esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials
#### 2.1.3. Coconut Fibers  
Young and mature coconut fibers are natural fibers obtained as by-product of agricultural enterprises. In this study, the coconut fibers were prepared using steam explosion technology by a company in Thailand. Figure 2 illustrates the geometry of the fibers; however, any incomplete elements in the figure should be revised to ensure clarity and proper interpretation. Young coconut fibers (YCF) were derived from young coconuts and were thicker and stronger compared to mature coconut fibers (MCF), which were obtained from mature coconuts. The higher moisture content of MCF contributed to its lower flexibility and strength. Conversely, YCF contained more hemicellulose, enhancing its flexibility and strength. A notable color difference was observed between the two fiber types, with YCF being darker due to its thicker and more robust fibers. For testing split tensile strength and moisture content, the fibers were transferred to the Faculty of Engineering at Rajamangala University of Technology Thanyaburi. The fibers were dried in an oven at 110 \(\pm\) 5 \({}^{\circ}\)C until no moisture remained before being shredded using a fiber cutter machine and passed through a 1-inch sieve for use in fibrous cement board production. The physical characteristics of YCF and MCF are presented in Table 3. YCF exhibited a split tensile strength of 148.2 MPa, while MCF achieved 141.5 MPa. The moisture content of YCF and MCF was 11.11% and 11.61%, respectively. Additionally, YCF demonstrated an elongation of 21.39%  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Composition (\%)** & **OPC** \\ \hline Silicon dioxide (SiO2) & 20.2 \\ Aluminum oxide (Al2O3) & 4.3 \\ Ferric oxide (Fe2O3) & 3.5 \\ Calcium oxide (CaO) & 63.7 \\ Sulfur trioxide (SO3) & 0.26 \\ Magnesium oxide (MgO) & 1.3 \\ Sodium oxide (Na2O) & 0.32 \\ Potassium oxide (K2O) & 2.62 \\ Loss on ignition (LOI) & 2.77 \\ \hline
**Physical properties** & \\ \hline Specific gravity & 3.15 \\ Retained on a 325 sieve (\% by weight) & 20.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical and physical compositions of ordinary Portland cement (OPC) type 1.  
Figure 1: Fine natural aggregate (FNA) (**left**) and crushed rock dust (CRD) from limestone (**right**).  
and a diameter of 208.52 \(\upmu\)m, whereas MCF had an elongation of 27.72% and a diameter of 116.48 \(\upmu\)m.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 6 Results
Assuming \(T_{R}\) = 2000 km, ND returns lower BPs than RR and FLD for moderate to low numbers of \(R\) (compare the lines in Figure 11a,b. However, for \(T_{R}\) = 1600 km ND returns the highest value of BP when compared to the ones returned by both RR and FLD for moderate to high values of \(R\). Moreover, whereas FLD returns significantly lower BPs than RR for both \(T_{R}\) = 1600 km and \(T_{R}\) = 2000 km, RR returns slightly lower BP than FLD for \(T_{R}\) = 2400 km. Figure 10: Blocking probability as a function of the total number of regenerators in the CoreNet topology assuming transparent reaches of: (**a**) \(T_{R}\) = 1600 km; (**b**) \(T_{R}\) = 2400 km, each compared against the reach of \(T_{R}\) = 2000 km. Figure 9: Blocking probability as a function of the total number of regenerators in EuroNet topology using k-shortest path routing algorithm for: (**a**) \(k\) = 3; (**b**) \(k\) = 5, each compared against the \(k\) = 1 case. Finally, we also investigated the number and the arrangement of the essential nodes in the analyzed topologies considering the transmission reaches of \(T_{R}\) = 1600 km, \(T_{R}\) = 2000 km, and \(T_{R}\) = 2400 km, as shown in Table 1. In this table, we provide the number of essential nodes in each topology \(T_{R}\) scenario, as well as the node indexes of the essential nodes in the topologies. One can note that, as expected, the number of essential nodes reduces as the transmission reach increases. This occurs because the higher the transmission reach, the lower the number of required regeneration points along a given route.
# Technology Readiness Levels (TRLs) in the Era of Co-Creation
## 4 Results  
In our empirical case, that of an innovative development in explainable energy demand forecasting, at a certain point in time, we realized that the specific development was inherently limited in terms of its market perspective and could therefore not reach beyond a TRL 6/7. We understood that co-creative setups were a possible and promising way towards providing a market potential for this development, a direction we are currently working on.  
We also found that the classical formulation of the TRL model of innovation has important shortcomings in describing the details of our approach. The classical TRL has emerged based on notions of producer innovation and not on collaborative and co-creative setups that present challenges and opportunities not adequately traced on the ladder. However, such setups are more and more frequently encountered in the innovation arena and foster the need for an adaptation of the TRL approach.  
It is important at this point to draw the line between classical teamwork and co-creation in the way we experienced it and presented it above. Teamwork typically has a common agenda, known from the very beginning. Here, by co-creation, we imply something well beyond teamwork. Essentially, we imply a type of synergy that may be created by bringing together different developments and the opportunities created by this for some of the individual components to assume a critical role in the co-creative setup--one that they could never enjoy outside of it.  
We presented a conceptual framework to extend the TRL ladder to account for co-creation. Three more steps were found to be necessary: to define the co-creative value, to define the co-creators, and to make sure that the economics of the co-creation are clear and rational to all, as well as to secure the interoperability of their solution items. The challenges of each one of these were briefly discussed.  
Our case was one of late co-creation, meaning that the co-creative idea occurred later, while the innovative result was already at TRL 6/7. However, most of the considerations are also pertinent in early co-creation when the co-creative setup is in place from the very onset.  
As confirmed by extensive research of the literature, we also found that the communication costs are the main concern for the viability of a co-creative pathway. These have to be counterbalanced by the additional value resulting independently and for all co-creators, and not just in aggregate terms. This is a most critical condition for the viability of the scheme and for securing an effective move up the TRL ladder.
## 3 Experimental Observation of Rabi Spectroscopy and Dephasing
### Sideband-Resolved Spectroscopy and High-Resolution Rabi Spectroscopy
Additionally, \(\delta=\omega_{\mathrm{c}}-\omega_{\mathrm{p}}\) is the detuning (\(\omega_{\mathrm{c}}\) is the resonance frequency of the clock transition), while \(\Omega_{\mathrm{n}_{\mathrm{z}},\mathrm{n}_{\mathrm{r}}}=\Omega\mathrm{e}^{-( \eta_{\mathrm{z}}^{2}+\eta_{\mathrm{z}}^{2})/2}L_{\mathrm{n}_{\mathrm{z}}}( \eta_{\mathrm{z}}^{2})L_{\mathrm{n}_{\mathrm{r}}}(\eta_{\mathrm{r}}^{2})\) is the modified coupling strength of probe light to the clock transition in the external states \(\mathrm{n}_{\mathrm{z}}\), \(\mathrm{n}_{\mathrm{r}}\), where \(\eta_{\mathrm{z}}\) and \(\eta_{\mathrm{r}}\) are the longitudinal and transverse Lamb-Dicke parameters, respectively [13]. Herein, \(L_{\mathrm{n}}\) (\(\cdot\)) is the 0 th order generalized Laguerre polynomial and \(\Omega\) is the bare state coupling strength (without FE) extracted from the Rabi oscillation. Figure 2: Sideband-resolved spectroscopy. Red open squares and blue open triangles represent the cases without and with energy filtering process, respectively. The solid lines are the combinations of fits to the carrier, the red sideband (red sb.) and the blue sideband (blue sb.) according to reference [13]. The longitudinal temperatures of the red squares and blue triangles are \(T_{\mathrm{H}}\approx 3.8\) uK and \(T_{\mathrm{L}}\approx 2.3\) uK, respectively. When the resolved sideband approximation holds \(\Omega_{\text{n}_{x}\text{n}_{x}\text{n}}\left|\mathbf{J}_{\text{n}}(\mathbf{M}) \right|\ll\omega_{\text{s}}\) (the largest \(\Omega_{\text{n}_{x}\text{n}_{x}}\) is less than \(5\times 2\pi\) Hz, but the \(\omega_{\text{s}}\) is 100 \(\times\) 2\(\pi\) Hz in this experiment), the coupling strength between the clock laser and atoms of the nth Floquet sideband can be expressed by \(\Omega_{\text{J}}\)[2A], where \(\text{J}_{\text{n}}\)[\(\cdot\)] is the Bessel function of the first kind [10].
## 2 Overvoltage of a Battery
### Problem of Low Cell Balancing Current
#### 2.1.3 Comparison of Simulation Results for the Influence of Low Cell Balancing  
In this session, the simulation results were compared (see Table 5). It was observed that the maximum voltage of Bat #3 increased more with a lower cell balance current than when there was no cell balance. This phenomenon can be attributed to the increased range of SOC usage, as indicated by the experimental results. Additionally, it was confirmed that as the allowable capacity deviation increased, the maximum voltage of Bat #3 even more significantly increased. This was because the difference in SOC usage range increased due to the increase in the allowable capacity deviation, resulting in an increase in the DCIR difference and voltage redistribution, which in turn increased the voltage difference between each battery [51]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Serial Connected**} & \multicolumn{4}{c}{**With Cell Balance**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Bat \#1**} & \multicolumn{2}{c}{**Bat \#2**} & \multicolumn{2}{c}{**Bat \#3**} & \multicolumn{2}{c}{**Bat \#4**} & \multicolumn{2}{c}{**Bat \#1**} & \multicolumn{2}{c}{**Bat \#2**} & \multicolumn{2}{c}{**Bat \#3**} & \multicolumn{2}{c}{**Bat \#4**} \\ \hline Voltage & Min & 3.112 & 3.200 & 2.996 & 3.112 & 3.115 & 3.206 & 2.998 & 3.115 \\ (V) & Max & 4.114 & 4.099 & 4.245 & 4.114 & 4.114 & 4.009 & 4.243 & 4.114 \\ SOC & Min & 7.87 & 9.10 & 6.57 & 7.87 & 7.91 & 9.20 & 6.60 & 7.91 \\ (\%) & Max & 97.74 & 96.35 & 99.22 & 97.74 & 97.75 & 96.36 & 99.21 & 97.75 \\ SOH & Max & 62.04 & 63.90 & 60.18 & 62.04 & 62.04 & 63.90 & 60.18 & 62.04 \\ (Ah) & Max & & & & & & & & \\ \hline Time (Cycles) & \multicolumn{4}{c}{1 Cycle} & \multicolumn{4}{c}{1 Cycle} \\ \hline Voltage & Min & 3.536 & 3.620 & 2.998 & 3.536 & 3.527 & 3.614 & 2.984 & 3.527 \\ (V) & Max & 4.125 & 4.112 & 4.309 & 4.125 & 4.122 & 4.115 & 4.331 & 4.122 \\ SOC & Min & 8.48 & 10.99 & 5.59 & 8.48 & 17.74 & 25.39 & 4.29 & 17.74 \\ (\%) & Max & 97.04 & 95.18 & 99.11 & 97.04 & 95.22 & 92.42 & 99.39 & 95.22 \\ SOH & Max & 24.67 & 28.51 & 20.09 & 24.67 & 24.66 & 28.51 & 20.09 & 24.66 \\ \hline Time (Cycles) & \multicolumn{4}{c}{4297 Cycles} & \multicolumn{4}{c}{4296 Cycles} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Aging simulation results with allowable capacity deviation of \(\pm\)3%.
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
## 3 Results
### Comparison of Objective Evaluation Indicators  
In this experiment, SRCNN [3,4], VDSR [5], Lap-SRN [16,17], LESRCNN [20], and Meta-SR [21] are selected as reference methods for comparative experiments. BSD100 is selected as the test dataset, and the upscaling factor is 1.1-1.9. In Table 2, we compare the PSNR value between IDMF-SR and state-of-the-art SR methods. It can be seen that IDMF-SR is slightly better than the PSNR value of Meta-SR [21], but has a similar PSNR value to RCAN [19]. Compared with LESRCNN [20], IDMF-SR almost comprehensively outperforms LESRCNN. Under \(\times 2\), the performance is slightly different. It can be seen from the PSNR and SSIM that IDMF-SR has improved PSNR and SSIM performance indicators compared to Meta-SR [21] and RCAN [19] methods. As shown in Table 3, the PSNR index of IDMF-SR can reach 40.15 dB on the Manga109 test data set with factor of 2, which is 2.8 dB, 0.91 dB and 1.42 dB higher than Meta-SR [21], RCAN [19], and LESRCNN [20]. Figure 8: Feature mapping schematic diagram. Under \(\times 4\), on the Urban100, the PSNR value of IDMF-SR reaches 27.10 dB, which is 1.28 dB and 0.22 dB higher than Meta-SR [21] and RCAN [19]. When the scale factor is 8, on the SetIt4 dataset, the PSNR value of IDMF-SR reaches 25.50 dB, which is 1.18 dB and 0.07 dB higher than Meta-SR [21], RCAN [19], and LESRCNN [20], as shown in Table 3. It can be seen from the data that when the magnification factor is large and the image details are difficult to recover, the PSNR value of the IDMF-SR is slightly higher than the other algorithms. In summary, from the perspective of objective data, IDMF-SR can effectively restore image details. The objective evaluation index is higher than other algorithms, and the reconstruction effect is good. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \(\times\)**1.1** & \(\times\)**1.2** & \(\times\)**1.3** & \(\times\)**1.4** & \(\times\)**1.5** & \(\times\)**1.6** & \(\times\)**1.7** & \(\times\)**1.8** & \(\times\)**1.9** \\  & **PSNR** & **PSNR** & **PSNR** & **PSNR** & **PSNR** & **PSNR** & **PSNR** & **PSNR** & **PSNR** \\ \hline Bicubic & 36.56 & 35.01 & 33.84 & 32.93 & 32.14 & 31.49 & 30.90 & 30.38 & 29.97 \\ SRCNN [3,4] & 38.01 & 37.21 & 35.87 & 34.40 & 33.28 & 32.30 & 31.94 & 31.85 & 31.04 \\ VDSR [5] & 39.67 & 38.16 & 36.43 & 35.18 & 34.39 & 33.12 & 32.50 & 32.36 & 31.58 \\ Lap-SRN [16,17] & 40.35 & 39.12 & 37.85 & 35.99 & 34.97 & 34.01 & 33.82 & 32.97 & 31.95 \\ Meta-SR [21] & 42.82 & 40.40 & 38.28 & 36.95 & 35.86 & 34.90 & 34.13 & 33.45 & 32.86 \\ RCAN [19] & 42.83 & 40.39 & **38.30** & **36.97** & 35.86 & 34.91 & **34.14** & **33.46** & **32.89** \\ LESRCNN [20] & 42.91 & 40.35 & 38.29 & 36.93 & 35.85 & 34.88 & 34.10 & 33.45 & 32.88 \\ IDMF-SR & **42.83** & **40.40** & 38.29 & 36.95 & **35.87** & **34.92** & **34.14** & **33.46** & 32.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The PSNR value of IDMF-SR under non-integer upscale factors.
## 2 Receiver Architecture
#### Dll  
The main purposes of the DLL is to maintain a local code \(C_{loc,m}[n]\) of the receiver locked to the incoming PRN code \(C_{m}[n]\). To do this, DLL estimates the relative delay between two codes by using a correlation operation. However, the typical implementation of the DLL is not based on the estimation of a delay, rather, on the estimation of a code frequency \(f_{c}\). To maintain locked \(C_{loc,m}[n]\) and \(C_{m}[n]\), every integration time \(T_{int}\) (i.e., 1 ms), the output of the DLL's discriminator is used to correct \(f_{c}\) and this mechanism yields to have a variable number of samples every \(T_{int}\). The presence of filters in the loop introduces a delay in the receiver chain or better, a transient with a duration dependent on the filter bandwidth: narrow band means less noise but long transient and vice versa. To align the incoming PRN code with a locally generated code, a DLL must include integrators, a code loop discriminator, and code loop filters. In our scheme the signal Equation (1) is first demodulated by multiplying it by two local carrier waves at frequency \(f_{IF}\), one for each branch of the receiver (I and Q). The resulting signal is multiplied by three local codes (called early, prompt, late), generally shifted by \(-1/2\), \(0\) and \(+1/2\) chip time, and the outputs are integrated and dumped to implement the correlator.  
The model chosen for the discrimination function is a _non-coherent_ scheme, independent of the phase of the local carrier, and with the expression  
\[\frac{(I_{e}^{2}+Q_{e}^{2})-(I_{l}^{2}+Q_{l}^{2})}{(I_{e}^{2}+Q_{e}^{2})+(I_{l }^{2}+Q_{l}^{2})}\]  
called Normalised Early minus Late power, where \(I_{e}=AR_{x}(\tau-d/2)\cos(\phi_{e})\), \(Q_{e}=-AR_{x}(\tau-d/2)\sin(\phi_{e})\), \(I_{l}=AR_{x}(\tau+d/2)\cos(\phi_{e})\), \(Q_{l}=-AR_{x}(\tau+d/2)\sin(\phi_{e})\), and \(R_{x}(\cdot)\) is the correlation function between the local and incoming code, \(A\) is an amplitude factor, \(\tau\) is the code delay of the signal, \(\phi_{e}\) is the estimated carrier phase error and \(d\) is the correlator spacing between early and late.  
We are interested in investigating what happens on the DLL parameters, when a disturbance, e.g., an MP, affects the received signal.
## 4 Experimental Setup and Results
### Adaptive Boosting  
Figure 4 shows the analysis done using the AdaBoost approach. The AdaBoost approach is analyzed due to its inherent similarity to the Random Forest classification approach. For this analysis, the AdaBoost uses the J48 trees as the base classifier. Figure 4 shows a similar trend of the F-measure to that of the Random Forest approach. In Figure 4, for the AdaBoost, the nonreduced F-measure is 0.786, which is almost similar to that of the SVM. The F-measure for Subset Evaluation is 0.772, Correlation Evaluation is 0.772, the Gain Ration Evaluation is 0.735, the Info Gain Evaluation is 0.782, OneR Evaluation is 0.79, Principal Components is 0.758, Relief Evaluation is 0.772, and the Symmetrical Uncertain Evaluation F-measure is 0.761. From the visual perspectives, the trending of the F-measure of Figure 4 is almost similar to that of the Random Forest of Figure 3. For AdaBoost and the Random Forest, though there is a difference in actual F-measure, the trend is almost similar, which shows an interesting similarity of behavior even in large datasets, thus augmenting the overall results and analysis of the proposed work.  
Figure 4: AdaBoost performance based on the F-measure for the Actual 1024 attributes and the F-measure based on the attributes selected by the corresponding algorithm.
## 4 Numerical Simulation  
### Stress Characteristics  
As shown in Figure 4a, there is a positive approximate linear relation between impact speed and peak stress in all cases with different pre-stress levels. For specimens under the same impact speed with different pre-stress levels, though the peak stress varies to some extent, the slopes of their impact speed and peak stress curves are very similar to each other. With the impact speed increasing from 4 m/s to 14 m/s, the peak stress increases from 25.2 MPa to 53.2 MPa when \(k_{s}\) is 0 and from 35.6 MPa to 60.1 MPa when it is 0.8, respectively. Overall, the influence of the impact speed on the dynamic strength of coal specimens is very significant in spite of varying pre-stress levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle Basic Parameters** & **Parallel Bond Parameter** \\ \hline Particle contact modules \(E_{c}\) (GPa) & 1.8 & Elasticity modulus \(\overline{E_{c}}\)(GPa) & 1.8 \\ Stiffness ratio \(k_{n}/k_{s}\) & 1.0 & Stiffness ratio \(k_{n}/k_{s}\) & 1.0 \\ Particle friction coefficient \(\mu\) & 0.577 & Cohesion \(\overline{c}\)(MPa) & 12.3 \\ Particle density \(\rho\)(g/cm\({}^{3}\)) & 1.4 & Tensile Strength \(\overline{\sigma_{c}}\)(MPa) & 7.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mesoscopic parameters of the numerical specimens.  
Figure 4: Peak stress of coal specimens subjected to different pre-stress levels and impact speeds.  
Figure 3: Experimental results of coal sample subject to uniaxial compression.  
Compared to impact speed, pre-stress has less influence on the dynamic strength of the coal specimens. As shown in Figure 4b, under the lower impact speed of 4 m/s, the peak stress increases linearly from 25.1 MPa to 35.6 MPa with \(k_{\mathrm{s}}\) increasing from 0 to 0.8. With the increase in impact speed, the relation between the pre-stress coefficient and peak stress gradually becomes non-linear while the growth rate of the peak stress with the increase in the pre-stress coefficient also decreases. When the impact speed reaches 14 m/s, the peak stress increases from 53.2 MPa to 59.1 MPa by 11.0% when \(k_{\mathrm{s}}\) increase from 0.0 to 0.1 and remains stable when the pre-stress coefficient further increases to 0.8. In such a case, the influence of the pre-stress is not obvious anymore.
# PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)
## 1 Introduction  
Intelligent attacks utilizing documents with malicious codes have been increasing rapidly in recent years as file transfers expand. The majority of Internet users are aware of the risk posed by execution files that are attached to emails or web pages. However, because users are unaware of the documents, they serve as an effective means of spreading malware. Because Portable Document Format (PDFs) are more flexible than other document formats, they are one of the main attack vectors among the malware that has been identified. The majority of malicious PDF documents include JavaScript or binary scripts that exploit certain security flaws and carry out destructive deeds, as explained in [1]. On the internet, there are countless billions of PDF files. Not all of them are as benign as one might expect. In actuality, PDF files may include a variety of objects, including binary or JavaScript codes. These items might occasionally be harmful. Malware software could try to infect a computer by finding a reading weakness [2]. In 2017, Adobe Acrobat Reader was found to have sixty-eight vulnerabilities. There are about fifty of them that may be used to run arbitrary codes. Each reader has certain weaknesses, and a malicious PDF file may discover a method to exploit them [3].
## 6 Results and Discussion
### Overall Evaluation  
In Table 3, we report an overall comparison of the results obtained by the different platforms in the Experiments (A), (B), (C) and (D).  
In Experiment (A), all platforms, except Wit.ai, obtain good results, with a F-Score greater than 0.8. However, considering Experiment (B) Dialogflow and Watson have remarkably better results when the training set is very small. In terms of response time (Experiment (C)), the difference between the platforms selected is not particularly important. In fact, all platforms have a response time lower than 500 ms and a difference of 50 ms (such as the one between Watson and Wit) is not perceptible by the users. Considering Experiment (D), we find that Watson has a much greater robustness as concerns the spelling errors, while the other platforms have, on average, an error rate which is almost twice as high as the one obtained by Watson. Therefore, the extensive research conducted in the previous sections pushed us to choose IBM Watson as the NLU engine for this project since it is very robust, and it has outcomes comparable to Dialogflow.