esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Assimilation of Soil Moisture with the WOFOST Model Using the EnKF algorithm  
The EnKF assimilation method was implemented for the 10 m grid cells with winter wheat pixels. The winter wheat yield was validated at the 110 field experiment plots. In this study, an ensemble of 50 members was adopted for the simulations of winter wheat yield in the water-limited mode.  
From the assimilation strategy (Figure 3) we know that the soil moisture retrieved from the water cloud model will be assimilated into WOFOST model when the observation is available. As can be seen in Figure 6, the gray solid line encircled by three red circles represents the results of EnKF assimilation and the soil moisture content increased after data assimilation. Figure 7 shows the comparison results of the regional winter wheat yield estimation, with and without EnKF assimilation. The spatial difference of the winter wheat yield map without assimilation is not obvious and most of the estimated yields were around 6500 to 6800 kg/ha. As for the result with EnKF assimilation, more realistic spatial variability throughout the study area was observed. The yield estimation ranged from 5900 to 7700 kg/ha. Generally, the northern part of Hengshui city showed lower winter wheat yield estimation values, while the central and southern part generally had a relatively higher yield estimation. This is likely due to the differences in temperature, solar radiation, and agronomy activities [31]. In Figure 8, the WOFOST simulation without SM assimilation could not estimate the winter wheat yield well, with R\({}^{2}\) of 0.21 and RMSE equal to 1330 kg/ha. The winter wheat yield estimation accuracy with EnKF assimilation was improved, with a higher coefficient of determination (R\({}^{2}\) = 0.35) and a much lower RMSE (RMSE = 934 kg/ha). Overall, the correlation of the assimilated winter wheat yield is higher  
Figure 4: Maps of soil moisture in the study area retrieved from the water cloud model (**a**) 1 April, (**b**) 7 May and (**c**) 1 Jun. (**d**) Retrieved soil moisture content of 22 measured sites in Hengshui city over 3 time periods. (Every county has 2 measured sites).  
than that without assimilation, although the error of the yield estimation results with assimilation is not negligible (RMSE = 934 kg/ha).  
Figure 5: Validation of the retrieved soil moisture from the water cloud model; (**a**) 1 April, (**b**) 7 May, and (**c**) 1 June. Note: 55 sample points were used for validation.  
Figure 6: Comparison of the SM curves simulated by WOROST model with and without the EnKF-based assimilation method (a sample point in Shenzhou county).
## 2 Related Works  
### Image-to-Image Translation  
[PERSON] et al. [13] proposed GAN models in which the generators and discriminators that are adversarial toward each other gradually improve each other's performance so that the generators learn to generate data that is as close as possible to the final target data. [PERSON] et al. [14] suggested an improvement on GAN called Conditional GAN (cGAN). Since GANs generate the output data from random noise input data, control over the output is difficult. However, cGAN can control the output partly by adding conditions to the GAN. Since then, cGAN has been applied in many fields such as image generation [15; 16; 17; 18], image domain transfer [19; 20; 21; 22], image super-resolution [23; 24], and image editing [25].  
For image-to-image translation problems, [PERSON] et al. [19] introduced cGAN to solve the problem of blurring of the resultant image as a result of pixel-to-pixel translation based on CNN. [PERSON] et al. [20] proposed an unpaired image-to-image translation model to solve the problem that existing image-to-image translation models need the paired images of input and output as training data. Existing image-to-image translation models can successfully translate images between two domains but scalability and robustness are limited for more than two domains; [PERSON] et al. [21] suggested StarGAN to solve the problem of translating images between multiple domains in a single model.  
In this study, LP images were generated using the three state-of-the-art GAN-based image-to-image translation models mentioned earlier. Moreover, it was verified through experiments that the generated LP images are similar to real ones and that they were sufficient for use as training data for the LPCR.  
Figure 1: Sample LP images by various LP-GAN generators and real LP images.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Lead Selenide Films  
The deposition of lead selenide films took place on various substrates (glass and ITO glass) by using lead (II) acetate (Pb(CH\({}_{3}\)CO\({}_{2}\))\({}_{2}\)), triethanolamine, and sodium selenosulfate, at room temperature and under normal pressure [116]. The obtained films were adherent and metallic. The influence of hydrazine hydrate (H\({}_{6}\)N\({}_{2}\)O) on the properties of films was reported [117]. Experimental results showed that the highest crystallinity and maximum conductivity could be observed for the films prepared by using 5 mL of hydrazine hydrate. The band gap, average crystallize size, and roughness were 1.425-1.803 eV, 18.18-33.37 nm, 13.7-76.3 nm, in all samples.
# How to Prevent Aseptic Loosening in Cementless Arthroplasty: A Review
## 4 Implant Properties
### Acetabular Cup
#### 4.1.3 Design  
Acetabular cups without screw holes have a lower rate of revision compared to ones with screw holes, as shown by [PERSON] et al. in a study on 22,725 primary total hip replacements [12]. The authors showed that the rate of aseptic loosening was not statistically significant between the two, but a limitation of the study was a mean follow-up of only 3.4 years (0-18) [12]. On the other hand, randomized clinical trial on 100 patients showed no difference between solid cups and cluster hole cups at 10 years follow-up [13].  
Moreover, [PERSON] et al. performed a meta-analysis on the cumulative survival at 10 years to study the rate of aseptic loosening [14]. The following acetabular cups had the lowest rate of aseptic loosening, accordingly: Weber Hemispheric(r), Trabecular Metal Monoblock Acetabular Component System(r), JRI Thread Cup(r), Filtmore(r), Conserve Plus(r), Morscher Press Fit(r), Zweymuller-Alloclassic Screw Cup(r), Arthropor(r), ACS Triloc+, Titan(r) and Spectron(r) [14].
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Distinguishing Attack Traffic from Benign Traffic  
We used a statistical analysis detection method to distinguish attack traffic from benign traffic. In the statistical detection model, the time window \(\Delta\)T of the MFPK method and threshold \(\delta\) are two important parameters. We collect network traffic periodically at the ingress router, and the time window will be a key feature that affects the detection result. If the time window is too small, some features may take a long time to capture and it may not be possible to capture all the traffic. If the time window is too large, this will take a long time to detect. Therefore, the time window \(\Delta\)T affects the performance of the statistical model. If \(\delta\) is too high, many DDoS packets will be judged as normal packets, which will decrease the similarity between normal and DDoS packets. If \(\delta\) is too small, many normal packets will be deemed DDoS attacks. To obtain the best parameters for \(\Delta\)T and \(\delta\), we performed experiments and analyses.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
###### Abstract  
To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. To enhance predictive accuracy of water trace, spectral data were preprocessed using smoothing combined with first-derivative processing, and optimal selection of absorption wavelength feature was performed using interval Partial Least Squares (iPLS). Cross-batch external validation demonstrated a Limit of Detection (LOD) of 0.026% with 95% confidence which satisfies the rapid screening requirements for water exceedances (>0.1%) in industrial applications. These findings provide a robust technical foundation for developing handheld, in situ water detection devices.  
micro-electro-mechanical systems (MEMS); near-infrared; ethylene glycol; water content determination Article
## 5 Results and Discussion  
### Results of Magnetic Simulation  
The magnetic density of the proposed mount is presented in Figure 6a,b, in which all components have a magnetic density close to the magnetic saturation point (less than 2 (T)). In particular, in the MRF layer, the magnetic density is uniformly distributed from up to down, and from outside to inside in the range of 1.0-1.2 (T), which is very close to the saturation value of MRF in Figure 6b. Similarly, Figure 6c,d show the magnetic field intensity in the mount. The magnetic intensity at the working MRF region (\(H_{m}\)) peaks at approximately 200 (kA/m) and has uniform distribution from up to down and from outside to inside in the range of 120-140 (kA/m), and the remaining areas (such as disc and core) have a magnetic intensity of less than 20 (kA/m).
## 1 Introduction
From the perspective of secure communication, we consider a different communication model in Figure 2, _i.e._, the wiretap channel with information embedding on actions. In this setup, the transmitter aims to send the confidential message to the receiver and keep it secret from the wiretapper as much as possible. We use equivocation (_i.e._, the uncertainty about the confidential message) at the wiretapper to measure the level of information leakage. Meanwhile, like [11], a specific decoder is introduced to retrieve a portion of the confidential message (see \(m_{1}\) in Figure 2). The specific decoder observes a function of the message-dependent actions, which affects the formulation of the channel states. Our work is novel in the sense that we consider the secrecy constraint in the information transmission, and we try to characterize how much information could be embedded in the actions without increasing the information leakage. For the new channel model described above, this paper obtains the inner and outer bounds on the capacity-equivocation region of such a channel with noncausal (and causal) channel state information. Furthermore, the lower and upper bounds on the sum secrecy capacity are also attained. Through a special case where no message needs to be retrieved by the specific decoder, we get a new outer bound on the capacity-equivocation region of the wiretap channel with action-dependent states and prove that it is tighter than the existing outer bound. To illustrate the tradeoff between the sum secrecy rate and the information embedding rate under the secrecy constraint, we provide a binary example. It shows that the sum secrecy rate is reduced when the information embedding rate increases. We find that the secrecy constraint and the communication requirements of the specific decoder have a negative impact on improving the secrecy transmission rate of the given communication link. Figure 1: Information embedding on actions [11]. Figure 2: Wiretap channel with information embedding on actions. The rest of the article is organized as follows. Section 2 describes the wiretap channel with information embedding on actions and outlines the main results. Section 3 discusses the results and presents a binary example. We conclude in Section 4 with a summary of the whole work and some future directions.
###### Abstract  
Battery thermal management systems (BTMS) are hugely important in enhancing the lifecycle of batteries and promoting the development of electric vehicles. The cooling effect of BTMS can be improved by optimizing its structural parameters. In this paper, flow resistance and heat dissipation models were used to optimize the structure of BTMS, which were more efficient than the computational fluid dynamics method. Subsequently, five structural parameters that affect the temperature inside the battery pack were analyzed using single-factor sensitivity analysis under different inlet airflow rates, and three structural parameters were selected as the constraints of a stud genetic algorithm. In this stud genetic algorithm, the maximal temperature difference obtained by the heat dissipation model was within 5K as the constraint function, where the objective function minimized the overall area of the battery pack. The BTMS optimized by the stud genetic algorithm was reduced by 16% in the maximal temperature difference and saved 6% of the battery package area compared with the original BTMS. It can be concluded that the stud genetic algorithm combined with the flow resistance network and heat dissipation models can quickly and efficiently optimize the air-cooled BTMS to improve the cooling performance.  
Keywords:flow resistance model; heat dissipation model; sensitivity analysis; stud genetic algorithms; structural optimization; battery +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Study Characteristics  
The compilation of published papers in Figure 5 shows how the field of study on IoT technology for electrical transformer monitoring has evolved significantly over time. The data, which span the years 2014 to 2024, show that both the amount of research being conducted on this issue and the interest in it are increasing. The fact that only three studies were published in 2014 suggests that before 2018 little research has been conducted on the use of IoT technology for transformer monitoring. The research landscape did not acquire traction until fifteen and twelve new publications were published in 2019 and 2020, respectively. The observed increase indicates that more people are becoming aware of the potential advantages of IoT technology for transformer monitoring. The most significant movement happened in 2021 and 2022, where there noticeably were 24 and 35 new studies published, respectively. This increase can be ascribed to the increased availability of implementation tools and data, as well as the growing public awareness of IoT applications. Researchers and practitioners seem to be quite interested in the idea of using the IoT to improve transformer status monitoring. Research question two shows that conference papers make up the bulk of the landscape with 55% of publications, while journal articles only account for 45% of publications.  
Figure 5: Trends over the years on IoT-based transformer condition monitoring.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects  
For this review article, a comprehensive literature search was conducted to gather relevant studies elucidating the role of fungi in food production and processing. The search strategy encompassed electronic databases, including Google Scholar, PubMed, Web of Science, and ResearchGate. Keywords such as \"fungi\", \"food production\", \"food processing\", \"fermentation\", \"mycotoxins\", and \"food spoilage\" were utilized in various combinations to identify peer-reviewed articles, reviews, and book chapters published between 2000 and 2024. The initial search yielded a broad selection of literature, which was subsequently refined based on the relevance to the scope of the review. Studies focusing on fungal ecology, physiology, enzymology, and their implications in food systems were prioritized. Additionally, articles detailing fungal interactions with food substrates, biotechnological applications, and food safety concerns were included. The selected literature was critically analyzed to extract the key findings, trends, and insights regarding the diverse roles of fungi in food production and processing. Special attention was paid to studies elucidating fungal-mediated processes such as fermentation, enzymatic activities, mycotoxin production, and spoilage mechanisms. Moreover, efforts were made to incorporate recent advancements in fungal biotechnology and their implications for enhancing food quality, safety, and sustainability. Overall, this literature review provides a comprehensive synthesis of existing knowledge on the multifaceted interactions between fungi and food systems, serving as a foundation for understanding their pivotal role in shaping the modern food industry.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 3 Smart Grid Cyber-Physical Attacks
### Power System Attack Scenarios  
Under the pressure of various cyber-physical attacks, the power systems are facing challenges from infrastructure vulnerabilities, operational vulnerabilities, energy efficiency, generation costs, extreme climate, etc. Table 1 examines some representative real-world power system attack scenarios and their devastating consequences in the real world.  
Real-world power system attack scenarios are valuable resources for understanding the vulnerabilities and risks facing the power grid. As a critical national infrastructure, the power grid's security and resilient architectures need to be studied and tested urgently to avoid any damage to the power system.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Year** & **Place** & **Attack Methods** & **Impact** \\ \hline
2003 [10] & Ohio & The Slammer worm attacked and disabled the supervisory system system & Ohio Davis–Besse nuclear plant supervisory system disabled for 5 h. \\ \hline
2010 [10, 11] & Iran & Replay attack. The Stuxnet worm tempered the power frequency of nuclear centrifuges rapidly in between high and low speeds and sent the normal measurements to SCADA. \\ \hline
2015 [10, 11] & Ukraine & BlackEnergy3 was designed to conduct spear-phishing attacks to collect internal staff’s VPN credentials and deploy a telephonic DoS attack to outage report. \\ \hline
2016 [11] & Israel & Ransomware attack through phishing email against Israel Electric Authority. & Suspend the operations of affected computers and company facing 12,610 megawatts electricity demand. \\ \hline
2021 [12] & Texas & Extreme weather winter storm & Short of 1.6 million megawatt-hours electricity and electricity generation cost increased by USD 52.6 bn. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power system attack scenarios study.
## 1 Introduction  
The concept of using microalgae as a source of energy was first proposed half a century ago, though the 20 th century saw the real advancement of diverse applications. The first production of monoalgae crops (_Chlorella vulgaris_) was in 1890. Since 1948, endeavors started within the United States, Japan, and Germany in applications of microalgae (_Chlorella, Spirulina_, and _Dunaliella salina_), focusing mainly on their utilization in nutrition and then to explore wholesome and therapeutic applications [1]. In the 1980s and 1990s, the U.S. Department of Energy financed the Aquatic Species Program, pointing at the generation of oils and inevitably biofuels from microalgae [2].  
The current study thoroughly reviews the application of microalgae culture to efficient wastewater bioprocessing with concurrent sequestration of CO2 and development of value-added products for human and animal health, as well as of sustainable biofuels, along with microalgae acting as bioindicators for assessing the condition of ecosystems.  
Specifically, in this section, Section 1, the biology, cultivation systems, harvesting methods, and processing technologies for microalgal biomass are introduced. Then, in the following sections, Section 2 discusses the microalgae-based wastewater treatment; Section 3, CO2 capture and utilization for microalgae photosynthesis; Section 4, generation of value-added health products, and, in Section 5, biofuels; Section 6, specific applications of wastewater bioprocessing; Section 7, the microalgae bioreinery concept; and Section 8, the use of microalgae in environmental biomonitoring.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 4 Conclusions  
The compensation/rejection of perturbations is crucial for achieving accurate trajectory tracking. In this study, a disturbance-rejection algorithm was developed and thoroughly analyzed. The algorithm combines sliding mode control (SMC) with a proportional integral derivative (PID) sliding surface and extended-state observer (ESO). The ESO was utilized to estimate the system state and the perturbation, with the estimated perturbation used to reject any disturbances. This perturbation rejection led to improved system performance, including small tracking errors and effectiveness against the low-frequency elements of the perturbation. The control design only requires minimal information about the system, making it easy to implement. The simulation results in MATLAB/Simulink demonstrated the superiority of the proposed algorithm.  
Conceptualization, S.J.A.; methodology, S.J.A.; software, S.J.A.; validation, S.J.A.; formal analysis, S.J.A.; investigation, S.J.A.; resources, S.L.; writing--original draft preparation, S.J.A.; writing--review and editing, S.J.A. and S.L.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the BK21 Four program through the National Research Foundation of Korea (NRF), funded by the Korean government (grant number: 5199990714521), Smart Drone Convergence, Korea Aerospace University.  
The authors declare no conflict of interest.
###### Abstract
Addressing the limitations of existing methods, this paper proposes a combined prediction model that integrates signal decomposition, feature screening, and deep learning. The key contributions of this study are outlined as follows:  
1. To address the complex energy consumption data of large public buildings, the CEEMDAN method is employed to decompose the data, resulting in decomposed IMF components and residuals and effectively decoupling the energy consumption data of large public buildings. 2. A combined feature screening method, MIC-FCBF, is proposed, ranking the regression feature importance for each decomposed IMF component and residual. Simultaneously, the BiLSTM model is utilized to predict each component and residual. Figure 1: Article structure chart. 3. The accuracy and robustness of the model are validated using data from a large public building in Xi'an, Shaanxi Province, China. The application of the model is discussed, and its superiority is verified through comparisons with multiple benchmark methods. The remaining sections of this paper are organized as follows. Section 2 describes the methods employed in this study. Section 3 presents the data collection process for the investigated building energy consumption experiments. Section 4 analyzes the experimental results. Finally, Section 5 provides the conclusions and prospects of the research.