esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stability Based Coordinated Control of Active Suspension and DYC
### Active Suspension Roll Controller
#### 3.1.1 Initial Values Optimized by Genetic Algorithm  
In order to further improve the roll stability, the body roll angle should be reduced as much as possible while ensuring that the dynamic displacement of the suspension and the tire are not too large. To achieve the maximum reduction in the energy consumption, the active force of the actuator is also considered. In addition, the overshoot and oscillation should be reduced as well. Therefore, the fitness function of the genetic algorithm is:  
\[F=\int_{0}^{T}[Q_{1}|e(t)|+Q_{2}\cdot\Delta Z+Q_{3}\cdot\Delta Z_{r}+Q_{4}\cdot u ]dt+Q_{5}\cdot\max(|e(t)|). \tag{21}\]  
where \(T\) is the simulation duration, \(Q_{1}\) to \(Q_{5}\) are the weight coefficient, and \(\Delta Z\), \(\Delta Z_{r}\), and \(u\) are the average of the four suspension dynamic strokes, four tire dynamic displacements, and four suspension active forces, respectively.  
The parameters of the genetic algorithm are shown in Table 2.  
Additionally, the genetic optimization results are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 30 \\ Number of iterations & 20 \\ Crossover probability & 0.65 \\ Mutation probability & 0.08 \\ Initial population interval & [1 1; 7000 7000] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of genetic algorithm.  
Figure 1: Flow chart of genetic fuzzy PI algorithm.
## 4 Discussion
### Advantages of CGANs over CNNs  
Even though this paper mainly focused on CGANs, we also investigated other deep learning architectures, such as convolutional networks (CNNs) for our image translation task. CNNs have been widely used for image-processing tasks [55], more specifically, [PERSON] et al. [23] used CNN for downscaling L8 imagery to S2-like spatial resolution, [PERSON] et al. [26] trained a super-resolution CNN model to predict the S2-like red edge bands at 10 m from L8. However, CGANs have an advantage over CNNs for image regression tasks, including image translation due to their adaptable loss function. To demonstrate the benefits of generative adversarial training, we compared CGANs with CNNs. We trained the CNN on the training dataset for the same task (translating S2-like spectral bands from L8). Even though CNNs could marginally outperform CGANs during training, they would likely underperform during testing. It was found that CNNs were not robust to outliers. When CNNs were tasked to predict from out-of-the-distribution \"real world\" training pairs that may be considered an outlier, for example, misregistered pairs of L8 and S2, the translations were less accurate than CGANs (Figure 9). This sensitivity to out-of-the-distribution training data reduces model stability. This suboptimal performance of CNNs is likely due to its predefined loss function, which primarily attempts to reduce the overall mean error between the input and target image pairs. CGANs, on the other hand, learn from the original distributions of both sets--L8 and S2, enough to differentiate between the two products. We encountered several suboptimal training pairs that had misregistration issues due to the pixel mismatch between the spatial resolutions of L8 and S2 (described in Section 2.2). CNN erroneously learned to produce blurry images for such samples, while CGAN-generated images are more likely to belong to real S2 imagery. Figure 9 demonstrates two cases of misregistered image pairs. We observe that the images predicted by CGANs appear sharper than the blurry images predicted by the CNNs. The results are consistent with the observations by [PERSON] et al. [30] with respect to the characteristics of these two network architectures. Since achieving spatial generalizability with any model is non-trivial, GANs would be a more reliable architecture for image-to-image translation tasks.
# Studies on Influence of Chromium Layer on Inner Surface of Steel Tube on Heat Transfer
## 5 Conclusions  
Calculations of the heat transfer in a tube with different layers of chromium, i.e., 0, 10, 20, 30 and 200 \(\upmu\)m, on its inner surface were carried out for temperature-dependent thermophysical parameters, i.e., thermal conductivity, specific heat and density (in the range from RT to 1000 \({}^{\circ}\)C), of 30 HN2 MFA steel and Cr. The results are summarized as follows:  
1. The calculations of the heat transfer indicate that the division of the tube into 30 zones allows for a reasonable time of several hours to calculate the temperature distribution over the entire length of the tube.
2. After the first and subsequent heat impulses, as the thickness of the chrome layer increases, the highest peak temperature in each zone drops, while the temperature inside the steel wall of the tube increases. Such a heat transfer mechanism is related to the high thermal conductivity of chromium. With each subsequent impulse, the lowest peak base temperature of the inner surface of the chrome-plated tube increases relative to the same temperature of the inner surface of the non-chrome-plated tube. Additionally, it can be assumed that the so-called minimum temperature of the peak base is actually the temperature of the tube to a depth of approximately 0.5 mm below its surface.
3. Already after the first heat impulse, the temperature of the wall at a depth of 0.5 mm below the inner surface is twice as high for a tube with a 200 um thick chrome coating compared to a tube without this coating. After the fourth heat impulse, the temperature difference stabilizes and amounts to approximately 60 \({}^{\circ}\)C and, after the seventh impulse, a little more, i.e., 65 \({}^{\circ}\)C.
4. The presented model of heat transfer calculation in a tube with an inner channel covered with a thin layer of protective material can be used for the analysis of heat engines, internal combustion engines, etc.  
Conceptualization, M.Z., P.K. and Z.S.; methodology, P.K., M.Z. and Z.S.; software, M.Z. and Z.S.; validation, P.K. and M.Z.; formal analysis, P.K., M.Z. and Z.S.; writing--original draft preparation, P.K., M.Z. and Z.S.; writing--review and editing, P.K., M.Z. and Z.S. All authors have read and agreed to the published version of the manuscript.  
The methods and results presented in this paper were obtained thanks to funding from the university research project UGB-821 of the Military University of Technology, in 2023 (Warsaw, PL) entitled \"Numerical simulations of heat transfer in the barrel of a 30 HN2 MFA steel, the inner surface of which was covered with a layer of chromium of various thicknesses. Complementary tests of thermophysical properties of Maraging 350 and WCL steels\".  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Modulus of Rupture  
The MOR results are shown in Figure 2. For softwood, the highest values were recorded for plywood with lupine flour filler at 10 pbw--113 N mm\({}^{-2}\) and for filler with a content of 15 pbw--111 N mm\({}^{-2}\). The lowest MOR value was recorded for the spirulina filler sample, constituting 15 pbw--72 N mm\({}^{-2}\). Analyzing hardwood plywood, the highest MOR values were recorded for plywood with coconut flour, where the filler content was 20 pbw--177 N mm\({}^{-2}\), and for plywood with lupine flour for variants with 10 and 20 pbw of alternative filler, where the values were 163 N mm\({}^{-2}\) and 164 N mm\({}^{-2}\), respectively. The only statistically significant differences in mean MOR for birch plywood were found between 15 and 20 pbw coconut plywood. For pine plywood, such differences were found for REF plywood when referring to all spirulina samples and 10 pbw coconut plywood, as well as between 20 pbw bamboo and 10 pbw coconut plywood.  
Some of the studies conducted so far concern using hep flour as an alternative to rye flour in plywood technology. The proportion of hep flour in these studies was 15, 20, 25, and 30 pbw. The highest MOR values were obtained for the variant in which the flour constituted 20 and 25 pbw [4]. The results obtained for coconut flour and lupine flour are consistent with the results mentioned above, and in this case, the MOR value also increased with the increase in filler content.  
Another group of researchers studied the use of meals derived from milling the bark of various species, including walnut, chestnut, fir, and spruce. The highest MOR value was recorded for the variant where fir meal was used (accounting for 10 pbw)--78.45 N mm\({}^{-2}\)[49]. These values are similar to those we obtained for spirulina in coniferous plywood. The low MOR results in these cases may be related to the chemical structure of the fillers used and their origin.  
A different study was devoted to plywood, where the alternative filler was dust from grinding birch bark post-extraction waste with suberic acid [37]. Analyzing hot-pressed plywood, the MOR increased as the filler content increased, as in our study.  
Figure 2: Modulus of rupture values for different filler types.
## 3 Survey of Techniques for Updating
### Traffic Reduction
#### 3.2.2 Incremental Network Programming (INP)  
This uses the _rsync_ algorithm [41] to provide incremental software updates [44]. It differs from [PERSON] work in that the algorithm is processor independent; in fact it does not require any knowledge of the program code structure. Energy is saved by sending software updates as a _differences script_. The script can either specify an unmodified block to be copied (possibly to a new address), or can contain a modified form of S-Records for the new program image. The sender asks the receiver for the checksums of its current blocks, and after the receiver responds, it sends only the changed blocks. The script commands are disseminated using the XNP protocol, and are stored by a node in external memory during the Download operation; the host then queries the node for missing commands; finally a Decode command is sent. The program images are stored in two sections of external memory (EEPROM), one for the old image, and one for the new image (a third is used for the script). The network programming module passes a parameter to the boot-loader (modified from XNP) to select the image to read into program memory at boot time.  
As for the Reijers scheme, the only features relevant to autonomous updates are the inherent advantages introduced by the size reduction with reference to autonomous updates.
## 3 Results  
### Results at 1.5 T  
The investigation of the extracted tangential electric field along the paths of CIEDs inside the model of Duke at 1.5 T are shown in Figure 4, where the W values were separated for pathways leading to the right side of the patient from those leading to the left placement of the IPG device. The distribution inside the rest of the models is provided in the Supplementary Materials (Figure S1). In particular, the distribution of the W values over all the imaging positions is presented, where the input power of the coil was adjusted to generate B\({}_{1}\)+ RMS = 2 uT at the iso-center (Figure 4a) or to assess the limiting value of the global SAR (Figure 4b). The dotted lines present the mean values of the W values. The most unfavorable scenario (with the highest mean W value) at 1.5 T, assuming a constant B\({}_{1}\)+ RMS at the coil's iso-center, occurred during thoracic imaging (LD\({}_{3}\)). Conversely, when fixed SAR was taken into account, the highest electric field was estimated during neck imaging. For both approaches, the electric field along the cardiac paths decreases with moving the human away from the center of the coil. Therefore, the selection of the appropriate imaging landmark is a dominant factor for reducing the deposition of the RF power at the tip of a CIED. Tables S1 and S2 in the Supplementary Materials summarize this obtained reduction in dB of the mean W value for each landmark relative to the worst case, where, at the low part of the lumbar and at the pelvis, the electric field reduced by more than 10 dB.  
Furthermore, Figure 5 displays the impact of the coil's dimensions on the extracted electric field at the position LD\({}_{3}\) for both considerations. The deviation in dB for each configuration relative to the worst case (highest mean W value) is provided in Tables S3 and S4 in the Supplementary Materials. The values of electric field scaled to constant B\({}_{1}\)\({}^{+}\)RMS at the iso-center were always higher for longer coils, whereas, when they were scaled to the limiting values of SAR, they were higher for shorter coils.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 2 Materials and Methods
### Model Updating
#### 2.2.2 Mode Shape Residual  
The mode pairing is performed by the method explained in Section 2.2.1 to make the comparison among corresponding modes. In addition, the measured mode shape \(\phi_{i,meas}\) and the analytic mode shape \(\phi_{s\text{s}}\big{(}=\widetilde{S}\phi_{i}\big{)}\) that have been paired may differ by a definite ratio and should thus be scaled by an appropriate method.  
Mode scaling can be done for the measurement mode shape as well as for the analysis mode shape. Here, the measurement mode shape is scaled with respect to the analysis mode shape. The scaled measurement mode shape \(\phi_{si,meas}\) can be obtained as follows using the modal scale factor (MSF) [13] of the analytic mode shape \(\phi_{si}\) for the measured mode shape \(\phi_{i,meas}\).  
\[\phi_{si,meas}=MSF(\phi_{si},\phi_{i,meas})\phi_{i,meas} \tag{38}\]  
where  
\[MSF(\phi_{si},\phi_{i,meas})=\frac{\phi_{si}^{T}\phi_{i,meas}^{*}}{\phi_{i,meas }^{T}\phi_{i,meas}^{*}} \tag{39}\]  
where superscript \({}^{*}\) denotes the complex conjugate.  
Once the scaled measurement mode shape is obtained, the residual and sensitivity matrices can be constructed as follows.  
\[r^{k}=\phi_{si,meas}-\phi_{s}\big{(}\theta^{k}\big{)} \tag{40}\]  
\[\frac{\partial\phi_{si}}{\partial\theta_{j}}=\frac{\partial\big{(}\widetilde{ S}\phi_{i}\big{)}}{\partial\theta_{j}}=\widetilde{S}\frac{\partial\phi_{i}}{ \partial\theta_{j}} \tag{41}\]  
where the derivative \(\frac{\partial\phi_{i}}{\partial\theta_{j}}\) with respect to the eigenvector can be obtained by previous studies [14; 15]. The method of [PERSON] and [PERSON] [14] necessitates the whole eigenvectors to obtain the derivative for the \(i\)th eigenvector, whereas the method of [PERSON] [15] provides a higher calculation efficiency since it uses only the \(i\)th eigenvector to obtain the derivative.
## 2 Brief Introductions to SSA and LS-SVM
### Least Squares SVM
\begin{tabular}{c c c} \hline
**Domain** & **Indexes** & **Formula** \\ \hline \multirow{7}{*}{Time} & Shape factor \(T_{sf}\) & \(T_{sf}=x_{rms}/(\sum_{i=1}^{n}|x_{i}|/n)\) \\  & Crest factor \(T_{sf}\) & \(T_{cf}=\max\{x_{i}\}/x_{rms}\) \\  & Kurtosis \(T_{k}\) & \(T_{\text{k}n}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})^{4}}{n^{3}}-3\) \\  & Skewness \(T_{sk}\) & \(T_{sk}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})^{3}}{n^{3}n^{3}}\) \\  & Kurtosis factor \(T_{sf}\) & \(T_{sf}=T_{k}\,/x_{rms}\) \\ \hline \multirow{7}{*}{Frequency} & Stabilization ratio \(F_{sf}\) & \(F_{sf}=(\sum_{i=1}^{\frac{n}{2}}f_{i}^{2}P_{i})/\sqrt{\sum_{i=1}^{n/2}(f_{i}^{ 4}P_{i})\sum_{i=1}^{n/2}P_{i}}\) \\  & Wave-height ratio \(F_{wr}\) & \(F_{wr}=\max\{P_{i}\}/\sqrt{\frac{n}{n}\sum_{i=1}^{n/2}P_{i}^{2}}\) \\  & Average Frequency \(F_{sf}\) & \(F_{sf}=\sqrt{\sum_{i=1}^{n/2}(f_{i}P_{i})}/\sqrt{\sum_{i=1}^{n/2}P_{i}}\) \\  & Frequency high-low ratio \(F_{fbr}\) & \(F_{fbr}=\left(\sum_{i=1}^{\frac{n}{2}}P_{i}\right)/\left(\sum_{i=1}^{\frac{n}{2 }}P_{i}\right)\) \\  & Modified equivalent bandwidth \(F_{mb}\) & \(F_{mb}=\sqrt{\sum_{i=1}^{n/2}\left(\left(f_{i}-\overline{f}\right)^{2}P_{i} \right)}/\sqrt{\sum_{i=1}^{n/2}P_{i}}\) \\ \hline \end{tabular}   
* \(x_{i}\) is a signal series for \(i=1,2,\ldots,n\), \(n\) is the number of data points, \(\overline{x}\) and \(S\) are the mean and the standard deviation of \(x_{i}\), \(x_{rms}=\sqrt{\sum_{i=1}^{n}x_{i}^{2}/n}\); \(f_{i}\) is the frequency signal with \(x_{i}\) by FFT, \(P_{i}\) is the power spectrum of \(f_{i}\), \(\overline{f}=\sqrt{\sum_{i=1}^{n/2}f_{i}/n}\).
## 4 Experiments
### Analysis of Experimental Results
#### 4.3.3 Performance Comparison Experiments  
In order to fully evaluate the improved YOLOv5s detection algorithm in this paper, six algorithms were selected for experimental comparison, including the unimproved YOLOv5s, the earlier YOLOv4, and the updated YOLOX. Meanwhile, Faster-RCNN with the highest two-stage accuracy, and a lightweight network, EfficientDet, were compared. The mean average precision (mAP) was selected as the evaluation index of different detection algorithms, which can scientifically and reasonably evaluate the target detection and positioning ability of different detection algorithms. The experimental comparison results are shown in Table 5.  
In this paper, the performance of the proposed algorithm and some other classical algorithms for rail surface defect detection were compared experimentally. The experimental results are shown in Table 5, where it can be seen that the improved YOLOv5s algorithm proposed in this paper achieved the highest detection accuracy. Compared with YOLOv4 [27], the unimproved YOLOv5s algorithm, YOLOX [39], Faster R-CNN [18], and EfficientDet, the improved YOLOv5s algorithm achieved the highest detection accuracy, with mAP increased by 6.53, 1.47, 1.06, 2.47, and 6.09 percentage points, respectively.  
The prediction results in Figure 11 show the detection effects of YOLOv4, the unimproved YOLOv5s, YOLOX, Faster R-CNN, EfficientDet, and the improved YOLOv5s of this paper. It can be seen from the figure that YOLOv5s and Faster R-CNN both have missed detections, while YOLOv4 has inaccurate positioning. The improved YOLOv5s algorithm proposed in this paper detects all the defects in the obtained images, and compared with the unimproved YOLOv5s algorithm, the accuracy of the predicted border is higher. Thus, a better detection effect is achieved.  
Figure 11: Comparison of the Detection Effect of Each Algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **p** & **R** & **[EMAIL_ADDRESS]** & **Times/ms** \\ \hline YOLOv4 & 92.05 & 78.74 & 90.37 & 38.4 \\ YOLOv5s & 96.55 & 93.34 & 95.43 & 16.3 \\ YOLOX & 96.95 & 91.62 & 95.84 & 22.7 \\ Faster-RCNN & 94.39 & 74.61 & 94.43 & 160.3 \\ EfficientDet & 96.49 & 71.20 & 90.81 & 54.1 \\ Ours & 97.13 & 94.53 & 96.90 & 29.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of comparison experiments.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 8 Viruses
### Iridescent Virus  
Named for the characteristic blue hue seen in host cells, iridescent virus (IV) is a non-occluded isoahedral virus that has been documented to infect a range of arthropod taxa, including insects of the orders Diptera, Coleoptera, and Lepidoptera [235, 236]. IV has a broad host range, and even the same type of IV is capable of infecting different species [237]. IV is also capable of infecting insects during all life stages. Various types of IV have been documented in scarab pests, including pruninosc scarab (_Servicesthis pruninose_) in Australia [238], the New Zealand grass grub (_C. zealandica_) [64], African black beetle (_H. arator_) in South Africa [65, 239], and May beetle (_Phyllophaga anxia_) in Canada [95] and Puerto Rico (_Phyllophaga vandine_) [240], as well as an introduced population of Japanese beetle in the Azores [92]. IV has not been documented in any scarab species in the U.S. The virus can be fast-acting under laboratory condition, with mortality occurring within a week of infection, but it appears to exhibit very low infectivity rates in the field [95, 241]. [PERSON] et al. [240] reported mortality rates of only 30% in artificially infected May beetle larvae but did observe effective transmission from infected to uninfected individuals. Though not a scarab species, [PERSON] et al. [197] reported greater than 60% mortality of adult bull weevils fed a _Chilo_-derived IV. They also reported persistence of infectivity across 5 days when the virus was mixed with a bait formulation and sprayed onto cotton leaves. Although IV is not available in any commercial formulation, largely due to its instability outside of a host, it may have future potential in biocontrol programs, particularly those utilizing pathogen vectors such as parasitoids or nematodes. Certain IV types have been shown to infect insect cell lines, including that of the scarab _Anomala costata_[242], which will be useful for future research in the development of these viruses for biocontrol. Epizootics of IV have been reported in several non-scarab hosts, including corn earworm (_Helicoverpa zea_), blackfly (_Simulium_), velvetbean caterpillar (_Anticarsia gemmatalis_), and the mole cricket (_Sapteriscus borelli_) [237, 243]. Despite being found in many scarab species, no incidences of epizootics in scarab populations have been reported.
# A Nighttime and Daytime Single-Image Dehazing Method
###### Abstract  
In this study, the requirements for image dehazing methods have been put forward, such as a wider range of scenarios in which the methods can be used, faster processing speeds and higher image quality. Recent dehazing methods can only unilaterally process daytime or nighttime hazy images. However, we propose an effective single-image technique, dubbed MF Dehazer, in order to solve the problems associated with nighttime and daytime dehazing. This technique was developed following an in-depth analysis of the properties of nighttime hazy images. We also propose a mixed-filter method in order to estimate ambient illumination. It is possible to obtain the color and light direction when estimating ambient illumination. Usually, after dehazing, nighttime images will cause light source diffusion problems. Thus, we propose a method to compensate for the high-light area transmission in order to improve the transmission of the light source areas. Then, through regularization, the images obtain better contrast. The experimental results show that MF Dehazer outperforms the recent dehazing methods. Additionally, it can obtain images with higher contrast and clarity while retaining the original color of the image.  
nighttime dehazing; daytime dehazing; mixed-filter; high-light area transmission compensation; regularization 2023