esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Plan
### Material Properties  
The compressive strength tests of concrete cylindrical specimens of dimensions \(\varphi\)100 \(\times\) 200 mm were carried out according to the specifications of KS F 2405 [35]. The longitudinal displacement and strain were simultaneously measured using two linear variable differential transformers (LVDTs) of capacity 10 mm and two concrete strain gauges of gauge length 60 mm. The load was measured using a load cell mounted in a universal testing machine (UTM). The tensile tests of the rebar and steel rod were performed in accordance with KS B 0802 [36]. The elongated displacement and strain were measured using an extensometer of gauge length 50 mm and two strain gauges of gauge length 5 mm, respectively. Table 2 and Figure 3 present the material test results. The average compressive strength of concrete at 28 d was 30 MPa, and the D13 compression rebar exhibited a yield strength of 544 MPa and tensile strength of 655 MPa. The D19, D22, and D25 tension rebars exhibited yield strengths of 493, 530, and 445 MPa, respectively, and tensile strengths of 631, 658, and 628 MPa, respectively. The \(\varphi\)22 mm and \(\varphi\)28 mm external rods exhibited yield strengths of 655 and 625 MPa, respectively, and tensile strengths of 805 and 765 MPa, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Diameter** & **Sectional Area** & \(f_{y}\)**or\(f_{yy}\)** & \(f_{u}\)**or\(f_{mu}\)** & \(\varepsilon_{y}\)**or\(\varepsilon_{py}\)** & \\  & **(mm)** & **(mm\({}^{2}\))** & **(MPa)** & **(MPa)** & **(\(\mu\epsilon\))** & \\ \hline Reinforcements & D13 & 126.7 & 544 & 655 & 2720 & \\  & D19 & 286.5 & 493 & 631 & 2465 & \\  & D22 & 387.1 & 530 & 658 & 2650 & \\  & D25 & 506.7 & 445 & 628 & 2232 & \\ \hline External rod & \(\varphi\)22 & 380.1 & 655 & 805 & 5196 & \\  & \(\varphi\)28 & 615.8 & 625 & 765 & 5050 & High-strength steel \\ \hline Concrete & & & & \(f^{\prime}_{c}\) = 30 MPa & \\  & & & & & \\ \hline \multicolumn{6}{c}{Notes: \(f_{y}\) and \(f_{u}\) are the yield and tensile strengths, respectively, of the mild steel reinforcements; \(f_{yy}\) and \(f_{fu}\) are the yield and tensile strengths, respectively, of the external rods; \(\varepsilon_{y}\) and \(\varepsilon_{yy}\) are the yield strains of the reinforcement and external rods, respectively; \(f^{\prime}_{c}\) and \(E_{c}\) are the compressive strength and modulus of elasticity, respectively, of the concrete.} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material test results.  
Figure 2: Details of the strengthening specimen.
## 1 Introduction
### Our Contribution  
As the illustrating example of PEE shows, EC depends on the prediction accuracy of the pixels. When PEE is applied, the data bits are embedded only when the prediction-error is \(-1\) or \(0\). Hence, we have the following hypothesis.  
**Hypothesis 1**: _As the prediction accuracy is improved, the performance of the PEE techniques for RDH is enhanced._  
In this paper, we devote our efforts in validating this hypothesis. Specifically, _we aim at improving the prediction accuracy in PEE using deep an artificial neural network (ANN), which has been developed rapidly and extensively studied in the past decade._ We propose a novel method based on a _multilayer perceptron_ (MLP), which is a well-known ANN consisting of multiple sequential fully connected layers and providing nonlinear mapping between input data and output data with nonlinear activation functions. Moreover, we consider _eight octants in the three-dimensional space for embedding_, which makes better use of space (c.f. [5] which considers only the first octant for the embedding). We conduct experiments by applying our proposed method on six test images, including Lena, Baboon, Boat, Peppers, Airplane (F-16), and House. The experimental results well support our hypothesis. The EC greatly increases and is 1.9-9.8 times of previous methods. On the other hand, the image quality is still well maintained in terms of low PSNR, which is competitive compared with previous work.  
**Remark 1**: _Our MLP consists of layers of nodes. The nodes between consecutive layers are fully connected by weighted edges. Each node receives input from nodes on the previous layer and sends output by passing the aggregated input to a nonlinear activation function. It has been shown that the well-trained MLP can be used to approximate any smooth and measurable function [28]. The MLP has been proven to be an effective alternative to more traditional statistical techniques [29]. Recently, the MLP has been widely used in many different fields of research (e.g., see [30, 31, 32, 33, 34] for more details). Our proposed method applies MLP to the pixel prediction phase of prediction-error histogram modification. We train the MLP network and use it to derive more accurate pixel prediction. Unlike other statistical techniques, the MLP makes no prior assumptions on the data distribution and can be accurately applied even when new or unseen data appear. These features of the MLP make it an attractive alternative when developing numerical models and choosing between statistical methods._
## 4 Data and Estimation Results
### Bivariate GARCH Model  
Table 3 presents the estimated results of the bivariate GARCH-CCC model. The two estimated GARCH parameters (\(\alpha^{ELIA}\) and \(\beta^{ELIA}\) for EUA, and \(\alpha^{WTI}\) and \(\beta^{WTI}\) for WTI) are positive and significant (\(p\)-value \(<\) 1%). These results support non-constant volatilities in carbon-energy markets. Notably, the sum of the two estimated GARCH parameters is near to unity (e.g., \(\alpha^{ELIA}=0.1226\) and \(\beta^{ELIA}=0.8777\)). This result implies a volatility-clustering phenomenon in the GARCH-based volatility, in that a high/low volatility follows another high/low volatility.  
Figure 1: Return rates on EUA carbon price and WTI crude oil price.
## 5 Experimental Results  
### Evaluation Metric: ROC Curve  
In binomial classification, the results can be depicted by a confusion matrix, as shown in Table 2. Two important evaluation metrics based on the confusion matrix, including true positives rate (_TPR_) or sensitivity and false positives rate (_FPR_) or 100-Specificity, are defined as below:  
Figure 4: Generated images with different values of the hyper parameter \(\gamma\) (some noise-like regions are marked with red).  
\[TPR =\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{19}\] \[FPR =\frac{\mathit{FP}}{\mathit{FP}+\mathit{TN}}\]  
where _TP_, _FN_, _FP_, and _TN_ are described in Table 2. In the ROC curve [30], the _TPR_ is plotted in function of the _FPR_ (100-Specificity) for different cut-off points. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold.  
To compare two classifiers using ROC curve (for example M1 and M2), AUC [30], the area under the ROC curve is used. If ROC curve M1 occupies a larger AUC than ROC curve M2, then the M1's classifier is considered to achieve better performance compared to the M2's classifier.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 3 PID Controller Parameter Optimization  
The performance of the PID controller depends on the tuning of three parameters: \(k_{p}\), \(k_{i}\), and \(k_{d}\). However, these parameters are often set based on experience, which makes it difficult for the controller to achieve optimal performance. This study uses an optimization algorithm to automatically adjust the PID parameters, improving control accuracy, reducing overshoot and oscillation, and speeding up system response. Compared to manual tuning, optimization algorithms are better equipped to handle complex environments and uncertainties, ensuring the controller operates stably under variable road conditions. This approach reduces reliance on experience and finds the global optimal solution, significantly enhancing system performance.  
Using a genetic algorithm to optimize PID parameters offers notable advantages. Its global search capability avoids local optima and finds better parameter combinations. Genetic algorithms are well suited for complex, nonlinear, multivariable problems and remain effective even when dealing with discontinuous or noisy systems. They also exhibit strong robustness, being less sensitive to initial parameters, thus ensuring the stability of PID control. The parallel processing ability makes the optimization process more efficient, quickly finding the optimal parameters.
# Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage
## 7 Conclusions  
In this paper, the problem of storage and retrieval of cloud-based video surveillance system are discussed. To save storage space while ensuring data security, we present a dynamic redundant replicas mechanism based on security levels. It has been proved that the dynamic redundant replicas mechanism can dynamically adjust the number of redundant replicas according to retention time and access hit. The method ensures the security of video files and appropriates to save storage resources. Performance comparisons show that our approach is effective. In video retrieval, it also has been proved that the data cache strategy based on location correlation can effectively reduce the response time of video playback effectively. The above two strategies are conducive to optimizing storage and retrieval of the massive video surveillance. Their combination can improve the access speed of video data in the case of saving resources. In terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods. Therefore, the two optimization strategies proposed in this paper provide a solution to the efficient management of massive video data and can meet the needs of the current and future development in distributed video surveillance systems. In future, we would incorporate replica placement strategy with SL-DRM strategy, speeding up the search for system indexes. We believe that our proposed approach can adapt such settings.  
This work is partially supported by The National Key Research and Development Program of China (No. 2017 YFB0701600), Shanghai Innovation Action Plan Project under the grant No. 16511101200, Shanghai University Material Genetic Engineering Institute (No. 14 DZ2261200), and the Japan Society for the Promotion of Science under Grants-In-Aid for Scientific Research 15H04678.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Method
### Data and Measurement Instrument  
This study was conducted with secondary data obtained from independent safety culture evaluations. Analysis data were obtained by administering the following measurement instruments: (1) a survey including four standardized scales given to all nuclear power plant members and (2) three behavioral anchored rating scales given to a representative sample of each nuclear power plant. Both BARS and surveys were administered physically on paper (years 2015, 2018, and 2019). The survey comprised four scales, all of them with Likert-type responses with seven fixed-choice options: safety [53], risk perception [53], safety conscious work environment (SCWE) [54] and organizational resilience. The authors of this last scale also suggested that it could be approached as two subscales: planning capacity and adaptive capacity [55; 56]. The upper part of Table 2 shows a content summary of these scales together with theoretical elements identified to describe safety culture. In addition to surveys, the NOMAC methodology uses BARS as a measurement instrument. BARS are an evaluation instrument used to establish behavioral norms within a continuous scale [57]. They are scales providing specific examples of behavioral norms to which a numerical value is assigned. In other words, they are an evaluation tool linking a set of specific narrative examples of behavior to a numerical scale [58], meaning each example is associated with a score (1 through 5) for favorable, moderate or unfavorable behaviors (high > 3, medium = 3, low < 3) related to a specific attribute. The design of BARS includes the definition of an attribute followed by five graded behavioral examples, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & & **Survey Sample N (\%)** & **Total N (\%)** \\ \hline \multirow{3}{*}{Organization} & Nuclear Power Plant 1 & 533 (12.32\%) & \multirow{3}{*}{4326 (100\%)} \\  & Nuclear Power Plant 2 & 1975 (45.70\%) & \\ \cline{1-1}  & Nuclear Power Plant 3 & 1818 (42.03\%) & \\ \cline{1-1}  & Facility & 3767 (88.84\%) & \\ (Location) & Headquarters & 473 (11.16\%) & 4240 (100\%) \\ \cline{1-1}  & Contractual situation & & \\  of workforce & Own staff & 1715 (42.85\%) & \\ (Contract) & Contractors & 2287 (57.15\%) & 4002 (100\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Study survey sample and descriptives. from which individuals are asked to choose the one which best describes the organizational scenario in that specific attribute [59; 60]. As explained by [PERSON] et al. [61] (p. 606), \"BARS methodology results in explicit statements regarding requisite job behaviors and their perceived value.\"  
As opposed to surveys that were distributed to all nuclear power plant members, BARS were administered only to personnel participating in individual interviews and focus groups (some 10-15% of personnel in the organization).
# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 2 Related Work
### Deep Learning Method  
In recent years, deep learning has become prevalent, and its robust feature representation and nonlinear mapping capabilities have made it widely used in image processing. [PERSON] et al. [21] were the first to put forward the low illumination image enhancement algorithm based on deep learning LLNet, which is a kind of stack-based sparse denoising method since the encoder, from the low illumination image identification signal char acteristics, and in the high dynamic range image adaptively lighting image without an over/saturated lighter parts, loss function using sparse regularization reconstruction. Later, [PERSON] et al. [22] proposed an MSRNet algorithm imitating MSR to directly learn end-to-end mapping between dark images and bright images. Most parameters in the model are reversely optimized, which is superior to the traditional method. However, this method uses synthetic datasets and has some gaps with authentic images. Some scholars have put forward an enhancement algorithm based on the idea of decomposition, which divides the network into the decomposition part and the enhancement part and shares parameters. However, details will be lost in the process, the noise will be amplified, and the visual effect will be damaged. There are also some effective methods, such as the multi-exposure depth single-image contrast enhancement algorithm proposed by [PERSON] et al., which mainly focuses on single-image contrast enhancement and aims at solving the low-contrast problem of under-exposure and over-exposure. [PERSON] et al. [23] proposed a new convolutional network, which is characterized by feature extraction and fusion at different levels in the network. A recent loss function was proposed, including structural loss, content loss, and regional loss. Structural loss was combined using SSIM and MS-SSIM measures [24; 25]. Content loss is a similar feature extracted by VGG. Region loss guides the network to focus more on low-light areas in the image.
# Advanced First-Principle Modeling of Relativistic Ruddlesden--Popper Strontium Iridates
## 4 Results and Discussions
### Electronic Structures
#### 4.2.3 \(Gw\) vs. LSDA+\(U\)
In fact, \(GW\) gives a renormalization factor \(Z\) for the \(J_{\rm eff}=1/2\) bands close to the Fermi level of 0.61, yielding a mass enhancement of 1.64, far lower than the experimental value of 6 [11]. This is also reflected by the absence of the characteristic QP peak close to the Fermi level detected by ARPES [152]. These failures imply that the type and degree of correlations included in the \(GW\) self-energy are not adequate enough to accurately describe the local band topology of Sr\({}_{2}\)IrO\({}_{4}\) and Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\) as well as the correlated metallic state of SrIrO\({}_{3}\) and that going beyond the \(GW\) approximation is needed, such as DFT+DMFT [154]. Finally, we would like to mention that the gap in Sr\({}_{2}\)IrO\({}_{4}\) is unstable upon doping: both electron and hole doping induce an insulator-to-metal transition associated with the emergence of a Fermi surface [155; 156; 153]. The basic features of the transition are well captured by LSDA+\(U\)[142]. Figure 6 show a comparison between calculated (middle panels) and experimental (right panels) Fermi surfaces of Sr\({}_{2-x}\)La\({}_{x}\)IrO\({}_{4}\) for different doping concentrations (\(x=0\), 6.25%, and 12.5%). The Fermi surface are extracted from La-doped supercell using the band-unfolding techniques (left panels), whereas the measured data refer to ARPES. The closing of the gap is attributed to a progressive reduction in the electronic correlation in electron-doped samples and by gradual filling of the bottom of the conduction bands. The agreement between theory and experiment is excellent, apart from the feature at \(\Gamma\) in the simulated undoped Fermi surface due to the overestimation of the top of the valence band at \(\Gamma\), as previously mentioned. Figure 5: Electronic band structures and density of states (DOS) obtained from (**a–f**) LSDA+\(U\)+SOC with \(U\) calculated from cRPA and (**g–l**) \(GW\)+SOC calculations for three iridates (upper panel for Sr\({}_{2}\)IrO\({}_{4}\), middle panel for Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\), and bottom panel for SrIrO\({}_{3}\)). The Fermi energy has been aligned to zero. The high-energy Ir-\(e_{\rm g}\) states are not shown due to the large crystal field. Reproduced with permission from Reference [100]. Copyright 2018 by the American Physical Society.
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
### Optimization of Distribution Grid Energy Storage with Distributed Photovoltaics
#### 3.2.1 Distributed PV Energy Storage Methods
No equalization management topology photovoltaic lithium battery energy storage system [61], to solve the traditional design process of lithium battery equalization management, single overshoot problem, enhance the safety of the system, the system can be uploaded through the controller and then the prediction of the relevant data, but only theoretically feasible, there is no actual prediction with the counterpart. For the problem of slow response speed of compressed air energy storage, a virtual inertia coordinated control strategy between different optical storage units is proposed [62], which combines compressed air energy storage and supercapacitors, greatly improving the response speed of the system and also accurately realizing the crossover frequency control and so on. The hybrid energy storage system based on a supercapacitor [63] suppresses the voltage fluctuation when distributed PV is connected to the distribution network, which is of practical significance for the consumption of distributed PV. However, the shortcoming is that only a PV plus an energy storage system is studied, and whether the energy storage system can be applied for multiple power supply configurations remains to be studied. The liquid-flow battery energy storage optimization configuration method [64], which is applicable to PV systems, can suppress the intermittency and volatility of the power generation system through rational planning and configuration. The method pin mainly on the economic problems of the energy storage system and the configuration of the energy storage system in the local area network (LAN), which improves the economy of the LAN power supply and the new energy consumption rate. Hydrogen energy storage is a new type of energy storage technology, which is based on the principle of converting electric energy into hydrogen energy and then generating electricity by burning hydrogen. For the study of distribution network systems containing hydrogen energy storage [65], nine working mode judgments can be proposed, through which hydrogen energy storage can be utilized to participate in peak shaving and valley filling of the distribution network and to improve the consumption capacity of the distribution network. However, there are many simplifications in the calculation process of this study, and the actual conclusions are still debatable, but hydrogen energy storage has a good development prospect for photovoltaic distribution systems. As an emerging technology, photovoltaic energy storage has great application prospects and development potential. With the continuous development of technology and cost reduction, it is believed that PV energy storage will become a very important research direction in the future renewable energy field.