esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
Firstly, the chosen algorithm should be applied using coefficients considering at least various levels of columnar water vapour and view angles. Performance differences between using coefficient set Case A (only view angle considered) and Case C (view angle, total columnar water vapour, and first guess LST considered) could exceed 1 K in case of the [PERSON] and [PERSON] 1990 algorithm--which is a multiple of the before mentioned warming per decade. Second, the algorithms should generally show good performance and low sensitivity, as presented in this study. Maximum differences between algorithms in presented total sensitivity was found to be 3.7 K for the mono-window algorithms and 1.6 K for the split-window algorithms. The general level of presented total sensitivities (except [PERSON] and [PERSON] 2003) of about 2 K is not representative of general LST retrieval, but still points, third, to the importance of the quality of the input data to the LST algorithms. Forth, only LST values carrying a low uncertainty can be used--this would exclude surfaces under very humid atmospheres or surfaces acquired by a large sensor view angle. The performance of even the best ranking algorithms in this study approaches 1 K (RMSD) in case of very humid atmospheres--in case of large view angles even 2 K.  
Besides the considerations on performance and sensitivity, data from the AVHRR instruments are influenced by several other factors which complicate direct use of the data [40]. The orbital drift and the differentiation of the NOAA satellites into morning and afternoon passes for example lead to different acquisition times over the lifespan of a single sensor and the whole AVHRR time series. It follows that AVHRR LST data cannot be directly be used for climate change studies, but should be further processed to match temporal requirements of such analyses. AVHRR LST further forms valuable input as an additional source of information to studies using a multitude of sensors.
# Residential Demand Response Scheduling with Consideration of Consumer Preferences
## 1 Introduction
Another critical point in the related scheduling problems is the relation between the \"flexibility\" of the loads and the associated electricity cost. The literature shows that when the time horizon for job scheduling is expanded, the customer demand can be met at a lower cost. The existing literature employs binary values to represent the operation of appliances at each time step, e.g., binary 1 shows that the appliance is \"on\" and consumes power, while binary 0 shows that the appliance is turned \"off\". On the other hand, the behavior and the appliance usage habits in households of different sizes with varying occupants and ages can notably differ. For instance, young professionals may not prefer to do the laundry after midnight, whereas this can be a good money-saving option for pensioners. It is important to note that such habits are not dependent on the financial aspect cost [2]. Similarly, one of the missing parts of the existing models is that the comfort levels are only limited to HVAC (heating, ventilating and air conditioning) appliances, and they are measured by the output temperature. Nevertheless, the idea of the \"satisfaction-cost\" trade-off has been neglected for other household devices. Here, we use the term \"satisfaction\" in the sense of using the appliance at times that are more or less convenient. The idea is that by adding such a property to job requests and by granting a minimal level of \"satisfied preferences\" to all of the consumers, the flexibility of time periods for jobs would increase; hence, the scheduling problem can be solved at a lower cost. In this paper, we propose a model for the underlying scheduling problem that takes the preference/satisfaction levels into account. To be exact, we define a multi-objective optimization problem and analyze the relation between the two objectives by exploring the Pareto front on problem instances based on real-world household usage data from [21] with real-world electricity prices. The paper is organized as follows. In Section 2, we present the model and the corresponding mixed integer program. In Section 3, we give the details of how real-world data are incorporated into the model. The results of the conducted computational experiments are presented in Section 4.
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 3 Methods and Design
### Trajectory Prediction of a Car
#### 3.3.1 Data Set  
We used the same data set in Section 3.1.1, and the technique in Section 3.1 was applied as well. We made use of car object detection to detect the car in the road image. It simultaneously obtained the car coordinates, representing the car location in the image. We could gather these coordinate changes over times, and they could become a certain car's trajectory. Finally, these coordinates were saved in an Excel file, as shown in Table 5. The x and y contained in the Excel file referred to the x coordinates and y coordinates of the car in the road image.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 3 Results
### Machine Learning Evaluation Result  
The importance of the 11 features used in the proposed MLFB algorithm model was investigated using the SHapley Additive exPlanations (SHAP) method [29]. The SHAP values for each feature are shown in Figure 5. As seen, phase coherence was the most important feature, with a value of 95%. The next most important features were the standard deviation and amplitude average of the POS signal, with values of 40% and 25%, respectively.  
The SVM used the features representing various signal characteristics to label the sub-bands with binary values of 1 and 0. As explained in Section 2.3.2, these labels were then compared to the referenced binary series derives from the cPPG. The accuracy, F1-score, sensitivity, and specificity across different combinations of model features are summarized in Table 4. Evidently, most models achieved an accuracy of over 97% and a specificity of  
Figure 5: Importance of the 11 features on refining rPPG signal based on SHapley Additive exPlanations (SHAP) values. The features listed on the ordinate orderly: phase coherence, POS standard deviation, POS amplitude, red standard deviation, frequency variability, green standard deviation, blue amplitude, green amplitude, red amplitude, POS, and blue standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Signal-to-Noise Ratio**} \\ \hline \multicolumn{5}{c}{**Noise Reduction**} \\ \cline{2-7}
**Algorithm** & **G** & **G-R** & **PCA** & **ICA** & **CHROM** & **POS** \\ \hline None & 1.70 *** & 3.44 *** & 2.24 & 3.06 * & 6.00 *** & 6.31 *** \\ BPF & 2.33 *** & 4.08 ** & 2.79 & 3.60 & 6.30 *** & 6.61 *** \\ ASF & 1.81 *** & 3.65 ** & 4.63 * & 3.97 & 6.03 *** & 6.35 *** \\ MLFB & 4.42 & 5.98 & 2.52 & 4.60 & 9.85 & 10.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average signal-to-noise ratio on pulse signal is computed by the combination of six pulse extraction and four noise reduction algorithms. Statistical significance and differences between the results of the machine learning-based filter bank (MLFB) and other algorithms within the same pulse extraction algorithm were evaluated using the Wilcoxon non-parametric test (denoted by *: \(p\) < 0.05, **: \(p\) < 0.01, and ***: \(p\) < 0.001).  
over 98%, indicating the effectiveness of the SVM in identifying sub-bands with prominent cardiac activity. Interestingly, there were no significant differences in performance between models using different numbers of features.  
Table 5 lists the performance and computational time based on a combination of these three features. When all 11 features were used, the computational time was significantly (\(p<0.001\)) longer than when three or fewer features were used. However, no significant differences were observed between the models.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 3 Angle Conversion Algorithms
### Optimization-Based Approach
#### 3.3.1 Forward Function  
In the context of the optimization-based algorithm, the conversion of \(n\) roll positions into \(n-1\) tangential angles represents the forward function. Its equations are derived from the geometry in Figure 8. The figure shows two consecutive outer rolls with different radii at the coordinates \((x_{1},y_{1})\) and \((x_{2},y_{2})\). To calculate the tangential angle \(\rho_{\text{calc}}\), the distance  
\[d=\sqrt{(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}} \tag{21}\]  
between the midpoints is calculated first using the Pythagorean theorem. The inverse tangent function is used to find  
\[\alpha=\arctan\frac{x_{2}-x_{1}}{y_{1}-y_{2}} \tag{22}\]  
which represents the angle of the midpoint connection line. Note, that the numerator and denominator are chosen such that they yield positive results for this geometry. The equations still hold, however, when these differences are negative, for example, when \(y_{2}>y_{1}\). To consider the influence of the different radii, a residual angle  
\[\beta=\arcsin\frac{r_{1}-r_{2}}{d} \tag{23}\]  
must be calculated. Finally, the tangential angle  
\[\rho_{\text{calc}}=\alpha-\beta \tag{24}\]  
can be determined. Evaluating these equations for every consecutive outer roll pair yields a set of \(n-1\) tangential angles as the result of the forward function.  
Figure 8: Geometry of the forward function of the optimization-based algorithm. The conversion from roll positions to tangential angles is unambiguous and straightforward.
## 2 The Dynamic Traveling Salesman Problem
### Generating Dynamic Environments  
There are two main dynamic versions of the TSP in which: (a) the values associated with the arcs are changing [20; 21; 12; 22]; and (b) the cities to be visited are changing [11; 13; 23]. The dynamic benchmark generator (Available at [[https://github.com/Mavrovouniotis/ACODTSP](https://github.com/Mavrovouniotis/ACODTSP)]([https://github.com/Mavrovouniotis/ACODTSP](https://github.com/Mavrovouniotis/ACODTSP)) (accessed on 23 February 2023)) used in [10] is adopted to generate DTSP test cases. Each DTSP test case is characterized by the type of change (either the weights or the nodes change), the magnitude of change (either small, medium, or severe), and the frequency of change (either fast or slow).  
For the DTSP with weight changes, a factor that increases or decreases the value of arcs connecting cities \(i\) and \(j\) is assigned every environmental period \(T\), i.e., \(d_{ij}(T)\), where \(T=\lceil t/f\rceil\), \(f\) is the frequency of a dynamic change, and \(t\) is the algorithmic iteration count. The value of the arc \((i,j)\) changes as follows:  
\[d_{ij}(T+1)=\begin{cases}d_{ij}(0)+\mathcal{R}_{ij},&\text{if arc}(i,j)\in A _{rnd}(T),\\ d_{ij}(T),&\text{otherwise},\end{cases} \tag{2}\]  
where \(\mathcal{R}_{ij}\) is a random number drawn from a normal distribution with a zero mean and standard deviation proportional to the initial value of the arc \(d_{ij}(0)\), \(A_{rnd}(T)\subset A\) is a set of randomly selected arcs with size \(\lceil mn(n-1)\rceil\) in which their values are subject to change at environmental period index \(T\), and \(m\in(0,1)\) is the magnitude of change. Therefore, the quality value of solution \(s\) for the DTSP depends on the parameter \(t\) and, thus, is evaluated as \(\phi(s,t)\). In other words, the quality of a solution \(s\) at time \(t\) may be different at time \(t+1\).  
For the DTSP with node changes, a newly generated set of cities, i.e., \(N_{rnd}(T)\), is initialized, with \(n\) random cities in the range of the actual set of cities \(N\). A dynamic change is generated for every \(f\) function evaluation, in which exactly \(\lceil mn\rceil\) cities are randomly chosen from \(N_{rnd}(T)\) to replace exactly \(\lceil mn\rceil\) randomly chosen cities from \(N\), where \(f\) and \(m\) define the frequency and magnitude of a dynamic change, respectively, as above.
# Effect of Paper Sludge and Dendromass on Properties of Phytomass Pellets
## 4 Discussion and Conclusions  
Many manufacturers are looking for solutions to prevent the problem of the low ash melting temperatures of plant biomass. They try to solve this by modifying parts of the combustion device (e.g., burner) or by producing pellets with admixtures of various raw materials and additives that would suppress these undesirable properties. The increase of ash melting temperature was already investigated in a previous work, and data showed that 10% of sludge raised the temperature to around 250 \({}^{\circ}\)C [18].  
This work was focused on the production of pellets from straw, wood sawdust, and paper sludge, which would contribute to the improvement of some properties compared to pure straw. A total of three independent measurements were performed for each sample of pellets under steady-state conditions. The sample SSPS 45/45/10 seem to be a promising option in terms of the examined parameters. Boiler heat output boiler during the combustion of this sample was higher in comparison to the pure straw sample, and the concentration of CO emissions was half as low compared to the S-100 sample. The measured NOx emissions for SSPS-45/45/10 were slightly higher (around 10%) compared to pure straw. In the case of CO\({}_{2}\) the difference between all of the samples was negligible. The PM concentration measured for the SSPS 45/45/10 sample was 154 mg\(\cdot\)m\({}^{-3}\), and this is approximately 35.6% less than S-100. The positive properties of the paper sludge were verified in its ability to raise the melting temperature of the plant biomass ash. After addition of 10% sludge, an increase of deformation temperature from 1025 to 1328 \({}^{\circ}\)C was noticed compared to the reference pellet. The combustion of the samples confirmed the possibility of seamless operation of the boiler.  
Co-firing of straw, saw dust, and a small amount of paper sludge (about 10%) in the form of pellets can be an effective way to utilize straw in thermal energy production. The combination of these materials should be experimentally measured in a full-scale power plant to verify the results.  
R.N.; experimental methodology, investigation, data collection, writing--original draft, A.B. and P.D.; preparation of the testing equipment, formal analysis, M.H.; analysis of the data and validation, J.J.; writing and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by KEGA No. 038 ZU-4/2019 Piping systems in heat supply and VEGA No. 1/0479/19 \"Influence of combustion conditions on production of solid pollutants in small heat sources\".  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 7: (**a**) Comparison of average NOx emissions, and (**b**) comparison of average particulate matters (PM).
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 6 Conclusions and Future Work  
CP improves traffic safety and efficiency by supporting various CAV applications. However, one source of inefficiency in this new paradigm is that the same object can be detected and tracked by multiple CAVs on the road, resulting in redundant data transmissions over the network. Without coordination, three observations of the same object will often be sent over the network. Additionally, if the network is overloaded, messages may take longer to send, which makes them less useful for collective perception. In this situation, we need data redundancy mechanisms to make collective perceptions more accurate and useful.  
In this article, we discussed the implications and benefits of the redundancy reduction mechanism for CP in the context of CAVs. In addition, an evaluation of a redundancy mitigation mechanism, called VILE, was introduced to reduce communication costs and maintain the system's overall perception. Simulation results show the efficiency of the VILE compared to the baseline to ensure a collective perception with 55% less generated packets and 75% fewer redundant objects. Finally, we discuss collective perception trends in the context of CAVs, as well as potential research challenges and opportunities.  
In future work, we aim to compare the VILE mechanism with other proposals in the literature and evaluate it in more complex scenarios. We also intend to consider the perception and processing latency from the sensors, for example, by adding the processing model defined by activity estimation [52], as well as evaluating the transmission delay and a prediction model that chooses the CAV that will stay longer inside the hex region. The security of the provided proposal will also be examined in future work. We will also assess how the introduction of misleading information affects the behavior of the approach and the perception of CAVs.  
Conceptualization, W.L., P.M. and D.R.; methodology, W.L., P.M. and D.R.; software, W.L. and D.R.; validation, W.L., P.M., D.R., E.C. and L.A.V.; formal analysis, W.L., P.M., D.R., E.C. and L.A.V.; investigation, W.L., P.M., D.R., E.C. and L.A.V.; resources, P.M., D.R. and L.A.V.; data curation, W.L. and D.R.; writing--original draft preparation, W.L., P.M. and D.R.; writing--review and editing, W.L. and D.R.; funding acquisition, P.M. and D.R. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Sao Paulo Research Foundation (FAPESP) for the financial support, grant #2019/19105-3.  
Simulation data can be provided after contacting the corresponding author.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Recent Advances in Sample Preparation for Cosmetics and Personal Care Products Analysis
## 2 Sample Preparation Strategies for Cosmetics Analysis
### Microextraction Techniques for Cosmetic Analysis
#### 2.7.1 Liquid-Phase Microextraction Techniques
The use of cyclohexane, a low-density and less toxic solvent to disperse the sample (LDS-DLLME) was proposed for the determination of alkanolamines and alkylamines [114]. This approach allows the target analytes to transfer into acidic solutions, while liposome substances were dissolved in cyclohexane. The complete extraction was accomplished within 13 min and further purification, or clean-up steps were not necessary. One decade ago, [PERSON] et al. [141] proposed a modification of the original DLLME called reversed-phase DLLME (RP-DLLME), where a small volume of water, used as extraction solvent, is dispersed into a bulk organic solution containing the polar target analytes. RP-DLLME has recently been applied to extract n-nitrosamines [115; 116] and free formaldehyde [117] from cosmetics and personal care products before LC-MS [115] or HPLC-UV analysis [116; 117]. In all cases, the employed volume of water ranged between 50 and 125 \(\mu\)L, with recoveries and LODs similar to those obtained for the analysis of free formaldehyde by UAE-CPE-UV-Vis [40] or n-nitrosamines employing SPE-LC-MS/MS [44]. Ultrasound-Assisted Emulsification Microextraction (USAEME)  
USAEME was introduced by [PERSON] et al. in 2009 [142]. It relies on the emulsification of the organic extractant in the sample by ultrasonic energy without the need for a disperser solvent, which favours efficiency, achieving the complete extraction of the analytes into the organic phase. This technique shows the advantage of using US to accelerate the mass-transfer process between two immiscible phases, reducing the extraction time. USAEME has been applied to extract preservatives, hormones, fragrances and phthalates from cosmetics matrices [118; 119; 120; 121; 122]. The use of ionic liquids (ILs) to substitute the organic solvents (ILs-USAEME) has been proposed as an environmentally friendly alternative prior to HPLC-UV to determine three glucocorticoids, one androgen and five progestogens in cosmetics [119]. The combination of USAEME with SFO was applied to determine fragrances and phthalates in both rinse-off and leave-on cosmetic samples [120; 121]. In both cases, a few microliters of 2-dodecanol and 1-undecanol were employed, respectively, to perform the extraction. Several parameters influencing the extraction efficiency were optimized. Another environmentally friendly approach involves the use of ultrasound-assisted surfactant-enhanced emulsification microextraction (UASEME), that employs a surfactant and less toxic organic solvent which offers an environmentally friendly procedure. A method based on UASEME-HPLC-UV to determine preservatives in different cosmetic samples was developed. Obtained LODs were similar to those provided by classical USAEME [118].
# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
## 3 Experimental Results
### Vocal Fold Localization Performance  
Though the vocal fold localization is not the main contribution of this work, good localization accuracy is important because the needle route computation directly depends on it. In our previous work [7], extensive evaluations were performed which showed an error as low as \(2.33\pm 1.17\) mm. In the current study, we again focus on the acceptability or accuracy of localization (i.e., to verify whether the vocal fold is localized in the desired region with a safe margin from the paraglottic membrane). We report the localization accuracy in Table 2. We also present the accuracy for the paresis cases separately in this table. Overall, the vocal folds were localized with high accuracy of 93.02% and 94.12% according to observers 1 and 2, respectively. The localization accuracy was comparable to the inter-observer agreement of 96.69%. Similar to the findings of the previous study [7], the RL agent showed a greater performance over the standard end-to-end deep localization models (Table 3).  
\begin{table}
\begin{tabular}{l c} \hline
**Attribute** & **Description** \\ \hline Total cases & 136 \\ Age & \(62\pm 16\) years \\ Sex & Male: 69, female: 67 \\ Paresis cases & Unilateral: 23, bilateral: 6 \\ Avg. volume dimension & \(512\times 512\times 160\) \\ Avg. voxel dimension & \(0.44\times 0.44\times 0.96\) \\ \hline \end{tabular} Inter-observer agreement: 96.97% (right fold), 95.45% (left fold).  
\end{table}
Table 1: Dataset description.