esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Marshall Test and Rutting Test Results  
The Marshall stability of all OGFC mixtures is shown in Figure 7. As shown in Figure 7, the OSW as partial aggregate replacement in OGFC could be effective in enhancing the MS of OGFC mixtures. The higher MS value indicates that the OGFC mixture is stiffer and has better resistance against permanent deformation. The MS value of OGFC, SBS-OGFC, and SM-OGFC were found to be 6.47, 9.12, and 9.63, respectively. It should be noted that the MS value of SM-OGFC was 48.8% higher than OGFC, even 5.6% higher than SBS-OGFC. It is shown that OSW helps to increase the adhesion ability among aggregates of OGFC. The main reason for the increase in adhesion among aggregates in SM-OGFC is that OSW with porous structure has a strong tendency to absorb asphalt binder, which can make the content of the structural asphalt binder increase, and the content of free asphalt binder is decreased as a result. This structural change improves the overall ability of the OGFC to bear loads. Moreover, The RSM predicated the MS value of the optimal SM-OGFC to 9.78, the measured MS value of SM-OGFC was 9.63, and the difference between the two was 1.5%, indicating that the RSM has high prediction accuracy. Figure 8 shows the dynamic stability of all OGFC mixtures. As seen, the DS value of SM-OGFC was 48.9% higher than OGFC, reached 94.7% of SBS-OGFC. This observation indicated that SM-OGFC had better resistance to permanent deformation at high temperature.  
Figure 7: Marshall stability for all Open graded friction course (OGFC) mixtures.  
\begin{table}
\begin{tabular}{c c} \hline \hline Asphalt-aggregate ratio & 5.18\% \\ Silane coupling agent content & 2.50\% \\ Mixing temperature & 153 °C \\ Compaction times & 50 \\ Desirability & 0.056 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal conditions obtained by Numerical optimization.
## 3 Experimental Results
### Speaker Identification Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Speaker\_YAMNet** \\ \hline Precision (\%) & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Recall (\%) & 100 & 99.78 & 98.56 & 99.73 & 100 & 99.78 & 99.67 \\ \(F\)-measure (\%) & 100 & 99.89 & 99.27 & 99.86 & 100 & 99.89 & 99.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Speaker identification results in terms of precision rate, recall rate, and \(F\)-measure. Figure 4: Waveform of speaker identification results, where the red box denotes the current analysis frame: (**a**) Blue speaker; (**b**) [PERSON] speaker. Figure 5 shows the confusion matrix of identified results by the Speaker YAMNet. Only eight frames are incorrect in the 2422 test frames, with an accuracy rate of 99.67%. We find that the incorrectly identified frames mainly appear in the frames of the first half of the speech after speaker switching. The misidentified frames mainly appear in the frames at the beginning of the changing speaker. In addition, the similar intonation and speech spectrum characteristics among the speakers [PERSON], [PERSON], and [PERSON] caused the Speaker YAMNet to misidentifify the speaker [PERSON] as [PERSON] (three frames) and [PERSON] (two frames). The speech contents of the speakers [PERSON] and [PERSON] were misidentified as the speaker [PERSON] by one frame each. So, speakers with similar intonation and speech spectrum characteristics were misidentified as the speaker [PERSON]. Therefore, there is confusion among speakers with similar intonation and spectral characteristics. The speaker \"[PERSON]\" was misclassified as \"[PERSON]\" in only one frame; the confusion level is low.
# Solid-Phase Extraction of Organic Dyes on Mixed-Ligand Zr(IV) Metal-Organic Framework
## 1 Introduction  
Zirconium-based metal-organic frameworks (Zr-MOFs) have become the focus of research in analytical chemistry due to their promising properties, including good selectivity, high performance, low cost, and regeneration, as well as the strong interaction of framework metal centers with functional groups of organic pollutants [1, 2, 3, 4, 5, 6, 7]. The strong bonds between the metal and carboxyl groups are responsible for the high stability of Zr-MOF [8, 9, 10, 1]. As one type of stable MOF, Zr-MOFs show high efficiency in removing dyes [11], antibiotics [12], and heavy metal ions [13].  
The disadvantages of Zr-MOFs include low adsorption rates and a long time for the establishment of an equilibrium state. Of the methods for increasing the adsorption capacity, the following should be singled out. Thus, the use of crystallization modulators in the synthesis makes it possible to increase the size of Zr-MOF crystallites by four orders of magnitude (from 10 to 100 ). Typical modulators are monocarboxylates to bind to metal clusters (coordination modulators) and thus to compete with the linker [14, 15, 16, 17], or acidic species to reduce the deprotonation of linkers (protonation modulators) [19]. The stability of such solids is well suited for further study of the effect of functionalization on their properties, and several experimental studies have established the possibility of additionally introducing new functional groups into the structure of these compounds [20].  
Among the generally accepted methods of synthesis, including stepwise and bottom-up, the mixed-ligand strategy has become widespread in MOF design [21, 22]. The term \"mixed ligands\" means that two types of linkers with similar spatial architectures or completely different structures are used for MOF synthesis. Regarding ligand attachment, a carboxyl ligand is often used in MOF construction due to the variety of coordination modes, and a polypyridine organic ligand can bind or chelate a metal ion. As an example, the use of a mixed-ligand strategy to produce Zr-MOF, including, in addition to carboxylate ions, various polypyridine ligands such as 2,2'-bipyridine and 1,10-phenanthroline can be mentioned [23, 24, 25, 26, 27, 28, 29, 30, 31]. In relation to our study, 1,10-phenanthroline, which has a rather rigid,flat, geometric structure, is of the greatest interest. In addition, the electron-conjugated heterocyclic aromatic ligand imparts a significant redox inertness to the complex, which favorably distinguishes MOF synthesized by us from those described earlier. The presence of a vacant \(\pi\)-orbital in the phenanthroline molecule makes it possible to expect that the formed mixed-ligand complex will have a significant charge transfer from the metal atom to the ligand, possibly in the visible and UV regions, which was also not previously described for MOFs of this type.  
The aim of our study was to use a ligand-mixed Zr-MOF containing terephthalidic acid and 1,10-phenanthroline as linkers in the solid-phase extraction (SPE) of organic dyes.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Transmission Latency (L)
#### 3.2.4 L for NR-V2X Protocol (L\({}^{\rm NR}\))  
For NR-V2X, \(L^{\rm NR}\) is determined by the number of time slots required to transmit data \(P_{b}\) (in bytes) multiplied by the slot duration \(t_{slot}^{NR}\), such as  
\[L^{NR}=\left\lceil\frac{n_{RB}^{NR}}{n_{RB-slot}^{NR}}\right\rceil\cdot t_{slot }^{NR} \tag{8}\]  
where \(n_{RB}^{NR}\) is the number of resource blocks needed to transmit \(P_{b}\) bytes, and \(n_{RB-slot}^{NR}\) is the number of resource blocks per slot (see [37], Table 5.3.2-1).  
To calculate \(n_{RB}^{NR}\) resource blocks, we use  
\[N_{RB}^{NR}=\left\lceil\frac{P_{b}.8}{n_{d-RE}^{RE}n_{nbm}^{NR}}\right\rceil \tag{9}\]  
where \(n_{d-RE}^{NR}\) is the number of resource elements available for data transmission, \(n_{nbm}^{NR}\) is the spectral efficiency of a given MCS obtained from (see [33], Table 6.1.4.1-1), and \(P_{b}\) is the payload size.
# A Novel Inductor-Based Non-Dissipative Equalizer
## 3 Simulation
### Equalization Simulation for SSIE, PAE and NIBNDE with Four Cells  
Since the circuits for comparison are affected by the initial distribution sequences of the batteries, all the distribution sequences will be simulated and compared. The total number of different distribution sequences of the four batteries is \(A_{4}^{4}=24\), of which twelve samples are independent. The initial voltages of the four batteries are shown in Table 1, and the equalization threshold is 3 mV.  
The circuit diagrams of the three equalizers are shown in Figure 5. Table 2 gives the balancing time of SSIE, PAE and NIBNDE in the twelve independent samples, while Table 3 provides a comprehensive comparison of them. Compared to the SSIE, the proposed NIBNDE requires four less MOSFETs and one more inductor, while reducing the average balancing time by 29.23%. Compared to the PAE, the NIBNDE uses the same MOSFETs and one less inductor. The PAE spends 80.08% less balancing time than the NIBNDE on average.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A** & **B** & **C** & **D** \\ \hline Voltage & 3.98 & 3.96 & 3.92 & 3.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The initial cell voltages of four batteries (voltage unit: V).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **ABCD** & **ABDC** & **ACBD** & **ACDB** & **ADBC** & **ADCB** & **BACD** & **BADC** & **BCAD** & **CBAD** & **DABC** \\ \hline SSIE & 0.7865 & 0.7867 & 0.7865 & 0.7864 & 0.7875 & 0.7877 & 0.7864 & 0.7865 & 0.7875 & 0.7864 & 0.7869 \\ PAE & 0.059 & 0.059 & 0.1216 & 0.1214 & 0.1525 & 0.1522 & 0.059 & 0.059 & 0.1525 & 0.1213 & 0.1209 & 0.1525 \\ NIBNDE & 0.6355 & 0.6916 & 0.2775 & 0.564 & 0.6905 & 0.5651 & 0.6985 & 0.7543 & 0.29 & 0.7542 & 0.4707 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The balancing time in different distribution sequences for SSIE, PAE and NIBNDE (time unit: seconds).  
Figure 5: Circuit diagrams of the single switched-inductor equalizer (SSIE) (**a**), the parallel architecture equalizer (PAE) (**b**) and the NIBNDE with four cells (**c**).  
In summary, although the SSIE uses only one inductor, the equalization time and the number of MOSFETs for the NIBNDE are both less than the SSIE. The equalization speed of the PAE is faster than the NIBNDE, but the NIBNDE uses one less inductor.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix A Two-Stage Detection Algorithm
### Step # 1: Censoring and Calculation of the Noise-Power Reference Level
Considering the \(H_{0}\) hypothesis (i.e., observation of samples only containing noise) and that the noise is a stochastic processes following a complex Gaussian distribution, \(\mathcal{CN}(0,\sigma^{2})\), therefore, each one of the PDP values follows a Gamma distribution, \(\Gamma(1,N_{\text{Synch}}\sigma^{2})\) with mean given by:  
\[\mathbb{E}[\text{PDP}^{(\cdot)}]=N_{\text{Synch}}\sigma^{2}. \tag{12}\]  
Therefore, since:  
\[\frac{\sum_{i=0}^{k-1}\text{PDP}^{(i)}}{k}\approx\mathbb{E}[\text{PDP}^{( \cdot)}]=N_{\text{Synch}}\sigma^{2}, \tag{13}\]  
the equation defining the probability of false disposal, \(P_{FD}\), can be accurately approximated as:  
\[P_{FD}\approx P\left\{\text{PDP}^{(k+1)}\geq T_{k}kN_{\text{Synch}}\sigma^{2} \right\}. \tag{14}\]  
The approximation given by (14) becomes more and more accurate as the amount of reference PDP samples, i.e., \(k\), increases. Moreover, given that the PDP sample \(\text{PDP}^{(k+1)}\) is a random variable with Gamma distribution, \(\Gamma(k,\theta)\), with the shape parameter, \(k\), equal to one, therefore, as demonstrated in [31; 32], its CDF can be simplified and expressed in closed-form by:  
\[F(y,k,\theta)=P(Y\leq y)=1-e^{-y/\theta}\sum_{i=0}^{k-1}\frac{1}{i!}\left( \frac{y}{\theta}\right)^{i}, \tag{15}\]  
where \(\theta\) and \(k\) are the scale and shape parameters of the Gamma distribution, respectively. Consequently, the probability of false censoring given by (A13) can be computed as:  
\[ \begin{split} P_{FD}=1-P\left\{\mathrm{PDP}^{(k+1)}\leq T_{k}kN_{ \mathrm{Synch}}\sigma^{2}\right\}\\ =e^{-T_{k}k},\end{split} \] (A15)  
where in (A14), \(y\) has been set to \(T_{k}kN_{\mathrm{Synch}}\sigma^{2}\). Next, we establish that \(T_{CME}=kT_{k}\), where \(T_{CME}\) is an input parameter to the FCME algorithm and is computed by solving (A15). Therefore, the probability of false censoring, \(P_{FD}\), can be thought of as the desired rejection rate to obtain a clean sample set. The censoring/discarding threshold level is defined as \(T_{k}\sum_{i=1}^{k}\mathrm{PDP}^{(i)}\). This way, PDP samples that present values greater than or equal to the threshold level are censored (i.e., removed from the set). Next, the new set of samples consists of samples that are considered to be noise-only observations. The outcome of the discarding step consists of (i) the noise-power reference level, \(Z_{\mathrm{REF}}\), and (ii) the number of noise-only samples, \(k\). The noise-power reference level is computed as the summation of all PDP samples that present values that are less than the discarding threshold level. The noise-power reference level, \(Z_{\mathrm{REF}}\), is given by:  
\[Z_{\mathrm{REF}}=\sum_{i=1}^{k}\mathrm{PDP}^{(i)}.\] (A16)
###### Abstract
They defined the optimization problem as a multi-period optimal control problem (OCP), where a nearly bang-bang optimum solution could be obtained for the non-discretized control (decision) variables simply by using a traditional gradient-based optimization algorithm. In this method, however, the optimization was conducted based on the simulation results for the entire time horizon, so thus sometimes failed in obtaining a bang-bang solution as the problem scale became larger with a long-time horizon. As reviewed above, most precedent optimization approaches dealing with the HEN cleaning scheduling problems have undergone some limitations, mainly originating from the complexity and nonlinearity of the models [1; 17]. Moreover, these limitations become critical as the problem scale increases due to a long-time horizon. In this case, moving window algorithm can be a good supplementary method because it can efficiently handle such complicated large-scale problems. [PERSON] et al. [31] used the moving window method to predict crude oil fouling in a preheat train and successfully proved its robustness. [PERSON] and [PERSON] [15] also compared a moving window decision-making algorithm with other methods, but rather criticized the moving window scheme only identifying local minimums inferior to other solutions. Their study, however, did not consider the effect of moving window size, which could be critical in an extremely complicated HEN cleaning scheduling problem. According to our literature survey, there has been no research applying the moving window algorithm over such a long-time horizon to make an issue of scale problem. In this study, we propose a moving window decision-making algorithm based on the multi-period OCP framework presented by [PERSON] et al. [18]. The primary distinction of our method is that a small size of window continuously slides over the entire time horizon making decisions sequentially at each moving step, so the complexity of the optimization problem could be greatly reduced. We compare the performance of our approach with the method using a fixed time horizon with respect to the optimality of solution and the computational time required as well.
## 3 The Diurnal Reference Check (DRC)
### Setting Maximum Amplitude of the Diurnal Signal
Figure 6d shows the correlations \(C_{LA}\) of \(T_{SAT}-T_{LA}\) and \(T_{IS}-T_{LA}\). For S-NPP, Aqua, and NOAA-15/18/19 at \(D\) > 2.5 K, they are higher than the corresponding \(C_{NIGHT}\)s in Figure 6c and remain relatively high at \(D\) > 5.5 K because the deviations of \(T_{SAT}-T_{LA}\) and \(T_{IS}-T_{LA}\) are both affected by the same variations in \(T_{L4}\). This raises a question of the quality of the reference SST in the presence of significant DS in the buoys' records. Remember that the 'foundation' L4 analyses are not intended to reproduce the diurnal warming cycle. In particular, the CMC does not assimilate SSTs sampled during the day under wind speeds \(<\) 6 m/s between 25\({}^{\circ}\) S and 25\({}^{\circ}\) N or elsewhere within 45 days of the summer solstice [25; 26]. It is instructive, therefore, to explore the quality of the \(T_{IS}-T_{LA}\) statistics in the presence of diurnal SST variations. Figure 5: (**a**) Time series of the total monthly number of (1) daily segments in DTM and (2) daily segments with identifiable DS. (**b**) Fraction of daily segments with identifiable DS, in which the ADS exceeds \(D\), as a function of \(D\).
# Abnormal Ship Behavior Detection Based on AIS Data
## 3 Materials and Methods
### Overview of Study Area and AIS Data Preprocessing  
#### 3.1.1 Overview of the Study Area  
There is a large number of sea-going ship trajectories. In order to mine the characteristics of the ship's behavior pattern, we partitioned the ship based on its location.  
China is located in the east of the Asian continent, facing the Pacific Ocean. The Bohai Sea, Yellow Sea, East China Sea, and South China Sea are the four sea areas under the jurisdiction of the People's Republic of China. Marine Regions integrates and serves geographic information from the VLIMAR Gazetteer and the MARBOUND database and proposes a standard of marine georeferenced locations, boundaries, and regions. Based on this website, we downloaded the boundary data of the global sea area and intercepted the boundary data of the four major sea areas of China as the data of our research area.
## 2 Experimental Procedure
### SHPB System  
A 50 mm diameter split Hopkinson pressure bar (SHPB) apparatus with 500 mm-long striker, 2500 mm-long incident bar and 2000 mm-long transmitted bar was used to test BFRC specimens, as shown in Figure 1. The properties of all the bars are shown in the Table 3. A high dynamic strain indicator (produced by Qinhuangdao Longke Measurement Technology Co., Ltd., Qinhuangdao, China) was used for data acquisition through the strain gauges mounted on the surface of the incident and transmitted bar. The technical parameters of high dynamic strain indicator are as follows: coefficient of strain (K) is 2.0, sensitivity (k\({}_{\text{p}}\)) is 1v/1000 \(\mu\varepsilon\). The confining pressure device applied pressure to specimens by means of hydraulic pressure and kept pressure stable. The pulse shaping technique [25] which could eliminate the high frequency oscillation, reduced the wave dispersion effects, and extended the rising time of the incident pulse was used in SHPB test. In this research, through the comparative analysis of the tests, the pulse shaper used copper sheets that were 1 mm in thickness and 25 mm, 20 mm, 15 mm in diameters corresponding different impact velocities: 5.4 m/s, 8.8 m/s, and 11.3 m/s.  
Figure 1: Schematic diagram of split Hopkinson pressure bar (SHPB) apparatus.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Density (kg/m\({}^{3}\))** & **Elasticity Modulus (GPa)** & **Longitudinal Sound Velocity (m/s)** & **Yield Strength (MPa)** \\ \hline Bars & 7800 & 210 & 5124 & \(\geq\)1200 \\ \hline \end{tabular}
\end{table}
Table 3: The properties of the bars.  
During the impact process, the striker impacted incident bar through propelling by pressurized nitrogen gas. Impact wave passed through specimens form incident bar to transmitted bar and the specimens would be destroyed. Based on the theory of one-dimensional wave propagation, the stress, strain and strain rate of the specimens can be calculated by the following equations:  
\[\left\{\begin{array}{l}\sigma(t)=\frac{E_{0}A_{0}}{2A_{s}}[\varepsilon_{I}(t)+ \varepsilon_{R}(t)+\varepsilon_{T}(t)]\\ \dot{\varepsilon}(t)=\frac{C_{0}}{L_{s}}[\varepsilon_{T}(t)-\varepsilon_{I}(t )-\varepsilon_{R}(t)]\\ \varepsilon(t)=\frac{C_{0}}{L_{s}}\int_{0}^{t}[\varepsilon_{T}(t)-\varepsilon _{I}(t)-\varepsilon_{R}(t)]dt\end{array}\right. \tag{1}\]  
where \(A_{0}\), \(E_{0}\), \(C_{0}\) are cross-sectional area, elasticity modulus and longitudinal sound velocity of incident or transmitted bar, \(A_{s}\), \(L_{s}\) are cross-sectional area and length of specimens, \(\varepsilon_{I}(t)\), \(\varepsilon_{T}(t)\) and \(\varepsilon_{R}(t)\) are incident pulse, transmitted pulse and reflected pulse, \(\sigma(t)\), \(\varepsilon(t)\) and \(\dot{\varepsilon}(t)\) are the stress, strain and strain rate of the specimens.
## 2 Models and Methods  
### Physical Model  
Figure 1 illustrates the physical model of the liquid bridge that initially forms between two horizontal plates. The liquid bridge has an initial length of \(R\) and a height of \(H\) and is surrounded by a gas medium in a rectangular container with a height of \(H\) and a length of \(2R\). The liquid bridge comprises a binary mixture of toluene and n-hexane with a concentration ratio of 0.24 to 0.76. The initial state of the gas-liquid interface is vertical. The upper and lower plates are subjected to various temperatures and concentrations that satisfy \(T_{1}>T_{0}\) and \(C_{1}>C_{0}\), that is, the temperature difference between the upper and lower plates can be expressed as \(\Delta T=T_{1}-T_{0}\), while the concentration difference is represented by \(\Delta C=C_{1}-C_{0}\).  
A succession of assumptions is introduced to simplify the research. Firstly, it is assumed that the upper and lower plates are fully insulated and impermeable. Additionally, the slip-free condition is applied to all walls of the computational domain. The impact of viscous dissipation is considered negligible, and it is assumed that the flow is laminar. Moreover, gravitational effects are ignored. Finally, the binary mixture fluid under investigation is an incompressible Newtonian fluid. It should be noted that the physical parameters of the fluid remain unchanged, except for the fluid surface tension. The fluid's surface tension can be expressed as a linear function with respect to both temperature and concentration, which is interpreted as \(\sigma=\sigma_{0}-\sigma_{T}\) (\(T-T_{0}\)) - \(\sigma_{C}\) (\(C-C_{0}\)), where \(\sigma_{0}\) represents the initial surface tension, \(T\) and \(C\) represent coefficients of the temperature and concentration of the fluid, respectively, and \(\sigma_{T}\) and \(\sigma_{C}\) represent the coefficient of surface tension varying with temperature and concentration, respectively. Furthermore, \(\sigma_{T}=-\partial\sigma/\partial T=\text{constant}\) and \(\sigma_{C}=-\partial\sigma/\partial C=\text{constant}\).