esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
[11] described a mean predictability of 72.8% for changes in the width of the upper arch; however, in contrast to the present findings, they reported that the intercanine width was the most predictable measure, with the distance between the gingival margins of first molars being the least predictable. [PERSON] et al. [26] observed that the predictability of expansion was only 40.5% at the incisor and canine level in both arches, mainly attributing the expansion movement to dental crown tilt; however, they did not study the predictability at the premolar and molar levels. [PERSON] [27] investigated the accuracy of clear aligners in 20 adult patients, determining differences between the predicted and observed movements in both arches. Greater accuracy was achieved for upper interpremolar expansion than for upper intercanine expansion, which significantly differed between the predicted (2.09 mm) and observed (1.60 mm) outcomes. These results are in line with the present findings and explained by the longer roots of maxillary amines and the morphology of conical crowns, with fewer undercuts to enhance aligner retention. [PERSON] et al. [10] concluded that expansion planned with the ClinCheck(r) virtual model was not predictable for gingival or cuspid widths at the canine level, first or second premolar levels, or first molar level. In comparison to the present study, their study sample was larger (n = 116), but no information was provided on the planning of malocclusion correction; in addition, they treated patients with Ex30(r) material, whereas both Ex30 and SmartTrack(r) materials were used in the present study, hampering the comparability of findings. To our best knowledge, this is the first study to compare the efficacy of clear aligners among patients with different types of crossbites, and more research is required on any differences and their clinical implications. [PERSON] et al. have shown that the prescribed movement, the moved teeth, the employment of attachments, and the frequency of aligner changes may influence the lack of correction [8]. Considering the planned movement, the more the planned movement increases, the more the lack of correction increases. Regarding the aligner change regime, he found significant differences for certain movements when comparing 7-days to 14-days aligner change. No significant differences were found between the 7-days and 10-days categories. All these factors should be taken into account when planning the correction of different types of dentoalveolar posterior crossbites. Further CBCT studies are warranted to determine how much of the expansion obtained in the present study is attributable to coronal tilt. Previous studies have shown that tipping and torque movements are the most difficult movements to control with aligners [13], and the loss of information increases movement toward the distal portion of the aligner [8].
## 3 Results and Discussion
### Model Comparison by Sum of Ranking Differences  
Let us be good natured (benevolent) and assume that estimating the course of the epidemic in five countries starting at different times is a reasonable goal. (I personally disagree, but we can assume it). Eight models (summarized in Figure 3) were compared by using three minimum search algorithms (GA, PSO, and GWO), for 24 combinations altogether. Why just these algorithms were selected from the plethora of available ones (_cf._, Section 2.3) is unknown. Fortunately, the authors provided detailed tables (Tables 3-10 in ref. [2]) with two evaluation (performance) criteria: correlation coefficient (\(r\)) and root mean square error (RMSE).  
A data table can be constructed from \(r\) and RMSE in the rows and algorithm combinations to be compared in the columns. Row maxima for \(r\) and row minima for RMSE served as the benchmark reference. Such a benchmark realizes the hypothetical best combination: the largest correlation coefficients and the smallest residual errors. The proximity of the different alternatives to the hypothetical best combination is measured by SRD.  
Figure 5 shows the SRD values and the cumulated relative frequencies of random ranking.  
Even a superficial evaluation reveals that something is not in order. No clear winner can be established, neither from the models nor from the minimum search algorithms, though some tendencies are observed. Why could not all minimum search algorithms find the global minimum? They should! The possible reasons might be stopping too early, wrong starting values, running into local minima, the presence of outliers, or a combination of these reasons.
## 3 Background
### Security Requirements
#### 3.3.7 Privacy  
A driver's daily routine is one of the types of private information that should not be made public. No unauthorized access to the network should be allowed since it may put people's lives at risk [63].
## 2 Materials and Methods
### Contact Parameters
#### 2.3.2 The Coefficient of Restitution  
The coefficient of restitution indicates the capacity of one material to restore its original form after impact. Considering that frequent collisions occur between FTB and harvesting equipment, as well as among bulbs themselves, during pre-treatment processes, the coefficient of restitution was measured by a high-speed camera and coordinate paper as reference tools [31; 32; 33].  
The free-fall method was employed to measure the coefficient of restitution between FTB and a stainless steel plate [34; 35; 36]. The free-fall motion was captured by a high-speed camera to collect trajectory data, as shown in Figure 5. Each FTB sample was released from a fixed height \(H_{1}\) and allowed to fall freely onto a stainless steel plate. The rebound height \(H_{2}\) was subsequently recorded by the high-speed camera. Because only the gravitational force acts on the sample during this process, the effect of both dynamic and static friction coefficients between the sample and the surface can be neglected. The coefficient of restitution e\({}_{1}\) between FTB and stainless steel is calculated by Equation (5):  
\[e_{1}=\sqrt{\frac{H_{2}}{H_{1}}} \tag{5}\]  
The experiment was conducted 20 times, from which the coefficient of restitution between FTB and the stainless steel plate was determined as \(e_{1}\) = 0.606.  
To evaluate the coefficient of restitution between FTB samples themselves, the suspension method was used, as illustrated in Figure 6. In this setup, sample B was suspended vertically, while sample A was raised to a height, \(H_{1}\), and then released to collide with sample B. The extent of oscillation following the collision reflects the restitution behavior between the two samples. The motion was captured by a high-speed camera, and the swing  
Figure 5: Collision test between FTB and the stainless steel plate. 1. High-speed camera. 2. Stainless steel plate. 3. Calibration ruler. 4. Coordinate paper. 5. FTB samples.  
heights of sample A (\(H_{2}\)) and sample B (\(H_{3}\)) were recorded by using coordinate paper as a reference. The coefficient of restitution between two samples, denoted as e\({}_{2}\), was calculated by Equation (6):  
\[e_{2}=\frac{\sqrt{H_{3}}-\sqrt{H_{2}}}{\sqrt{H_{1}}} \tag{6}\]  
The experiment involved 20 FTB samples, and each measurement was repeated 10 times. Based on the result, the coefficient of restitution (\(e_{2}\)) between FTB samples themselves was calculated to be 0.366. The inherent parameters of FTB are shown in Table 1.
## 5 Numerical Implementation  
The proposed optimization model was implemented in the Yalmip toolbox (R20230622 version) [41], using the Gurobi 9.5.1 solver [42] in the MATLAB 2021a software. A Dell Inspiron 15 7000 Series (Intel Quad-Core i7-7700 HQ @2.80 GHz) PC (Dell Inc., Round Rock, TX, USA; Intel Corporation, Santa Clara, CA, USA) with 16 GB RAM and 64-bit Windows 10 Home Single Language (Microsoft Corporation, Redmond, WA, USA) was used to carry out the simulations. Furthermore, the exact optimization model (1)-(16) was also implemented in the GAMS software (23.5.1 version).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Par.** & **Value** & **Unit** & **Par.** & **Value** & **Unit** \\ \hline \(C\) & 0.1390 & USD/kWh & \(T\) & 365 & Days \\ \(\Delta_{h}\) & 0.50 & hour & \(k_{1}\) & 6/2190 & \(1/\)Days \\ \(k_{2}\) & 10 & years & – & – & – \\ \hline \multicolumn{5}{c}{Coefficients of the FACTS devices} \\ \hline Device & \(\omega_{1}\) & Unit & \(\omega_{2}\) & Unit & \(\omega_{3}\) & Unit \\ \hline SVC & 0.30 & USD/Mvar\({}^{3}\) & \(-\)305.10 & USD/Mvar\({}^{2}\) & 127.380 & USD/Mvar \\ TCSC & 1.50 & USD/Mvar\({}^{3}\) & \(-\)713.00 & USD/Mvar\({}^{2}\) & 153.750 & USD/Mvar \\ UPFC & 0.30 & USD/Mvar\({}^{3}\) & \(-\)269.10 & USD/Mvar\({}^{2}\) & 188.220 & USD/Mvar \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter values for the objective functions.  
Figure 3: Variation in the considered active and reactive power curves.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(k\) & \(m\) & \(R_{\text{k,m}}\) (\(\Omega\)) & \(x_{\text{k,m}}\) (\(\Omega\)) & \(P_{k}\) (\(k\)W) & \(  
The four scenarios shown below were proposed to evaluate the performance of the proposed mixed-integer convex model (25)-(42). All scenarios consider a maximum of three FACTS.  
* The proposed model was compared against GAMS, the black widow optimization (BWO) algorithm proposed in [10], and the vortex search algorithm (VSA) presented by [26] for the IEEE 33-bus test system.
* The proposed convex model was evaluated in the IEEE 69-bus test system and compacted to the BWO and VSA, with the aim of installing the SVC devices with a fixed and variable operation.
* The model was evaluated in a large test system, namely the IEEE 85-bus test system. Furthermore, it was compared to the BWO algorithm regarding the installation of SVCs with a fixed and variable operation.
* A comparison regarding the installation of different FACTS technologies was analyzed (i.e., SVC, TCSC, and UPFC devices).
## 1 Introduction  
Bioinformatics represents a set of standard methods applied in research on peptides, including those of food origin [1; 2; 3]. Recently, the importance of the computational approach has been highlighted in, for example, the search for anti-SARS-CoV2 peptides [4; 5; 6]. The in silico approach includes molecular docking studies of peptides, which appear to be an emerging tool for predicting the bioactivity of food peptides [4; 6; 7; 8; 9]. Its application for educational purposes has been recently described by [PERSON] and coworkers [10]. Molecular docking is usually deployed together with experimental confirmation of biological properties of molecules, e.g., enzyme inhibition potential, but there are also investigations restricted to computational predictions. Another experimental model includes the in silico screening of many compounds, of which only one or a few of the most promising are then subjected to laboratory testing. Although in silico predictions are not fully equivalent to experimental works, they provide valuable information about the biological functions of compounds. However, critical interpretation of such results is always necessary. Strong and weak points as well as opinions concerning computational predictions of bioactivity of chemical substances have been recently discussed by [PERSON] et al. [11]. Taking into account the pros and cons of in silico predictions concerning bioactive peptides, they may be annotated in a separate database.  
The BIOPEP-UWM database [12] has become a popular information resource in research concerning the bioactivity and taste of peptides, mainly those derived from foods. It is also used for students' education [13]. The existing peptide databases, the BIOPEP-UWM database of bioactive peptides and the BIOPEP-UWM database of sensory peptides and amino acids, contain only data concerning peptides with bioactivity or taste evaluated in vitro or in vivo. The newly launched database provides the results of computational predictions of the biological activity or the taste of peptides, especially those derived from foods, which have yet not been confirmed in laboratory conditions.  
To date, our policy concerning the annotation of peptides in the BIOPEP-UWM database only took into account the compounds with biological properties confirmed experimentally. Submissions of peptides that function was predicted via the in silico method were declined. Thus, the launching of a new database is a result of the change of this policy. Results concerning the bioactivity or taste of peptides, obtained experimentally or predicted in silico and confirmed experimentally, will still be presented in our twin BIOPEP-UWM databases, i.e., of bioactive peptides and of sensory peptides and amino acids, whereas results obtained using computational methods only (considered virtual) will be presented in the new database.  
The aim of this publication was to announce and provide a brief description of the new tool serving as a repository of data concerning peptides with the biological activity or taste predicted in silico and other recent changes in the BIOPEP-UWM database.
## 3 Synthetic Examples
### Synthetic Wavelet Example
In addition, the waveform of its Ricker wavelet is closer to the original noise-free waveform, unlike the waveforms of EEMD and  
Figure 5: Waveforms and spectrograms of ten components obtained by variational mode decomposition (VMD). (**a**) Waveforms of components; (**b**) spectrograms of components. Figure 6: The values of Pearson’s correlation coefficient (CC) and their difference \(\Delta CC\) of the ten components decomposed by VMD. CEEMD, which have a certain degree of distortion. The signal processed by VMD-AIC has the least noise and the cleanest waveform compared with the other three results. In order to quantitatively compare the denoising effect by the above methods, the signal-to-noise ratio (\(SNR\)) is introduced as an evaluation index, which is defined as  
\[SNR{=}\ 10\log\left(\frac{\sum\limits_{i=1}^{N}{{{(x_{i})}^{2}}}}{\sum\limits_{i= 1}^{N}{{{(x_{i}-\overline{x_{i}})}^{2}}}}\right) \tag{14}\]  
where \(x_{i}\) is the noiseless signal, \(\overline{x_{i}}\) is the signal after noise reduction by a certain method, and \(N\) is the number of sampling points. The larger the \(SNR\) is, the closer the denoised signal is to the original signal. Table 1 shows the values of \(SNR\) before and after noise reduction by VMD-AIC, VMD, EEMD and CEEMD. Due to the existence of mode aliasing, the \(SNR\)s of EMD and CEEMD have only an approximate improvement of 4.5, and the value of VMD-AIC reaches 23.49, which is almost twice that of VMD. This huge increase is due to the fact that the noise in the non-signal segment occupies a considerable part of the total noise, and eliminating it results in a significant reduction in the numerator of the formula and an increase in \(SNR\). Both the above qualitative and quantitative results testify the effectiveness of this method in dealing with this simple synthetic signal.
# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 6 Discussion
Moreover, the developed stationary and mobile target nodes are simple, low in cost, and small in size, with no requirement of extra complicated sensor devices to accomplish the positioning phase, and can be attached to any object in a reliable manner. As presented earlier in Table 3, increasing the number of reference points will increase the time required for manually gathering the RSS values from the allocated reference points. For instance, the time required to collect RSS values from 65 reference points is approximately 65 min, whereas 126 reference points requires 132 min, and finally the 194 reference points requires 197 min. Hence, collecting more reference points will increase the training and testing accuracy, increasing the localization accuracy. However, additional labor and cost are required to accomplish the offline phase, which may be unavailable in certain scenarios. On the other hand, this paper investigates the impact of reference point density on localization accuracy. The results obtained from three different sizes of the RSS datasets with various reference point density values were analyzed and discussed. As a result, the medium and large density values offer high localization accuracy compared to the small RSS fingerprint dataset. However, the time required to accomplish the offline phase for the medium and large density values are longer than the small density environment.
## 2 Theoretical Background and Literature Review
### Blockchain and Smart Contracts
#### 2.2.1 Blockchain Technology: The Theoretical Foundation
[73] focus on developing a lightweight blockchain architecture tailored for 5G-enabled IoT networks. Their research evaluates both local and public transactions within IoT devices, assessing the architecture's performance. The study particularly emphasizes the architecture's enhanced security and efficiency, demonstrating its superiority over traditional 5G networks.
# Physico-Mechanical Properties of Alkali-Activated Based Composites Using Recycled Fire Fibers
## 3 Results
### Thermal Conductivity  
The thermal conductivity (k) results are summarized in Table 3. The incorporation of TFs into the mortar structure decreased the material's thermal conductivity as the amount of TFs in its composition increased. The mortars with partial siliceous and limestone aggregate substitution for TFs achieved lower thermal conductivity than conventional PC concrete (\(\lambda\) = 2.24 W/m\(\cdot\)K) [38]. The mortars with the limestone aggregate obtained lower thermal conductivity values than those with the siliceous aggregate. This can be attributed to the conductivity difference between both aggregates, which is larger for quartz (7.69 W/mK) vs. calcite (3.59 W/mK) [39]. The network of pores generated by the nonpolar nature of TFs also influenced the results because of the fibers' capacity to trap air on their rough surface. As the TF content increased, the presence of pores also grew [26; 27; 40; 41]. Figure 12 presents selected photographs of the specimen surfaces after testing, which confirmed the high pore content in the 100% TF composite. In the composites with 100% TF (2/1 to 1/1), the results had similar values among the six mixtures, with the ones containing a higher binder content achieving slightly better outcomes. This was because more compact and more uniform mortars were achieved on their testing surfaces compared with those with lower ratios. This resulted in less compact and less uniform specimens, given the lower binder content. The values of all six mixtures fell within the typical intermediate range for concrete with expanded clay [42]. ACI committee 213 R-03 proposed Equation (1) for correlating density (\(\rho\)) and the thermal conductivity value (k), while [PERSON] et al. [36] proposed Equation (2) to obtain them in concrete samples.  
\[k = 0.0864\;e^{0.0125\rho} \tag{1}\] \[k = 0.0625\;e^{0.0015\rho} \tag{2}\]  
In the TF 2/1 composite, when applying both equations, the corresponding values from Equations (1) and (2) were respectively 0.516 and 0.534 W/mK, which were similar to the experimental data (0.542 W/mK). However, the values for TF 1/1 obtained from the proposed Equations (1) and (2) were respectively 0.417 and 0.413 W/mK, and were significantly lower than for the experimental one (0.551 W/mK). The thermal conductivity of tire rubber fell within the 0.19-0.22 W/mK range [43]. Theoretically, the k value for TF 1/1 should be lower than that for TF 2/1, but the experimental results showed similar k  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **k (W/mK)** \\ \hline CTRLSIL & 0.969 \(\pm\) 0.030 \\ TF SIL15 & 0.948 \(\pm\) 0.103 \\ TF SIL25 & 0.926 \(\pm\) 0.033 \\ CTRLCAL & 0.925 \(\pm\) 0.043 \\ TF CAL15 & 0.881 \(\pm\) 0.024 \\ TF CAL25 & 0.849 \(\pm\) 0.049 \\ \hline TF 2/1 & 0.524 \(\pm\) 0.097 \\ TF 1.8/1 & 0.500 \(\pm\) 0.068 \\ TF 1.6/0 & 0.524 \(  
values for both composites. This behavior could be attributed to the larger total volume related to the interfacial transition zone gap when increasing the TF volume fraction [44].
# Simultaneous DC Railway Power System Analysis Method Using Model-Based TPS
## 2 Background of the TPS Algorithm
### Train Motion Equation  
The acceleration that a train can produce is related to the traction force of the motor and the train resistance [17]. The acceleration of a train \(a_{i}\), is the value obtained by dividing the effective traction force of \(F_{eff}\) in kN by the dynamic mass of \(m_{dyn}\) in tons, and it is expressed in Equation (1). The suffix \(i\) stands for calculation step for time, distance or velocity.  
\[a_{i}=\frac{F_{eff,i}}{m_{dyn}} \tag{1}\]  
Train set mass \(m_{dyn}\) is a sum of the empty masses, inertial masses (for each type of the train) and passenger mass. Additionally, the inertial mass is an empty car mass times inertia coefficient. \(F_{eff,i}\) can be expressed by subtracting the motor traction force \(F_{mtf,i}\) from the train resistance \(R_{i}\) in kN.  
\[F_{eff,i}=F_{mtf,i}-R_{i} \tag{2}\]  
and the \(R_{i}\) consists of curve, running, and gradient resistance as Equation (3).  
\[R_{i}=R_{Curve,i}+R_{Running,i}+R_{Gradient,i} \tag{3}\]  
In the TPS algorithm, those train resistance values are calculated by referencing track information for specific train position. Additionally, the specific speed limits can be determined by the track information as well.
# Transcriptomic Analysis of the Candidate Genes Related to Aroma Formation in _Osmanthus fragrans_
## 4 Materials and Methods
### qRT-PCR Validation  
Nine DEGs involved in terpenoids were chosen for qRT-PCR validation. The first-strand cDNA was reverse transcribed with 5 \(\upmu\)g of total RNA isolated from each of the three flowering stages and biological replicates using a Revert-Aid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Wilminton, DE, USA). The primers, designed using the Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, CA, USA), are listed in Table S2. TheqRT-PCR was performed on 10 \(\upmu\)L reactions systems containing a cDNA template, forward and reverse primers, SYBR Premix Ex Taq (TaKaRa, Ostu, Japan), and water, with an ABI Step One Plus Systems (Applied Biosystems, Carlsbad, CA, USA). The PCR reaction conditions were as follows: 95 \({}^{\circ}\)C for 30 s, followed by 40 cycles of 95 \({}^{\circ}\)C for 5 s, 60 \({}^{\circ}\)C for 30 s, and 95 \({}^{\circ}\)C for 15 s. _OfRPB2_ was used as the reference gene to calculate the relative fold-differences based on the comparative cycle threshold (2\({}^{-\Delta\Delta\text{Ct}}\)) values [59]. Every reaction for each gene used three biological replicates, with three technical replicates.