esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The structure of the matrix defines the strength of the correlations between variables based on the similarities between points. In co-kriging, parameters are selected with the use of a variogram. A variogram [31] describes the spatial differentiation of data. A variogram or a semivariogram, a simplified version of a variogram, is used to describe the spatial structure of data, and it provides information about the range, strength, and direction of the correlations between data. A semivariogram is half a variogram, where the semivariance increases with an increase in distance [32]. Semivariance increases to a certain point, after which point, it is stabilized. The maximum variance of a dataset is represented by a sill, which is used in large-scale studies to describe pairs of distant points. If a dataset contains random errors, measurement errors, or other inconsistencies, the semivariance increases rapidly for small distances (semivariogram limits), and this increase is referred to as the nugget effect [31]. A variogram is optimized with the use of lag intervals. Every dataset is described using an empirical variogram that is not linear due to the presence of random variables in the dataset. A variogram can only be used in interpolation if it is described using an environmental function. Predefined functions are fit to data with an empirical variogram. A highly irregular dataset (with many random variables) can be described with several functions. Typical predefined functions include the nugget effect, spherical function, exponential function, Gaussian function, linear function, and power function [33]. Datasets can be divided into the following categories based on the randomness of the data:  
1. Isotropic: On isotropic surfaces, changes in attribute values are influenced by distance but not by direction; therefore, attribute values change identically in all directions. 2. Anisotropic: On anisotropic surfaces, changes in attribute values are influenced by both distance and direction; therefore, attribute values change irregularly in space and they differ in various directions. Anisotropic surfaces can be further divided into:  
1. Geometric anisotropy: Attribute values change similarly in all directions but vary with distance; therefore, the same variability is achieved in different directions when points are separated by a varied distance. 2. Zonal anisotropy: Variability is not regularly distributed in space; this type of anisotropy results from data trends. The type and parameters of anisotropic datasets are identified with directional variograms. A variogram map presents an alternative to several directional variograms. The analyzed area is covered by a single semivariance map. The anisotropic direction is described by generating variograms for angular segments with a given tolerance. The tolerance defines the angle at which points will be analyzed relative to the defined direction.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 4 Dam Deformation Prediction Model Based on IEALL  
### Overall Framework of Model  
The data on dam deformation are affected by a variety of factors, and the combined model's forecast accuracy needs to be increased. This paper proposes a combined prediction model based on IEALL for single measurement point scenarios based on the concept of decomposition-prediction-combination. The overall idea of the model is shown in Figure 2. Firstly, the EEMD algorithm is used to decompose the complex dam deformation series into linear and nonlinear components, which are processed by ARIMA and LSTM, respectively. In order to address the issue that the relationship between the linear and nonlinear prediction values in the dam deformation combination model cannot be explained by simple linear addition, the model fits the relationship between the two components using the strong self-learning ability of the LSTM model, dynamically determining the relationship and weight rather than using the static addition relationship. Due to the random initialization of the neural network parameters, the model is susceptible to local optimization or to not being fully trained. In order to optimize the LSTM model in EALL, the improved gray wolf optimization approach is used in this study. The final prediction result is obtained when the historical relationship between the linear result and the nonlinear result sequence is fitted using historical real data.
## 3 Results and Discussions
### FST Results  
The major rheological parameters, the complex shear modulus, and the phase angle were determined using FST. From the test results, isothermal plots, a black space diagram, and master curves were plotted to characterize the shear stiffness of the binder, to evaluate the effect of molasses on the BB, and to characterize the binder for a wide range of temperatures and frequencies. The black space diagram and isothermal plot for MMB20 are shown in Figures 7 and 8, respectively, as sample diagrams.  
Figure 7: Log Isothermal plot of MMB20.  
From the FST results, the black space diagram, isothermal plot, and the master curve was developed and presented in Figures 7-11. The figures indicate that, at a lower temperature, higher frequency and percentage replacement led to the higher complex shear modulus. But, at a higher temperature, the result was the opposite. An increase in the percentage replacement of molasses from 5% to 20% at higher temperatures led to a decrease in the complex shear modulus. At lower to higher temperature and frequency, the phase angle (\(\delta\)) of MMB5 and MMB10 gradually increased and then decreased slowly for MMB15 and MMB20.
## 4 Results
### Suitable LULC Allocation Scenario to Minimize Sediment and Nutrient Export  
The average ESCI values for sediment and nutrient (N and P) exports in ten periods of three LULC allocation scenarios were compared, in order to identify suitable LULC allocation scenarios to minimize sediment and nutrient export, in terms of ecosystem service change, as shown in Figure 18 and summarized in Table 34.  
As a result, the average cumulative ESCI values for sediment and nutrient export ecosystem services under the LULC allocation of Scenario II (Maximization of ecosystem service values) provided the lowest value, with an average ESCI value of 0.1575 among different LULC scenarios. Additionally, Scenario II created the lowest yield of sediment, nitrogen, and phosphorus exports between 2000 and 2029 among the scenarios, with average values of about 37,678 tons, 196,136 kg, and 43,317 kg, respectively.  
Figure 18: Comparison of average ESCI value of sediment and nutrient export on ecosystem services among the three scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multicolumn{4}{c}{**Scenario I**} & \multicolumn{4}{c}{**Scenario II**} & \multicolumn{4}{c}{**Scenario III**} \\ \cline{2-13}  & **Sediment** & **N** & **P** & **Avg.** & **Sediment** & **N** & **P** & **Avg.** & **Sediment** & **N** & **P** & **Avg.** \\ \hline
2019–2020 & 0.5686 & 0.0241 & 0.0329 & 0.2085 & 0.5510 & 0.0189 & 0.0271 & 0.1990 & 0.6324 & 0.0366 & 0.0471 & 0.2387 \\
2019–  
Therefore, the LULC allocation of Scenario II (Maximization of ecosystem service values) was selected as the suitable LULC allocation scenario to minimize sediment and nutrient exports into Kwan Phyao from the Upper Ing watershed. These findings can serve as crucial information to allocate LULC in the Upper Ing watershed by land-use planners, land managers, and decision-makers in order to minimize sediment and nutrient loads into Kwan Phyao in the future.
# Experimental Evaluation of Rigidity Center
## 1 Introduction
They also concluded that an adjusted rigidity center close to the mass center was not sufficient to prevent torsional irregularity; in addition, slabs were one of the important elements to reduce torsional irregularity [34]. A 17-story reinforced concrete building in half scale was tested on the shake table. In this test, it was stated that torsional effects were dominant in the asymmetrical building, and damage occurred in the so-called flexible part when there was no shear wall in the floor plan, [35]. According to the study findings, the story mass eccentricity is primarily to blame for the building's torsion movement. On the other hand, the shaking table was found to have a considerable impact on the response of the building to twisting [36; 37]. The rigidity center can also be calculated from the vibration records. This method is based on the criterion that the coherence of the translational motions with the rotational motions at the rigidity center is minimal [38; 39]. It is known that there are several analytical approaches to calculating the rigidity center location. These approaches do not calculate the rigidity centers close to each other in asymmetric buildings. As significant differences were observed in the calculation of the center of stiffness with the analytical methods, it was decided to seek verification by conducting an experimental study. In this study, we tried to experimentally determine the rigidity center of buildings using a specially designed test setup. Five square-planned buildings with only four vertical members, six square-planned buildings with equally spaced columns and shear walls, four rectangular-planned buildings and five buildings revised from real buildings were manufactured using balsa wood.
## 4 Results
### Ablation Study  
Ablation Study are a research methodology for validating the necessity and contribution of model components by systematically removing or modifying them [21], and are centered on parsing the workings of a complex model through a control variable approach. The implementation involves first constructing a baseline model with all target components, and then generating ablation variants by removing specific modules one by one (e.g., sequence-aware attention or specific layers), replacing them with simplified versions (e.g., replacing depth-separable convolutions with ordinary convolutions), or partially adjusting them (e.g., reducing the number of channels). Strict control of other parameter consistency, including training data, optimization strategies, and hyperparameter settings, is required during the experimental process to ensure the reliability of the comparison results. In the performance evaluation stage, multidimensional indicators such as accuracy, loss value, inference speed, etc. need to be recorded, and the credibility of the result differences should be verified by statistical significance tests (e.g., \(t\)-test).Table 7 shows the data from our ablation study.  
Can be obtained from the above Ablation Study:  
Removing the BiLSTM layer leads to a 7.69% decrease in F1 value, which verifies the effectiveness of BiLSTM in modeling cross-character error propagation. The experiments show that BiLSTM can capture the common context-dependent patterns in Chinese spelling errors (e.g., the idiomatic error \"\(\bigtriangleup\)\" \(\bigtriangleup\)\" \(\bigtriangleup\)\"), and its implicit state enhances the pre-trained model's ability to perceive the chain spread of local errors.  
The F1 value decreases by 3.20% after removing the adapter layer, proving the key role of this layer in heterogeneous feature fusion. The adapter aligns the 768 \(\times\) 2 dimensional features output from BiLSTM with the 768 dimensional hidden space of BERT through  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Accuracy (\%)** & **F1 (\%)** & **Discrepancy (F1\%)** \\ \hline Remove the BiLSTM layer & 78.63 & 85.56 & –7.69 \\ MacBERT + BiLSTM (w/o Adapter) & 91.05 & 90.05 & –3.20 \\ Joint Training (no freeze) & 90.48 & 84.75 & –8.50 \\ MacBERT-BiLSTM & 93.37 & 93.25 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study.  
linear transformation + layer normalization, avoiding the loss of information due to dimensional mismatch.  
Direct fine-tuning (no freezing) reduces the F1 value by 8.50%. This indicates that freezing the BERT parameters at the initial stage can effectively preserve the pre-trained linguistic knowledge, prevent early noise gradients from corrupting the semantic representation, provide stable context initialization for the BiLSTM module and reduce the risk of model overfitting for a small number of error-correcting samples.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Resistance to Statistical Attacks
#### 5.5.2 Correlation of Two Adjacent Pixels  
In image data, adjacent pixels show very strong correlations with each other. Good encryption algorithms must remove this strong correlation to resist statistical attacks. The correlation among adjacent pixels calculated via Equation (18).  
\[C_{r}=\frac{N\sum_{j=1}^{N}\big{(}x_{j}y_{j}\big{)}-\Big{(}\sum_{j=1}^{N}x_{j} \Big{)}\Big{(}\sum_{j=1}^{N}y_{j}\Big{)}}{\bigg{(}N\sum_{j=1}^{N}x_{j}^{2}- \Big{(}\sum_{j=1}^{N}x_{j}\Big{)}^{2}\bigg{)}\bigg{(}N\sum_{j=1}^{N}y_{j}^{2}- \Big{(}\sum_{j=1}^{N}y_{j}\Big{)}^{2}\bigg{)}} \tag{18}\]  
The results that were obtained from the above relation demonstrate that the proposed algorithm efficiently destroys the correlation among adjacent pixels, as shown in Table 5 and Figure 9. Therefore, no information can be obtained through the correlation of the pixels.  
\begin{table}
\begin{tabular}{c c c} \hline
**Image** & \multicolumn{2}{c}{\(\chi^{2}\)**-Text**} \\ \hline  & **Plain Image** & **Encrypted Image** \\ \hline Lena (grayscale) & 158360 & 270 \\ Baboon (grayscale) & 211370 & 281 \\ Peppers (grayscale) & 549150 & 276 \\ Airplane (grayscale) & 728690 & 286 \\ House (grayscale) & 880954 & 291 \\ Lake (grayscale) & 181220 & 273 \\ \hline \end{tabular}
\end{table}
Table 4: Chi-squared test results for histogram uniformity.  
Figure 8: Histogram analysis. (**a**) Original Lena image histogram; (**b**) Encrypted Lena image histogram.
_Article_  
**Influence of Rotational Stiffness Modeling on the Joint Behavior of Quasi-Rectangular Shield Tunnel Linings**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 1\({}^{,}\)*\({}^{\copyright}\), [PERSON] 2 and [PERSON] 1\({}^{,}\)2**  
1 Department of Structural Engineering and Building Materials, Ghent University, 9000 Ghent, Belgium;  
[PERSON]ESS] (W.Z.); [PERSON]RESS] (L.T)  
2 College of Civil Engineering, Tongji University, Shanghai 200092, China; [EMAIL_ADDRESS]  
* Correspondence: [PERSON]] check for  
**Featurered Application: (1) In quasi-rectangular tunnels, a new type of special-section shield tunnels, the effect of nonlinear joint behavior is much more important than in circular tunnels. (2) An iterative nonlinear spring-beam model for the prediction of lining deformations and bending moments in a shield tunnel is presented. (3) The predictions are validated against unique full ring test results. (4) The relevance of the presented method, compared to a constant joint stiffness approach, is demonstrated. (5) A parametric study into the design parameters for quasi-rectangular tunnels is presented.**  
**Abstract: A beam-spring model with constant rotational stiffness is a practical tool for the prediction of the general deformations and bending moments in circular tunnel linings. However, in reality, the rotational stiffness of a segmental joint is not constant, due to nonlinear deformations and local yielding in the vicinity of the joint. These are a result of the specific geometry at the joint, which is related to water-tightness measures and buildability issues. For quasi-rectangular tunnels this nonlinearity should not be neglected, as the bending component in the lining is significantly larger compared to circular linings. To date, there are only few studies that have investigated a calculation method for consideration of the joint's nonlinear moment-axial force and shear-axial force interaction behavior and its consequences on the calculated lining behavior. In this paper, an iterative incremental method is proposed to tackle this issue, based on rotational stiffness curves derived from 3D nonlinear finite element modelling of the joints, and substantiated by testing. The significance of the variable rotational stiffness is highlighted through a comparison with results based on a constant stiffness assumption. Further, using the proposed calculation method, the effects of the circumferential joints, the bending moment transmission and several other parameters on the full-ring behavior of quasi-rectangular tunnels are discussed for a wide interval of design parameters. The results provide some new insights into the behavior of this non-traditional tunnel type. Although the presented results are related to specific overall and local geometries, the presented method is considered to be useful for the design of other special tunnel geometries.**  
**Keywords: iterative incremental method; quasi-rectangular shield tunnel; joint behavior; experimental validation; parametric study**
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 2 Internal Calibration System
### Echo Compensation
Based on Formulas (8)-(10), the expressions for phase compensation and amplitude compensation can be further derived:  
\[\varphi_{\text{C=}}\varphi_{H_{sys}}-\left(\varphi_{CN_{A}^{a}}+\varphi_{CR_{ 3}^{a}}+\frac{1}{2}\varphi_{CR_{4}^{a}}-\frac{1}{2}\varphi_{CR_{1}^{a}}-\frac {1}{2}\varphi_{CR_{5}^{a}}\right)+\left(\varphi_{CN_{A}^{b}}+\varphi_{CR_{3}^{ b}}+\frac{  
The accuracy of the internal calibration of the BiSAR system largely depends on the stability of the calibration network. Therefore, it is necessary to conduct sufficient tests on the system calibration network under different temperatures to extract the phase (amplitude) temperature dependence of instrument drift. In the design of the internal calibration hardware, temperature telemetry points are reasonably set in the antenna calibration network and the internal calibrator. During ground testing, the phase-temperature curve and amplitude-temperature curve of the internal calibrator, antenna calibration network, and coaxial cables are obtained through temperature experiments. During the in-orbit internal calibration operation, the satellite will downlink telemetry temperature data, internal calibration data, and synchronization data. In ground processing, the telemetry temperature values and existing phase-temperature and amplitude-temperature data can be used to perform temperature compensation on the calibration data. Combined with the in-orbit internal calibration data and synchronization data, the echo compensation of the bistatic mode can be calculated. The proposed internal calibration strategy consists of pre-flight phase and in-orbit phase, as shown in Figure 2.  
The various calibration items in Equation (9) can be flexibly combined to form different calibration sequences. The duration of executing a complete internal calibration sequence in-orbit is in the order of milliseconds, so the effect of environmental temperature changes during calibration is negligible. Since the internal calibrator, antenna calibration network, and coaxial cables are distributed in different locations on the satellites, their environmental temperatures differ to some extent. Thus, the measurement errors of the calibration items in Equation (10) can be considered uncorrelated random variables, and the total measurement error is the root mean square of these error items. In temperature experiments, the average of multiple measurements can be used as the reference for temperature compensation, and the root mean square of the compensated errors can be taken as the calibration accuracy. The calculation formulas are as follows:  
\[\hat{\mathrm{f}}=\frac{1}{N}\sum_{1}^{N}h_{i} \tag{13}\]  
\[\sigma=\sqrt{\frac{1}{N}\sum_{1}^{N}\left(h_{i}-\hat{h}\right)^{2}} \tag{14}\]  
Figure 2: The strategy for the internal calibration system of LT-1.
## 3 Results and Discussion  
### Verification Analysis of Double-Cutterbar Configuration Methods  
An example of the fitted curves of rice plant before cut by upper cutterbar, rice stalk before cut by lower cutterbar, fitted point of the panicle lowest point, and their corresponding measured values of a single plant is shown in Figure 7. The results showed that the fitted curve of stalk before cut by the upper cutterbar was close to the measured one, with the free end error and the maximum error being only 5.8% and 8.6%. The predicted panicle lowest point was also close to the actual one, with the horizontal and vertical error being only 4.3% and 1.75%. This suggests that the calculation methods used could provide considerable accuracy in the upper cutterbar configuration. On the other hand, the measured curve of the stalk before cut by the lower cutterbar was compared to an inclined straight line with the error between the two endpoints being 5.9 %. It was indicated that using Equation (9) would obtain a \(x_{lc}\) that was a bit larger than the actual value of stalk endpoint displacement, while this could be beneficial to the stalk cutting of the lower cutterbar by providing more space between stalk and cutterbar. Therefore, the methods developed for the configuration of the upper and lower cutterbars could considerably implement their functions of lowering the straw-grain ratio and keeping stubble height as required.  
Figure 7: The contrast of fitted and measured rice plant.  
The deformation calculation methods developed can be applied to other stalk crops, such as wheat, oat, sorghum, and sunflower, etc. However, the parameters used in calculation should be collected and calculated prior to harvesting operations, which could be time-consuming, and the results obtained can only be used to one specific variety. As the mechanical properties of a crop may differ from variety to variety, one database of their deformation properties could be established to provide baseline information for the design of harvesting machines.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 4 Performance Evaluation
### Experimental Settings  
To evaluate the performance of the proposed scheme and the existing schemes, a real network dataset and a dataset generated by adjusting the network capacity and seed value with Network Simulator 3 (NS3) were used [30]. For the real network dataset, a broadband dataset provided by the FCC, a 3G/HSDPA mobile dataset from Norway, and a 4G/LTE mobile dataset from Belgium were used [31; 32; 33]. The real network dataset was used as the agent's training data, and the dataset created using NS3 was used to operate the algorithm of the proposed scheme. Table 2 shows the characteristics of the network dataset used for the test, and Table 3 shows the characteristics of the existing schemes to be compared with the proposed scheme.  
Before presenting the experimental results of the proposed scheme and the existing schemes, the video used, the evaluation scenario, and other factors are described. The video is constructed based on the real video of the DASH-246 JavaScript reference player [34]. The video is encoded with {240p, 360p, 480p, 720p, 1080p} resolution and the H.264/MPEG-4 codec. The segment playback length is set to 2 s. Clients can experience different network environments by randomly choosing a bandwidth from the test set. The maximum buffer level of the client is set to 60 s, and online learning is performed using a 24 h dataset. The experimental results are derived based on the model after the online learning is completed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value** \\ \hline \(\gamma\) & Discount factor & 0.99 \\ \hline \(\lambda,\rho\) & Weight parameters used in the & 1.0, 4.3 \\  & QoE function & \\ \hline \(\alpha,\ \alpha^{\prime}\) & Learning rate for the actor-critic network & \(10^{-4},\ 10^{-3}\) \\ \hline \(\beta\) & Entropy weight & 1 to 0.1 \\  & & (over \(10^{6}\) iterations) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and their values used in the proposed scheme.