esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
If we define an equivalent volumetric heat capacity \(\rho_{\text{eff}}\) as a weighted average of the AlAs and GaAs heat capacities, the equivalent thermal diffusivity is  
\[\alpha_{\text{eff}}=\frac{\text{k}_{\text{measured}}}{\left(\rho\text{c} \right)_{\text{eff}}}(\rho\text{c})_{\text{eff}}\equiv\ \left(\text{I}_{\text{GaAs}}/1\right)\ \rho_{\text{GaAs} \text{cGaAs}}+\left(\text{I}_{\text{AlAs}}/1\right)\ \rho_{\text{AlAs}} \text{c}_{\text{AlAs}} \tag{13}\]  
where 1 is the superlattice period thickness.  
Figure 10 presents a relation between measured and calculated values of thermal diffusivity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & \multicolumn{2}{c}{**Frequency Domain PTR**} & \multicolumn{2}{c}{**Frequency Domain TR**} \\ \hline  & Advantages & Disadvantage & Advantages & Disadvantage \\ \hline Measurement of cross-plane thermal transport properties & Possible & & Difficult but possible \\ \hline Measurement of transport properties & & Difficult but possible & Possible \\ \hline \begin{tabular}{c} Measurement of \\ in-plane thermal transport properties \\ \end{tabular} & & \begin{tabular}{c} Relatively easy \\ \end{tabular} & & \begin{tabular}{c} Relatively \\ difficult \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Temperature- \\ measurements at \\ low temperatures \\ \end{tabular} & Difficult & Possible \\ \hline
\begin{tabular}{c} Temperature \\ measurements at \\ high temperatures \\ \end{tabular} & Possible & Possible \\ \hline \hline \end{tabular}
\end{table}
Table 5: The summary of frequency-domain photothermal radiometry (PTR) and frequency-domain thermoreflectance.  
As can be seen in Figure 10, the measured and calculated thermal diffusivities are in good agreement. In superlattices, the in-plane thermal conductivity is two times bigger than cross-plane. Figure 11 demonstrated this relation in AlAs/GaAs superlattice sample. The thermal anisotropy of the superlattice layer confirms the expectation that the thermal conductivity is (about two times) higher in the plane than perpendicular to it which is consistent with phonon reflections at the GaAs - AlAs interfaces.  
As can be observed in the figure, the tested relationship is valid for temperatures below 50 \({}^{\circ}\)C. In this range, the in-plane thermal conductivity is twice that of the cross-plane. For 50 \({}^{\circ}\)C, a slight discrepancy between the tested relationship and the obtained values can be observed, but the results are within the limits of measurement uncertainty.  
Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.  
Figure 11: Relation between in-plane and cross-plane thermal conductivity measured for AlAs/GaAs superlattice sample as function of temperature.  
Figure 10: Relation between measured and calculated values of thermal diffusivity as function of temperature.
## 5 Case Studies for Construction Site Layout Planning and Managerial Implication
### Case Study 1
The convergence graphic of competing algorithms is shown in Figure 4. Therefore, the HSOS-LO proposed in this case study can be successfully used for resolving practical construction engineering problems for construction site layout. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{10}{c}{**Location**} \\ \cline{2-11}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline
1 & 0 & 15 & 25 & 33 & 40 & 42 & 47 & 55 & 35 & 30 & 20 \\
2 & 15 & 0 & 10 & 18 & 25 & 27 & 32 & 42 & 50 & 45 & 35 \\
3 & 25 & 10 & 0 & 8 & 15 & 17 & 22 & 32 & 52 & 55 & 45 \\
4 & 33 & 18 & 8 & 0 & 7 & 9 & 14 & 24 & 44 & 49 & 53 \\
5 & 40 & 25 & 15 & 7 & 0 & 2 & 7 & 17 & 37 & 42 & 52 \\
6 & 42 & 27 & 17 & 9 & 2 & 0 & 5 & 15 & 35 & 40 & 50 \\
7 & 47 & 32 & 22 & 14 & 7 & 5 & 0 & 10 & 30 & 35 & 40 \\
8 & 55 & 42 & 32 & 24 & 17 & 15 & 10 & 0 & 20 & 25 & 35 \\
9 & 35 & 50 & 52 & 44 & 37 & 35 & 30 & 20 & 0 & 5 & 15 \\
10 & 30 & 45 & 55 & 49 & 42 & 40 & 35 & 25 & 5 & 0 & 10 \\
11 & 20 & 35 & 45 & 53 & 52 & 50 & 40 & 35 & 15 & 10 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Travel distance between locations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Best**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Worst**} & \multicolumn{2}{*}{**Standard**} & \multirow{2}{*}{**Best–Average**} & \multirow{2}{*}{**Best–Worst**} & \multicolumn{1}{c}{**Average**} \\  & & & & & & & & & \\ \hline PSO & 12,546 & 12,581.24 & 12,984 & 79.02 & 0.281\% & 3.491\% & 0.062 \\ DE & 12,546 & 12,561.88 & 12,760 & 47.36 & 0.127\% & 1.706\% & 0.085 \\ SOS & 12,546 & 12,560.56 & 12,792 & 42.52 & 0.116\% & 1.961\% & 0.075 \\ HSOS-LO & 12,546 & 12,553.86 & 12,672 & 19.37 & 0.063\% & 1.004\% & 0.084 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Travel distance between locations.
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 4 HP-LSTM Detection Model
### Hawkes Process Formulation Module  
In order to simulate the temporal logic behavior of DDoS attackers, we make the following assumption regarding the number of IP addresses within a certain period of time (a window): the IP address is fixed. We employ separate one-dimensional Hawkes intensity functions to each IP address within each window. The occurrence of a packet with the source IP address \(IP_{i}\) is considered an event occurrence for the \(i\)th one-dimensional Hawkes process. During a DDoS attack orchestrated by attackers, the adversaries utilize a multitude of legitimate IP addresses to inundate the victim with a significant volume of fabricated packets, consequently resulting in notable discrepancies between the Hawkes parameters associated with the spoofed IP addresses and those under normal circumstances. The differences in these parameters are advantageous for our model to better distinguish between attacked and unattacked entities [23].  
In Equation (6), we introduce the most fundamental intensity function of a one-dimensional Hawkes process, while Equation (7) represents the intensity function used in our experimental process. Since the only difference among different IP addresses lies in the parameter values of the intensity function, this section focuses solely on presenting the one-dimensional Hawkes process for a single IP address.  
Figure 4: Attack scenario: DDoS attack on SOME/IP protocol.  
\[\lambda(t)=\theta_{0}+\sum_{x^{\tau}<t}\alpha e^{-\beta(t-x^{\tau})} \tag{7}\]  
We now provide a more in-depth explanation of the parameters in the intensity function as follows:  
* \(\theta_{0}\) represents the baseline intensity of event occurrences, indicating the initial trend of packet-forwarding events.
* \(\alpha\) denotes the amplitude parameter, which reflects the abrupt increase in the intensity function for each event occurrence. It signifies the positive influence of each event occurrence on subsequent events.
* \(\beta\) is the decay parameter controlling the decay rate of event occurrences, indicating that the positive influence of event occurrences diminishes over time.  
**Using the likelihood function for parameter estimation:** To estimate the parameters of a one-dimensional Hawkes process, according to [PERSON]. [PERSON] [37], the likelihood function for a one-dimensional Hawkes process is given by  
\[L(\lambda,t)=\prod_{t=t_{1}}^{l}\lambda(x^{\tau})e^{-\int_{0}^{t_{1}}\lambda(t )dt} \tag{8}\]  
The likelihood function is further transformed into the following:  
\[\ln L(\lambda,t)=\sum_{t=t_{1}  
After obtaining the likelihood function as shown in Equation (11), we compute the partial derivatives and Hessian matrix of our likelihood function [37]. Subsequently, we utilize the gradient descent method to estimate the parameters \(\alpha\), \(\beta\), and \(\theta_{0}\). Through this computation, each one-dimensional Hawkes process yields a set of estimated parameters.
## 3 Results
### Spatial Resolution (Pixel Size and Study Areas)  
To assess the impact of climate change or various EWEs, research has been conducted at different spatial extents and resolutions. Figure 7 shows the relationship between spatial extent and its spatial mapping resolution. Due to the wide range of pixel sizes and study area sizes, the axes are logarithmic. In around 13.5% of the studies, the size of the study area was not stated or could be found out retrospectively; these publications are not included in Figure 7.  
Figure 7: The relationship between the spatial extent and mapping resolution among the reviewed articles. The color code represents the observed extreme weather events or trend changes.  
The spatial extent varies greatly, from local studies with 0.15 ha [104] to multi-continent extent up to the entire northern hemisphere [88], while the pixel size ranged from 0.07 m [105] to 8000 m [106; 107; 108; 109; 110; 111; 112]. The average study area size is above 3.7 million km\({}^{2}\), while the median lies at approximately 35,000 km\({}^{2}\). This is due to a number of large-scale study areas. The distribution of scatter points shows a correlation between pixel size and the size of the study area (Figure 7). Studies with high-resolution sensors tend to examine small areas, while studies with coarse-resolution sensors tend to examine large areas. The Spearman correlation, useful to quantify nonlinear relationships between two variables [113], is 0.64. About 27 studies have a study area size of more than about 1,000,000 km\({}^{2}\), with only one study using a sensor with a resolution of less than 250 m. Eleven studies have a study area size of 10 km\({}^{2}\) or less, with a predominance of high-resolution sensors and a pixel size of up to 10 m. Parallel to the \(x\)-axis, the most frequently used sensors are clearly visible: Sentinel-2 with 10 m, Landsat with 30 m, MODIS between 231 and 1000 m, and AVHRR with 8000 m pixel size.  
While drought studies have been conducted at all study area sizes and spatial resolution levels, other extreme weather or climate change phenomena can be clustered more distinctly. For example, trend analyses can only be found at a certain resolution and study area size, which is due to the longer time period available for the coarser resolution sensors. The same applies to studies of recurring extreme events. In contrast, the effects of storms in temperate forests are only investigated in smaller studies with higher resolution sensors. Five of the eight storm event studies have a study area size of 40-140 km\({}^{2}\). The clustering effect is even more pronounced in terms of resolution, with seven of the eight studies using sensors with a pixel size of 10 m or less. The studies investigating the effects of late frost used only the MODIS sensor, and therefore did not use pixel resolutions smaller than 250 m.
## 3 Results and Discussion  
### Characteristics of Obtained Preparations  
Extruded preparations mainly consist of starch and other carbohydrates, since a significant component of this formulation was rice flour rich in starch, maltodextrin, and red potato pulp starch. As the proportion of red potato pulp increased, there was a reduction in the content of starch carbohydrates. At the same time, the content of fiberfractions and total dietary fiber, ash, fat, and protein increased (Table 2). The total content of fiber and the fractions of soluble and insoluble fiber, ash, fat, and protein increased with the increase in the proportion of potato pulp (preparations E30 PP and E40 PP) 100-fold, 60-fold, 45-fold, and 18-fold, respectively, and 11-fold compared to the E0 PP formulation (Table 2). Considering the total phenolic content (TPC) of extruded rice flour and maltodextrin preparations with or without red potato pulp, a significant amount of these compounds was found, especially in formulations containing 30 and 40% of pulp in comparison to the reference preparation (E0 PP). The content of TPC in the 30 and 40% pulp preparations was 12 and 15 times higher, respectively, compared to the non-pulp preparation (E0 PP) (Table 2). For the E0 PP preparation, the determination of total polyphenol content (TPC) using [PERSON] et al. 's [33] method was crucial as the Folin-Ciocalteu (F-C) reagent reacts not only with polyphenols but also with other compounds such as ascorbic acid, glycoalkaloids, sugars, and amino acids [40; 41]. Importantly, the latter two components form the Maillard reaction products, which greatly increase the \"apparent\" amount of TPC in the sample. Therefore, TPC was also determined by the method of [PERSON] et al. [34] as modified by [PERSON] et al. [35]. The results of this analysis show lower TPC values, but the trend in the range of all samples is convergent. It should be emphasized that the amount of polyphenols in these preparations increased significantly after the use of pulp containing red potato polyphenols (mainly chlorogenic acid, neo- and cryptochlorogenic acids, ferulic acid, gallic acid, p-coumaric acid, and pelargonidin and acylated peonidin derivatives) [23; 42; 43]. It is important to acknowledge that the spectrophotometric determination of total polyphenol content (TPC) using the Folin-Ciocalteu reagent ([PERSON] et al. [33] method) provides a
# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 3 Method and Model
### Data Collection  
The monitoring data used in this paper mainly comes from the monitoring system we set in the research place, and the monitoring system is mainly built by connecting countless sensors scattered in the site through communication transmission. The main equipment on the site, one is a real-time power generation data collector of the solar cell module, another is a pyranometer used to analyze the module maximum power which is mainly affected, and temperature of the array module, and the other is a hygro-thermograph and anemometer. By the above equipment, we can analyze the correlation of atmospheric environment factors and power generation.  
In order to strengthen environmental-climate factors that we lacked on-site, and we through collecting the history monitoring data at the Environmental Observation Station of the Environmental Protection Administration and the Central Weather Bureau, including the related feature data set of the wind direction, ozone, particulate matter, sulphur dioxide, and rainfall. To strengthen the original data that we lacked in the monitoring system, we expect to find the potential factors of the solar power generation prediction, and then improve the results of prediction effectively.  
Installation Situation of the Solar Power Plant  
The site for this study was selected as a solar power plant to be built in southern Taiwan (i.e., Tainan City). The total area of the land is about 7.1 hectares. The set-up capacity of the site using idle vacant land is 6388 kWp. The concentrated solar inverter is used to convert the electric energy waveform. Subsequently, the transformer is used to increase the voltage to 11.4 kV and then merged into the Taipower grid. It is roughly estimated that the average annual cumulative power generation can reach Approximately 9.02 million kWh. Converted into basic electricity consumption for 2580 households for one year.
## 4 Experiments and Results  
### Dataset  
We used the LUNA16 challenge dataset to evaluate the performance of the network. The LUNA16 dataset was derived from a larger public dataset, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [22]. The LUNA16 dataset is an LIDC-IDRI dataset that removes CT images of slice thicknesses greater than 3 mm, as well as those with space inconsistencies, and missing partial slices, resulting in 888 CT images. It contains 1186 real nodules, where each nodule is an irregular circle with a diameter of more than 3 mm, and most of them are located in the lung parenchyma central, with a few in the lung parenchyma periphery. The transverse plane size of each CT sample is \(512\times 512\).  
For false-positive reduction of pulmonary nodules, the LUNA16 challenge data provide two versions (V1 and V2) of the datasets. Each dataset contains the central coordinates of candidate nodules, the classification labels, and the corresponding patient identifier (ID). The dataset V1 was detected by three existing candidate detection algorithms [21; 24; 25] and contains 551,065 candidate nodules. The dataset was separated into nodule and non-nodule groups, where 1351 were labeled as nodules and 549,714 were labeled as non-nodules. Out of the 1186 real nodules previously recognized, 1120 of the 1351 labeled nodules are real nodules, and 231 are non-nodules. The dataset V2 contains 754,975 candidate nodules obtained by References [21; 24; 25; 26; 27], where 1557 were labeled as nodules, and 1166 are matched with the 1186 real nodules. Table 1 summarizes the candidate nodule statistics of the dataset in the LUNA16 challenge.
## 2 Materials and Methods  
### Dispersive Raman Instrument with 1064 nm Excitation Laser  
A schematic of the dispersive Raman instrument with a 1064 nm excitation laser is depicted in Figure 1A. The compact spectrometer (Wasatch Photonics, Logan, UT, USA) offered a spectral range of 250-1850 cm\({}^{-1}\) using a holographic transmissive grating. The spectral resolution was 10 cm\({}^{-1}\) at a slit size of 50 \(\upmu\)m. The spectrometer featured a 512-pixel InGaAs array detector (Hamamatsu G9214-512S, Hamamatsu, Japan, pixel size \(25\times 500\) nm), which was thermoelectrically cooled to \(-15\)\({}^{\circ}\)C. A 1064 nm laser with a linewidth < 0.1 nm corresponding to <0.9 cm\({}^{-1}\) (Innovative Photonics Solution, Plainsboro Township, NJ, USA) was coupled via a 105 \(\upmu\)m multimode fiber to a Raman probe with a focusing lens of numerical aperture 0.3, offering a working distance of 12 mm (Wasatch Photonics, Logan, UT, USA). The Raman probe focused the laser to a spot diameter of roughly 200 \(\upmu\)m. A collection fiber with a 600 \(\upmu\)m core diameter guided the Raman scattered signal to the spectrometer. Raman images were acquired using an inexpensive three-dimensional stage (SainSmart Genmitsu CNC 3018-PRO, Shanghai, China). A USB-coupled full HD digital microscope camera (Facamword, Chicago, IL, USA) was installed next to the fiber probe, and the offset between the microscope and Raman probe was calibrated to define the region of interest. In-house LabVIEW (National Instruments, Austin, TX, USA) routines were developed to control the components and spectra acquisition. Raman images were acquired from bladder tissue samples with an exposure time of 10 s, laser power of 450 mW, and 400 to 500 \(\upmu\)m step size.
## 2 Method for Estimating Dynamic Mechanical Behaviors of RC Columns  
### Predictive Models of Dynamic Modified Coefficient  
The results of available experiments on RC columns indicate that with the increase of the loading rate, the bearing capacity is enhanced while the ductility is likely to be reduced [6; 7]. Similar to the widely used empirical models of dynamic increase factor (DIF) [30; 31; 32], which were established at the material level, the dynamic modified coefficient is defined as the ratio of the mechanical behavior parameter under the dynamic loading rate (denoted by the subscript 'd') to the corresponding value under static loading rate (denoted by the subscript's'). In this paper, the predictive models of _DMC_ for the yielding bearing capacity \(F_{y}\), the ultimate bearing capacity \(F_{u}\) and the ductility coefficient \(\mu\) are expressed as  
\[DMC_{F_{y}}(input_{1},input_{2},\cdots,input_{n}) =F_{yd}/F_{ys} \tag{1}\] \[DMC_{F_{u}}(input_{1},input_{2},\cdots,input_{n}) =F_{ud}/F_{us}\] (2) \[DMC_{\mu}(input_{1},input_{2},\cdots,input_{n}) =\mu_{d}/\mu_{s} \tag{3}\]  
where \(input_{1}\), \(input_{2}\), \(\ldots\), \(input_{n}\) denote the input variables that have a considerable influence on the mechanical behavior and dynamic effect of RC columns. Based on the available experimental observations, 5 structural design parameters of RC column members, including the axial load ratio (\(n_{0}\)), shear span ratio (\(\lambda\)), strength of longitudinal reinforcement (\(f_{y}\)), compressive strength of concrete (\(f_{c}^{\prime}\)) and stirrup spacing (\(S\)) are selected as input variables. Moreover, a loading rate related parameter, i.e., the strain rate index (\(SRI\)) [33], is incorporated into the predictive models, which is defined as  
\[SRI=\lg(\dot{\epsilon}_{d}/\dot{\epsilon}_{0}) \tag{4}\]  
where \(\dot{\epsilon}_{d}\) and \(\dot{\epsilon}_{0}\) denote the dynamic and quasi-static strain rates, respectively, and the quasi-static strain rate is taken as \(1\times 10^{-5}/\)s [18].  
Due to the inadequacy of dynamic loading test data on RC columns, this paper develops the predictive models of _DMC_ based on the numerical results of mechanical behavior parameters of RC columns. To ensure the accuracy and reliability of predictive models, it is noted that the numerical simulation needs to sufficiently consider the influence of the dynamic effect on the mechanical behavior and deformation mechanism of RC columns under different loading rates. More importantly, a large number of training data are required in the model development when employing the random forest method.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
[PERSON] et al., [31] extended the ATV model to the anisotropic total variation Lp-quasinorm shrinkage (ATPV) model for impedance inversion. [PERSON] et al., [32] put forward the Lp-norm-based sparse regularization model for license plate deblurring. Inspired by the aforementioned studies, we propose a model that combines the first-order and high-order ATV with Lp-pseudonorm shrinkage to construct a new image-deblurring model, hereafter referred to as HTV-Lp. The proposed model addresses the following limitations of the conventional TV model: (1) staircase artifacts; and (2) the limited capability of the L1-norm to depict the sparsity of sparse variables. To realize the new HTV-Lp model, the alternating direction method of multipliers (ADMM) framework [33] was adopted. Fast Fourier transform (FFT) was used to improve the algorithm efficiency. To validate the newly proposed HTV-Lp model, experiments were performed to compare its performance with existing models including ATV [7], isotropic total variation (ITV) [34], ATPV [31], FTV4 Lp [6] and non-convex hybrid TV (Hybrid TV) [10]. Objective indicators of the six methodswere evaluated, such as the peak signal-to-noise ratio (PSNR) [35], structural similarity (SSIM) [36], and gradient magnitude similarity deviation (GMSD) [37]. The major contributions that have led to significant improvements in the quality of deblurred infrared images are as follows: (1) considering the sparsity of a high-order gradient field based on the ATV model; (2) introducing Lp-pseudonorm shrinkage to express the sparsity of a first-order and high-order gradient field; (3) combining first-order TV and high-order TV with Lp-pseudonorm shrinkage organically; (4) adjusting the parameters in the model separately. This paper is organized as follows: Section 2 presents the ATV deblurring model. The HTV-Lp model proposed in this paper is described in Section 3. The algorithm for solving the HTV-Lp model is presented in Section 4. Next, the publicly available datasets, evaluation metrics, results of the extensive experiments conducted for evaluation of the six models, and experimental analysis are presented in Section 5. Finally, Section 6 concludes the paper.