esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Relationship Analysis
#### 4.6.1 Model Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Hidden Layer**} & \multicolumn{8}{c}{**Input Layer**} \\ \cline{2-10}  & **RO** & **BT** & **WT** & **CT** & **SD** & **W/B** & **C/S** & **W/S** & **Intercept** \\ \hline
**Node.1** & 1.119438 & 0.917696 & 0.847112 & \(-\)1.89394 & \(-\)13.4908 & 0.931048 & 9.864061 & 49.80315 & \(-\)129.322 \\
**Node.2** & 0.213831 & -0.26982 & -0.41637 & -0.74098 & 3.62122 & 1.504040 & 21.65496 & \(-\)10.2356 & 26.70857 \\
**Node.3** & 0.312775 & 0.383161 & 0.162746 & \(-\)0.05831 & 0.135491 & 1.999971 & 18.3255 & 60.20646 & \(-\)7.13486 \\
**Node.4** & \(-\)0.00944 & 0.072808 & -1.54386 & -0.44443 & 7.39233 & -10.8965 & 0.897476 & \(-\)11.9191 & 7.70851 \\
**Node.5** & \(-\)0.17237 & -0.37229 & 0.566364 & -1.06437 & 7.197474 & 10.92726 & \(-\)9.69878 & 30.96016 & 55.7529 \\
**Node.6** & 1.247616 & 0.572948 & 0.72043 & \(-\)0.5876 & 2.999194 & \(-\)18.2851 & \(-\)13.8395 & \(-\)7.85648 & 36.09561 \\
**Node.7** & \(-\)1.24127 & -0.3231 & -1.0167 & -1.0962 & 0.883434 & 0.556747 & \(-\)25.2614 & \(-\)11.3999 & 13.79037 \\
**Node.8** & 0.889906 & \(-\)0.70768 & -0.5373 & 0.033132 & \(-\)12.02503 & \(-\)13.0231 & \(-\)3.73304 & 21.21806 & \(-\)0.8187 \\
**Output Node (C.S)** & \(-\)0.23215 & -0.50744 & 0.619237 & 0.127134 & 0.179312 & 0.199791 & 0.086341 & \(-\)0.00164 & 0.778489 \\ \hline \hline \end{tabular} Note: CS-Compressive Strength, RO-Ratio of Mortar, BT-Bond Type, WT-Water, CT-Cement, SD-Sand, W/B-Water/Binder, C/S- Cement/Sand ratio, W/S-Water/Sand ratio.  
\end{table} 
Table 9: Parameter estimates for artificial neural network (ANN) model.
# DIFFBAS: An Advanced Binaural Audio Synthesis Model Focusing on Binaural Differences Recovery
## 2 Methodology
### Definition of IPD  
The human's ability to localize sound sources predominantly relies on interaural time differences(ITD) between the signals received by the left and right ears. Different incident angles of sound waves will lead to different time differences, allowing humans to perceive relevant spatial information. Taking the horizontal plane as an example, as shown in Figure 2(a), if the role of the head is omitted, the ears are approximated as two points \(2a\) apart (\(a\) is approximately the radius of the head). For plane sound waves incident in direction \(\theta\) (can be regarded as generated by a point sound source at infinity), ITD can be calculated as  
\[ITD(\theta)=\frac{2a}{c}sin\theta \tag{5}\]  
where \(c\) is the speed of sound, and if ITD is greater than 0, it means it reaches the right ear first. If ITD is less than 0, it means it reaches the left ear first.  
If we further consider the shape of the head, as shown in Figure 2(b), the head is approximated as a sphere with a radius \(a\), and the ears are approximated as two opposite points on the spherical surface. The formula for the binaural time difference is approximately  
\[ITD(\theta)=\frac{a}{c}(sin\theta+\theta) \tag{6}\]  
From Equations (5) and (6), it can be seen that ITD is related to the azimuth angle and is a key factor for human spatial positioning.  
The moment a sudden or transient sound reaches the basilar membrane of the human ear, the hair cells in the ear become excited and sensitive, so that the human ear can keenly distinguish the difference in arrival time [21]. However, due to the human ear's adaptability to sound, a continuous audio sequence may cause subsequent sounds to arrive at the same ear and mask previous sounds, thus weakening the significance of temporal differences. In this case, the human ear mainly infers the interaural time difference by analyzing the  
Figure 3: Schematic diagram of horizontal ITD (interaural time difference) calculation.  
interaural phase difference (IPD) of the sounds in the left and right ears [22]. According to Equation (6), IPD can be defined as  
\[IPD(\theta,\omega)=2\pi\omega\frac{a}{c}(sin\theta+\theta) \tag{7}\]  
where \(\omega\) is the frequency of the sound wave. The human auditory system realizes the perception of sound direction by comprehensively processing the information of perceived sound frequency and interaural phase difference. Therefore, in the digital signal of binaural audio, we can use STFT to analyze the phase spectrum difference of short-term audio to quantify the spatial information contained in it.
## Appendix A
### Radar Data Curation  
Radar data post-processing was carried out in Python 3.10. The Python libraries Pandas, NumPy, datetime, Matplotlib and openpyxl were used. All radar data were extracted into a Pandas DataFrame. In multi-target scenarios, objects were placed successively within the DataFrame, by descending SNR. The DataFrame contained the following data columns: Time {_datetime_}, Distance {_float_}, Velocity {_float_}, SNR {_float_} and Object number {_int_}. Since radar timestamps did not correspond to local time, datetime shifts were calculated based on the previously corrected _date and time_ of each _Radar logging_ instance, and the _date and time_ with which the radar output files were named (cf. Table 10). Datetime shifts were added to the Time column, correcting all measurements. All radar data were merged into one DataFrame and saved as a Pickle file prior to curation.  
Filters 4 (target section) and 5 (downsampling) are illustrated in Figures 11 and 12.  
Figure 14 shows a 7-min period for truck 2. Chosen targets are blue, discarded ones purple, and the moving average turquoise. Averaging across 10 loggings, instead of fewer, reduced noise in turbulent situations. Inspection of radar data, e.g., when traversing _Roundabouts_, showed that approximately 10 loggings were needed after the video code ended, for distance to the preceding truck to stabilize.  
The presence of lone blue data points located away from the blue trend in Figure 14, shows that this algorithm may choose the wrong target. Erroneous selections are those data points which clearly suppress the moving average distance value. Figure 15 shows a 10-min excerpt of downsampled radar data (blue), alongside data after filter 1  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Filtering Step**} & \multicolumn{6}{c}{**Truck Number**} \\ \cline{2-9}  & \multicolumn{6}{c}{**2**} & \multicolumn{6}{c}{**3**} \\ \cline{2-9}  & **Min** & **Avg** & **Max** & **Std** & **Min** & **Avg** & **Max** & **Std** \\ \hline Raw & \(-280.9\) & \(-5.7\) & \(281.0\) & \(40.4\) & \(-280.7\) & \(-9.4\) & \(280.7\) & \(45.9\) \\
1 & \(-280.9\) & \(-7.7\) & \(281.0\) & \(43.7\) & \(-280.7\) & \(-11.3\) & \(280.7\) & \(48.9\) \\
2 & \(-29.9\) & \(-0.5\) & \(29.9\) & \(5.5\) & \(-29.9\) & \(-0.5\) & \(29.9\) & \(5.5\) \\
3 & \(-29.9\) & \(-0.3\) & \(29.9\) & \(4.8\) & \(-29.9\) & \(-0.3\) & \(29.7\) & \(4.6\) \\
4 & \(-29.9\) & \(-0.2\) & \(29.9\) & \(4.4\) & \(-29.9\) & \(-0.2\) & \(29.7\) & \(4.2\) \\
5 & \(-29.6\) & \(-0.2\) & \(28.6\) & \(4.3\) & \(-29.9\) & \(-0.2\) & \(27.2\) & \(4.0\) \\
6 & \(-29.6\) & \(-0.1\) & \(28.6\) & \(4.2\) & \(-29.7\) & \(-0.1\) & \(27.2\) & \(3.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relative velocity statistics (km/h) for trucks 2 and 3 after each filtering step.
# Simulation of a Low Concentrator Photovoltaic System Using COMSOL
## 3 Results and Discussion
### Optimum Inclination Angles of Mirrors
#### 3.1.2 Partial Coverage  
One method of reducing the cost of an LCPV system is to reduce its size through the reduction of the size of its attached mirrors. A major drawback of this reduction in the overall size of the system is that only partial coverage of the PV panel occurs. To investigate the performance of a partial coverage case, the city of Jeddah was chosen to conduct a partial coverage simulation, which can be later compared with the results of this city with double coverage, reported above.  
Figure 6 illustrates the irradiance falling on the PV panel vs. the inclination angles for the noon period. As can be seen, the highest amount of irradiance occurs in the solar hours 12:00 and 13:00, when the mirrors M1 and M2 were inclined at the angles \(\theta_{1}=\theta_{2}=65^{\circ}\). These inclinations are nearly the same inclinations that gave maximum B\({}_{\rm PV}\) in the noon period with double coverage, in Jeddah. However, it should be noted that the value of B\({}_{\rm PV}\) at these optimum angles for partial coverage was lower by 200 W/m\({}^{2}\) than for double coverage.  
Figure 5: The irradiance B\({}_{\rm PV}\) at different inclination angles in the case of double coverage for the solar time from sunrise to 9:00 for the selected cities.  
Figure 6: The irradiance B\({}_{\rm PV}\) at different inclination angles in the case of partial coverage for the solar time from 10:00 to 14:00 (\(\theta_{1}=\theta_{2}\)) for Jeddah.  
As for the morning period, Figure 7 illustrates the amount of \(\mathrm{B_{PV}}\) falling with partial coverage vs. mirror inclination angles during the morning hours. It can be seen from Figure 7, when compared with Figure 5, that the optimum angles for partial coverage differed slightly from those of double coverage.  
As was mentioned previously, due to the symmetrical shape of the CPV, the optimum angles for the afternoon period were the same as that for the morning period.
## 5 Real-Time Experimental Validation
### Case One: Experimental Results for Control Objectives Realization  
In this scenario, the performance of the proposed FDSC in achieving its control objective is validated. A resistive load, \(R_{L_{1}}\) = 10 \(\Omega\), is initially connected to the bus, with the droop control activated and the secondary control inactive. The secondary controls for all DERs were enabled at \(t=2\) s. Figure 25 illustrates that with the droop control alone in action, the bus voltage was 40 V, which is less than its nominal value. Our proposed FDSC, when enabled, could regulate and maintain the bus voltage at its nominal value, 48 V. The same effect was observed in Figure 26, showing the current distribution results. The proposed FDSC guaranteed a proper current distribution, ensuring a consensus value of approximately 7.2 A. These results validate the simulation analyses of the control performance in realizing the control objectives.
# Heat Transfer Enhancement of Falling Film Evaporation on a Horizontal Tube by Thermal Spray Coating
## 4 Conclusions  
Falling film evaporation of HFC-134a on a horizontal tube was experimentally evaluated. Two copper tubes with different surface conditions, a smooth surface and an arc-wire thermal spray coated surface using copper as the coated material, were used as the test tubes. Moreover, the obtained results were compared with those of pool boiling to consider the contribution of nucleate boiling heat transfer. The results are summarized as follows.  
1. A high heat transfer enhancement factor up to 5.2 could be obtained by the thermal spray coating.
2. The heat transfer coefficient increased with increasing liquid flow rate because of the spreading wetting area and then became saturated. The liquid flow rate for the saturation was lower for the coated tube than for the smooth tube due to the spreading easily through the porous structured surface.
3. The hysteresis effect of the heating procedure, as seen in the pool boiling, was observed only for the coated tube. The heat flux decreasing process produced higher heat transfer coefficients due to the retention of the activated nucleation sites.
4. In comparison with the results of pool boiling, for the smooth tube, heat transfer coefficients for falling film evaporation were slightly lower than those for pool boiling, especially in the high heat flux range, because of partial dry out. On the contrary, for the coated tube, the values for falling film evaporation were higher than those for pool boiling due to bubble agitation in the liquid film and increased evaporation from the liquid film surface.  
Conceptualization, T.U., H.A., and K.S.; methodology, K.S. and H.A.; investigation and formal analysis, T.U., H.A., and K.S.; writing--original draft, T.U.; supervision and writing--review and editing, H.A.; project administration, H.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors are indebted to TOCALO Co., Ltd. for processing the thermal spray coating. The authors declare no conflict of interest.
# Parameter Study for the Ice Adhesion Centrifuge Test
## 3 Results
### Durability of Test Surfaces  
The materials used in this study were selected because of their expected durability to deliver reproducible results over the complete test period. Additionally, due to COVID-19-related restrictions regarding the presence of staff, the duration of this study had to be prolonged (200 days). To guarantee the comparability of the test results over the complete period, test set-ups for static ice (centrifuge acceleration speed: 200 rpm/s) and impact ice (95 m/s; centrifuge acceleration speed: 200 rpm/s) were conducted repeatedly. The results of the ice adhesion strength measurements are summarized in Figure 5a,b (no quantitative results were available for the primer coating due to cohesive ice failure).  
For the Standox material, the data points over time were on comparable levels with the resulting means of 39 kPa for impact ice (95 m/s) and 185 kPa for static ice. No trends were identified that indicate material degradation or parameter variances within the test set-ups. The same applied for the PTFE-tape material, with means of 63 kPa for impact ice and 131 kPa for static ice. For the PUR C25 material, the test results remained at a stable  
Figure 4: Adhesion strength evolution in the Fraunhofer IFAM centrifuge for: (**a**) 3 g of ice at various acceleration speeds; (**b**) 200 rpm/s acceleration speed and various ice masses.  
low level over the complete test period for impact ice (mean of 15 kPa) and static ice (mean of 41 kPa).  
No visible defects for the used materials were reported. A comparison of the test results showed no time-dependent changes for the three quantifiable materials Standox, PUR C25, and PTFE-tape. The results already showed differences between surfaces and ice types, which is further assessed in the following sections.
# Resilience Analysis Grid-Rasch Rating Scale Model for Measuring Organizational Resilience Potential
## 3 Results
### Responding Person Fit Statistics  
As shown in Table 3, all organizations demonstrated infit and outfit mean square (MNSQ) statistics within the recommended range of 0.6 to 1.4, indicating acceptable fit to the Rasch model expectations. The standardized fit statistics (ZSTD) for both infit and outfit were also within the acceptable threshold of \(\pm 2\), suggesting that the response patterns of the organizations were neither overly predictable nor erratic. However, organization 3 showed infit and outfit MNSQ values approaching the upper limit of the acceptable range (infit MNSQ = 1.32; outfit MNSQ = 1.32), with corresponding standardized infit and outfit ZSTD values of 0.82 and 0.81, respectively.  
While still within acceptable limits, these higher values may indicate some unpredictability in that organization's responses, suggesting that its response pattern was less consistent with model expectations compared to other organizations. Conversely, Organizations 7 and 8 had infit MNSQ values near the lower limit of the acceptable range  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & \begin{tabular}{c} **Infit** \\ **MNSQ** \\ \end{tabular} & \begin{tabular}{c} **Infit** \\ **ZSTD** \\ \end{tabular} & \begin{tabular}{c} **Outfit** \\ **MNSQ** \\ \end{tabular} & \
\begin{tabular}{c} **Outfit** \\ **ZSTD** \\ \end{tabular} &
\begin{tabular}{c} **Difficulty** \\ **SE** \\ \end{tabular} \\ \hline R1 & 2.94 & 2.79 & 2.89 & 2.74 & 0.10 & 0.52 \\ R2 & 1.01 & 0.15 & 1.05 & 0.22 & \(-1.20\) & 0.51 \\ R3 & 0.96 & 0.05 & 0.88 & \(-0.15\) & \(-0.14\) & 0.75 \\ R4 & 0.62 & \(-0.69\) & 0.64 & \(-0.64\) & \(-3.55\) &(Organization 7 rift MNSQ = 0.64; Organization 8 rift MNSQ = 0.63); their standardized rift ZSTD values were \(-\)0.83 and \(-\)0.85, respectively. Although these values are within acceptable limits, the lower MNSQ statistics suggest that their response patterns were slightly more predictable than expected, potentially indicating limited variability in their responses.  
Organization ability estimates (\(\theta\)) were examined to determine each organization's comparative position on the latent trait continuum. Organizations with positive ability estimates, particularly organization 1 (\(\theta\) = 2.55) and organization 2 (\(\theta\) = 1.06) (see Figure 1a,b), showed higher levels of resilience potential as measured by the responding scale; in particular, organization 1, with the highest ability estimate, appeared to show the strongest potential for responding ability. In opposition, organizations with negative ability estimates demonstrated lower levels of the latent trait.  
Figure 1: Organizational resilience potential estimates for eight synthetic organizations: (**a**) organization 1; (**b**) organization 2; (**c**) organization 3; (**d**) organization 4; (**e**) organization 5; (**f**) organization 6; (**g**) organization 7; (**h**) organization 8.
## 2 Materials and Methods
### Viability Assay  
Cell viability was assessed using the MTT assay (3-(4,5-dimethylthiazol-2 yl)-2,5-diphenyltetrazolzolium bromide). HepG2 cells (1.5 \(\times\) 10\({}^{4}\) cells/well) were seeded in 96-well plates and treated after 24 h with doxorubicin (0.3-100 uM), either alone or in combination with 100 ug/mL of _Muscari comosum_ methanol/water (70:30 \(v/v\)) extract (MET70) for 72 h. Following treatment, cells were incubated with medium containing 0.75 mg/mL MTT for 4 h at 37 \({}^{\circ}\)C. The medium was then removed and formazan crystals were solubilized with a 1:1 DMSO:isopropanol solution containing 1% Triton X-100. Absorbance was measured at 570 nm, with background subtraction at 630 nm, using a microplate reader (Multiskan(tm) GO Microplate Spectrophotometer, Thermo Scientific, Waltham, MA, USA). The half-maximal inhibitory concentration (IC\({}_{50}\)) of the extract was determined by non-linear curve fitting using GraphPad Prism 6 (GraphPad Software, San Diego, CA, USA). Cell viability was expressed as a percentage of control cells treated with vehicle only. All experiments were performed in triplicate and repeated three times.
## 3 Theoretical Background
### Proposed DCGOSM-Based Speech Enhancement Technique
#### 3.9.2 Process of Speech Enhancement Using the Obtained Sensing Matrix  
1. Speech enhancement is performed only on the noisy speech frames separated by VAD during the generation of sensing matrix. Additionally, the enhancement is performed on a frame-by-frame basis using the obtained \(\phi_{i,USE}^{j}\) matrix, the final noise variance (\(v_{n}^{final}\)), and the OMP algorithm. However, because the dominating columns for speech signal components (\(V_{s}^{i}\)) are already known, the OMP is only iterated through these columns instead of all the columns of \(\phi_{i}^{norm}\).
2. The enhanced speech frames are then converted into the time domain by taking IDCT and finally converted into a single enhanced speech stream through the de-framing operation.
## 1 Introduction
### Novelty and Contributions  
In previous publications on the categorization of STBCs, only non-relaying emissions were investigated. Nevertheless, relaying communications is increasingly valued in practical settings. The most interesting new aspects and useful contributions of this study are described below.  
* We create an STBC categorization approach for use in AF relaying networks.
* The structure of the gathered signals is mathematically employed to produce a collection of second-order correlation functions that serve as the foundation for the categorization technique.
* Keeping this finding characteristic in mind, we design two novel evaluation tests based on the idea of a fast Fourier transform (FFT) process.
* The described solution's strengths include its independence from channel status information, noise power, modulation schemes, and the beginning of STBC blocks.
* The given strategy is adaptable in that it can recognize many STBC waveforms so long as the correlation functions produce pulses for certain waveforms and not others.  
The following provides an overview of the remaining tasks. Section 2 explains the system architecture and problem description. Section 3 discusses the correlation functions of different STBCs waveforms over AF relaying schemes. Section 4 develops the proposedSTBC categorization approach. Section 5 describes the simulation outcomes. This research is concluded in Section 6.
# Distributed Software Build Assurance for Software Supply Chain Integrity
## 7 Related Work
### Hash and Merkle Tree Applications for Securing Integrity  
Our work builds on the Merkle tree and hash function to construct our scheme for software integrity. Previous research has utilized the Merkle tree and hash function for integrity in other applications and purposes than ours, including in over-the-air vehicular software updates [36; 37], vehicular firmware updates [38; 39], transactions, blocks, and the consensus mechanism in blockchain [40; 41], cryptocurrency and cellular networking software assurance [9; 10]. Version control systems (VCSs) used for project collaborations include Git [42], Subversion [43], and Mercurial [44]. Our work uses the Merkle tree to achieve different goals than these previous works, i.e., we use it to assure software integrity.