esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation Results
### Implementation  
Our studies with optical-OFDM schemes and various cameras have been repeated multiple times (different distances and exposure times) to demonstrate the frame variation effect. For the asynchronous procedure, the SN length was determined appropriately. Figure 5 depicts the architecture of a missing component and the data merging process. Table 1 also includes some experimental results and settings for OCC demonstration. The OFDM system was deployed with a PointGrey rolling-shutter camera with a frame rate variation of between 50 and 60 frames per second. The experimental setup is shown in Figure 8, and the OFDM waveform of the receiving camera is shown in Figure 9.  
Table 1 displays the results of experiments carried out numerous times with varying optical clock rates. The data has been inserted into the asynchronous packet with the desired SN. The data merging and loss detection techniques are shown in Figure 5. In addition, the experimental results contain a variety of optical clock rates for various symbol lengths (128 bits and 256 bits) to assess the system's performance. The table demonstrates that if the symbol length is 128 bits, the data rate of 2.56 kbps is achieved, and the data rate of 5.12 kbps is achieved when the symbol length is 256 bits. As previously stated, the data  
Figure 6: Simulation of BER ACO-OFDM versus pixel SNR.  
Figure 7: Simulation of BER DCO-OFDM versus pixel SNR.  
Figure 10 displays the results of RS-OFDM schemes employing the Van De Beek algorithms and a deep learning neural network with varied distances at a 100 us exposure time (2-18 m). With a deep learning neural network, we can achieve BER of \(10^{-5}\) at 2 m, while with the conventional scheme, we just achieve BER of \(10^{-4}\). When the same distance and environment were used, the deep learning neural network decoder considerably enhanced the system's performance in the mobility environment over the conventional approach. For LED detection, a deep learning neural network was also used. It can also help with data decoding, which helps enhance performance for long communication distances and mobile contexts. In our implementation, we used a velocity of 2 m/s (walking velocity) to ensure that the proposed technique would suit indoor applications, considering the mobility effect. We implemented this scheme at a communication distance of 1 m, a visual representation of which is given in the Supplementary Materials section of this paper.
# Performance Assessment of Waste Cooking Oil-Modified Asphal Mixtures
## 4 Results and Discussions
### Performance Assessment of Waste Cooking Oil on Balanced Asphal Mixtures
#### 4.2.1 Cracking Performance Evaluation of WCO-Modified Asphalt Mixture  
Figure 4 presents the average results of CT\({}_{\text{Index}}\) for all tested specimens with the cracking threshold (i.e., a minimum of 26.4). The error bars indicate a plus/minus one standard deviation. It can be observed that the control mixture (i.e., 0% WCO) had an average CT\({}_{\text{Index}}\) of 29.7, which satisfies the performance threshold. The data showed that CT\({}_{\text{Index}}\) has an excellent direct linear relationship (i.e., CT\({}_{\text{Index}}\) = 315.99 [WCO%] + 31.03) with the variation WCO dosage (i.e., R\({}^{2}\) of 0.87). Such relationships indicated that CT\({}_{\text{Index}}\) increased by 3.15 for each 1% increment in WCO dosage. The WCO dosages of 0%, 2%, 3%, 4%, 5%, and 7% had CT\({}_{\text{Index}}\) of 29.7, 42.5, 38,0, 42.8, 44.5, and 54.8, respectively. Such findings indicate that the mixture resistance to cracking improved with the increment in WCO dosages. ANOVA and Tukey's analysis indicated a statistically significant difference between the control and modified mixtures with high WCO dosages (e.g., 5% and 7%). Furthermore, the data showed a low variability with an average COV of less than 15%. This finding agrees with reported results in the literature [24; 86; 87].  
Figure 3: Designed balanced asphalt mixtures.
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
## 3 Influence of Different Young's Modulus on Stress Sensitivity  
### Matrix-Type Carbonate Reservoirs  
For matrix-type carbonate reservoirs with a high Young's modulus, the rock has relatively low rigidity and is more susceptible to both elastic and plastic deformation. When subjected to increased stress, plastic deformation of the rock matrix may occur, leading to pore closure and deformation, thereby affecting permeability. Therefore, matrix-type carbonate reservoirs with a high Young's modulus exhibit a higher degree of stress sensitivity.  
For matrix-type carbonate reservoirs with a low Young's modulus, the rock has relatively higher rigidity and is less susceptible to plastic deformation. When subjected to increased stress, the rock matrix may undergo limited deformation, but that of pore closure and deformation are relatively small and insignificant, leading to minor changes in permeability. Therefore, intragranular matrix-dominated carbonate reservoirs with a low Young's modulus exhibit relatively low stress sensitivity. If the Young's modulus E of these rocks is changed to 10\(\sim\)30 GPa, the stress sensitivity curve would exhibit characteristics as shown in Figure 4.  
In general, the stress sensitivity of matrix-type carbonate reservoirs is relatively low, when compared to other types of rocks. As the Young's modulus increases, the rate of permeability decrease gradually intensifies. The maximum loss of permeability is less than 4%. Furthermore, the stress sensitivity of intragranular matrix-dominated carbonate reservoirs uniformly decreases with increasing Young's modulus. This indicates that the higher the Young's modulus of the rock, the greater the rock's stiffness, and the smaller the effect of external stress on it. As a consequence, the rock's stress sensitivity weakens.
## 4 Advancements in Industry 4.0 and Pandemic Management
### Comparative Analysis of AI, IoT, and Cloud Computing in Industry 4.0
#### 4.7.3 Cloud Computing  
Cloud computing offers scalable resources for storage and computation, supporting the vast data requirements of Industry 4.0. It allows for the flexible deployment of applications and analytics tools, which are pivotal for predictive modeling. However, cloud technologies face challenges such as latency in data access, data privacy concerns, and reliance on continuous internet connectivity.
## 2 Materials and Methods
### Bacteriocin-Like Inhibitory Substance Preparing and Inhibitory Activity Testing  
Firstly, bacteriocin activity was checked using the qualitative method as previously described by [PERSON] et al. [14]. The principal indicator strain _Enterococcus avium_ EA5 (the most sensitive faecal strain isolated from piglets from our collection) was used in the test [14]. A partially purified bacteriocin substance (precipitate) was prepared according to the following protocol: LPa 12/1 strain (0.1% inoculum) was grown overnight in 60 mL of MRS broth (Merck, Germany) at 37 \({}^{\circ}\)C. Then, it was centrifuged at 10,000\(\times\)\(g\) for 30 min. The cell-free supernatant (pH 4.5) was treated with Chelaton-EDTA III (Merck) and heated at 80 \({}^{\circ}\)C for 10 min to eliminate the other organic substance effects. Then, the supernatant was filtrated using a 0.22 \(\upmu\)m filter (Millipore corporation, Bedford, MA, USA). The filtrate was precipitated with ammonium sulphate (70% saturation) overnight at 4 \({}^{\circ}\)C. After precipitation, it was centrifuged (10,000\(\times\)\(g\)) for 30 min. The precipitate was resuspended in a minimal volume of phosphate buffer (5 mL, pH 5.0). Its inhibitory activity was checked against the indicator _E. avium_ EA5 and expressed in arbitrary units per ml (AU/mL). In other words, this is the highest dilution of bacteriocin-like inhibitory substance (precipitate) that caused the growth inhibition of indicator strain. In addition, the following indicator bacteria were used: 10 vancomycin-resistant strains of _Enterococcusecium_ (VRE2, VRE3, VRE5, VRE 9, VRE 10, VRE 11, VRE 12, VRE 13, VRE15, VRE16, kindly provided by Dr. [PERSON] from Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Slovakia), 11 faecal strains of _E. faecalis_ from dogs (EE211-EE221), and 5 faecal strains of _Staphylococcus chromogenes_ from cows (kindly provided from Dr. [PERSON], University of Life Sciences in Lublin, Faculty of Veterinary Medicine, Poland). Moreover, _Listeria monocytogenes_ LM 7223 (Veterinary Institute, Olomouc, Czech Republic) was used as an indicator (as a frequent contaminant in milk). Altogether, 28 indicator bacteria were tested.  
The enzymes such as \(\alpha\)-chymotrypsin, trypsin, proteinase K and pepsin (Merck) were used to treat precipitate. The inhibitory activity of the precipitate was checked again against the EA5 strain.  
The precipitate was also tested for its thermostability. It was exposed for 1 h at 60 \({}^{\circ}\)C and at \(-\)20 \({}^{\circ}\)C storage for 2 weeks. The remaining activity was tested against the strains EA5 and LM 7223 and expressed as formerly mentioned.
## 5 Results and Discussion
### Satellite Spectral Analysis
#### 5.2.2 Spectral Analysis of Iron Oxide
Likewise, at the site of Naukratis (western Nile Delta), there is a comparable casemate building (termed the \"Great Mound\") located inside the southern half of the Great Temenos. This building, like Installation G at Hermopolis, comprises numerous cells or what are described as forty-one deep vertical \"chambers\" [61]. Most of the cells were filled solid with mudbricks, while others were filled with rubble from masons' shops. Considering that cellular or casemate constructions were common ancient Egyptian building styles, which were not limited to military use [61], it is therefore difficult to deduce the function of Installation G as having military, domestic, or administrative purposes solely based on the presences of the cells/chambers. For the purpose of validation, a map with the previously detected structures from earlier archaeological excavations of the site [32] was georeferenced and digitized in ArcGIS (Figure 11). A clear match was found between the digitized (known) lower part of the wall and those detected by remote sensing (IPVI and WV-II). From this map, it is apparent that the newly identified structures in the eastern section of Hermopolis were previously unmapped by earlier projects and are located outside the excavated areas of the site. Figure 10: Casemate structure at Tell el-Balamun detected using magnetic survey [59]. The remotely sensed structure (Installations G) at Hermopolis, shown in Figure 9, resembles the Tell el-Balamun structure.
## 4 Numerical Simulation Results and Discussion
### Numerical Simulation Results and Discussion
#### 4.2.1 Comparison of Blade Fillet  
Figure 16 shows the contact force, contact force range and standard deviation of three kinds of disc cutter with the same blade width (22 mm) and different blade fillets (0, 5 and 9 mm) at different penetration depths (5, 10, 15, 20 and 25 mm). From Figure 16a,b, it can be seen that with increasing penetration, the contact force of the three kinds of disc cutter gradually increase. It is also obvious that, with the same penetration depth, the larger the blade fillet, the smaller the contact force. As can be seen from Figure 16c,d, with increasing penetration, the standard deviation of the contact force changes little and is relatively stable, but the range of contact force fluctuates slightly and increases. We can see that with increasing blade fillet, the range and standard deviation of the contact force both decrease. It can be seen that the disc cutter with a 22 mm blade width and a 9 mm blade fillet performs better in various indicators.  
Figures 17 and 18 show the equivalent stress and stress contour of rock breaking using three kinds of disc cutter with different blade fillets and a 22 mm blade width. It can be seen that the influence range of the disc cutter during rock breaking decreases with increasing blade fillet. In addition, the shape of the influence range gradually transitions from an approximate circle to a trapezoid and finally a triangle.  
Figure 16: The contact force of BW 22 mm disc cutter with different blade fillets. (**a**) Normal force; (**b**) Rolling force; (**c**) Range and STD of normal force; (**d**) Range and STD of rolling force.  
Figure 17: Equivalent stress isosurface of rock breaking using disc cutter with 22 mm blade width. (**a**) Isosurface of equivalent stress (Blade filler 0 mm); (**b**) Isosurface of equivalent stress (Blade filler 5 mm); (**c**) Isosurface of equivalent stress (Blade filler 9 mm).  
Figure 18: Stress contour of rock breaking by disc cutter with 22 mm blade width. (**a**) Stress contour of rock breaking (Blade filler 0 mm); (**b**) Stress contour of rock breaking (Blade filler 5 mm); (**c**) Stress contour of rock breaking (Blade filler 9 mm).
## 3 Results
### Effects of Ultrasonic Vibration on the Fluidity of Cement Paste after CO\({}_{2}\) Absorption
#### 3.3.3 Porosity  
The total porosity of hardened cement pastes after different CO\({}_{2}\) AA under ultrasonic vibrating agitation is shown in Figure 8. Clearly, the porosities of the hardened cement paste under mechanical agitation are 17.5%, 17.1%, 16%, 15.1%, 14.3%, and 13.8% when the CO\({}_{2}\) AA are 0%, 0.44%, 0.88%, 1.32%, 1.76%, and 2.20%, respectively. Under ultrasonic vibrating agitation, the porosities of the hardened cement paste are 15.6%, 15.1%, 14.6%, 13.4%, 12.1%, and 11.5%, which are 10.9%, 11.7%, 8.8%, 1.3%, 15.4%, and 16.7% lower compared to those under mechanical agitation, respectively. Moreover, the porosities of hardened cement paste decrease by 3.2%, 3.3%, 8.2%, 9.7%, and 5% when the CO\({}_{2}\) AA increases at the rate of 0%, 0.44%, 0.88%, 1.32%, 1.76%, and 2.20% continuously under ultrasonic vibrating agitation.  
According to the above analyses, conclusions can be drawn that the porosity of the hardened carbonized cement paste whether mechanically agitated or ultrasonically agitated decreases when CO\({}_{2}\) AA increases. However, the porosity decrement is more obvious when ultrasonic agitation is applied. Reasons for the significant porosity decrement when ultrasonic agitation is applied lies in two aspects. On the one hand, cement particles can be minimized by ultrasonic waves, which is advantageous for the hydration degree of cement particles. On the other hand, the flocculation structures formed by cement particles can be broken by ultrasonic agitation, which is advantageous for cement particles to get accesses to water to improve the hydration degree. The improved hydration degree of cement particles indicates that more hydration products of C-S-H and CH are formed, which improves the density of the hardened cement paste, thus reducing porosity of the hardened cement paste.
# Using Multimodal Foundation Models for Detecting Fake Images on the Internet with Explanations
## 3 Results and Discussion  
The results in Table 1 show that the models had varying levels of success with both zero-shot and n-shot prompting.  
The Llava model had a relatively steady and moderate increase in all metrics as the model gained more context due to relative prompts. It had the highest accuracy, precision, and Matthews correlation coefficient with four-shot prompting. Each prompt seemed to train the model better and add clarity to the model. The few-shot learning strategy helped it steadily improve.  
The CLIP model for image classification performed the worst of all the models. This could be because CLIP is not a text generator, rather it is an image classifier. Image classification models are typically specialized to categorize given images into predefined classes. Although CLIP allows the customization of labels, it is still based largely on pretraining. Because we are not attempting to classify images into predetermined classes, this could explain why CLIP was not quite as successful.  
Both Moondream2 and Gemini 1.5 Flash performed similarly in terms of their overall results. The results from one-shot prompting were better than that of zero-shot prompting;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Prompting Technique** & **Accuracy** & **Precision** & **F1 Score** & **MCC** \\ \hline \multirow{4}{*}{Llava} & 0-Shot & 0.62 & 0.73 & 0.583 & 0.347 \\  & 1-Shot & 0.65 & 0.77 & 0.609 & 0.38 \\  & 2-Shot & 0.65 & 0.76 & 0.613 & 0.39 \\  & 3-Shot & 0.66 & 0.77 & 0.613 & 0.391 \\  & 4-Shot & 0.696 & 0.794 & 0.657 & 0.424 \\ \hline \multirow{4}{*}{CLIP} & 0-Shot & 0.48 & 0.446 & 0.407 & \(-\)0.07 \\  & 1-Shot & 0.452 & 0.460 & 0.567 & \(-\)0.122 \\  & 2 Shot & 0.488 & 0.502 & 0.609 & \(-\)0.020 \\  & 3-Shot & 0.488 & 0.505 & 0.558 & \(-\)0.057 \\  & 4-Shot & 0.500 & 0.451 & 0.505 & 0.024 \\ \hline \multirow{4}{*}{Moondream2} & 0-Shot & 0.632 & 0.784 & 0.579 & 0.375 \\  & 1-Shot & 0.650 & 0.594 & 0.732 & 0.435 \\  & 2-Shot & 0.583 & 0.529 & 0.660 & 0.248 \\  & 3-Shot & 0.583 & 0.578 & 0.705 & 0.188 \\  & 4-Shot & 0.667 & 0.639 & 0.760 & 0.300 \\ \hline \multirow{4}{*}{Gemini 1.5 Flash} & 0-Shot & 0.55 & 0.6804 & 0.5148 & 0.2075 \\  & 1-Shot & 0.6375 & 0.6990 & 0.5839 & 0.3181 \\ \cline{1-1}  & 2-Shot & 0.6 & 0.5741 & 0.5512 & 0.1095 \\ \cline{1-1}  & 3-Shot & 0.575 & 0.7749 & 0.5344 & 0.3253 \\ \cline{1-1}  & 4-Shot & 0.6875 & 0.6966 & 0.6325 & 0.4013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics for various models.  
however, the two- and three-shot prompting results were worse as the metrics dipped once again. Four-shot prompting seemed to be enough for both models to determine consistent patterns across the dataset and generate better results.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 2 Background Concepts
### Power Factor  
The power factor (PF) can be defined as a measure of electrical energy consumption efficiency. PF refers to the relationship between the active or useful power and the apparent or total power provided by the electrical network. Therefore, the PF value falls between 0 and 1. Ideally, this value should always tend to 1, since it would be taking advantage of all the power of the electrical network. However, the real input voltage and current waveforms are not purely sinusoidal and are out of phase. This is because of the large amount of electronic equipment connected to the electricity distribution network. Such loads, which are mainly non-linear, distort the input current of the system and therefore do not correspond to the traditional definition of PF. Figure 1 shows the waveform of the grid voltage (red), which is modified by the inherent resistance of the power supply cables delivering the AC voltage to a power supply unit equipped with a rectifier bridge and capacitor filter. This configuration results in a pulsating current (blue) derived from the grid, which aligns with the charging cycles of the filter capacitor. This current is defined as the repetitive peak current, which increases in magnitude and decreases in duration as the capacitance of the filter capacitor is increased.  
In order to formally define the PF, the RMS voltage and current values of the nth harmonic (\(V_{(n,\ RMS\ IN)}\)): \(I_{(n,\ RMS\ IN)}\) and their phase shifts between them (\(\varphi_{n}\)) shall be taken into account, as presented in (5), which is the fraction between the active power (\(P\)) and the apparent power (\(S\)). For sinusoidal voltage \(v(t)\) and non-sinusoidal current \(i(t)\), the PF can be expressed in a simplified form as presented in (6), where \(cos\varphi_{1}\) is the voltage and current displacement factor (\(DF\), Equation (7)), and \(Kp\) is the distortion factor defined in (8).  
\[PF=\frac{\frac{1}{\sqrt{1+THD^{2}}}}{2} \tag{5}\]
# A Piecewise Linear Regression Model Ensemble for Large-Scale Curve Fitting
## 2 Continuous Piecewise Linear Regression  
Continuous piecewise linear regression has been found to be helpful since it is a powerful and flexible modelling technique that allows for obtaining different functional forms. Its simplicity, together with its adaptability to any problem, have made it a very popular tool.  
One-dimensional regression can be easily understood thinking about curve fitting from scatter plot data (e.g., see Figure 1, top). Consider a dataset of \(N\) points (\(i=1,2,...,N\)) made by a one-dimensional input \(x\) and an output variable \(y\). In addition, we will consider that each data point can have a weight \(w_{i}\), that is one in the standard case. The output variable is generated through the relationship (see, e.g., chapter 2 of [4])  
\[y_{i}=f(x_{i})+\epsilon_{i}, \tag{1}\]  
where \(f(x)\) is the true underlying function and \(\epsilon_{i}\) is normally distributed random noise with zero mean and constant variance \(\sigma^{2}\). The objective is to find a simple enough model \(\hat{f}(x)\) of the true function of Equation (1) by minimising the error, measured by the overall (weighted) mean squared error (MSE).  
\[\text{MSE}=\frac{1}{\sum_{i=1}^{N}w_{i}}\sum_{i=1}^{N}w_{i}[y_{i}-\hat{f}(x_{ i})]^{2}. \tag{2}\]
## 6 Conclusions  
To improve the accuracy of the lateral offset control threshold during an incremental launching construction and to address the low computational efficiency of finite element models, this study developed a predictive model for the local maximum stress of the beam under the combined influence of unsynchronized jacking parameters, based on the SSA-SVR algorithm. The following conclusions were drawn:  
1. The SSA-SVR algorithm demonstrates a small Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), with relative errors within 3%, indicating reliable computational results. Additionally, the model exhibits a strong generalization ability and fast convergence.
2. Using the trained predictive model, the time required to calculate the local maximum stress of the beam corresponding to a set of unsynchronized jacking parameters is 0.21 s, which is significantly lower than the time required by the finite element model. Utilizing this predictive model can greatly enhance computational efficiency.
3. According to the change rule in the beam stress with the difference in the jacking height, it is suggested that the control threshold of the jacking height difference should be set to 20 mm. When the jacking difference is less than 20 mm, according to the calculation results of the prediction model, the corresponding lateral offset value is determined by the current actual jacking height difference and the stress limit required by the specification, which is the maximum allowable offset value of the jacking point. This method can realize the dynamic adjustment of the lateral offset control threshold according to the reading of the hydraulic jack and improve the control accuracy to 1 mm.  
Conceptualization, J.J. and R.Y.; Methodology, J.J. and R.Y.; Software, H.G. and Q.L.; Validation, R.Y. and H.G.; Writing\(-\)Original Draft Preparation, H.G.; Writing\(-\)Review and Editing, Q.L.; Resources, J.J. and R.Y.; Data Curation, H.G. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
Authors [PERSON] and [PERSON] was employed by the company Henan Transportation Investment Group Jiaozheng Expressway Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.