esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 1 Introduction
The DCU positions become the challenge for the AMI RF-mesh network, where location optimization and  
Figure 1: The AMI architecture. coverage area enhancement are of interest. Channel information and modeling are useful for investigating the wireless communication performance in the service areas. The channel models are used in the network performance simulation and evaluation. The channel models are usually used in AMI wireless propagation channel [5; 6; 7] and vehicle-to-vehicle (V2V) wireless propagation channel [8], especially in urban and suburban areas where static or movable obstacles are presented in the realistic environment. In [5], the channel models of two Wi-SUN smart meters used in different modem brands are modeled by using the received signal strength indication (RSSI) measurement in Seoul, Korea. Smart meters are installed on the house's exterior wall which is different from Thailand where smart meters are on a utility pole. In [6], the simulation of the Wi-SUN network in a laboratory is proposed, in which the Rayleigh fading path is used as the channel model. In [9], the channel model for a wireless M-Bus standard smart meter is modeled by the large number of RSSI which are collected by the driving data concentrator unit. In this article, the statistical RSSI at all allowed frequencies in Thailand, as described in Sections 2 and 6, are used to model the channel fading and combine a bias function to reduce the measured RSSI distribution error to the residual at the tail of the distribution curve. The Rician fading model for the RSSI estimation in the Wi-SUN channel is studied by using the empirical model due to a low complexity property, given a tradeoff in a compromised accuracy in the model. This article is organized as follows. Section 2 briefly discusses the components of AMI architecture and last-mile communication. The conventional channel modelling method and the biased method are presented in Sections 3 and 4, respectively. In Section 5, the measurement setup is described. In Section 6, the experimental results and analysis are shown. The conclusion is stated in the last section.
## 3 Theory and Methods  
### Modal Decomposition Methods  
#### 3.1.1 Empirical Mode Decomposition (EMD)  
EMD, a method for processing signals in the time-frequency domain, is suitable for signals that are both nonlinear and non-stationary. This approach eliminates the need for defining basis functions and instead separates signals based on their time-scale properties. Through the EMD technique, time series data are broken down into multiple intrinsic mode function (IMF) sequences and a single RES sequence sorted by frequency, starting from the highest to the lowest. Each IMF series captures unique characteristics of the original data at varying frequencies, while the RES sequence captures the overall trend. The decomposition process of EMD for a given time series data includes the following steps:  
Step 1. Obtain the maximum value and minimum value of the original sequence, use the interpolation function to fit the original sequence, obtain the upper envelope and lower envelope, record the mean values of the upper envelope and the lower envelope as \(m(t)\), and convert the original sequence subtracted \(m(t)\) from \(x(t)\) to get the new sequence \(h_{1}(t)\). The formula for \(h_{1}(t)\) is the following:  
\[h_{1}(t)=x(t)-m(t) \tag{3}\]  
Step 2. The \(h_{1}(t)\) sequence also needs to be verified, so verify that the number of extreme points and zero-crossing points of the entire sequence is equal, and verify whether the average values of the upper envelope and lower envelope are zero. If the above conditions are met, \(h_{1}(t)\) is the first IMF component. If not, the first step needs to be repeated until the conditions are met.  
Step 3. Subtract \(h_{1}(t)\) from \(x(t)\) to get the new sequence as the original sequence and repeat the above steps until the final IMF sequence is a monotonic sequence. The decomposition result is expressed as the following:  
\[x(t)=\sum_{1}^{n}h_{i}(t)+res(t) \tag{4}\]  
where \(n\) denotes the number of decompositions, \(h_{i}(t)\) is the \(i-th\) IMF component, and \(res(t)\) is the residual sequence.  
We used the EMD method to decompose the DO data used in this article, obtain eight IMF components and one RES component, and draw their spectrum diagrams to facilitate our comparison with the decomposition results of the VMD method. The EMD decomposition results and the spectrum diagrams corresponding to each component are shown in Figure 6.  
Figure 6: EMD decomposition results for DO data: (**a**) IMF1-IMF4 sequences, (**b**) IMF1–IMF4 spectrum diagrams, (**c**) IMF5–IMF8 sequences, (**d**) IMF5–IMF8 spectrum diagrams, (**e**) IMF9 sequence, and (**f**) IMF9 spectrum diagram.
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 3 Connection-Aware DT for 5G-MANETs  
This paper aims to provide an accurate virtual representation of 5G-MANETs deployed in physical scenarios, enabling the precise monitoring of network events and resources. To achieve this, a DT is applied to represent the set of nodes, their connections, and the transmitted messages. DT technology enables the continuous synchronization between the physical network and its digital counterpart, allowing real-time monitoring, analysis, and simulation of network conditions. Through this digital replica, it becomes possible to observe the behavior of individual nodes, track data transmission patterns, and perform potential network simulations. Given the dynamic nature of the nodes and the potential variations in the availability and strength of the 5G signal, the proposal includes routing mechanisms to transmit information from disconnected nodes using connected devices such as gateways. To detail the operation of the proposal, this section addresses the overview of the system, the architecture and components behind its functioning, and the proposed routing algorithm for information transmission.
## 5 Conclusions  
The relationship between the UPV and strength of normal concrete is substantially influenced by the mixture proportions and the age of the concrete. In this study, regression analyses were conducted separately on the water-to-cementitious-material ratio and age, and the relationship curves thus obtained were mutually independent and scattered. Thus, no simplified single curve could be obtained for the relationship between concrete UPV and strength.  
Based on the idea that differences in mixture proportion and material type are eventually reflected in the final properties of hardened concrete, this study proposes a method for characterizing the relationship between the evolution index of UPV and strength and  
Figure 10: Comparison between the concrete strength measurements and estimations made using the unified equation: (**a**) estimation results of concrete at all ages; (**b**) comparison of the estimation results within 3 days of setting and at all ages.  
aims to establish a unified equation to represent the relationship between the UPV and the strength evolution index. The following conclusions were drawn from the testing results:  
1. Converting the relationship between the UPV and strength into that of UPV and the strength evolution index yielded sufficiently convergent correlations for normal concrete with various mixture proportions and at different ages. Moreover, this convergence did not decrease when pozzolanic materials were added.
2. This study established a unified equation for representing the relationship between the UPV evolution index and the strength evolution index of concrete. The UPV and strength values at 28 days for normal concrete and those at 70 days for pozzolanic concrete were used as the terminal UPV and strength values required for the calculation of the evolution index.
3. The proposed unified equation could be used to estimate the strength of concrete at different ages. The estimation results indicated that almost all specimens over 3 days old achieved an error of less than \(\pm 15\%\); therefore, the proposed unified equation has substantial value for practical application in major construction procedures (e.g., applying post tensioning or removing formwork supports).  
Conceptualization and methodology, Y.L. and Y.-C.L.; measurements, Y.-C.L.; data analysis, Y.L., Y.-C.L. and C.-C.C.; writing-original draft preparation, Y.-C.L. and Y.L. All authors have read and agreed to the published version of the manuscript. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable.  
The authors declare no conflict of interest.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 3 Methods
### System Model
#### 3.1.3 Optimization  
In order to optimize the use of the client and expert side and improve efficiency, our primary objective is to minimize the maximum completion time, which indicates the moment when all tasks are finished. As shown in Figure 3, the execution sequence of tasks can significantly impact the overall performance, as demonstrated in Figure 3a,b. Moreover, different allocation strategies lead to varying results, as depicted in Figure 3b,c. The optimal scheduling strategy is illustrated in Figure 3c, achieving a maximum completion time of 90.  
**Optimization Objective 1: Minimize the Maximum Completion Time.**  
\[T_{1}=\text{max}\ X_{t}^{et},\quad\forall X_{t}\in\mathcal{N}_{X} \tag{18}\]  
To avoid the scenario illustrated in Figure 4, where the schedule in Figure 4b is superior to that in Figure 4a even with the same maximum completion time, we also aim to reduce the average completion time \(T_{2}\) to improve the overall efficiency. While minimizing the maximum completion time \(T_{1}\) is our main goal, reducing the average completion time \(T_{2}\) is an auxiliary target.  
Figure 4: Comparison of Scheduling Policies with the Same Minimum Completion Time: (**a**) schedule with higher average completion time, and (**b**) schedule with lower average completion time.  
**Optimization Objective 2: Minimize the Average Completion Time.**  
\[T_{2}=\max\Bigl{(}\overline{C^{ct}},\overline{S^{ct}}\Bigr{)} \tag{19}\]  
where \(\overline{C^{ct}}\) and \(\overline{S^{ct}}\) are the average completion times for clients and servers, respectively:  
\[\overline{C^{ct}}=\frac{1}{|\mathcal{N}_{C}|}\sum_{i=1}^{|\mathcal{N}_{C}|} \max\textit{Time}^{ct}(C_{m},X_{t}),\quad\forall X_{t}\in\mathcal{N}_{X} \tag{20}\]  
\[\overline{S^{ct}}=\frac{1}{|\mathcal{N}_{S}|}\sum_{i=1}^{|\mathcal{N}_{S}|} \max\textit{Time}^{ct}(S_{n},X_{t}),\quad\forall X_{t}\in\mathcal{N}_{X} \tag{21}\]  
In order to balance these two objectives during optimization, a parameter \(\gamma\in[0,1]\) is introduced to ensure that one objective \(T_{2}\) does not dominate the other \(T_{1}\). Thus, the optimization problem is defined as follows:  
\[\min(T_{1}+\gamma T_{2}) \tag{22}\]  
subject to constraints (2)-(16).
## 2 The Interacting Multiple Model Unscented Kalman Filter
The sigma vectors are propagated through the nonlinear function to yield a set of transformed sigma points:  
\[\mathbf{y}_{i}=f(\mathbf{X}_{i})\quad i=0,...2n \tag{3}\]  
The mean and covariance of \(\mathbf{y}_{i}\) are approximated by a weighted average mean and covariance of the transformed sigma points as follows:  
\[\mathbf{\bar{y}}_{u}=\sum\limits_{i=0}^{2n}W_{i}^{(m)}\mathbf{y}_{i} \tag{4}\]  
\[\mathbf{\bar{P}}_{u}=\sum\limits_{i=0}^{2n}W_{i}^{(c)}(\mathbf{y}_{i}-\mathbf{ \bar{y}}_{u})(\mathbf{y}_{i}-\mathbf{\bar{y}}_{u})^{T} \tag{5}\]  
As compared to the EKF's linear approximation, the unscented transformation is accurate to the second order for any nonlinear function. The UKF algorithm is summarized in Appendix. Incorporation of the STA (also provided in Appendix) into the UKF results in the strong tracking unscented Kalman filter (STUKF). The IMMUKF algorithm uses model (Markov chain state) probabilities to weight the input and output of a bank of parallel UKFs at each time instant. The approach takes into account a set of models to represent the system behavior patterns or system model. The overall estimates is obtained by a combination of the estimates from the filters running in parallel based on the individual models that match the system modes. An IMM cycle consists of four major stages: interaction (mixing), filtering, model probability calculation, and estimate combination, as described in the following subsections.
## 4 Discussion
### Limitations of the Study  
This study was conducted in a highly specialized rehabilitation center where all physiotherapists had an expertise in robotic rehabilitation and used these devices for more than one year. Therefore, this could limit the extension of our results to different rehabilitation units with lower levels of expertise. However, we believe that this limitation does not severely impact on the application of the results, since a high level of expertise is required to efficiently use these devices. Moreover, this study was not aimed to evaluate the validity and the reliability of the used outcome measures. Finally, this study did not compare the effect of different physiotherapist/patient ratios on the outcome of the rehabilitation process. These aspects should be addressed in future studies.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.3 Impact of Delay Deadline  
This study investigates the impact of delay deadline on on-time packet delivery ratio of the delay sensitive packets for QoS-aware protocols as illustrated in Figure 6. Here, the bit error rate changes randomly ranging from \(10^{-6}\) to \(10^{-2}\). The performance of the protocols is evaluated considering two traffic loads \(1.5\) pps (Moderate load) and \(4\) pps (high traffic load).  
As the figure shows, at moderate traffic load at \(1.5\) pps, the delay deadline has little impact on on-time packet delivery ratio for both the protocols. Although during low traffic load, at a strict deadline of 200 ms, a slight difference on on-time delivery ratio of delay sensitive packets is observed between TMQoS and TLQoS, however, both the protocols show almost similar behavior at relaxed deadlines. In contrast, during high traffic load at \(4\) pps, TLQoS shows much better performance at strict deadline. However, the performance gap for this metric between two protocols reduces gradually with increasing deadline. The situation is characterized by the fact that, during high traffic load, TLQoS shows comparatively better performance than TMQoS due to its localized behavior. Hence, the delay sensitive packets successfully meet the deadline even at a lower value of the deadline.
## 3 Problem Formulation
### System Model
**Metrics:** Target \(V_{k}\) is observed when it is within the sensing area of at least one UAV, which can be denoted as  
\[O_{k,t}=\left\{ \begin{array}{l}1,\text{ if }\exists U_{i}\ \|v_{k,t}-\mathbf{\mu}_{i,t}\|\leq R_{s}\\ 0,\text{ else}\end{array} \right., \tag{5}\]  
where \(O_{k,t}\) is the observation state of target \(V_{k}\) at timestep \(t\). The observation rate \(\alpha_{k}\) of target \(V_{k}\) can be denoted as  
\[\alpha_{k}=\frac{1}{T}\sum_{t=1}^{T}O_{k,t}, \tag{6}\]  
which denotes the proportion of the time under surveillance to total mission time. Therefore, one of the objectives for UAVs is to maximize the average observation rate \(\bar{\alpha}\) of \(N_{V}\) targets, which can be represented as follows:  
\[\bar{\alpha}=\frac{1}{N_{V}}\sum_{k=1}^{N_{V}}\alpha_{k}. \tag{7}\]In addition, since the targets' quantity is unknown, the multi-UAV system must persistently search the mission area to detect new targets. Therefore, another objective of the multi-UAV system is to maximize the exploration rate \(\beta\) of mission area, which can be represented as  
\[\beta=\frac{1}{T}\frac{1}{L_{x}L_{y}}\sum_{x=1}^{L_{x}}\sum_{y=1}^{L_{y}}F_{x,y}, \tag{8}\]  
where \(F_{x,y}\) indicates the latest observed time for the cell \(G_{x,y}\). The maximum value of \(\beta\) is 1, which represents that all the cells in mission area are being monitored during the whole mission. However it cannot be achieved since the maximum sensing area of the UAVs is less than the mission area, i.e., \(\underset{U_{i}\in U}{\cup}\mathbb{S}_{i,t}<\mathbb{O}\). In the scenario of cooperative searching and tracking for moving targets, the multi-UAV system needs to balance between monitoring perceived targets and exploring mission area [25].
## 2 Stated Antenna Design Analysis
### Parametric Optimization of the Stated Design  
The parametric optimization and analysis of the proposed antenna and selection of the best parameters are important steps that will be discussed in this sub-section. Parametric optimization has proven the suitability of the selected parameters for the stated implantable antenna design. At 915 MHz, the reflection coefficient can be changed by adjusting the values of critical parameters, such as the length of the L-slot in the ground '\(L_{9}\)', the width of the upper horizontal '4'-shaped slot '\(W_{11}\)', the length of the slot in the ground near the probe '\(L_{5}\)', the width of the horizontal slot in the patch '\(W_{5}\)', and the position of the via.  
Figure 7a shows how the frequency band is moved from 1.6 to 0.915 GHz by changing the value of '\(L_{9}\)' from 3.5 to 3 mm (685 MHz).  
When the '\(W_{11}\)' value is modified from 3 mm to 4.5 mm, the frequency spectrum shifts from 0.6 to 0.915 GHz (500 MHz), as illustrated in Figure 7b.  
Figure 7c shows that by increasing the value of the '\(L_{5}\)' from 1.5 mm to 1.9 mm the band range changes from 1.5 to 0.915 GHz (585 MHz).  
When the value of '\(L_{6}\)' is altered from 2.1 mm to 2.7 mm, the frequency moves from 1.38 to 0.9 GHz (480 MHz), as illustrated in Figure 7d.  
The frequency band is pushed higher than the targeted frequency band by adjusting the location of the via. The frequency band is 1.3 GHz when the through is positioned at position 1, 0.75 GHz at position 2, and 0.88 GHz at position 3. Finally, by moving the via from position 3 to position 4, as illustrated in Figure 7, the desired frequency band of 0.915 GHz is attained (e).  
As illustrated in Figure 7, the position of the via not only influences the antenna's impedance matching, but it also plays a key role in tuning the target frequency. Hence, it is seen that parametric optimization plays an imperative role in the impedance matching, tuning, and accomplishment of the desired frequency.  
Figure 6: Current distribution at 915 MHz throughout the (**a**) patch and (**b**) ground.
# Competitive Analysis of Algorithms for an Online Distribution Problem
## 6 Computational Results
### A Real-World Instance  
We first show the performance of the online algorithms with transportation prices obtained from a professional website to check what happens under real market conditions. We consider a load of 20 pallets, with a weight of 1000 pounds each, sent from New York to Los Angeles. The minimum price \(p\) is set to USD 3000, while the default price \(P\) is set to USD 6000. The deadline \(H\) is set to 8 days. For this load, we obtained the prices shown in Figure 1. Given these prices, we analyze the performance of the online algorithms when the penalty cost \(h\) is set equal to USD 0.01, 0.1, 1, 10, 100 to cover different practical situations. In the Secretary problem algorithms, \(k\) is set equal to 1 and 3.  
The results reported in Table 3 show that \(\text{ALG}_{1}^{0}\) is significantly dominated by \(\text{ALG}_{3}^{p_{3}^{5}}\). In fact, the average cost increase for \(\text{ALG}_{1}^{0}\) is 29.07%, while for \(\text{ALG}_{3}^{p_{3}^{5}}\), it is only 1.77%. Moreover, \(\text{ALG}_{4}^{p_{4}^{5}}\) is able to outperform \(\text{ALG}_{3}^{p_{5}^{5}}\) in all cases, with the only exception of the case with \(h=100\), due to the high penalty cost. On average, the cost increase is 2.12%. The Secretary problem algorithms \(\text{ALG}_{5}^{1}\) and \(\text{ALG}_{5}^{3}\) perform worse than \(\text{ALG}_{3}^{p_{5}^{5}}\), as their cost increase is 4.43% and 2.03%, respectively. Finally, as expected, \(\text{ALG}_{5}^{3}\) dominates, on average, \(\text{ALG}_{5}^{1}\).
# Overview of Active Ingredients Used in Deodorants and Antiperspirants Available on EU Market
###### Abstract  
Deodorants and antiperspirants available on the market are designed to reduce the discomfort associated with sweating. This study examined the types of active substances contained in deodorants and antiperspirants from international cosmetic brands available in Poland (part of the EU market) and the frequency of their use. Product compositions were analysed based on INCI (International Nomenclature of Cosmetic Ingredients) product labels. The investigation included the following 170 cosmetic products: 50 spray deodorants (from 50 different brands); 50 roll-on deodorants (from 50 brands); 20 stick deodorants (from 20 brands); 40 roll-on antiperspirants (from 40 brands); and 10 stick antiperspirants (from 10 brands). The most popular active components were Triendly Citrate (51/120 formulations; 42.5%), followed by Alcohol (25.8%), Ethlyhexylglycerin (25.0%), Cappylyl Glycol (12.5%), and Potassium Alum (10.0%). Antiperspirant products were dominated by aluminium-based compounds, with the most frequently used being the following aluminium-based salts: Aluminium Chlorohydrate (67.5%), Aluminium Sesquichlorohydrate (25.0%), and Aluminium Chloride (12.5%). In contrast, aluminium-zirconium complexes, such as Aluminum Zirconium Tri-, Penta-, and Octachlorohex Gly, were rarely used by cosmetic manufacturers. Additionally, composition complexity, i.e., the number of deodorizing and anti-sweating ingredients per single formulation, was examined for roll-on deodorants, stick deodorants, and roll-on antiperspirants. All tested antiperspirants and most deodorants contained fragrance-imparting ingredients; the most popular were Partum/Fragrance, Limonene, Linalool, Citronellol, Citral, Benzyl Salicylate, Hexyl Cinnamal, and Geraniol.  
antiperspirants; cosmetic ingredients; deodorants; frequency of use; market trends; odour; sweating +
Footnote †: journal: Applied sciences  
0000-0002-2300-3300][PERSON]
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 4 Method
### Adpscan  
In this section, we summarize the work above, and the pseudocode of ADPSCAN is shown in Algorithm 3.  
```
Input: Graph \(G=(V,E)\) Output: Clusters
1 similarity \(\leftarrow\) calculate_similarity(\(G\));
2\(\rho\leftarrow\) calculate_rho(similarity, \(|V|\));
3\(\delta\leftarrow\) calculate_delta(similarity, \(\rho,|V|\));
4 peak_list\(\leftarrow\) automatic_density_peaks_selection(\(\rho,\delta\))(\(Algo.1\));
5 clusters \(\leftarrow\) get_clusters_reclassify_noise(\(G,peak\_list\));
6 distance_similarity \(\leftarrow\) calculate_distance_structural_similarity(\(G,peak\_list,hop\));
7 clusters \(\leftarrow\) reclassify_noise(\(G,peak\_list,distance\_similarity\));
8 returnclusters;
```  
**Algorithm 3**ADPSCAN  
Firstly, we calculate the structural similarity for all nodes in the graph (line 1). From this similarity, we calculate each node's local density (\(\rho\)) and dependency similarity (\(\delta\)) (lines 2-3). We then identify density peaks using an automated density peaks selection method (line 4). We traverse each density peak node, initializing a cluster for unvisited peak nodes and adding their neighboring nodes to a pending queue. We continuously extract nodes from this queue and incorporate them into the current cluster. If these nodes are also density peak nodes, we add their unvisited neighbors to the queue to expand the cluster (line 5). Finally, for unprocessed nodes, identified as noise, we calculate the average distance-based structural similarity to the clusters and assign them to the cluster with the highest structural similarity (lines 6-7).