esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Overall Liking and Perceived Sensory Intensity Scores  
Consumers' acceptability and perceived sensory intensity mean scores are shown in Table 3. BaristaRTD(A) and its blind duplicate, BaristaRTD(B), scored similarly in all liking and intensity categories, and no significant differences (\(p>0.05\)) between them were evident. This indicates that comparing consumers' evaluations from two different sessions was reliable. However, mean liking scores were generally low, ranging between \"dislike moderately\" to \"like slightly.\" BaristaRTD ranked the highest for liking in terms of overall liking, bitterness and sourness liking while the BabinskiRTD sample scored highest for coffeeID liking. Overall, the liking scores for BabinskiRTD and BaristaRTD were relatively higher, whereas the cold brew (CB) and coffee maker (CM) samples scored lower. Specifically, KenyaCM scored the lowest in every liking category, and consumers gave the highest sourness intensity to Colombia and Kenya samples. FolgersCM samples scored the highest for bitterness intensity. The highest perceived sourness intensity of Kenya samples might have decreased consumers' sourness liking. Regarding coffeeID intensity, the CS sample scored the highest, whereas RTD samples received relatively lower scores. Consumers were asked to freely express their opinions about the coffee samples as an open-ended comment. The most frequently used words were sourness (386) and bitterness (297), showing that these attributes were dominant characteristics of the coffee samples. Interestingly, oriental medicine was also mentioned about 19 times. The medicinal attribute in the CATA list referred to sterile characteristics such as alcohol and iodine but consumers seemed to consider it as oriental medicine. Korean consumers seemed to taste the herbal medicine attribute when they tried too much bitterness, which could be attributed to the oriental food culture. Korean panel produced and used more particular descriptors about perilla oil than panels from different country [22]. Considering most Koreans have experienced oriental medicine, strong coffee flavor and bitterness may have influenced perception of oriental herbal medicine-like flavor. Since there were no herbal medicine attributes in the CATA questionnaires, there was the possibility of the dumping effect [23] for medicinal attributes or other bitterness-related attributes.
# Real-Time HIFU Treatment Monitoring Using Pulse Inversion Ultrasonic Imaging
## 3 Results
### Effect of Target Motion on the Performance of the Proposed Method
As shown in Figure 9b and Table 2, the energy of the remaining HIFU interference was exponentially increased as the target moving distance increased. Note that the energy was increased by only 4% when the moving distance was changed to 10 \(\upmu\)m (i.e., from 0.173 \(\pm\) 0.003 to 0.180 \(\pm\) 0.003). The results in Figure 9 were obtained under the condition of 1.1 MHz HIFU (i.e., a wavelength of 140 \(\upmu\)m) and can be generalized by normalizing the moving distance to the wavelength as shown in Table 2. Note that the change in the center frequency of an imaging transducer has nothing to do with the efficiency of HIFU interference elimination although the axial resolution of monitoring images may be degraded at a given moving distance of the target. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Moving Distance (\(\upmu\)m)**} & \multicolumn{4}{c}{**Magnitude (dB)**} \\ \cline{3-6}  & & **1.1 MHz** & **2.2 MHz** & **3.3 MHz** & **4.4 MHz** \\ \hline Ultrasound Only & - & \(-\)54.32 & \(-\)13.92 & 15.25 & \(-\)40.84 \\ \hline \multirow{4}{*}{Ultrasound + HIFU} & 0 & \(-\)32.10 & 4.06 & 16.37 & \(-\)26.78 \\  & 10 (0.071\(\uplambda\)) & \(-\)21.80 & 12.64 & 16.29 & \(-\)14.23 \\ \cline{1-1}  & 30 (0.214\(\uplambda\)) & \(-\)13.79 & 19.24 & 24.72 & \(-\)5.93 \\ \cline{1-1}  & 50 (0.357\(\uplambda\)) & \(-\)11.08 & 21.34 & 27.73 & \(-\)3.26 \\ \cline{1-1}  & 100 (0.714\(\uplambda\)) & \(-\)4.84 & 26.12 & 34.17 & 2.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spectral magnitude at the fundamental and harmonic frequencies of the transmitted HIFU, which was measured from the frequency spectra of the A-mode signals in Figures 6 and 7. \(\uplambda\) is the wavelength of the 1.1 MHz HIFU transducer, which was calculated at a sound speed of 1540 m/s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Moving Distance (\(\upmu\)m)**} \\ \cline{2-5}  & **0** & **10 (0.071\(\uplambda\))** & **30 (0.214\(\uplambda\))** & **50 (0.357\(\uplambda\))** & **100 (0.714\(\uplambda\))** \\ \hline Correlation & 1.000 \(\pm\) 0.005 & 0.952 \(\pm\) 0.014 & 0.802 \(\pm\) 0.046 & 0.643 \(\pm\) 0.035 & 0.331 \(\pm\) 0.034 \\ Energy & 0.173 \(\pm\) 0.003 & 0.180 \(\pm\) 0.003 & 0.234 \(\pm\) 0.015 & 0.366 \(\pm\) 0.023 & 1.029 \(\pm\) 0.091 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation coefficient between the reference scanline and other scanline signals and energy of each scanline. The measured values are expressed as mean \(\pm\) standard deviation. \(\uplambda\) is the wavelength of the 1.1 MHz HIFU transducer, which was calculated at a sound speed of 1540 m/s.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Learning Policy with the Sarsa Method
#### 4.2.1 Sarsa Method  
The [PERSON] method is an on-policy temporal-difference learning method, where the agent attempts to update the policy that is used to make decisions for the decision making UAV \(f\) at the same time. Different from most reinforcement learning methods where the main goal is to estimate the optimal value function, the Sarsa agent learns an action-value function \(Q(s,a)\) rather than a state-value function \(V(s)\). For the enemy avoidance problem, the Sarsa agent updates its action-value function \(Q(s,a)\) after every transition from a state \(s\in S\), where \(s\) is not an episode ending situation. That is to say, \(s\
otin S_{T}\). If the state \(s^{\prime}\in S_{T}\), then \(Q(s^{\prime},a^{\prime})=0\).  
The updating rule is given in Equation (6), where \(\gamma\) is the discount rate, and \(\alpha\) is a step-size parameter. Every element of the quintuple of the enemy avoidance events, \((s,a,r,s^{\prime},a^{\prime})\), are used in this updating rule. Such a quintuple makes up a transition from one state-action pair of the enemy avoidance problem to the next, and therefore gives rise to the name Sarsa for the algorithm.  
\[Q(s,a)\gets Q(s,a)+\alpha(r+\gamma Q(s^{\prime},a^{\prime}-Q(s,a)) \tag{6}\]In the proposed framework, the Sarsa algorithm [13] is adapted into the enemy avoidance problem for the agent to learn the policy, which is presented in Algorithm 1.  
```
1:Initialize \(Q(s,a)\) arbitrarily
2:for each episode do
3: Initialize \(s\) as all the UAVs having entered the problem region
4: Choose \(a\in A\) for \(s\) using policy derived from \(Q\) with \(\epsilon-\)greedy
5:for each step of episode do
6: Take action \(a\), observe \(r\), \(s^{\prime}\)
7: Choose \(a^{\prime}\in A\) for \(s^{\prime}\) using policy derived from \(Q\) with \(\epsilon-\)greedy
8:\(Q(s,a)\gets Q(s,a)+\alpha(r+\gamma Q(s^{\prime},a^{\prime})-Q(s,a))\)
9:\(s\gets s^{\prime};a\gets a^{\prime}\)
10:endfor
11: until \(s\in S_{T}\)
12:endfor
```  
**Algorithm 1** Sarsa Algorithm for the Enemy Avoidance Problem
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 4 Proposed Scheme
### Time Complexity Analysis  
In this subsection, a study of the worst-case performance of the proposed scheme in terms of the time complexity as a function of \(|\Re_{\text{w}}(s)|\), \(|\Re_{\text{w\_sel}}(s)|\), and \(\ell_{i}\), with \(i=1,2,...,c\), is provided. In the SACcomponent, the amount of time required to perform comparisons is referred to using the constant \(a_{1}\), while the amount of time required for statements is referred to using the constant \(a_{2}\). In the TPE component, the amount of time required for statements is referred to using the constant \(a_{3}\). In the SAD component, the amount of time required to perform comparisons is referred to using the constant \(a_{4}\), while the amount of time required for statements is referred to using the constant \(a_{5}\). In the FBA component, the amount of time required for statements is referred to using the constant \(a_{6}\). In addition, the FBA component sorts the fair assignment vectors, and the worst-case cost for the FBA component is indicated by \(O(|\Re_{w}(s)|\left(\sum_{i=1}^{c}\ell_{i}\right)\log(\sum_{i=1}^{c}\ell_{i}))\). In the FPA component, the amount of time required for statements is referred to using \(a_{7}\)\(|\Re_{w,\text{sel}}(s)|\), where \(a_{7}\) is a constant. As such, the worst-case time complexity of the proposed scheme can be calculated as \(O(|\Re_{w}(s)|\left(\sum_{i=1}^{c}\ell_{i}\right)\log(\sum_{i=1}^{c}\ell_{i}))\). It should be noted that \(c\) typically falls in the range of 2-5 (with an especially high likelihood of being equal to or smaller than 3) in today's environments. As a result, the complexity should generally be low.
# The Role of a DirectDensity(r) CT Reconstruction in a Radiotherapy Workflow: A Phantom Study
## 2 Materials and Methods
### Calibration Curve Building  
To convert the CT numbers of the CT scan into a density map of the reconstructed region, a relationship between the CT numbers and density (electron density or mass density) is needed. Since each reconstruction algorithm has its own mechanism to represent images in CT numbers, in addition to being typically kVp-dependent, calibration curves are also algorithm-dependent.  
To build the DD calibration curves and verify their behavior when varying kVp, CT images of the Cheese Phantom Mod. 467 Gammerx (Gammerx, Giessen-Allendorf, Germany) were acquired at 80, 100, 120, and 140 kVp using the SOMATOM go.Open Pro CT scanner (Siemens Healthcare, Erlangen, Germany), which is currently dedicated to radiotherapy simulation scans at our institute (Istituto per lo Studio dei Tumori Dino Amadori, IRCCS IRST--Meldola, Italy). The Cheese phantom contained inserts with certificated densities ranging from 0.30 g/cm\({}^{3}\) (LN-300 lung) to 1.82 g/cm\({}^{3}\) (cortical bone), miming human tissues. Table 1 shows the materials used, with their corresponding densities. Acquisitions were carried out using a slice thickness of 3 mm, pitch = 1, to follow the current clinical procedure, adjusting the value of mAs to keep constant noise levels. Raw data were reconstructed with DirectDensity(r) (DD in this work, but indicated as Sd40 in the SOMATOM workstation) and a standard convolution kernel (Qr40). Cylindrical regions of interest (ROIs) of volume about 3 cm\({}^{3}\) were drawn in the middle of each insert (with a total volume approximately 45.5 cm\({}^{3}\) each) and the mean HU was extracted using MIM Maestro(r) v. 7.1.4 (MIM Software Inc., Cleveland, OH, USA [15; 16]). The calibration curves for each kVp and reconstruction technique were built using the nominal density of each insert and the corresponding HU mean value.  
The acquisition and reconstruction processes were repeated with a titanium (mass density of 4.51 g/cm\({}^{3}\)) and a stainless-steel (mass density of 8 g/cm\({}^{3}\)) insert to study the dependency of the calibration curves on kVp at densities exceeding the range of human tissues (Table 1). In these cases, the images were also reconstructed with a combination between the DD algorithm and the iMAR algorithm (indicated as DD + iMAR), giving an additional set of calibration curves. Metallic regions were contoured according to the manufacturer's specifications with ROIs of about 13.5 cm\({}^{3}\).  
The mean calibration curves were obtained averaging the mean HU (or HUDD) value in each type of non-metallic ROI over the acquisitions without metal inserts, with the titanium insert and with the stainless-steel insert. These curves were extended at high density, adding the points associated with the metal ROIs.
###### Abstract
The assessment of many dispersion models, conducted by [PERSON] [39], at distances of 8 and 16 km from the emission source, showed that the CALPUFF model is characterized by a smaller error and a better correlation of the results than the stationary Gaussian slug models in the near field range area (<50 km). However, it requires a considerable amount of work associated with the preparation of data, adjusting the wind field model, and use of highly efficient calculation units. The research conducted by [PERSON] and [PERSON] [39] confirms that the CALPUFF model reflects high values of concentrations in the air well and predicts the results obtained for 3-h and 24-h averaging periods better than the AERMOD model. Its accuracy for neutral and permanent states of atmospheric stability is excellent, while in the case of unstable classes, some overprediction of calculation results can be expected. Because the CALMET/CALPUFF modeling system is characterized by a good correlation between results and observations when using high-resolution grids, it is advised to recognize the impact of the changes in grid spacing on the results of calculations, as well as identify the resolution recommended for the purposes of the air quality impact assessment especially in the case of more complex terrain and land cover. The use of grids with too low resolution may in the case of such area create large discrepancies when compared to the results of calculations obtained for a dense grid. However, this type of action significantly shortens the time spent on preparation of the simulations and on the calculations themselves. This paper presents an assessment of the impact of the horizontal grid resolution in the CALMET/CALPUFF models on the results of atmospheric dispersion modeling in a local scale characterized by a terrain of medium complexity and highly varying land use (urban, industrial and partly rural areas). The calculations were made for point emission sources related to the Municipal Solid Waste Incineration (MSWI) Plant in Krakow. To simplify the description of the problem, the calculations were limited only to one group of gaseous pollutants--nitrogen oxides (NO\({}_{\mathrm{x}}\))--assuming their emissions at a constant level of the daily average emission limit value established for waste incineration plants [40]. NO\({}_{\mathrm{x}}\) emissions from MSWI plants cause the most significant air pollution from all emitted pollutants [41; 42]. More detailed results of air quality impact assessment carried out for MSWI Plant in Krakow using CALMET/CALPUFF model with grid resolution of 100 m are presented in the literature [43]. The results of preliminary studies on the effect of horizontal grid resolution on modeling results for this facility are summarized in the literature [44].
## 2 Dual-Port MIMO Antenna
### Diversity Parameters  
#### 2.5.1 Envelope Correlation Coefficient (ECC)  
ECC quantifies the level of correlation between signals and indicates possible performance degradation caused by mutual coupling. Equation (2) calculates ECC using  
Figure 9: (**a**) Antenna arrangement for group delay measurement. (**b**) Group delay vs. frequency.  
scattering (S) parameters between different antenna ports [25; 26]. A smaller ECC value signifies improved isolation and superior diversity performance.  
\[\mathbf{ECC}=\frac{\left|\mathbf{S}_{ii}^{*}\mathbf{S}_{ij}+\mathbf{S}_{ji}^{*}\mathbf{S}_{ji}\right|} {\left(\mathbf{1}-\left|\mathbf{S}_{ii}\right|^{2}-\left|\mathbf{S}_{ji}\right|^{2}\right) \left(\mathbf{1}-\left|\mathbf{S}_{ji}\right|^{2}-\left|\mathbf{S}_{ij}\right|^{2}\right)} \tag{2}\]  
Figure 10 compares simulated and measured ECC. It shows ECC remains under 0.01 throughout the intended 3.1 to 10.6 GHz band. The total average ECC across all ports is calculated to be 0.002045, which is notably less than the widely accepted threshold of 0.5, indicating excellent diversity performance [25; 26].
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Experimental Setups  
#### 4.1.1 IQA Databases  
TLLFD and state-of-the-art NR IQA models are compared on four standard IQA databases: LIVE [32], TID2008 [33], CSIQ [34], and MICT [35], respectively. The basic  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Extraction** & **Regression Method** \\ \hline \multirow{3}{*}{NIQE} & The normalized information is fitted with the & \\  & generalized Gaussian distribution to obtain the & MVG \\  & statistical characteristics & \\ \hline \multirow{3}{*}{BIQI} & Wavelet decomposition is used for feature & \\  & extraction, and the statistical features are & \\  & obtained by fitting the generalized Gaussian & \\  & distribution & \\ \hline \multirow{3}{*}{DIIVINE} & The pyramid wavelet decomposition is used for & \\  & feature extraction, and the generalized Gaussian & \\  & distribution is fitted to obtain the & \\  & statistical features & \\ \hline \multirow{3}{*}{BRISQUE} & (1) & MSCN coefficient histogram is fitted with & \\  & generalized Gaussian distribution; & \\  & (2) & The four direction histograms of MSCN & \\  & coefficients are distributed by asymmetric & \\  & generalized Gaussian distribution. & \\ \hline \multirow{3}{*}{TLLFD} & (1) & The input image is normalized and & \\  & preprocessed, extracted CLBP weighted & \\  & histogram; & \\ \cline{1-1}  & (2) & The gradient features are fitted by Weibull & \\ \cline{1-1}  & distribution to obtain the & \\ \cline{1-1}  & statistical features. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature extraction and regression analysis of different methods.  
Figure 4: Flowchart of TLLFD NR IQA.  
information is listed in Table 2. For CSIQ and TID2008 databases, we only consider the four common distortion types (JP2K, JPEG, WN, GB). In addition, we exclude the 25 th synthetic reference image with its distorted versions from TID2008 database.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 5 Problem Solution
### Optimization Based on a Greedy Algorithm  
To achieve the objective of minimizing the average total energy consumption, the appropriate offloading decision should be found first. When minimizing the right side of Inequality (19),minimizing energy consumption while satisfying queue stability can be obtained. Ignoring the constant term on the right side of Inequality (19), the optimization problem can be formulated as  
\[\arg\min_{a(t)} \frac{1}{2}\sum_{k=0}^{M}(\sum_{m=1}^{M}a_{m,k}(t)\cdot A_{m}(t))^{2 }+VE(a(t))\] \[+\sum_{k=0}^{M}\sum_{m=1}^{M}Q_{m,k}(t)\cdot a_{m,k}(t)\cdot A_{m}(t)\] s.t. \[\text{C2}:\frac{D_{m}(t)}{B_{m,k}(t)}+\frac{\max\{Q_{k}(t)-f_{k },0\}+b_{k}(t)}{f_{k}}\leq T_{d}, \tag{20}\] \[\text{C3}:f_{0}>f_{m}>0\] \[\text{C4}:a_{m,k}(t)\in\{0,1\}\] \[\text{C5}:\sum_{k=0}^{M}a_{m,k}(t)=1,\]  
where C2 is to ensure that the task can be completed within the deadline. C3 refers to the computation resources provided by an MEC server being always greater than those provided by other PMs. C4 indicates that the status of the offloading decision is 0 or 1, and C5 means that a task can only be offloaded to one PM or MEC server. Definitely, the amount of computation may not be within the very specific range. In this case, the amounts of demanded computing resources are beyond the capability of the platooning and MEC. In other words, the delay/deadline constraint C2 cannot be met due to the limited computing resources that the platoon and MEC can provide, and the execution cannot be completed before the task deadline.  
Notice that each PM in this paper has \(M+1\) possible options. In order to obtain the optimal solution, it is necessary to repeat iterations \((M+1)^{M}\) times. This is a knapsack problem, and the problem scale is quite large with the increase of \(M\). The greedy algorithm is adopted to approximately solve the optimization problem as shown in Algorithm 1[38].  
```
1:Initialization: set Formulation (20) as \(Y(a_{mk}(t))\), let \(Z\) be a variable
2:for\(m=1:M\)do
3:\(Z=Y(a_{m0}(t))\)
4:for\(k=0:M\)do
5:if\(Y(a_{mk}(t))<Z\)then
6:\(Z=Y(a_{mk}(t))\)
7:else
8: CONTINUE
9:endif
10:\(k^{*}=argZ|_{k}\)
11:return\(a_{m}(t)=k^{*}\)
12:endfor
13:endfor
```  
**Algorithm 1** Greedy Algorithm  
In Algorithm 1, we set the value of Formulation (20) as \(Y(a_{mk}(t))\) first, and each vehicle finds its own offloading node with the smallest \(Y(a_{mk}(t))\) value as the optimal decision through iteration of all offloading nodes. In this way, a better offloading decision can be obtained while greatly reducing the complexity of the algorithm.
# Removal of Organic UV Filters Using Enzymes in Spent Mushroom Composts from Fungicultures
## 3 Results
### Determination of UV Filter Adsorption by SMCs  
It is time-consuming and difficult to produce large amounts of enzyme extracts of SMCs for wastewater treatment. Therefore, the direct application of SMCs for UV filter removal was tested. As shown in Figure 6, the ranges of the adsorption coefficients of EHS and HMS were 30.9 to 10.1. The range of the adsorption coefficients of EHMC was 18.3 to 11.3. These results suggested that SMCs exhibit higher adsorption ability for EHS and HMS than EHMC.  
Figure 4: (**A**) Laccase activity and (**B**) pH values of the enzyme extracts before and after batch experiments. Data from triplicate experiments are presented as the mean \(\pm\) SE. s: start of experiments, e: end of experiments. EHMC: ethylhexyl methanoycinamate, EHS: 2-ethylhexyl salicylate, HMS: homosolate. Red stars indicate the \(p\) values of \(t\) tests \(<\) 0.05.  
Figure 5: Intermediates of the biodegradation and biotransformation of homosalate (**A**), 2-ethylhexyl salicate (**B**) and ethylhexyl methanoxycinnamate (**C**) by enzyme extracts from the spent mushroom compost of _Pleurotus jamor_. Question marks indicate unidentified intermediates.
## 2 Mathematical Modelling
### Kinematics
#### 2.1.2 Inverse Kinematics  
Inverse kinematics is a crucial element for the functionality of manipulators with variable stiffness links. These manipulators are designed to be versatile and adaptable, and inverse kinematics plays a central role in achieving this adaptability. Variable stiffness link manipulators must respond to external forces and changing demands and dynamically adjust their joint variables to precisely position the end effector. BY applying inverse kinematics, the manipulator can calculate the joint variables needed to accomplish the desired end-effector position. This capability is important for tasks that need controlled interaction with the environment, such as grasping objects with varying levels of safety or avoiding obstacles. In applications such as advanced manufacturing and human-robot collaboration, where the manipulation environment is complex and dynamic, inverse kinematics in variable stiffness link manipulators ensures not only precise positioning but also increased safety and adaptability [43]. Having established that the VSL manipulator can be treated as a rigid link manipulator using forward kinematics, inverse kinematics can also be approached using the same framework for rigid link manipulators. Equations (9)-(12) define the inverse kinematics models for a rigid anthropomorphic manipulator with three rotational degrees of freedom, which have been solved and used extensively in previous studies [44; 45; 46].  
\[r=\sqrt{x^{2}+y^{2}+z^{2}} \tag{9}\]  
\[\theta_{1}=\tan^{-1}\Bigl{(}\frac{y}{x}\Bigr{)} \tag{10}\]\[\theta_{2}=-cos^{-1}\left(\frac{x^{2}+y^{2}+z^{2}-l_{2}^{2}\;l_{3}^{2}}{2 l_{2}l_{3}}\right) \tag{11}\] \[\theta_{3}=sin^{-1}\left(\frac{z}{r}\right)+\tan^{-1}\left(\frac{l_ {3}\text{sin}(\theta_{2})}{l_{2}+l_{3}\text{cos}(\theta_{2})}\right) \tag{12}\]