esa-sceva/satcom-chunk-collection huggingface dataset:
# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 5 Evaluation
### Setup  
To evaluate the technique proposed in this paper, we collected nighttime images as well as corresponding GPS data. For data collection, we implemented a mobile application and deployed on the roof of the car we used for driving. With the setup, we captured images while driving at night in three driving environments as show in Table 3, and logged the GPS data at the location where images were taken. To obtain hint information for our method, we used Google Street View images corresponding to the captured nighttime images with the GPS data.  
For each driving environment in Table 3, images converted using CycleGAN and Daydriex were prepared. As a result, the experiment was conducted with a total of 9 sets of images (3 sets of original images at night and 6 sets of images converted to daytime). Figure 12 is an example of the captured nighttime images (Figure 12a,d,g), the converted images using CycleGAN (Figure 12b,e,h) and the converted images using our method (Figure 12c,f,i). For image translation, we used the same HW setup and CycleGAN model mentioned in Section 3.  
To examine FID of translated results, we collected daytime videos recorded while driving along the same roads mentioned above; total length of videos is about 90 min. From these videos, we obtained daytime images which do not overlap each other and grouped them into the three cases corresponding to those in Table 3. We use these groups of images to obtain FID scores of daytime images translated by Daydriex and CycleGAN, respectively.  
Also, we conducted the user perception evaluation with 30 participants (ages between 21 and 33, mean: 24.4). We showed them videos made by using translated images and asked them to answer a 7-point Likert scale question as shown in Table 4. Each participant watched total 6 videos, two for each case including one by CycleGAN and the other by our method, and answered the question for each video.  
To evaluate the performance of Daydriex, we measure how much time is required for the main operations of the processing pipeline shown in Figure 4. For the purpose, we randomly select 10 nighttime images of case 1 in Table 3 and measure the time taken to translate them into daytime images. We report an average of the measured computation times.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**I Think This Video Shows Natural Daytime Scenes**} \\ \hline
1 & Strongly Disagree \\
2 & Disagree \\
3 & Slightly Disagree \\
4 & Neutral \\
5 & Slightly Agree \\
6 & Agree \\
7 & Strongly Agree \\ \hline \hline \end{tabular}
\end{table}
Table 4: 7-point Likert scale question used for evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline Case 1: rural (slow) & Slow driving in a rural area (avg. 20 km/h) \\ Case 2: rural (fast) & Fast driving in a rural area (avg. 60 km/h) \\ Case 3: urban & Driving in an urban area \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three cases for nighttime image collection.
## 3 Results and Discussions
### Residual Plots for BW and BH  
Residual plots depict the validation of the satisfactory results of the ANOVA. The ANOVA analysis is considered to be valid and suitable for the selected model, provided it satisfies some assumptions [37]. For this purpose, validation of the residual plots is very important. Figure 6 shows the residual pot for BW. It contains four plots. The normality plot represents linear development. It suggests the suitability of the model. The second plot of versus fits has shown that fits were entirely randomized around the source. The histogram plot depicts a bell-shaped curve that indicates the supportive data for good ANOVA. The absence of any particular trend for versus order plot verifies the ANOVA statistics. According to this, all four plots validated the ANOVA statistics for a better prediction of future results. Similar observations were obtained for BH as per Figure 7. This showed that all four plots successfully validated the ANOVA statistics for a better prediction of future results of BW and BH.  
Figure 4: Main effect plot for BW.  
Figure 5: Main effect plot for BH.
## 3 Results and Discussion
### Mechanical Properties of Green Composites
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Film** & **E (MPa)** & \(\sigma_{y}\) (MPa)** & \(\epsilon_{\mathbf{b}}\) **(\%)** \\ \hline PHBV & 2613.5 \(\pm\) 92.7 \({}^{\text{a}}\) & 27.4 \(\pm\) 2.1 \({}^{\text{a}}\) & 1.37 \(\pm\) 0.21 \({}^{\text{a}}\) \\ PHBV\_25\%MFC & 2684.9 \(\pm\) 77.1 \({}^{\text{b}}\) & 22.2 \(\pm\) 1.6 \({}^{\text{b}}\) & 1.09 \(\pm\) 0.13 \({}^{\text{b}}\) \\ PHBV\_5\%MFC & 2746.5 \(\pm\) 69.7 \({}^{\text{c}}\) & 21.6 \(\pm\) 0.9 \({}^{\text{c}}\) & 0.92 \(\pm\) 0.20 \({}^{\text{c}}\) \\ PHBV\_10\%MFC & 2873.1 \(\pm\) 73.5 \({}^{\text{d}}\) & 20.1 \(\pm\) 0.7 \({}^{\text{d}}\) & 0.78 \(\pm\) 0.14 \({}^{\text{d}}\) \\ PHBV\_ESAO & 2670.2 \(\pm\) 21.7 \({}^{\text{b}}\) & 27.3 \(\pm\) 1.2 \({}^{\text{a}}\) & 1.27 \(\pm\) 0.11 \({}^{\text{a}}\) \\ PHBV\_TGCIC+DCP & 2710.9 \(\pm\) 76.4 \({}^{\text{b}}\) & 27.6 \(\pm\) 1.7 \({}^{\text{a}}\) & 1.29 \(\pm\) 0.15 \({}^{\text{a}}\) \\ PHBV\_5\%MFC\_ESAO & 2863.1 \(\pm\) 55.6 \({}^{\text{d}}\) & 27.8 \(\pm\) 1.1 \({}^{\text{a}}\) & 0.91 \(\pm\) 0.09 \({}^{\text{c}}\) \\ PHBV\_5\%MFC\_TGIC+DCP & 3070.7 \(\pm\) 41.7 \({}^{\text{e}}\) & 29.3 \(\pm\) 2.3 \({}^{\text{e}}\) & 1.43 \(\pm\) 0.08 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} E = elastic modulus; \(\sigma_{y}\) = tensile stress at yield; \(\epsilon_{\mathbf{b}}\) = deformation at break. Mean values and standard deviation.  
\({}^{\text{a}}\) = Different superscripts in the same column indicate significant differences between formulations (\(p<0.05\)).  
\end{table} 
Table 5: Mechanical properties of the films of poly(3-hydroxybutyrate-\(co\)-3-hydroxyvalerate) (PHBV) with microfbilitated cellulose (MFC) and compatibilized with epoxy-based styrene-acrylic oligomer (ESAO) and with triglyceride l isocyanurate (TGIC) and dicumyl peroxide (DCP).
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 5 Symmetric Key Length  
The key length of a symmetric key is variable depending on the number of features present in the two images, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Test Name**} & \multicolumn{2}{c}{**Assess: 387,840**} & \multicolumn{2}{c}{**Assess: 387,840**} & \multicolumn{2}{c}{**Assess: 435,671**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Number of Bits: 30 Bits**} & \multicolumn{2}{c}{**Number of Bits: 41 Bits**} & \multicolumn{2}{c}{**Number of Bits: 43 Bits**} \\ \cline{2-5}  & \(p\)**-Value** & **Pass Rate** & \(p\)**-Value** & **Pass Rate** & \(p\)**-Value** & **Pass Rate** \\ \hline Frequency & 0.468595 & 29/30 & 0.559523 & 40/41 & 0.611108 & \(43/43\) \\ \hline Block Frequency & 0.671779 & 30/30 & 0.460664 & 41/41 & 0.460664 & \(43/43\) \\ \hline Cumulative Sums & 0.804337 & 30/30 & 0.811993 & 41/41 & 0.927083 & \(42/43\) \\ \hline Runs & 0.671779 & 29/30 & 0.663130 & 40/41 & 0.764655 & \(41/43\) \\ \hline Longest Run & 0.671779 & 29/30 & 0.258961 & 40/41 & 0.151616 & \(42/43\) \\ \hline Rank & 0.602458 & 29/30 & 0.414525 & 39/41 & 0.559523 & \(43/43\) \\ \hline Discrete Fourier Transform & 0.976060 & 30/30 & 0.663130 & 41/41 & 0.258961 & \(43/43\) \\ \hline Non- Overlapping Templates & 0.999896 & 30/30 & 0.986869 & 41/41 & 0.953553 & \(43/43\) \\ \hline Overlapping Templates & 0.602458 & 28/30 & 0.811993 & 39/41 & 0.811993 & \(41/43\) \\ \hline Universal & 0.602458 & 30/30 & 0.227773 & 41/41 & 0.811993 & \(43/43\) \\ \hline Approximate Entropy & 0.534146 & 30/30 & 0.460664 & 41/41 & 0.509162 & \(43/43\) \\ \hline Random Excursions & 0.739918 & 20/20 & 0.534146 & 24/24 & 0.875539 & \(23/23\) \\ \hline Random Excursions Variant & 0.991468 & 19/20 & 0.964295 & 24/24 & 0.941144 & \(23/23\) \\ \hline Serial & 0.253551 & 30/30 & 0.764655 & 41/41 & 0.559523 & \(43/43\) \\ \hline Linear Complexity & 0.213309 & 30/30 & 0.258961 & 41/41 & 0.371101 & \(43/43\) \\ \hline \end{tabular}
\end{table}
Table 2: P-values and success rates of Algorithm 1 for 30, 41, and 43 bits in NIST SP 800-22.
## 2 Materials and Methods
### Convergence  
Since Algorithm 1 is iterative, an end point must be chosen. Typically, one would let the algorithm run until the result has converged, where the standard definition of convergence requires any single player to find a remainder \(\mathbf{v}_{(t)}\) in iteration \(t\) that is sufficiently close to a remainder observed before,  
\[\|\mathbf{v}_{(t)}-\mathbf{v}_{(s)}\|\leq\mathcal{B}_{0}\quad,\text{for}\;1\leq s<t. \tag{5}\]  
This method demands the weights to converge. However, the optimal weights may depend heavily on a single data point. It is precisely this dependence that DP tries to cap. Furthermore, when adding noise in each round, the weights will absorb some of this noise, which could lead to a series of increasing remainders, so that convergence may never occur. For these reasons (5) is not an ideal convergence definition.  
At each iteration, the loss \(\mathcal{L}(\mathbf{\beta})\) is minimized. At iteration \(t\), a remainder \(\mathbf{v}_{(t)}=\mathbf{v}_{(t-1)}-\mathbf{X}\mathbf{\beta}_{(t)}\) with minimum length is passed on to the next player. However, after a certain number of iterations, the benefit of an additional round will become very small. One may define that convergence is reached when the length of the remainder  
\[\|\mathbf{v}_{(t)}\|_{2}^{2}\geq\|\mathbf{v}_{(t-1)}\|_{2}^{2}-\mathcal{B}_{C} \tag{6}\]  
hardly decreases or even increases. The bound \(\mathcal{B}_{C}\) for this would be defined at the initialization of the training. This definition is not very sophisticated, but it has the added advantage that it is directly related to the loss function, which is the objective of the training algorithm. Furthermore, it is applicable in virtually all situations. For example, it will also work in the case of increasing remainders, which may occur in a differentially private algorithm.  
Rather than using convergence as stopping criterion, the experiments described here use a fixed number of \(T=5\) iterations. This makes the analysis of the algorithm and its performance simpler. Five iterations are much less than typically used in BCD. The reason for this is that extra iterations are expensive in differential privacy.
## 3 Results and Discussion
### OPLS-DA Model Analysis  
The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, as another multivariate statistical method, was very helpful for the visualization of high-dimensional data [36]. To explore the relationship between volatile compounds and the groups of yak milk powder samples, we further established an OPLS-DA model to perform discriminant analysis on the volatile compounds in yak milk powders from different lactation periods, and the results are shown in Figure 5. As seen in Figure 5A, the yak milk powder sample points from different groups formed three distinct clusters. Specifically, the YCSP sample points were in the third quadrant, the YEMP sample points were in the fourth quadrant, and the YMMP sample points were concentrated at the intersection of the first and second quadrants. Additionally, the calculated model parameters in the OPLS-DA model showed that the R2 value was 0.996, and the prediction ability parameter (Q2) was 0.906. This indicated that the prediction ability of the OPLS-DA model was highly reliable [37].  
Figure 5: OPLS-DA model analysis results. (**A**): OPLS-DA score plot. (**B**): OPLS-DA model validation plot (results of 200 permutation tests).  
Although the OPLS-DA model was able to distinguish the differences in volatile compounds among the yak milk powder samples, we could not assess the robustness of the model, so a permutation test was still needed to verify whether the model was overfitted [37; 38]. The permutation test is a method that involves random rearrangement of the sample data followed by statistical inference, which can increase the sample size in the model, typically set at \(n\) = 200 [39]. The permutation test results are shown in Figure 5B, where the horizontal axis represents the retention degree of the samples and the vertical axis represents the values of R2/Q2. After 200 cross-validations, the regression line of Q2 crossed the horizontal axis, and all Q2 and R2 values in the permutation test were consistently lower than the original Q2 and R2 values. Additionally, the slopes of the regression lines for R2 and Q2 were both greater than 1, and the intercept of the Q2 regression line was negative (\(-\)0.4065). These results indicated that the OPLS-DA model had good fit and strong predictive ability [36; 39].
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 2 Materials and Methods
### Wind Power Prediction Model
#### 2.2.4 ForecastNet Methodology  
There is a certain difference in the output power of each wind turbine, and it is not only a large amount of work to build a model for each wind turbine, but also a tedious and complicated model training process. Therefore, the prediction is based on the average wind power. Specifically, the higher the active power (Patv) data of the wind turbines, the closer the output of the turbines to the theoretical output, which means that it is more appropriate to select the turbines with high wind speeds in the k-sums clustering. The data from the N wind turbines with the highest output and high spatio-temporal correlation are selected as the historical data for training the model. Predictions were made using these data as the output of other turbines, and the average of the predictions was used as the prediction for the other turbines in the wind plant.  
The new node data obtained from the data processed by the graph attention network mentioned in Section 2.2.2 contains not only the wind turbine feature data and historical wind power data of its own node but also the feature data of the neighboring nodes. These new node data are used as inputs to the ForecastNet model to build a prediction model and perform multi-step predictions. Each step predicts the data for the next 12 time points, and the prediction results obtained from the previous step are merged with the historical features before the next moment prediction. Together, they are used as inputs to the next model. This allows for more accurate learning of wind power trends over time while also fully considering the correlation of wind power data between inputs and outputs and between outputs at different moments. Following the multi-step prediction steps shown in Figure 9, a multi-step prediction of wind power is performed to obtain the prediction results at multiple future points in time. Adding a linear output model to the ForecastNet output layer allows for obtaining deterministic prediction results, and adding a Gaussian mixture density module [44] allows for obtaining probability density prediction results. The specific process framework diagram is shown in Figure 10.
## 1 Introduction  
### Research Background  
With the continuous acceleration of urbanization, urban traffic systems face unprecedented pressure and challenges. The high concentration of urban population, frequent economic activities, and accelerated personnel mobility have brought rapid economic and social development while also causing severe traffic congestion problems. Traffic congestion not only leads to reduced vehicle speeds in urban areas and increased travel time with greater uncertainty, but also results in increased fuel consumption, higher pollutant and greenhouse gas emissions, and increased road accident rates, among other serious socioeconomic impacts.  
Since the world's first traffic signal was installed in the Westminster area of London, England, in 1868, urban traffic control has developed over more than 150 years. As the primary means for cities to alleviate road congestion and rationally allocate road network resources, the importance of traffic signal control has become increasingly prominent. Fixed-time signal control adopts pre-determined periodic control schemes designed for specific scenarios and optimized offline. However, traditional signal control optimization heavily relies on accurate traffic flow data input, and obtaining such data faces numerous challenges.  
Traditional traffic data collection mainly relies on fixed detectors, including inductive loop detectors, radar detectors, and video detectors. Although these detection devices are technologically mature, they have numerous limitations. First, the installation and maintenance costs of the equipment are high, requiring regular inspection and replacement. Second, fixed detectors have limited spatial coverage and can only obtain cross-sectional data, making it difficult to reflect complete vehicle trajectories. Third, data quality is susceptible to environmental factors such as weather conditions and equipment aging. Fourth, these devices have difficulty obtaining complete vehicle path information and cannot provide fine-grained traffic flow characteristics.
## 4 Application of the Proposed Methodology
### Research Results  
Table 1 includes complete information regarding the identified technical, organizational and human causes, the cardinality of the occurrence of individual causes in the group of analyzed accidents, the percentage share of individual causes and the cumulative share of the subsequent causes, and also the classification of causes into the specific validity class.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Type of Cause** & \begin{tabular}{c} **Cardinality of** \\ **Occurrence \(a_{x}\)** \\ \end{tabular} & \begin{tabular}{c} **Share** \\ **\(u_{x}\) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Cumulative** \\ **Share \(\$x\) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Validity Class** \\ **of Cause** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{**Technical causes (\(\mathrm{\textbf{{T}}}\)); \(\mathrm{\textbf{T}}=\{t_{k}\mathrm{\textbf{:}}k=\mathrm{\textbf{1}},\ldots, \mathrm{\textbf{K}}\}\)**} \\ \hline \(t_{1}\) & Lack of or inadequate protective devices & 77 & 27.9\% & 27.9\% & A \\ \hline \(t_{2}\) & Lack of or inadequate measures of collective protection & 52 & 18.8\% & 46.7\% & A \\ \hline \(t_{3}\) & Improper spatial structure of scaffolding & 48 & 17.4\% & 64.1\% & A \\ \hline \(t_{4}\) & Improper stability of scaffolding & 46 & 16.7\% & 80.8\% & A \\ \hline \(t_{5}\) & Design defects of scaffolding that are a source of a hazard & 12 & 4.3\% & 85.1\% & B \\ \hline \(t_{6}\) & Use of substitute materials & 11 & 4.0\% & 89.1\% & B \\ \hline \(t_{7}\) & Lack of or incorrect signaling of hazards & 8 & 2.9\% & 92.0\% & B \\ \hline \(t_{8}\) & Inadequate strength of scaffolding & 6 & 2.2\% & 94.2\% & B \\ \hline \(t_{9}\) & Hidden material defects & 5 & 1.8\% & 96.0\% & B \\ \hline \(t_{10}\) & Failure to meet required technical parameters & 4 & 1.4\% & 97.4\% & C \\ \hline \(t_{11}\) & Inadequate maintenance of scaffolding & 3 & 1.1\% & 98.5\% & C \\ \(t_{12}\) & Excessive use of scaffolding & 2 & 0.7\% & 99.2\% & C \\ \hline \(t_{13}\) & Improper repairs and renovations of scaffolding & 1 & 0.4\% & 99.6\% & C \\ \hline \(t_{14}\) & Failure to meet required technical parameters & 1 & 0.4\% & 100\% & C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subsets of technical (\(T\)), organizational (\(O\)), and human (\(H\)) causes.
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
## 3 Experimental Results  
In the experiment, a prototype circuit (Example \(A_{3}\)) is selected to be fabricated on Rogers RT/5880 substrate by the printed circuit board (PCB) producing method. In order to implement the miniaturization, two short stubs, composed of \(Z_{2}\) and \(Z_{3}\) in series are bent. Figure 14 shows the layout and the photograph of the fabricated reconfigurable BSF. It can be seen that the size of the fabricated circuit is significantly reduced compared with the size of the previous work [19]. The measurement of the fabricated circuit is conducted by Keysight PNA NS227B, which is calibrated in the range of 0.010 to 3.000 GHz with a step frequency of 0.001 GHz. The corresponding circuit simulation, EM simulation, and measured results are summarized and compared in Figure 15 and Table 7. _BW@RL\({}_{S}\)_, _BW@SR\({}_{S}\)_, _BW@RL\({}_{D1}\)_, _BW@RL\({}_{D2}\)_, and _BW@SR\({}_{D}\)_ are the absolute BW corresponding to each ripple level (_RL\({}_{S}\)_, _SR\({}_{S}\)_
## 3 Results
### Histopathology Findings in Lungs of M. tb-Infected Mice with or without DEM Treatment  
Six mice from each group were sacrificed at 2 weeks, 4 weeks (Figure 6A) and 8 weeks (Figure 6B) post-infection, and portions of lungs, liver and spleen were collected for histopathology analysis. H&E-stained tissue sections were evaluated for immune cells, and bacterial loads were calculated from the fluorescent intensity of immunohistochemistry stained tissue sections. Histopathological study of lung tissue revealed large, well-formed granulomas with vast immune cell infiltration and inflammation in the _M. tb_-infected mice lung sections at 4 weeks post-infection without any treatment (Figure 6C,D). DEM-treated mice had ill-formed granulomas, reminiscent of scattered cellular aggregation in the majority of lung parenchyma (Figure 6E,F). The lungs showed vascular occlusion and  
Figure 5: Survival of _M. tb_ in the lung and spleen of mice that were sham-treated or treated with DEM. Survival of _M. tb_ was determined in the lung lysates at 2 weeks (**A**), 4 weeks (**B**) and 8 weeks (**C**) post-infection and in the spleen lysates at 2 weeks (**D**), 4 weeks (**E**) and 8 weeks (**F**) post-_M. tb_ infection. Statistical analysis was performed using GraphPad Prism software. Unpaired \(t\) tests were performed using Welsch correction. All values reported are representative of the mean values for each respective category and a p-value of <0.05 was considered significant. Any placement of an asterisk (*) denotes a direct comparison of the DEM-treated versus the untreated category. **\(p\)-value < 0.005. The sample size (_n_) includes six mice each in the untreated and DEM-treated groups.  
bronchial congestion. Lymphocytes were found scattered in the highly cellular areas. No visible necrosis of the lesion was noted (Figure 6E,F). The architecture of lung granulomas in _M. tb_-infected mice at 8 weeks post-infection resembled the architecture observed at 4 weeks. However, untreated mice had lymphocyte-rich cuffs surrounding the central core of the granulomas (Figure 6G,H). In contrast, the DEM-treated mice at 8 weeks post-infection had much bigger areas of immune cell infiltration and more congestion of bronchial, as well as vascular, structures (Figure 6I,J) compared to 4 weeks post-infection. GSH deficiency significantly increased the pulmonary and extrapulmonary bacterial load.
## 3 Results and Discussion  
### Sequence Comparison to Known Esterases  
The sequence analysis revealed that there were two open reading frames (ORFs), which could both encode the FAEs. These two ORFs consisted of 1500 bp and 1569 bp, thus encoding 500 and 523 amino acids (AAs), respectively. The amino acid alignment of reFAEs with other FAEs are presented in Figure 1. The deduced amino acid sequence of reFAEs was used to perform a BLAST search in the National Center for Biotechnology Information (NCBI) and SwissProt databases. It revealed a relative similarity between reFAEs and other FAEs, including alignment of the amino acid sequences among reFAE1, reFAE2, and other FAEs: AofaeB from _Aspergillus oryzae_ (GenBank: PDB: 3 WMT_B) [20] (28% and 32% identity, respectively), AnFaeB from _Aspergillus niger_ (GenBank: UniProtKB/Swiss-Prot: Q8 WZI8.1) [21] (29% and 30% identity, respectively), AN1772.2 from _Aspergillus nidulans_ FGSC A4 (GenBank: UniProtKB/Swiss-Prot: Q5B2G3.1) [22] (28% and 32% identity, respectively), and FofaeC from _Fusarium oxysporum_ (GenBank: SCN69328.1) [23] (29% and 31% identity, respectively).