esa-sceva/satcom-chunk-collection huggingface dataset:
# Creep Properties and Creep Modelling of Guilin Red Clay
## 4 Conclusions  
In this paper, triaxial shear and triaxial creep tests were carried out on red clay in the Guilin area under different cell pressures, and the internal friction angle, cohesion, and creep curves under different cell pressures and deviatoric stress levels were obtained. The Burgers' model was used to analyse the creep characteristics of red clay, and the main conclusions are as follows:  
1. The cohesion and angle of internal friction of red clay in Guilin were 57.20 kPa and 22.47\({}^{\circ}\), respectively, as obtained from a triaxial shear test under different cell pressures. The graded loading scheme of the triaxial creep test was designed according to the destructive deviatoric stress of the sample, and the creep curves of red clay under different cell pressures were obtained.
2. Using \"Chen's loading method\" to process the creep curves of the whole process, the triaxial creep curves of red clay were obtained separately. Based on the physical significance of the parameters in the Burgers model, a set of inversion methods for the model parameters was proposed, and the Burgers model parameters were obtained under different cell pressures and deviatoric stresses.
3. The curves calculated using the Burgers model were compared with the test curves, and the correlation coefficients \(R^{2}\) were all greater than 0.9. The Burgers model has a good fitting effect and can accurately describe the creep characteristics of red clay in Guilin.  
This paper was written by [PERSON] in collaboration with all co-authors. Data were collected by [PERSON], [PERSON] and [PERSON]. The first and final drafts were written by [PERSON] and [PERSON]. The results were analysed by [PERSON] and [PERSON]. The research and key elements of the models were reviewed by [PERSON] The writing work for corresponding parts and the major revisions of this paper were completed by [PERSON] and [PERSON]. All authors have read and agreed to the published version of this manuscript.  
This research was funded by the Key R&D and Promotion Projects in Henan Province (tackling key problems in science and technology) (Grant No. 232102321010) and the Foundation of Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Area (Grant No. CXZX2020002).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cell Pressure/kPa** & **Deviatoric Stress q/kPa** & **Correlation Coefficient R\({}^{2}\)** & **Cell Pressure/kPa** & **Deviatoric Stress q/kPa** & **Correlation Coefficient R\({}^{2}\)** \\ \hline \multirow{4}{*}{100} & 143 & 0.959 & & 271 & 0.928 \\  & 172 & 0.971 & 325 & 0.961 \\  & 201 & 0.975 & 300 & 379 & 0.963 \\  & 229 & 0.975 & & 433 & 0.996 \\  & 258 & 0.968 & & 488 & 0.939 \\ \hline \multirow{4}{*}{200} & 215 & 0.948 & & 331 & 0.954 \\  & 258 & 0.964 & & 397 & 0.972 \\ \cline{1-1}  & 301 & 0.965 & 400 & 463 & 0.976 \\ \cline{1-1}  & 345 & 0.961 & & 529 & 0.964 \\ \cline{1-1}  & 388 & 0.930 & & 596 & 0.970 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlation coefficients of curves under different cell pressures and deviatoric stresses.
## 4 Case Study Results
### The Application of [PERSON]'s Five Forces Model to the AeroLogic Joint Venture
#### 4.5.3 Bargaining Power of Suppliers  
Within the air cargo industry, suppliers can pose a threat by raising their prices. As such, powerful suppliers could in this context reduce the profitability of firms competing in the industry, if they are not able to recoup cost increases through an increase in their pricing. The aircraft manufacturers and -aviation fuel companies are powerful as these sectors are dominated by several firms, for example, Airbus and Boeing, and they are more concentrated than the airlines. Furthermore, their products delivered are vital for the consuming air cargo sector [175]. In the airline industry there are suppliers who either actually or potentially possess monopoly power. These include air traffic control (ATC) and airport services, with many carriers having to pay whatever ATC and airport services charges are levied upon them [120].  
AeroLogic's two joint venture partners--DHL Express and Lufthansa Cargo AG--are very well established and successful firms that are highly regarded. Boeing and Lufthansa share a strong partnership of innovation and a tradition of launching new aircraft programs together [185]. DHL Express' major European hub is located at Leipzig/Halle Airport and the firm and the airport authority work closely together. Thus, AeroLogic is in a relatively strong position vis-a-vis its suppliers.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design
### Phase and Communication Procedure  
Similar to OPC UA, SRMCP has multiple phases for establishing secured communication between the client and server, as shown in Figure 1.  
Figure 1: SRMCP communication procedures summary.
## 5 Conclusions  
In this study, the CNC turning of 3D-printed reinforced PETG material was investigated through the development of two ANFIS models, one for predicting surface roughness and another for cutting tool-tip temperature. Both models were structured with a 3-3-3 Sugeno-type configuration, trained on 27 experiments, tested with 12, and validated using an additional 12 experiments. The models demonstrated strong predictive performance, with prediction errors being well distributed across the dataset, confirming their generalization capability. Summarizing, the following concluding remarks were drawn:  
* The trials for determining the most suitable structure for the models revealed R\({}^{2}\) values equal to 0.9920 and 0.9598 for the roughness and temperature models, respectively.
* Sensitivity analysis revealed that feed rate and its interaction with depth-of-cut had the most pronounced influence on \(Ra\), followed by the interaction of cutting speed \(\times\) feed.
* On the other hand, cutting speed and its interaction with feed were the most influential parameters affecting tool-tip temperature.
* The interaction between \(Ra\) and \(T\) was further supported through a scatter plot, which highlighted a visible correlation, suggesting that conditions leading to lower \(Ra\) generally also resulted in reduced temperatures, though with some trade-offs.
* Finally, multi-objective optimization using the NSGA-II algorithm provided a set of optimal machining conditions balancing both surface quality and thermal performance. The Pareto front indicated that cutting speeds between 100 and 200 m/min, combined with low feed rates (<0.6 mm/rev) and small depth-of-cut (\(\sim\)0.5 mm), lead to favorable machining outcomes. These results can guide the selection of cutting parameters for efficient and high-quality finishing turning operations of 3D-printed CFRP parts.
## 3 VRIS Image Steganography Model
### Hybrid Loss Function
#### 3.4.2 Adam Optimizer  
In image steganography tasks, the model usually needs to deal with multiple aspects of the image, and the gradient variations of different parameters during training may vary greatly. Adam is a gradient descent-based optimization algorithm that combines the ideas of Force Momentum (Momentum) and RMSprop, with the property of adaptive learning rate. Its ability to adjust the adaptive learning rate enables the model to remain efficient and stable while dealing with these differences. Secondly, the Adam optimizer not only considers the first order moment estimation of the gradient (i.e., the mean of the gradient), but also the second order moment estimation, so that it can dynamically adjust the learning rate of each parameter, and quickly converge to find the optimal solution during the training process.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 4 Results and Discussion  
### Evaluation Methods  
In this section, the performance of the proposed ANC-TDE algorithm is evaluated by simulation and experiment. Suppose that the communication signal is single-frequency sinusoidal signal and frequency is 10 kHz. The sampling frequency is 1 MHz and the sample data is 14 bit.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Phage Abortive Infection  
Phage-abortive infection (Abi) is a type of phage resistance that induces the death of a single infected cell to ensure the survival of the remaining population [100]. This altruistic behavior of a single cell involves cell growth arrest after phage infection, caused by several different TA systems. Each TA protects against specific types of bacteriophages. The first TA loci that were identified to give resistance to phages comprised the Hok/Sok system on _E. coli_ plasmid R1 [101]. Pore-forming toxin Hok inhibits the propagation of T4 phage through the induction of membrane depolarization.  
Many sequence-specific ribonucleases have also been shown to provide protection against phage propagation. The activity of the TA toxin RnIA is induced after T4 phage infection. RnIA cleaves most of the cellular and phage-derived mRNA in the absence of the antitoxin RnIB, hence blocking the reproduction and spreading of T4 phage [85; 102; 103; 104; 105]. _mazEF_ antagonizes the induction of phage T4, as well as P1 [106; 107]. _toxicIN_ protects cells from multiple phages [88]. The digestion of mRNA is also the likely molecular basis behind _mazEF_ and _toxicIN_-led abortive infections.  
Since phages have genes on their genomes that specifically antagonize TA systems, it can be deduced that TA modules do indeed protect the hosts from phage propagation. Dmd protein, encoded from T4 phage, allows phage propagation in host cells by suppressing RnIA [83; 85]. Upon deletion of _dmd_, phages become sensitive to the RnIA activity and cannot propagate. Direct inhibition of RnIA activity by Dmd is shown both in vivo and in vitro [83; 85; 108]. T4 phages also encode ADP-ribosyltransferase (Alt) that can modify MazF [107]. ADP-ribosylated MazF has shown a reduced catalytic activity, thereby unable to block phage propagation [107]. Phages that are capable of escaping _toxicIN_-mediated abortive infections carry sequences similar to the antitoxin sequence. The study of mutants of phage TE that are insensitive to the _toxicIN_ system of _P. atrosepticum_ 1043 identified the expansion of repeats similar to repeats in antitoxin _toxic_[109]. By expressing antitoxin-like RNA sequences, phage TE mutants can escape the Abi by blocking the ToxN toxicity [109]. Phage T7 neutralizes TA-based defense systems by producing a protein, Gp4.5, which directly interacts with Lon and inhibits its activity [110].
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 1 Introduction
The integration of these techniques into the modem design offers a more reliable and efficient method for underwater communication that exceeds the capabilities of conventional systems. The subsequent sections detail the system architecture, experimental setup, and performance outcomes, underscoring the transformative potential of the approach presented in the realm of underwater acoustic communication. In Section 2, the software and hardware tools, as well as the methodology followed, are detailed. In Section 3, the main outcomes obtained showing the effectiveness of the system are described. In Section 4, the results and methods are discussed, and the final conclusions are drawn in Section 5, which also indicates future lines of research.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
###### Abstract  
The current evaluation models for earth-rock dam compaction quality seldom incorporate parameter uncertainty considerations. Additionally, the existing models frequently demonstrate constrained prediction accuracy and generalization capabilities. To resolve these issues, we present an intelligent evaluation method for the compaction quality of earth-rock dams that explicitly accounts for parameter uncertainty. The method utilizes a dynamic inertia weight, an adaptive factor, and a differential evolution strategy to enhance the search capability of the firefly algorithm. Furthermore, the random forest (RF) algorithm's _Ntree_ and _Mtry_ parameters are adaptively optimized through the improved firefly algorithm (IFA) to develop a dam compaction quality prediction model. This model aims to reveal the complex nonlinear mapping relationship between input influencing factors, such as compaction parameters, material source parameters, and meteorological factors, and the compaction quality. The proposed model improves the prediction accuracy, generalization ability, and robustness. The improved firefly optimization-based random forest (IFA-RF) is applied in practical engineering projects, and the results validate that this method can reliably and accurately predict the compaction quality of earth-rock dam construction in real time (R = 0.90107, MSE = 0.0000602, \(p=0.000\)) and thereby guide remedial measures to ensure engineering safety and quality compliance.  
earth-rock dams; compaction quality; uncertainty; improved firefly optimization-based random forest 2025
## 2 Study Area and Data
### Data  
The years from 1982 to 1984 were selected as the training period and 1985 was chosen as the testing period for the study. Twenty-one rainfall stations and four flow stations (in the locations of Donglin, Tuhuang, Qingxi, and Maoba) were selected within the study basin. The daily precipitation data and daily flow data were derived from the hydrology Yearbook published by the Hydrology Bureau of the Ministry of Water Resources of China. Terrain data were derived from digital elevation model (DEM) data with 30 m resolution provided by a geospatial data cloud [18]. Land use/vegetation cover type data obtained at 500 m grid scale were from USGS Land Cover Research Institute [19]. Soil data from the Food and Agriculture Organization of the United Nations (FAO) provided a global 1:5 million proportion of raster data [20]. The potential interception of reservoir (PET0)  
Figure 1: Location, digital elevation model (DEM), and ground observations in the Zhou River Basin (the Zhou River is a first-class tributary of the Jialing River).  
evaporation data with a 0.25-degree resolution were obtained from the Global Terrestrial Evaporation Amsterdam model (GLEAM) [21]. The potential evapotranspiration (EP) data were obtained from the Climate Research Unit (CRU) with a resolution of 0.5 degrees [22]. Leaf area index (LAI) data with a resolution of 0.05 degrees were adopted from the National Environmental Information Center (NCEI) [23]. All the above data sets were resampled to 1 km and input into the BTOP model for runoff simulation [24].
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
###### Abstract  
The accurate temperature background field plays a vital role in the numerical prediction of sea surface temperature (SST). At present, the SST background field is mainly derived from multi-source data fusion, including satellite SST data and in situ data from marine stations, buoys, and voluntary observing ships. The characteristics of satellite SST data are wide coverage but low accuracy, whereas the in situ data have high accuracy but sparse distribution. For obtaining a more accurate temperature background field and realizing the fusion of measured data with satellite data as much as possible, we propose a sparse data-extended fusion method to predict SST in this paper. By using this method, the actual observed sites and buoys data in the East China Sea area are fused with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5.0 SST data. Furthermore, the temperature field in the study area were predicted by using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) deep learning methods, respectively. Finally, we obtained the results by traditional prediction methods to verify them. The experimental results show that the method we proposed in this paper can obtain more accurate prediction results, and effectively compensate for the uncertainty caused by the parameterization of ocean dynamic process, the discrete method, and the error of initial conditions.  
deep learning; association relationship; heterogeneous clustering; extended fusion +
Footnote †: journal:
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 3 Factors Affecting the Yaw Motion  
The yaw motion of the moving platform depends on the platform aspect ratio, position of the platform's center of mass, driving force offset ratio, parallelism between the two LM Guides, and position of the LM Guides' center of stiffness [1,17,18].  
These factors can be classified into controllable and uncontrollable parameters, depending on the situation. In this work, the platform's aspect ratio and driving force offset were uncontrollable under the given conditions, while the parallelism between the LM Guides, the center of mass, and the center of stiffness was defined as a controllable parameter for design and implementation.  
The yaw motion of the moving platform may be described by the yaw angle (\(\theta\)) and settling time (\(\lambda\)), respectively, as functions of parallelism (\(PA\)), the center of mass (\(CM\)), the center of stiffness (\(CS\)), and force offset (\(FO\)).  
\[\text{Yaw motion}=f(\theta,\,\lambda) \tag{11}\]  
\[\theta=\left.\begin{array}{l}\theta(PA,CS,CM)\right|_{FO}\\ \lambda=\left.\lambda(PA,CS,CM)\right|_{FO}\end{array} \tag{12}\]  
The yaw angle can be predicted for the given size of the platform, the installation positions of the LM Guides, and the driving force from Equation (10).  
Figure 7: Schematic diagrams of (a) the relationship between the center of mass and the geometric center and (b) the zoomed view of the rotated center of mass.  
Numerical solutions to Equation (10) were obtained using the GEKKO package of python as a solver. GEKKO is a package for machine learning and the optimization of mixed-integer and differential-algebraic equations [22].
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Power Transfer Management and Technology Interchange  
#### 6.2.1 Energy Management and Efficient Power Transfer  
Energy management in passive device communications is crucial to supply continuous energy to inactive tags. A balance may be needed between harvesting rate and information transfer. Some optimal policies have been suggested for balancing the energy harvesting rate, which cannot be controlled by tags [136]. The proposed optimal stationary policy can maximize the long-term average read probability of tags. Moreover, the energy scheduler and the power amplifier were used; they are able to control the probability of successful transmissions by varying the drawn energy from the storage device.  
Efficient power transfer is always needed to enhance the lifetime of passive tags. [PERSON] et al. considered ambient power sources, rather than dedicated sources, for efficient power transfer in BackCom [12]. Similarly, RF-based efficient power transfer was suggested with energy beam-forming. This technique considers a power transmitter (reader) with multiple antennas for sending data and energy to various power receivers [127]. Figure 31 demonstrates a multi-antenna RFID reader with an \(M\) number of transmitting antennas and an \(R\) number of receiving antennas. A \(K\) number of RFID tags is equipped with a single antenna each. The reader in this scenario can perform channel estimation, energy beam-forming, and other signal processing operations, and each tag has an RF-energy harvesting module for its operation. The notation \(h_{Mk}\) represents the forward channel between the \(m\)-th transmit antenna. The \(g_{RR}\) shows the backward channel between it and the \(k\)-th tag [127]. Likewise, a rectifier design was suggested for the efficient harvesting of electrical energy to energize a WSN [137]. This technique works in dual load conditions by utilizing two distinct frequencies.
## 3 Optimization-Based Strategies
### Global Information-Driven Optimization
#### 3.2.2 Deterministic Dynamic Programming (DDP)
The interaction mechanism between the accuracy of the optimal results and the numerical issues of DP (such as the discretization degree of the state/control variables and the boundary issue) should be investigated in consideration of the computational burden in order to have the potential for practical application. Thus, [84] presents a DP-based EMS for the Toyota Hybrid System (THS-II) powertrain of the Prius. The potential of this DP-controller in reducing fuel consumption on regulatory cycles is better than a rule-based controller, and the fuel reduction enhancements of the DP-controller were achieved in real road tests in a MY06 Prius in Ile-de-France, when compared with the associated consumption measurements. To realize the adaptive energy management for real-time driving cycles, an adaptive energy management method based on DP for a PHEV was proposed in [85]. This approach classifies typical driving cycles into different driving patterns by considering the average and maximum speed as its classification parameters and identifies the driving types for the driving pattern recognition (DPR) process by a fuzzy logic controller. Meanwhile, the previous duration of historical information is determined to identify a real-time driving pattern in order to improve the real-time and robust performance of the energy management. Although the DDP-based strategy is powerful and effective in obtaining the global optimal fuel economy of HEVs, it presents some disadvantages for practical application:  
* The whole driving information needs to be acquired in advance;
* The method is difficult to implement in practical applications because of the tremendous time and memory consumption of the calculation process;
* The phenomenon of the \"curse of dimensionality\" occurs with the increase in the dimensions of the state variables \(x\) and the control variables \(u\), namely, the computation and storage capacity of traditional dynamics programming will increase significantly.