esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Experimental Validation Methodology  
#### 2.4.1 Statistical Requirements  
Our experimental validation adheres to rigorous statistical standards. First, the sample size was determined through power analysis, concluding that 500 iterations per scenario are sufficient to achieve statistical power greater than 0.95 for detecting performance differences of 5% or more between algorithms. Second, to account for the six pairwise algorithm comparisons conducted in each scenario, a Bonferroni correction was applied to control the family-wise error rate at \(\alpha=0.05\). Third, in accordance with APA guidelines, both \(p\)-values and Cohen's \(d\) are reported to provide clarity on both statistical and practical significance. Finally, to ensure reproducibility, all random seeds were fixed, and the complete experimental framework has been made available for replication.
## 2 TDC Architecture  
Figure 1 shows the architecture of the proposed TDL-TDC. The single-channel consists of a mixed-mode clock manager (MMCM), a coarse counter, a filter stage, a tapped delay line, a ring oscillator, a frequency counter, a ones-counter encoder, a histogram and a look up table (made by dual port RAM). The MMCM creates a 350 MHz clock from the 100 MHz system clock. The coarse counter is 24 bits at the time range of 47.9 ms. The tapped delay line is implemented with a cascading CARRY4 block. The ones-counter encoder module converts the thermometer code with bubble errors into binary code. The AXI BRAM controller module is used to calibrate the influence of the process and temperature on the tapped delay line. The main abbreviations applied in this paper are shown in Table 1.  
The TDL self-regulates and requires no additional reset logic. By utilizing the filter stage to filter the hit, the width of the hit becomes the time interval between the rising edge of the signal and the next rising edge of the clock, as shown in Figure 1. In the \"FF1\" stage, if the input of \"D\" is \(1\) and \"CLR\" is 0, the rising edge of the hit propagates along the TDL. When the hit transmitters conduct the first tap of the TDL, the \"FF2\" stage clears the \"FF1\" stage at the rising edge of the next clock and prevents the hit from entering the TDL. Once the entire TDL is cleared, the \"CLR\" is set to 0 again and the TDL is ready to receive a new pulse [25].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Description** \\ \hline TDC & Time-to-digital converter \\ FPGA & Field Programmable Gate Array \\ CARRY4 & Slice primitive: Fast Carry Logic with Look Ahead \\ LSB & least significant bit \\ PET & positron emission tomograph \\ ASIC & Application Specific Integrated Circuit \\ VDL & Verner delay line \\ MCP & multiple clock phases \\ TDL & tapped delay line \\ STA & static timing analysis \\ DNL & differential nonlinearities \\ INL & integral nonlinearities \\ MMCM & mixed-mode clock manager \\ LDO & low dropout regulator \\ IDELAYCTRL & Xilinx primitive: IDELAYE2/ODELAYE2 Tap Delay Value Control \\ IDELAYE2 & Xilinx primitive: Input Fixed or Variable Delay Element \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of abbreviations.  
Figure 1: Block diagram of the proposed TDC. The programmable logic (in orange) and processing system (in purple).
## 3 Transesterification for Producing Biodiesel from Animal Fats
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Animal Fat**} & \multirow{2}{*}{**Catalyst**} & **Weight** & **Reaction** & **AlcoholOil** & **Operating** & \multirow{2}{*}{**References**} \\ \cline{3-4}  & & **(\% to Fat)** & **Yield (\%)** & **Ratio** & **Conditions** & \\ \hline \multirow{2}{*}{Beef tallow} & Immobilized lipase from & & & & & \\  & _Burkhodera cspacia_ & 20 & 89.7 & 12:1 & 50 \({}^{\circ}\)C, 48 h & [75] \\ \multirow{2}{*}{Animal fat} & Immobilized lipase from _Candida_ & 10 & 79 & 50:6 & 40 \({}^{\circ}\)C, 6 h & [76] \\  & _antictictic_ & 10 & 87.4 & 3:1 & 40 \({}^{\circ}\)C, 30 h & [77] \\ \multirow{2}{*}{Lard} & Lipase from _Candida_ & 10 & 74 & 1:1 & 30 \({}^{\circ}\)C, 72 h & [78] \\ \multirow{2}{*}{Lard} & Lipase from _Candida_ & 2-6 & 97.2 & 5:1 & 50 \({}^{\circ}\)C, 20 h & [79] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._contents in free fatty acids and can perform esterification and transesterification simultaneously. Furthermore, the purification of biodiesel is avoided, and another advantage is the easier separation of the catalyst from the reaction products [94]. In cases where there is a high content of moisture and free fatty acids (e.g., animal fats), the enzymatically catalyzed transesterification is more attractive. Lipase converts both free fatty acids and triglycerides into biodiesel in the presence of an acyl acceptor. The main advantages are the mild reaction conditions, high selectivity and specificity of transesterification, broader substrate range, no soap formation, lower alcohol to oil ratio, lower requirements for purification and higher yields [95]. However, the major cost is due to the enzyme itself and its poor stability and longer reaction time, and the slow conversion rate because of the diffusion caused by its byproduct [96]. These troubles can be partly overcome through enzyme immobilization on an inert support that increases the enzyme stability, avoids the need for enzyme separation and improves process efficiency [97,98]. Other costs are derived from the deactivation of the enzyme at high molar ratios of alcohol to fat [96]. Three solutions have been proposed, which are using methanol stepwise addition, replacing methanol with an acyl acceptor like methyl acetate or ethyl acetate or using a solvent like t-butanol to get an improved solubility of methanol [99]. A variety of lipases from diverse microorganisms such as _Candida antarctica, Candida rugosa, Pseudomonas_ cepacia, Pseudomonas_ spp. and _Rhizomucor miehei_, and immobilized lipases (i.e., Lipozyme(r) RMIM from _Rhizomucor miehei_ or Novo(r) 435 from _Candida antarctica_), have been reported in the literature [100]. It must be pointed out that transesterification with alkaline catalysis is still preferred at industrial plants producing biodiesel. In spite of much research on heterogeneous and enzyme catalysts, biodiesel producers have not yet adopted these technologies [101].
## 4 The M_HERACLES Toolbox
### Step 2 (1): Automatic Segmentation and Classification of Structural Supports
#### 4.2.2 Results and Analysis

Table 6: Quantitative analysis on the results of step 2 for the detection and classification of columns in the Valentino dataset. Note that only the detected columns were taken into account here. %P is precision, %R is recall, and %F1 is the normalized F1 score. and 57.60% in F1 score (Figure 11). Although our algorithm managed to provide better results than the compared study, several remarks should nevertheless be taken into account. Firstly, in our study, only free-standing pillars were accounted for, whereas [36] also included engaged columns. Indeed, M_HERACLES did not manage to correctly detect the engaged columns. Secondly, the DL approach used in the other study has the potential to generate better results with more training data.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## Materials and Experimental Process
### Roughness of Structure Interface  
In this study, the surface roughness of the stainless-steel ring was specially prefabricated. The method was to cut the sandpaper into the same size as the surface of the steel ring and paste it on the surface of the steel ring with epoxy resin to simulate different interface roughness levels. The roughness of the contact surface of the stainless-steel ring made by this method was relatively uniform. In this paper, the different grit sizes of sandpaper were used as the evaluation parameters of the simulated sand-steel interface roughness (sandpaper grit #120, sandpaper grit #60). The grit size of the sandpaper indicated the grain size or thickness of the surface material of the sandpaper. Larger grit size implied that the material had fine particle size, and the undulation degree of the sandpaper surface profile was relatively small; that is, the interface roughness was relatively small. The opposite was true for smaller grit size, indicating that the undulation degree of the sandpaper surface profile was relatively large; that is, the interface roughness was relatively large. In addition, there was a steel ring without sandpaper as a relatively smooth interface. It should be noted that the smooth interface was not completely without roughness. It was just that the undulation of the steel surface was much smaller than that of the sand surface. Therefore, it could be approximated as a smooth contact surface. In this way, three kinds of roughness (\(R_{n1}\), \(R_{n2}\), \(R_{n3}\)) of the steel ring interface were produced, which corresponded to a relatively smooth interface, a medium rough interface and a rough interface, as shown in Figure 4.  
Figure 4: Three kinds of roughness interface formed by sandpaper and steel ring: (**a**) \(R_{n1}\) : relatively smooth interface; (**b**) \(R_{n2}\) : medium rough interface; (**c**) \(R_{n3}\) : rough interface.  
Figure 3: Interface shear testing apparatus. (**a**) The HJ-I ring shear apparatus. (**b**) Schematic diagram of shearing of sand–steel interface.
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 4 Control Strategies Comparison
### Comparison
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Parameters** & **Linear Control** & **FCS–MPC** & **Flatness-Based Control** & **Sliding Mode Control** \\ \hline
**Total number of control loops** & 5 & 2 & 3 & 3 \\
**Total number of measured variables** & 7 & 7 & 7 & 5 \\
**Averaged switching frequency** & 79,060 Hz & 79,140 Hz & 79,980 Hz & 85,680 Hz \\
**Ripple of inductor currents** & 5.2 A & 9.94 A & 5.23 A & 5.10 A \\
**THD\({}_{i_{k}}\)** & 3.85\% & 3.58\% & 2.59\% & 2.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main parameters of comparison. Finally, a summary with the main features of each control strategy is presented in Table 4. The output current of the inverter with the four control strategies complies with the IEEE standard. However, the linear control presents the highest current's THD, followed by FCS-MPC. The linear control scheme has the highest number of the control loops to regulate the DBI. Moreover, the design and tuning of the parameters of the PR controllers are non-trivial procedures. On the other hand, FCS-MPC presents a variable switching frequency, requiring a redesign of the grid filter to achieve the desired current's ripple. Furthermore, the tuning of the weight factor is a challenging task due to the different dynamics of the control objectives. Although the switching frequency obtained with sliding mode control is variable, the THD of the grid current presents the best result. Furthermore, this control strategy is focused on the current regulation of the inverter, which means that fewer sensors are used, since the capacitor voltage \(v_{\text{c1}}\), \(v_{\text{c2}}\) sensors are not required. Considering all the comparison parameters mentioned above, the sliding mode control appears to be the most attractive control strategy for the DBI.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 3 Sun Radiation
### Non-Parallel Rays  
The Sun's rays are not completely parallel, due to the non-negligible size of the solar disk seen from Earth (see Figure 4). On average, the maximum inclination amounts to \(\alpha_{max}=0.267^{\circ}\), enough to make a difference in some applications (for example a Cassegrain-type reflector [25; 26]). For some simulation ray randomly originated from the Sun's surface, defined by spherical angles \(\theta\) and \(\phi\), the non-parallel angle \(\alpha\) can be calculated from:  
\[\tan\alpha=\frac{R\sin\theta}{D} \tag{11}\]  
where \(R\) is the Sun's radius and \(D\) is the Earth-Sun distance.  
To generate \(\theta\) (0 to 90\({}^{\circ}\)) and \(\phi\) (0 to 360\({}^{\circ}\)) random angles, defining points randomly distributed over a spherical surface, the equations are (see [27] for a detailed discussion):  
\[\phi=2\pi R_{01} \tag{12}\]  
\[\theta=\arccos[1+R_{01}(\cos\theta_{max}-1)]\ \ \theta=\arccos(1-R_{01}) \tag{13}\]  
where \(R_{01}\) represent random numbers generated between 0.0 and 1.0.  
Once the random angles \(\phi\) and \(\alpha\), this through Equation (11), are known, the ray vector \(\overset{\rightarrow}{K}_{i}\) coming from Sun can be scrambled to \(\overset{\rightarrow}{K}_{f}\) to take into account this nonlinearity of the rays (see Figure 5), constructing a _xyz_ referential and then converting spherical to cartesian coordinates (see Equations (14)-(16)).  
\[\overset{\rightarrow}{K}_{i}\ \Rightarrow\ \overset{\rightarrow}{z}=\frac{ \overset{\rightarrow}{K}_{i}}{K_{i}}\ \Rightarrow\ \overset{\rightarrow}{z}\cdot \overset{\rightarrow}{y}=0\ \Rightarrow\ \overset{\rightarrow}{x}=\overset{ \rightarrow}{y}\times\overset{\rightarrow}{z} \tag{14}\]  
\[x=K_{i}\sin\alpha\cos\phi\ \ y=K_{i}\sin\alpha\sin\phi\ \ z=K_{i}\cos\alpha \tag{15}\]  
\[\overset{\rightarrow}{K}_{f}=x\overset{\rightarrow}{x}+y\overset{ \rightarrow}{y}+z\overset{\rightarrow}{z} \tag{16}\]
# Experimental Study of High-Strength Concrete-Steel Plate Composite Shear Walls
## 3 Experimental Results and Discussions
### Lateral Load and Dispalcement Capacity
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Specimen** & \multirow{2}{*}{**Direction**} & \multicolumn{3}{c}{**Yield**} & \multicolumn{3}{c}{**Peak**} & \multicolumn{3}{c}{**Ultimate**} & \multicolumn{3}{c}{**Pacility Factor**} \\ \cline{3-11}
**No.** & & \(P_{Y}\) **(kN)** & \(\Delta_{Y}\) **(mm)** & **Drift (\%)** & \(P_{mag}\) **(kN)** & \(\Delta_{mag}\) **(mm)** & **Drift (\%)** & \(P_{\tt u}\) **(kN)** & \(\Delta_{\tt u}\) **(mm)** & **Drift (\%)** & \(\Delta=\Delta_{\tt u}/\Delta_{Y}\)** \\ \hline  & + & 386 & 6.0 & 0.28\% & 469 & 18.2 & 0.84\% & 398 & 32.9 & 1.52\% & 5.45 \\ RCSW 2-1 & – & 405 & 6.9 & 0.32\% & 514 & 27.5 & 1.28\% & 437 & 35.4 & 1.64\% & 5.12 \\  & Average & 396 & 6.5 & 0.30\% & 491 & 22.9 & 1.06\% & 418 & 34.2 & 1.58\% & 5.27 \\ \hline  & + & 455 & 6.8 & 0.32\% & 540 & 14.9 & 0.69\% & 473 & 36.1 & 1.67\% & 5.29 \\ RCSW 2-2 & – & 468 & 6.3 & 0.29\% & 536 & 16.1 & 0.75\% & 456 & 34.8 & 1.61\% & 5.54 \\  & Average & 461 & 6.6 & 0.30\% & 538 & 15.5 & 0.72\% & 464 & 35.5 & 1.64\% & 5.41 \\ \hline  & + & 487 & 7.2 & 0.33\% & 570 & 15.8 & 0.73\% & 541 & 28.4 & 1.31\% & 3.97 \\ RCSW 2-3 & – & 488 & 5.9 & 0.27\% & 578 & 11.5 & 0.53\% & 522 & 35.4 & 1.64\% & 6.01 \\  & Average & 488 & 6.5 & 0.30\% & 574 & 13.6 & 0.63\% & 531 & 31.9 & 1.48\% & 4.89 \\ \hline  & + & 497 & 9.6 & 0.44\% & 581 & 17.1 & 0.79\% & 493 & 36.1 & 1.67\% & 3.78 \\ RCSW & – & 581 & 7.6 & 0.35\% & 697 & 20.1 & 0.93\% & 592 & 31.7 & 1.47\% & 4.19 \\  & Average & 539 & 8.6 & 0.40\% & 639 & 18.6 & 0.86\% & 543 & 33.9 & 1.57\% & 3.96 \\ \hline  & + & 536 & 8.4 & 0.39\% & 646 & 16.3 & 0.75\% & 592 & 30.0 & 1.39\% & 3.59 \\
2-2 & – & 569 & 5.2 & 0.24\% & 674 & 13.3 & 0.62\% & 573 & 23.3 & 1.08\% & 4.48 \\  & Average & 553 & 6.8 & 0.31\% & 660 & 14.8 & 0.68\% & 583 & 26.7 & 1.23\% & 3.93 \\ \hline  & + & 560 & 10.5 & 0.48\% & 674 & 15.0 & 0.69\% & 561 & 23.5 & 1.09\% & 2.25 \\
2-3 & – & 581 & 5.2 & 0.24\% & 701 & 11.5 & 0.53\% & 539 & 17.3 & 0.80\% & 3.33 \\  & Average & 571 & 7.8 & 0.36\% & 688 & 13.2 & 0.61\% & 550 & 20.4 & 0.94\% & 2.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lateral Load and Displacement Capacity of Flexure Specimens.
## 3 Networks and Methods  
### Data Processing by Time Serialization  
This study was conducted on a small amount of data, commensurate with real-world applications. The experimental data included only the voltage and current during battery discharge. The required number of sensors could be reduced using a small number of parameters to estimate the battery SOC, thereby significantly reducing the volume, cost, and failure rate of the battery.  
To eliminate the influence of different data ranges on the experimental results, we normalized the data before training the model to standardize the numerical range, thereby improving the training effect. Normalization can improve the convergence speed and accuracy of the neural network model, thus improving the accuracy of battery SOC estimation. The formula is:  
\[\mathrm{x^{\prime}=\frac{x-x\_min}{x\_max-x\_min}} \tag{1}\]  
where x is the original data; x_min and x_max are the minimum and max values, respectively; and x\({}^{\prime}\) is the normalized data.  
The \"many-to-one\" structure SOC prediction model used required that the data be processed into time series. Each time series included L= t - 1 historical data and current data. This approach allows each input sample of the model to contain multiple historical discharge data points, thereby improving the accuracy of model prediction.  
As shown in Figure 6, Xt-i denotes the discharge data at a given moment. When the sequence length is set to three, slicing operations are performed to obtain the input samples Xt = [Ut - 2, Ut - 1, Ut - 2, It - 1, It] such that each input sample Xt contains the  
Figure 4: Voltage and SOC trend plots for the DST dataset.  
Figure 5: Current and SOC trend plots for the DST dataset.  
current moment t and the discharge data of the battery in the previous two moments, t \(-1\) and t \(-2\). During the model-training process, historical data were used to predict the current SOC of the battery.
# Three-Dimensional Numerical Modeling of Artificially Freezing Ground in Metro Station Construction
## 1 Introduction
Through comparison with analytical calculations, monitoring optimization design ideas and data analysis methods were obtained. [PERSON] et al. [17] investigated the effect of freeze-thaw cycles on the performance of the combination supported by mineralogical investigation. [PERSON] et al. [18] and [PERSON] et al. [19] studied the development of the temperature field of a large-diameter cup-type frozen wall and the influence of different soil layers on its temperature field through numerical simulation. [PERSON] et al. [20] found that the spacing between freeze pipes can remarkably affect the freezing time and shape of the frozen body. [PERSON] et al. [21] carried out three-dimensional ground freezing simulations under high seepage flow ve-locity conditions, and the progression of the freezing front was found to be asymmetrical, which extended more in the flow direction. [PERSON] et al. [12] suggested that seepage would affect the position and the shape of the formed frozen wall and two different optimization methods were presented to get a better arrangement of the freeze pipes. Based on the microstructure of the rock and soil, some scholars [22; 23; 24; 25; 26] identified the propagation mechanism of the spatial variability of thermal and physical properties of geomaterials and provided new approaches for the quantitative characterization of the thermal physics of geothermal materials. It can be observed that researchers have conducted in-depth investigations into the frozen soil curtain stress field and the seepage field of the connecting passage, but only a few have undertaken the analysis of the field monitoring data in the study of the temperature field. However, the collapse of the cross-river tunnel of Shanghai Metro Line 4 originated from the connecting passage, which was constructed using the freezing method. The direct economic loss caused by the accident was as high as 150 million RMB. When using the freezing method to strengthen the soil around the connecting passage, it is still appropriate to conduct adequate theoretical research. The present investigation was based primarily upon the engineering background of using the freezing method to reinforce the No. 2 complex connecting passage of Binhu Road Station/Jinhu Square Station of Nanning Metro Line 3 and used finite element software (ADINA) to establish a three-dimensional numerical model of the connecting passage and its surrounding soil layers. Accordingly, the development and change behavior of the temperature field was studied and the evolution process of the frozen soil curtain was simulated dynamically, including the development characteristics of the frozen soil curtain, the relationship governing the intersections, the influence of average temperature development, and the consequent effective thickness variation. The obtained results provide a reference for similar engineering designs.
## 3 Optimum Parameters Selection
### Threshold Selection and Sequence Length  
Figure 4 shows the performance of the proposed RFI mitigation wavelet technique using the Haar wavelet, as a function of the threshold methods described in Section II and the sequence length, for the four different types of interferent signals (sine, Doppler, chirp, and pseudo-random noise), decomposition level = 12, and \(INR=100\).  
As it can be appreciated, at least 20 samples per period are required to reject RFI better than 20 dB, and as the sequence length increases, the estimated noise power error decreases (interference rejection increases) by the same factor, saturating around \(3\cdot 10^{-4}\) (\(INR>55\) dB) for the PRN signal at sequence lengths longer than \(2^{13}\). In general, the fixed threshold estimation provides the worst performances, except for the SURE thresholding with the chirp signal for lengths between 64 and 1024. In general,  
Figure 4: RFI mitigation performance using the Haar transform as a function of the threshold method and sequence length (number of samples per signal period) for four different types of interferent signals, and decomposition level = 12. Threshold method: squares = fixed threshold with single level noise estimation, circles = soft SURE, diamonds = soft heuristics SURE, and triangles = minimax. RFI signal: solid line = sine, dashed line = Doppler, dotted = chirp, and dash-dot = pseudo-random noise.  
for all signal types, the best performance is achieved using the heuristic SURE thresholding for any sequence length. Only for the PRN type of signal and below 256 samples, the minimax thresholding methods outperforms the heuristic SURE method.