and Numerical Simulation Analysis of a New Isolation System
## 3 Numerical Analysis of a Based Isolated System and Conventional Structure  
To evaluate the effectiveness of the isolation system, we used the finite element software SAP2000 to perform numerical simulations on a four-story reinforced concrete frame structure. Through finite element analysis, we compared the performance of the isolation system with isolated supports at the bottom and traditional seismic-resistant structures in terms of base shear, inter-story displacement, and floor acceleration, and evaluated the isolation effect based on these comparisons.
###### Abstract  
An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices, such as wristbands, are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection and activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with a convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.  
Orthopedic walker; dataset; IoT; fall detection; activity logging; inertial measurement unit; machine learning; deep learning Article  
Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification April. Sci. 2023131379. [[https://doi.org/10.3390/app132011379](https://doi.org/10.3390/app132011379)]([https://doi.org/10.3390/app132011379](https://doi.org/10.3390/app132011379))  
1
## 2 Related Works
### Residual Vector Quantization (RVQ)  
RVQ is a technique proposed to efficiently compress high-dimensional data. The core idea of RVQ initially involves quantizing the data vector in the first stage, calculating the resulting residual, and then using this residual as the input for the subsequent quantization stage. In subsequent stages, the residuals are continuously quantized in the same manner, with the magnitude of the residuals progressively decreasing at each stage. This iterative process ultimately achieves quantization performance that closely approximates the original data vector. When applied to neural network-based audio codecs, RVQ is effectively utilized for high-quality audio reconstruction. The quantization process of RVQ is outlined in Algorithm 1[9; 10; 12].  
```
1:RVQ(\(y\), \(Q_{1}\), \(Q_{2}\), \(\dots\), \(Q_{N_{q}}\))
2:Input\(y=enc(x)\), where \(y\) is the encoder output vector
3:Input\(Q_{i}\) for \(i=1,\ \dots,\ N_{q}\) (vector quantizers or codebooks)
4:Output: Quantized Vector \(\hat{y}\)
5:Initialize:\(\hat{y}\gets 0.0\), residual\(\gets y\)
6:for\(i=1\) to \(N_{q}\)do
7:\(\hat{y}+=Q_{i}(residual)\)
8:\(residual-=Q_{i}(residual)\)
9:return\(\hat{y}\)
```  
**Algorithm 1** Residual vector quantization (RVQ)
## 1 Introduction
Taking into account the above observations, the aim of this study was to determine the influence of the method of sample preparation and the applied measurement methods on the variation in the determination of heavy metal concentrations in soils. The study area was the area directly adjacent to the Arcelor Mittal metallurgical plant near Krakow in southern Poland. This area is characterised by a significantly polluted soil environment as a result of many years of operation of the steelworks. Soil samples prepared by two methods were analysed and the solutions obtained were determined using two analytical methods and different types of spectrometers. The contents of heavy metals in soil determined using two selected methods commonly used in this type of study, i.e., AAS and ICP-MS, were compared. Wide research of the published articles proves that most of them show only results based on one selected course of methodology without comparison with others. Many of them are based only on the influence of preparation methodology in plants, i.e., on _Pleurozium schreberi_ moss samples prior to use in active biomonitoring studies [37]. Four different methodologies were tested across four different sample locations with the conclusion that of the four methods used to prepare mosses for later exposure in active biomonitoring,the best method is averaging with simultaneous conditioning of mosses in demineralised water. Another published paper shows evaluation and comparison between a conventional acid digestion method and a microwave digestion system for heavy metals determination in _Mentha_ samples by ICP-MS [38]. The concentrations of selected heavy metals were measured in the stems and leaves of the _Artemisia herba-alba_ medicinal plant and soil samples were evaluated by [PERSON] et al. [39]. Results showed different results in heavy metal concentrations of washed and unwashed parts of plants, which is still the preparation method. The results of this study [39] revealed that there is a correlation between heavy metal levels in medicinal plants and their concentrations in soil. Usually, authors show that results depend on the preparation but choose only one measurement method. The novelty of the paper is the comparison of the measurements of heavy metal concentration in soil using two methods for adding soil components into solution and different laboratory methods and types of measuring equipment. Additionally, the article includes a discussion of the importance of the methodology. We believe that the conclusions may help to better understand how sample preparation and measurement methods applied may influence the results obtained. Innovation of our research is the comparison of the selected methods which shows how the methodology is important. Results are highly dependent on the preparation and measurement methods applied. We believe that the conclusions based on the comparison may help to better understand how the methodology applied may influence the results obtained.
## 3 Basic Principles of 3 MA
### Incremental Permeability (IP) Method  
The Incremental Permeability (IP) method provides measuring parameters, which are equivalently defined to the BN measuring parameters (see Table 4).  
The IP method requests two simultaneously acting excitation sources. These are a first high-amplitude low-frequency (f\({}_{\text{LF}}\)) excitation, as in case of the HA method (see Section 3.1) and a second low-amplitude high-frequency (f\({}_{\text{HF}}\)) excitation, as in case of the EC method (see Section 3.2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measuring Parameter** & **Description** \\ \hline \(\mu_{\text{MAX}}\) & Maximum amplitude \\ \hline \(\mu_{\text{MEAN}}\) & Amplitude averaged over one magnetization cycle \\ \hline \(\mu_{\text{R}}\) & Amplitude at remanence point \\ \hline \(\text{H}_{\text{C}\mu}\) & Coercive magnetic field, derived from Incremental Permeability \\  & (magnetic field strength at \(\mu=\mu_{\text{Max}}\)) \\ \hline \(\text{DH}25_{\mu}\) & Curve width at 25\% of \(\mu_{\text{MAX}}\) \\ \hline \(\text{DH}50_{\mu}\) & Curve width at 50\% of \(\mu_{\text{MAX}}\) \\ \hline \(\text{DH}75_{\mu}\) & Curve width at 75\% of \(\mu_{\text{MAX}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measuring parameters from Incremental Permeability (IP).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measuring Parameter** & **Description** \\ \hline \(\text{M}_{\text{MAX}}\) & Maximum amplitude \\ \hline \(\text{M}_{\text{MEAN}}\) & Amplitude averaged over one magnetization cycle \\ \hline \(\text{M}_{\text{R}}\) & Amplitude at remanence point \\ \hline \(\text{H}_{\text{CM}}\) & Coercive magnetic field, derived from Barkhausen Noise \\  & (magnetic field strength at \(\text{M}=\text{M}_{\text{Max}}\)) \\ \hline \(\text{DH}25_{\text{M}}\) & Curve width at 25\% of \(\text{M}_{\text{MAX}}\) \\ \hline \(\text{DH}50_{\text{M}}\) & Curve width at 50\% of \(\text{M}_{\text{MAX}}\) \\ \hline \(\text{DH}75_{\text{M}}\) & Curve width at 75\% of \(\text{M}_{\text{MAX}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measuring parameters from Barkhausen Noise (BN).  
The f\({}_{\rm LF}\) excitation by the magnetization yoke (see Figure 2, no. 3 and 4) generates magnetic hysteresis cycles in the material. Simultaneously, the EC transmitter coil (see Figure 2, no. 7) generates minor asymmetric hysteresis loops that are superimposed to the major hysteresis curve. The signal detected with the receiver coil (no. 8) is linked to incremental permeability. In contrast to BN, the IP method shows only little fluctuations during the hysteresis cycle. In addition, due to the applied narrow-band signal filtering, the IP method is relatively insensitive to electromagnetic distortions.
## 4 Distribution Flexible Alternative Current Transmission System (D-FACTS)  
Shunt D-FACTS devices can be classified into two main categories, namely the variable impedance type such as the distribution static var compensator (D-SVC) and the switching converter type such as the distribution static synchronous compensator (D-STATCOM).  
The configuration of the D-SVC connected to the distribution grid is shown in Figure 9.  
This figure shows a D-SVC consisting of a thyristor switched capacitor (TSC) part composed by two switching thyristors connected with capacitive reactance X\({}_{\text{TSC}}\); the other part is the thyristor-controlled reactor (TCR) composed by two thyristors connected with an impedance of an inductive reactance branch X\({}_{\text{TCR}}\). By controlling the angle thyristors (the angle with respect to the zero crossing of the phase voltage), the device is able to control the amplitude of the voltage at the point of common coupling (PCC) due to the changes in the angle resulting mainly in changes of the current. Therefore, the amount of the reactive power consumed by the inductor L, for an angle of \(\alpha=90^{\circ}\), the inductive circuit is activated, whereas for \(\alpha=180^{\circ}\), this inductive circuit is off.  
The configuration of the D-STATCOM connected to the distribution grid is shown in Figure 10.  
This figure shows the different blocks constituting this configuration of the control strategy, which consists of: A phase lock loop (PLL) for synchronization of the component of the positive sequence voltage with the primary voltage of the power distribution grid. An external control loop consists of controlling the DC bus voltage and the grid voltage. The outputs of the voltage controllers are the current references for the current controllers. The internal current control loop consists of the current  
Figure 9: Control configuration of the distribution static var compensator (D-SVC) connected to the point of common coupling (PCC) with a wind farm.  
controllers. The outputs of the current controllers consist in imposing the amplitude and phase of voltages of the D-STATCOM by generating Pulse Width Modulation (PWM) signals.
# Improved Acoustic Emission Tomography Algorithm Based on Lasso Regression
###### Abstract  
This study developed a novel acoustic emission (AE) tomography algorithm for non-destructive testing (NDT) based on Lasso regression (LASSO). The conventional AE tomography method takes considerable measurement data to obtain the elastic velocity distribution for structure evaluation. However, the new algorithm in which the LASSO algorithm is applied to AE tomography eliminates these deficiencies and reconstructs equivalent velocity distribution with fewer event data to describe the defected range. Three numerical simulation models were studied to reveal the capacity of the proposed method, and the functional performance was verified by three different types of classical concrete damage numerical simulation models and compared to that of the conventional SIRT algorithm in the experiment. Finally, this study demonstrates that the LASSO algorithm can be applied in AE tomography, and the shadow parts are eliminated in resultant elastic velocity distributions with fewer measurement paths.  
non-destructive testing; AE tomography; Lasso regression; LASSO regularization; LASSO algorithm +
Footnote †: journal: Applied  
11 October 2022  
18 November 2022  
such as SIRT, obtain the velocity distribution and visualize the internal damage of the structure, which is conventional and mature to apply in practice [1]. However, to construct the velocity distribution, the traditional algorithm requires a sufficient number of event data to maintain the quality of figures, which is a vital deficiency. For example, the SIRT utilizes a certain amount of event data to obtain the velocity distribution by iteration in each cell. Meanwhile, the characteristics of the defective area are local in comparison with the extent of the structures, and it satisfies the conditions for applying Lasso regression to obtain accurate velocity distribution while requiring a fewer amount of event data.  
In this paper, the second section introduces the basic theory of AE tomography. The third section introduces the fundamental hypothesis of Lasso regression, including the theoretical calculation process and conditions. Then, this section combines Lasso regression with AE tomography and determines the relevant parameters to ensure its feasibility in AE tomography systems. The capability of the proposed method was verified with three numerical models that simulated general damage patterns of concrete structures in the fourth section. In the fifth section, experiments were conducted to validate the proposed method by comparing the reconstructed velocity distribution of Lasso regression with fewer uniformly selected event data and the traditional SIRT algorithm in AE tomography. The discussion in the sixth section demonstrates different scenarios of AE tomography in theory and practice. In addition, the mechanism of the Lasso regression algorithm applied to AE tomography was illustrated in detail, and the principle of the Lasso regression algorithm was explained to be accurate and economical for velocity reconstruction. The conclusions are described in the final section.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 3 Optimization Problem Analysis
### Problem Formulation  
The optimization problem can be defined as a single-objective optimization problem with multiple constraints. The objective function aims to minimize energy consumption. This can be achieved by reducing the total energy used by the nodes during data transmission and extending the network's operational time through balanced energy consumption across all nodes. This optimization objective can be mathematically expressed as follows:  
\[\min E^{cc}_{total}=\sum_{t=1}^{T}\sum_{i=1}^{N_{or}}E^{i}_{total\_or}(t)+\sum _{t=1}^{T}\sum_{j=1}^{N_{su}}E^{j}_{total\_su}(t)+\sum_{t=1}^{T}\sum_{k=1}^{N_{ ad}}E^{k}_{total\_ad}(t) \tag{9}\]  
where \(E^{i}_{total\_or}(t)\), \(E^{j}_{total\_su}(t)\), and \(E^{k}_{total\_ad}(t)\) respectively represent the total energy consumption by the \(i\)-th ordinary node, \(j\)-th super node, and \(k\)-th advanced node at time \(t\).