esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sensory Profiling  
Sensory profiling of burgers was completed using Quantitative Descriptive Analysis (QDA) ([PERSON], 2013) with a trained sensory panel (n = 8; all female) with significant experience in the sensory assessment of beef ([PERSON], 2013; [PERSON], 2013). Sensory profiling was conducted under white light in a sensory laboratory (Teagasc, Dublin, Ireland) that meets the requirements of ISO 8589:2007 ([PERSON], 2013) using the computerised system, Compusense(r) (Guelph, ON, Canada).  
Nine sensory attributes (Table 2) were selected and defined following three 2 h training sessions. The reference materials used to demonstrate each attribute were determined via panel consensus.  
Panellists assessed the burgers in duplicate over two 1 h sessions, which took place between 10 a.m. and 12 p.m. A break of 30 min was enforced between sessions. To reduce first-order bias and calibrate the panellists, a warm-up sample was served at the beginning of each session. Panellists scored samples using the assessment protocol defined during panel training. The intensity of sensory attributes was assessed on a 10 cm unstructured linear graphic scale, with specific word anchors ranging from none on the left to very strong on the right.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Intendent Composition**} & \multicolumn{4}{c}{**Actual Proximate Composition (\%)**} \\ \cline{2-9}  & **95\%VL Beef** & **Salt** & **Moisture** & **Protein** & **Fat** & **Ash** & **Salt** \\ \hline
**O\% NaCl** & 100.0 & 0.00 & 73.18 & 21.49 & 4.86 & 1.13 & 0.19 \\
**0.7\% NaCl** & 99.3 & 0.70 & 74.41 & 20.62 & 4.16 & 1.74 & 0.70 \\
**1.05\% NaCl** & 99.0 & 1.05 & 73.61 & 20.06 & 4.60 & 1.94 & 0.95 \\
**1.15\% NaCl** & 98.9 & 1.15 & 74.18 & 20.02 & 4.00 & 2.13 & 1.16 \\
**1.4\% NaCl** & 98.6 & 1.40 & 73.76 & 19.97 & 4.36 & 2.29 & 1.38 \\
**1.75\% NaCl** & 98.3 & 1.75 & 73.57 & 19.98 & 4.28 & 2.62 & 1.61 \\
**2.1\% NaCl** & 97.9 & 2.10 & 73.62 & 20.04 & 3.92 & 2.97 & 1.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compositions of layers with varied salt distributions.
## 3 Simulation Results
### Distribution of Magnetic Field  
The appropriate distribution of magnetic field strength determines the field torque generated on the rotor's top surface. The higher magnetic field intensity verifies the practicality and accuracy of the suggested magnetorheological brake with a sector pole head arrangement with a maximum of 240 turns, based on the input NI that depends on varied input coil currents. According to the simulation findings, Figure 6 depicts the magnetic field intensity displayed for various input currents. The intensity of the magnetic field increases as the coil current increases from 0 Amp to 2 Amp at a set interval of 0.2 Amp. Uniformity index is observed from 0-1.2 Amp, and then a variation of low to peak intensity is observed between 1.6-2 Amp. A sector pole head shape in an MRB with a high input current of 2 Amp results in uniform magnetic field intensity, resulting in torque increase.  
Figure 5: Braking torque for variation of pole head gap.  
Figure 6: Magnetic field intensity for varied coil current.  
Figure 7 depicts the magnetic field intensity recorded on the rotor surface at a current input of 2 A to the core. The configuration demonstrates that the majority of the flux will proceed orthogonally to the rotor surface after passing through the MR fluid. The color variation describes the intensity of the magnetic field on the rotor surface. The blue hue represents a more severely impacted region, while the yellow color represents the surrounding area of the most severely afflicted area, where the strength is slightly reduced. When the magnetizing current is 2 A, the magnetic density around the coil achieves saturation, but the pole head experiences the typical magnetic field intensity depicted in orange due to the larger working area and the region's distance from the core. From Figure 8, we can determine that the magnetic field lines illustration indicates the pole head active zone of the working surface, where the magnetic field intensity is greater. It demonstrates that the highest magnetic field is created through the center of the pole head with no losses. This finding demonstrates that magnetic saturation is avoided throughout the construction.
# Thermal Investigations on Carbon Nanotubes by Spectroscopic Techniques
## 3 Results and Discussion
### Raman Spectroscopy  
The Raman spectrum of pure CNTs has several characteristic bands such as the D band at 1300 cm\({}^{-1}\) which can be attributed to the presence of defects in the structure of the CNTs, and the G band at 1575 cm\({}^{-1}\) which originated from tangential vibration modes of the bonds in carbon atoms with sp\({}^{2}\) hybridization. The D and G bands are typical Raman peaks of CNTs and the peak at 2668 cm\({}^{-1}\) represents the 2D band of carbon nanotubes (CNTs) [79; 80]. The G band at 1575 cm\({}^{-1}\) represents the phase vibration of the graphite lattice and the D band at about 1300 cm\({}^{-1}\) is related to the disorder of the graphite edges. The G band is composed only of sp\({}^{2}\) type C-C bonds, corresponding to the concentric cylinders of the graphene layers, while the D band is attributed to vibrations of carbon atoms with dangling bonds in the graphite structure (carbon atoms having sp\({}^{3}\) hybridization similar to diamond) [81; 82; 83]. Figure 2 shows, as an example, a comparison between Raman intensity versus wavenumber for the two investigated samples, i.e., CNT at T = 25 \({}^{\circ}\)C and CNT at T = 450 \({}^{\circ}\)C in the range 1250-3000 cm\({}^{-1}\). As above mentioned, the characteristic bands of CNT are present. As can be seen, by increasing the temperature up to T = 450 \({}^{\circ}\)C, several changes occur in the spectrum, especially in the 2500-2750 cm\({}^{-1}\) region.  
Figure 1: A comparison between the IR spectrum of CNTs at T = 25 \({}^{\circ}\)C and at T = 450 \({}^{\circ}\)C.  
Figure 2: A comparison between the Raman spectrum of CNTs at T = 25 \({}^{\circ}\)C and at T = 450 \({}^{\circ}\)C.
# Removal of the Previous Root Canal Filling Material for Retreatment: Implications and Techniques
## 1 Introduction
### The Importance of Complete Filling Removal  
The actual impact of the amount of residual filling material on the outcome of root canal retreatment is unknown and it is very difficult to evaluate in the clinical setting, as the potential methods for evaluation, such as periapical radiographs and cone-beam computed tomography (CBCT) may not have sufficient resolution to exhibit small filling remnants on the canal walls. These evaluations have usually been performed in ex vivo studies using either direct observation under the microscope [22; 23; 24] or micro-computed tomography [21; 25; 26; 27; 28; 29], which has a much higher resolution than radiographs and CBCT [30]. However, it is possible to infer the consequences of leaving residual material during retreatment. Histobacterialoid studies have clearly demonstrated the intracanal location of bacteria in teeth with post-treatment AP, including irregularities and recesses in the main canal lumen, dentinal tubules, isthmuses and ramifications [17; 31; 32]. If the residual filling material covers some of these areas, bacteria may be physically protected and will remain unaffected by the antimicrobial substances used during retreatment [10]. If the unaffected bacteria are those that are actually causing periradicular inflammation, then the retreatment outcome will be compromised [18].  
In teeth with an inadequate or absent coronal restoration, saliva may seep into the canal and bacteria may colonize the interface between the filling material and the dentinal wall [33; 34; 35; 36; 37]. If the previous material is not completely removed for retreatment, these areas will remain contaminated and may serve as a reservoir of bacteria to induce or maintain AP [38].  
One of the many challenges for the clinician during retreatment is to completely remove the previous filling material, especially in curved and well-filled canals [39]. The resistance imposed by the material makes its removal difficult, increasing the risk of procedural accidents such as ledges, perforation and instrument fracture. Furthermore, unlike gutta-percha, which is usually confined to the main canal, the endodontic sealer may penetrate into the dentinal tubules. Although this is required for a fluid-tight seal, the depth of sealer penetration and the blockage of the dentinal tubules can add an extra challenge to canal retreatment. Once the sealer penetrates into dentinal tubules, its total removal during retreatment is physically impossible [40].  
Non-surgical endodontic retreatment can be divided into two phases: filling removal and re-instrumentation. Considering that canal enlargement during re-instrumentation also helps remove the previous filling material, evaluations of canal cleaning after retreatment should always take into account both phases, that is, the whole procedure. Different retreatment techniques have been proposed, with the most recent ones based on engine-driven nickel-titanium instruments. Some systems have been specially designed for use in retreatment.
## 3 In Situ Measurement Methods
### Measurements Results  
The measurement results at receiver positions R1-R10 are reported in Table 1, expressed as T\({}_{20}\), C\({}_{80}\), and G acoustical parameters obtained with firecrackers at source positions S1 and S2. All the values are the averages of two or three repetitions at each receiver position and of the central 500 Hz and 1 kHz octave band frequencies, as indicated in ISO 3382-1 [2]. In accordance with the ISO 3382-1, spatial averages for each row were also reported in Table 1. It was assumed that each row can be considered as a homogeneous area, as in open-air theatres the direct sound and the distance from the source play a predominant role in the acoustic response. The Impulse Response-to-Noise Ratio, INR, (dB) is also reported as a parameter for judging the validity of the measurement, in order to establish the reliability of the outdoor acoustical measurements [31]. According to ISO 3382-1, the source level should be at least 35 dB above the background noise level in the corresponding frequency band for the case of T\({}_{20}\). All the measurements considered in this study had INR values well above 35 dB and up to 60 dB for the octave bands from 250 Hz to 8 kHz. It is important to underline that in the case of the T\({}_{20}\) values, the larger standard deviation is due to the presence of only one strong reflection from the orchestra after the direct sound (as shown in Figure 2), which determines an irregular course of the decay curve and a greater variability in the slope of the decay curve.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{Acoustical Parameters} \\ \hline
**Row** & **Receiver** & \multicolumn{3}{c}{**No. of Repetitions**} & \multicolumn{3}{c}{**Distance from Source (m)**} & \multicolumn{3}{c}{**T\({}_{\rm 20}\) (s) St. Dev.**} & \multicolumn{3}{c}{**C\({}_{\rm 00}\) (s) St. Dev.**} & \multicolumn{3}{c}{**G (dB) St. Dev.**} & \multicolumn{3}{c}{**INR (dB)**} \\ \cline{3-14}  & & **S1** & **S2** & **
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Methodology  
In this research, we compare the results of the proposed GWRFR model with those of five widely used machine learning methods. All predictors are standardized. To predict corn yield in the US Corn Belt, the data were split into training and testing datasets. A 10-fold cross validation procedure is applied to tune the hyperparameters of models. For crop yield prediction, 5-fold and 10-fold cross validation techniques are generally used; however, for county-level corn yield prediction, 10-fold cross validation technique has been found effective [38]. For each model, the best fit model is applied to each test year to predict corn yield in that specific year. The R\({}^{2}\) and RMSE for each test year are generated and reported along with the predicted results. The details on the machine learning models are presented below.
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
### Motivation and Contribution  
Based on RIS, NOMA, and CR advances, this paper proposes a cognitive RIS-aided NOMA communications network to improve spectrum efficiency and to overcome severe fading/obstacles. Different from the work in [42], we study a cognitive RIS-aided NOMA network with multiple UEs to improve efficient spectrum utilization. In addition, interference from the primary network (PN) with UEs of a secondary network (SN) is investigated to reflect real-world conditions. The main contributions of this work are as follows.  
* First, we propose a novel, cognitive RIS-aided NOMA network where a secondary source serves multiple SNs despite interference from the PN.
* Secondly, we analyze the performance of the suggested system in terms of outage probability and system throughput. The approximate closed-form analytical and asymptotic expressions for OP are obtained, providing helpful insights into the proposed system's configuration.
* Finally, we present a Monte Carlo simulation to validate the accuracy of the theoretical analysis, then we show the discussions about the impacts of (i) the number of reflective elements of the RIS, (ii) the number of PUs, and (iii) the interference from the primary source on the SN. We also present a comparison of the OP and system throughput between the cognitive NOMA system assisted by the RIS and its OMA equivalent.  
The remainder of this work is structured as follows. Section 2 describes the proposed system model. The channel model and performance analyses of the proposed system are in Section 3. The numerical results from analysis of our suggested system are provided in Section 4. Finally, conclusions are in Section 5.
## 3 Off-Design Model of SASCHCS
### Modeling of Absorption Subsystem  
The characteristic equation, a convenient method for the off-design simulation of the absorption chiller, was employed in the modeling of the absorption subsystem. In the characteristic equation method, the absorption chiller was treated as a black box, and the heat load of the component was calculated by the temperature as well as the flow rate of the external fluid [21; 24].  
The heat load of the generator, condenser, evaporator and absorber are described as follows:  
\[Q_{he,as}=(UA)_{he,as}z_{he,as}\Big{|}T_{he,as,ef,avg}-T_{he,as,if,avg}\Big{|} \tag{1}\]  
Here, the absolute guarantees that the temperature difference is positive.  
In addition, the heat loads of the generator, condenser and absorber can be expressed as a function of cooling capacity in the absorption subsystem:  
\[Q_{g,as}=GQ_{e,as}+Q_{loss,as} \tag{2}\]  
\[Q_{c,as}=CQ_{e,as} \tag{3}\]  
\[Q_{a,as}=AQ_{e,as}+Q_{loss,as} \tag{4}\]  
Here, the variables \(A\), \(C\) and \(G\) are thought to be constant, and can be obtained by the operational data of the absorption chiller [21]. We found that \(A\), \(C\) and \(G\) equal 1.1, 1.078, and 1.178, respectively, by the regression of the prototype's experimental data.  
The cooling output of the absorption subsystem is expressed as follows:  
\[Q_{e,as}=s\Delta\Lambda T_{as}-\alpha Q_{loss,as}=s(\Delta\Lambda T_{as}- \Delta\Lambda T_{as,min}) \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Test Equipment** & **Accuracy** \\ \hline Turbine flowmeter & 0.5\% of the reading \\ T-type Thermocouple & 2.5\% of the reading \\ \hline \end{tabular}
\end{table}
Table 2: Specification of test equipment.  
The parameter \(\Delta\Delta T_{as}\) can be calculated by the temperature of the external fluid:  
\[\Delta\Delta T_{as}=T_{g,as,ef,avg}-T_{a,as,ef,avg}-B\Big{(}T_{c,as,ef,avg}-T_{c,as,ef,avg}\Big{)} \tag{6}\]  
Here, the variable \(B\) is 1.15 for the \(\mathrm{LiBr/H_{2}O}\) solution.  
The parameter \(\Delta\Delta T_{as,\mathrm{min}}\) relies on \(\Delta\Delta T_{as}\)[24]:  
\[\Delta\Delta T_{as,\mathrm{min}}=1.9+0.01\Delta\Delta T_{as} \tag{7}\]  
The variable \(s\) and \(\alpha\) depend on the size of the heat exchanger:  
\[s=\frac{\frac{G}{(UA)_{g,as}z_{g,as}}+\frac{A}{(UA)_{a,as}z_{a,as}}+B\Big{[} \frac{C}{(UA)_{c,as}z_{c,as}}+\frac{1}{(UA)_{c,as}z_{c,as}}\Big{]}} \tag{8}\]  
\[\alpha=\Bigg{[}\frac{1}{(UA)_{g,as}z_{g,as}}+\frac{1}{(UA)_{a,as}z_{a,as}} \Bigg{]}s \tag{9}\]  
The coefficient of performance (COP) of the absorption subsystem is  
\[COP_{as}=Q_{e,as}/Q_{g} \tag{10}\]
###### Abstract
As shown in Figure 1, there are three rooms forming the complete scenario, and the user in each room can only detect and control the smart devices existing in the same room. The control region division method not only solves the low control accuracy problem but also solves the challenge of security issues as the devices cannot be connected or controlled when the user is not in the specific room. The VLP algorithm utilizes the directional angles of the smartphone and the position of the lightbulb to calculate the user's position and orientation. Our contributions are summarized as follows:  
1. We propose a VLC-enabled smart home control system with the combination of Bluetooth communication. The proposed system aims to solve the limited control accuracy and security issues existing in traditional RF-based control systems. This system consists of a VLC lightbulb as a transmitter, a smartphone application-based receiver, and a back-end cloud server. These three key elements cooperate to provide precise and secure control of smart devices by dividing the whole control region into different rooms according to the position of the VLC lightbulb. 2. We present the design of a driverless AC-powered VLC lightbulb for the practical implementation of the VLC system. This VLC lightbulb is designed to solve the problems and meet the real-world application requirements for our proposed smart home control system. It is characterized by compact size, standard form factor, and plug-n-play functionality. Moreover, it supports wireless control via Bluetooth for remotely managing its working states and transmitted information, which brings convenience to users. 3. We propose a directional-angle-assisted 3D indoor VLP algorithm to acquire the precise 3D world coordinates of the user to compensate for the low user position accuracy in SHSs. With this proposed VLP algorithm, the user's position can be calculated with centimeter-level accuracy. In addition, this algorithm can be adapted to orientation-based and location-based applications for extending diversity. The remainder of this paper is organized as follows: In Section 2, we summarize related works on smart home control systems. The methodology and architecture of our proposed VLC-based smart home control system with 3D indoor positioning algorithm are presented in Section 3. In Section 4, we demonstrate the prototype and analyze the experimental results. Finally, we conclude the paper in Section 5.
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 2 Related Work
### Density Peaks Clustering  
As mentioned above, DPC is one of the most extensively used density-based clustering algorithms and is very adaptable. Two assumptions underlie DPC: clustering centers have the highest local density and are relatively far apart [42].  
DPC mainly consists of [43]: firstly, local density \(\rho_{i}\) and relative distance \(\delta_{i}\) of each point are calculated. It is then determined which points are the initial clustering centers, based on the decision graph. After that, the remaining points are allocated to their appropriate clustering centers.  
In order to define the local density \(\rho_{i}\) of a data point, we use the following formula:  
\[\rho_{i}=\sum_{j=1}^{n}\chi\big{(}d_{ij}-d_{c}\big{)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \hline \multirow{2}{*}{\(\rho_{i}\)} & & & The minimum distance \\  & Density of point \(i\) & \(\delta i\) & from point \(i\) to point with \\  & & & higher density \\ \(d_{ij}\) & Euclidean distance between & & \\  & point \(i\) and point \(j\) & \(Score_{i}\) & Score value of point \(i\) \\ \(d_{c}\) & The cutoff distance & \(cell_{i}\) & The \(i\)th cell \\ \(DV(x_{i})\) & Divergence of point \(i\) & \(R_{i}\) & Rule \(i\) \\ \(DVdis(x_{i},x_{j})\) & Divergence distance between & & \\  & point \(i\) and point \(j\) & \(x(x_{1},x_{2},\ldots,x_{n})\) & A dataset of \(n\) points \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main notations.  
where \(n\) represents the number of data points and \(d_{ij}\) represents the distance between the point \(i\) and the point \(j\). \(d_{c}\) is the cutoff distance. \(\chi\) refers to an indicator function, when \(x<0,\chi(x)=1\), otherwise \(\chi(x)=0\).  
Furthermore, the Gaussian kernel function can be used to calculate the local density of data points in addition to the abovementioned method. The specific formula is as follows:  
\[\rho_{i}=\sum_{i\
eq j}\exp\biggl{\{}-\biggl{(}\frac{d_{ij}}{d_{c}}\biggr{)}^{ 2}\biggr{\}} \tag{2}\]  
The relative distance \(\delta_{i}\) of a data point is calculated by the following formula:  
\[\delta_{i}=\left\{\begin{array}{ll}\max\delta&\rho_{i}=\max\rho\\ \min\limits_{\{i,\rho_{i}>\rho_{i}\}}d_{ij}&\rho_{i}\
eq\max\rho\end{array}\right. \tag{3}\]  
Based on the above two variables, local density and relative distance, DPC constructs the decision graph with \(\rho\) as the abscissa and \(\delta\) as the ordinate. The user needs to manually select clustering centers in the decision graph, or to select the clustering centers through the decision value \(\gamma\), where \(\gamma=\rho\times\delta\). After finding the clustering centers, those remaining points belong to a cluster larger than the local density of their nearest neighbors.