esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results Analysis
### Analysis of the Results of Lignin Fiber-Improved Soil
#### 4.1.3 Hydraulic Conductivity Test  
Based on the results of the unconfined compressive strength and direct shear tests, the optimum content of lignin fibers was determined to be 8%. Consequently, a rigid wall permeameter was utilized to conduct the hydraulic conductivity test on soil improved  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lignin Fiber (LF)/(\%)** & **C/(kPa)** & \(\varphi\)/(\({}^{\circ}\)) & \(\mathbf{R^{2}}\) \\ \hline
0 & 166.83 & 16.12 & 0.923 \\
2 & 183.46 & 18.46 & 0.922 \\
4 & 200.64 & 20.39 & 0.931 \\
6 & 207.51 & 21.23 & 0.932 \\
8 & 210.53 & 26.00 & 0.972 \\
10 & 204.16 & 24.44 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fitting parameters of the cohesion and internal friction angle of the expansive soil improved by different contents of lignin fibers.  
Figure 4: The relationship curves of the shear stress and horizontal displacement of the expansive soil improved by different contents of lignin fibers.  
Figure 5: The fitting curves of the shear strength and vertical stress of the expansive soil improved by different contents of lignin fibers.  
with 8% lignin fiber. Untreated soil and soil improved with 2% lignin fiber served as the control group.  
As shown in Figure 6, the hydraulic conductivity of the untreated soil, 2% fiber-improved soil, and 8% fiber-improved soil is 3.67 \(\times\) 10\({}^{-7}\) cm/s, 6.74 \(\times\) 10\({}^{-7}\) cm/s, and 1.05 \(\times\) 10\({}^{-6}\) cm/s, respectively. It is observed that after the addition of lignin fibers, the hydraulic conductivity of the sample slightly increases, with the growth rate ranging from 45% to 65% as the content increases. This demonstrates that lignin fibers significantly influence the hydraulic conductivity of expansive soil. When lignin fibers are mixed with soil, larger macropores will be formed, which increase the permeability of the treated soils. These findings are corroborated by the detailed pore structure analysis presented in Figure 9, where the proportion of macropores in the soil significantly increases from 27.2% in the untreated soil to 33.1% and 37.8% in the 2% and 8% fiber-improved soil, respectively. This is consistent with the results of the hydraulic conductivity tests and supports the conclusion that lignin fibers enhance soil permeability by forming larger macropores. This aligns with the findings from [PERSON] and [PERSON] [41], who reported that the inclusion of fibers in kaolinite clay significantly increased its hydraulic conductivity, especially at higher fiber contents. Similarly, [PERSON] and [PERSON] [42] observed that within certain limits, the fiber content helped maintain the hydraulic conductivity at acceptable levels for waste containment applications, although higher contents significantly increased the conductivity.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 6 Electrochemical Flocculation
### Non-Sacrificial Electrode  
When non-sacrificial electrodes such as carbon are operated under direct current conditions, negatively charged algal cells move towards the anode electrodes. Once the algal cells reach the oppositely charged electrode, they lose their negative charges through neutralization, resulting in flocculation [58]. Algae harvesting efficiency largely depends on operating conditions such as current conductivity and salt content in the culture broth. For instance, using carbon electrodes significantly increased the harvesting efficiency of _C. sorokiniana_ from 66.0% to 94.5% with increasing NaCl concentration from 2 to 6 g/L under 1.6 kWh/kg biomass electric current conditions [124]. A chitosan-assisted ECF process incorporating carbon-graphene electrodes was developed for harvesting of _Chlorella_ sp., in a remarkable pilot-scale (1000 L) study by [PERSON] et al. [137]. In this study, various operating factors such as chitosan dosage, pH value, cell density, stirring rate, mixing time,voltage, current intensity, and inter-electrode distance were extensively investigated. One kilogram of algal biomass could be harvested with a high harvesting efficiency of 90% under optimized conditions (specifically, 0.2 L sulfuric acid, 23.7 g chitosan, and 0.42 kWh of electricity). If both high-efficient biomass recovery and low energy consumption are guaranteed, non-sacrificial carbon electrodes can be considered to be one of the more attractive solutions for large-scale electrochemical harvesting processes [124].
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 4 Grey-Box Model Architecture  
In this section, we present a detailed description of the proposed Grey-Box model which is based on a self-training methodology.  
In our framework, we aim to develop a Grey-Box classification model which will be interpretable, being at the same time nearly as accurate as a Black-Box and more accurate than any single White-Box. We recall that ML models which utilize very complex functions (Black-Box models) are often more accurate but they are difficult and almost impossible to interpret and explain. On the other hand, White-Box ML models solve the problem of explainability but are often much less accurate. Therefore, a Grey-Box model, which is a combination of black and White-Box models, can result in a more efficient global model which possess the benefits of both.  
In our model, we utilized an accurate classification model (Black-Box base learner) on a self-training strategy in order to acquire an enlarged labeled dataset which will be used for training an interpretable model (White-Box learner). More specifically, the Black-Box was trained on an initial labeled dataset in order to make predictions on a pool of unlabeled data, while the confidence rate of each prediction was calculated too. Then, the most confident predictions (m.c.p) were added to the initial labeled data and the Black-Box learner was re-trained with the new enlarged labeled data. The same procedure is repeated till a stopping criterion is met. At the end of iterations, the final augmented labeled data are utilized for training the White-Box learner.  
An overview of this Grey-Box model architecture and flow chart are depicted in Figures 1 and 2, respectively.  
It is worth mentioning that by the term White-Box model we mean that a White-Box learner is used in both left and right architecture displayed in Figure 1, while by Black-Box model we mean that a black box learner is used in both left and right architecture. Additionally, in case the same learner is utilized in both left and right architecture, then the proposed framework is reduced to the classical self-training framework.  
Figure 1: Grey-Box model architecture.
## 5 Discussion
### Geomorphic Changes  
#### 5.2.1 Adjustment of Bar and Channel Erosion Distribution  
The entire MYR is partitioned into three sub-reaches, namely the Yichang-Chenglingji reach (sub-reaches 1-6; 408 km), the Chenglingji-Hankou reach (sub-reach 7; 251 km), and the Hankou-Hukou reach (sub-reaches 8-9; 295 km), based on the river length and the relationship between the main stream and the lake. This division is employed to calculate the distribution of erosion and deposition within these sub-reaches. Figure 8 shows the annual bar and channel deposition in the MYR from 2003 to 2020. The results indicate that, in the post-TGD period, a more intense channel scouring was observed along the MYR, accompanied by slight yet relatively stable bar scouring (channel scouring being approximately 9-14 times more intense than bar scouring). The strongest scouring occurred in the sub-reach proximate to the dam, where the scouring intensity above the Chenglingji section was about three times greater than that in the section below Chenglingji.  
Overall, the erosion of bars and channels in the MYR displays relatively distinct spatial and temporal variability. Specifically, the annual channel erosion in Yichang-Chenglingji reach (7328 \(\times\) 10\({}^{4}\) m\({}^{3}\)/y), Chenglingji-Hankou reach (2864 \(\times\) 10\({}^{4}\) m\({}^{3}\)/y), and Hankou-Hukou reach (3993 \(\times\) 10\({}^{4}\) m\({}^{3}\)/y) are significantly greater than the corresponding bar erosion (784 \(\times\) 10\({}^{4}\) m\({}^{3}\)/y; 299 \(\times\) 10\({}^{4}\) m\({}^{3}\)/y; 286 \(\times\) 10\({}^{4}\) m\({}^{3}\)/y). However, the relatively pronounced channel erosion downstream of the Chenglingji reach occurred with a lag of approximately 10 years compared to the upstream Chenglingji reach.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 5 Experimental Results and Comparative Analysis
### Comparison of Losses with Boxplots  
The boxplot in Figure 16 visualizes the difference in the minimum validation and training losses across all our samples. We achieved further comparative insight regarding standard augmentation and GAN-based augmentation methods across the training and validation losses using boxplots.  
The study hypothesis is to show that the minimum validation and training loss differs significantly between the proposed augmentation pipeline and the standard augmentation method in classifying rice leaf diseases. Therefore, at a significance level of <= 0.05 (see Table 5), our analysis confirms our hypothesis that the minimum validation and training loss is significantly higher in the standard augmentation when compared to the proposed augmentation pipeline across the two object detection models. Hence, our result implies that the proposed augmentation pipeline for improving the performances of an object-detection model when faced with a small or imbalanced dataset is effective and therefore significantly improves the models' efficacy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset (k = 5)**} & \multicolumn{2}{c}{**Gan- Faster-rcnn**} & \multicolumn{2}{c}{**Standard Faster-rcnn**} & \multicolumn{2}{c}{**Gan-ssd**} & \multicolumn{2}{c}{**Standard-ssd**} \\ \cline{2-9}  & **Validation** & **Train** & **Validation** & **Train** & **Validation** & **Train** & **Validation** & **Train** \\ \hline k-fold 0 & 85.25 & 91.78 & 81.56 & 84.21 & 84.32 & 92.81 & 79.21 & 82.95 \\ k-fold 1 & 76.11 & 89.33 & 71.28 & 77.37 & 77.51 & 84.58 & 71.13 & 76.27 \\ k-fold 2 & 87.21 & 94.08 & 82.93 & 86.18 & 85.98 & 86.47 & 80.91 & 82.78 \\ k-fold 3 & 91.38 & 95.10 & 87.39 & 90.08 & 90.38 & 92.98 & 85.72 & 89.97 \\ k-fold 4 & 78.94 & 88.85 & 71.43 & 83.59 & 79.17 & 86.71 & 72.67 & 84.88 \\ \hline Average & 83.78 & 91.83 & 78.92 & 84.29 & 83.47 & 88.71 & 77.93 & 83.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: K-fold cross validation classification accuracy (%) of the four model methods.  
Figure 16: Boxplot visualizing the differences in the minimum training and validation losses between the proposed augmentation pipeline and the standard augmentation method.
###### Abstract  
Determining a reasonable main cable curve is the foundation of suspension bridge design, and the accuracy and efficiency of the curve-finding problem are key to the design of a suspension bridge. To accurately obtain the completed curve of a main cable, force equations, which are nonlinear equations, need to be solved. In this study, the improved particle swarm optimization (IPSO) algorithm with inertia weight is presented to solve these nonlinear equations. Then, taking a double-tower three-span steel-box girber suspension bridge as the research background, the accuracy and efficiency of the IPSO method in finding the main cable curve are studied and then compared with those of the N-R iteration method and the finite element method (FEM). The results show that the proposed IPSO method has a high accuracy and a fast computing speed. Furthermore, the convergence under different bridge parameters is discussed, which demonstrates that the IPSO method has a strong adaptability.  
suspension bridge; main cable; improved particle swarm optimization algorithm; nonlinear equation; inertia weight; N-R iteration method; finite element method; convergence
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 3 System Modeling
### Network Communication Model
Therefore, we can find out the SNR \(\varsigma=\frac{P_{t}G_{\text{leo,}t}G_{r}}{\zeta_{\text{max}}n^{2}}\), and the maximum coverage radius of the satellite and the base station are, respectively:  
\[x_{m}(t+1)=x_{m}(t)+\cos\theta\cos\phi d_{m}(t) \tag{2}\]2  
\[x_{m}(t+1)=x_{m}(t)+\cos\theta\cos\phi d_{m}(t) \tag{2}\]3  
In each time slot, if the distance \(L_{(m,n)}(t)\) between user \(n\) and UAV \(m\) is less than or equal to the communication coverage radius \(r_{\text{UAV,max}}\) of UAV, a communication link can be established. Similarly, a communication link can be established if the distance \(L_{\text{Leo,}n}(t)\) between user \(n\) and the satellite is less than or equal to the communication coverage radius \(r_{\text{leo,max}}\) of the satellite. A communication link can be established if the distance \(L_{\text{tbs,}n}(t)\) between user \(n\) and the TBS is less than or equal to the communication coverage radius \(r_{\text{tbs,max}}\) of the terrestrial base station.
## 6 Conclusions  
This work aims to improve fault classification on the CWRU bearing dataset and SoH detection on the NASA battery dataset by applying TD feature extraction and FSMs. A total of 15 features for the bearing dataset and 45 for the battery dataset were extracted, capturing key statistical and signaling features. Five FSMs, namely MI, FS, SFS, RFE, and RFI, were applied to determine the optimal feature sets. The performance of these methods was evaluated using SVM and LSTM techniques. The experimental study shows that RFI, an embedded Feature Selection Method, can effectively identify important features by reducing the number of redundant features, achieving an F1-score of 98.41% with 10 features out of 15 for the bearing dataset and 98.80% with 10 features out of 45 for the battery dataset. FSMs improve classification accuracy and reduce computational complexity by focusing on compelling features. In addition to these findings, the experimental study demonstrates that the P2P feature is a highly discriminative characteristic, consistently selected across both datasets. RFI, an embedded method, integrates feature selection directly into the model training process and has been noted for its strong performance.  
Conceptualization, M.S. and A.H.; methodology, M.S., A.H. and E.Y.; software, M.S. and A.H.; validation, M.S., A.H. and E.Y.; writing--review and editing, M.S. and A.H.; supervision, E.Y. All authors have read and agreed to the published version of the manuscript.  
This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under project numbers 118C083 (M.S.) and 118C100 (A.H.), within the scope of the 2244 Industrial Ph.D. Program.  
Not applicable.  
The dataset used to support the findings of this study is available upon request. The experimental data of the study use the bearing datasets from the Case Western Reserve University (CWRU) ([[https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)]([https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)) (accessed on 14 July 2022)) and the NASA AMES Prognostics Data Repository ([[http://ti.arc.nasa.gov/project/prognostic-data-repository](http://ti.arc.nasa.gov/project/prognostic-data-repository)]([http://ti.arc.nasa.gov/project/prognostic-data-repository](http://ti.arc.nasa.gov/project/prognostic-data-repository)) (accessed on 14 July 2022)).  
Author [PERSON] was employed by the Turkish Automobile Factory Inc. (TOFAS). Author [PERSON] was employed by the Turkish Aerospace Industries Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 1 Introduction
In present times, RPC is widely used in prefabricated components. For commercial RPC, steam-heat curing is typically used in order to accelerate the hydration process and strength development [31], but there are relatively few studies on the shrinkage of RPC after steam-heat curing and creep in RPC. Furthermore, there is no RPC shrinkage and creep model to predict shrinkage and creep of RPC at present, most of the existing shrinkage and creep models were developed from experimental data fitting, and the results of these models are mostly compared to data related to normal strength concrete. Hence, these models are suitable for normal strength concrete rather than RPC [32]. The applicability of the existing shrinkage and creep models for RPC needs to be further verified. In this study, the shrinkage after steam-heat curing and creep of RPC have been investigated. To this end, a total of 18 samples were prepared for compressive strength and elastic modulus tests, 9 samples for shrinkage tests, and 10 samples for creep tests. In addition, the influence of steel fibre on the compressive strength, elastic modulus, shrinkage, and creep of RPC was investigated. The effect of axial stress on the creep of RPC was also examined. Finally, the shrinkage and creep of RPC predicted by widely used models were compared with the experimental data to verify the applicability of the models for RPC, and new shrinkage and creep numerical models were developed for RPC.
## 3 Processing Methods
### Structure of MDISE Model  
The overall structure of the MDISE model proposed in this paper is shown in Figure 5. The model consists mainly of the multi-scale dilated convolution (MD) module, the 1D Inception module, and the channel attention mechanism (SE) module. The absorbance spectra of the target gas detected by SMWIR-IFTS are input into the model. First, the MD module extracts both global and local shallow features of the spectra. Then, two series-connected 1D Inception modules are used to further extract multi-scale deep features. The SE module is added after each 1D Inception module to enhance attention to important features and suppress redundant features. Finally, the processed features are passed through a global average pooling layer and a fully connected (FC) layer to obtain the gas concentration inversion results. The structure of each module in the model is shown in Figure 6.  
Figure 5: Overall structure of MDISE model.  
Figure 6: Structure of (**a**) MD module; (**b**) 1D Inception module; and (**c**) SE module.
# The Effects of AI-Driven Face Restoration on Forensic Face Recognition
## 5 Discussion
Firstly, the degree of blurring typically correlates with the loss of detail in an image, which directly affects the effectiveness of subsequent recovery algorithms. For instance, in cases of severe blurring, even advanced recovery techniques may fail to accurately restore all details, thus reducing the accuracy of recognition algorithms. Secondly, the choice of recovery method is crucial for ultimate recognition performance. Some methods may perform better with specific types of blur, such as motion blur or Gaussian blur, while others may be more suitable for dealing with low light conditions or noise interference. Therefore, selecting a recovery method that best matches the type of blur is key to enhancing recognition performance. In the study, different repair methods had high recognition accuracy under various levels of ambiguity, but as the degree of ambiguity increased, the recognition accuracy gradually decreased. The experiment also showed the limits of the recognition performance of face identification. Although face recognition technology has made significant progress in recent years, the recognition accuracy and robustness of the system still need to be improved in complex scenes and varying image quality. In addition, in the process of repairing damaged or missing images, more refined methods are needed to balance the completion of lost features with maintaining the authenticity of the original features. It is necessary to choose the most suitable image restoration method based on specific circumstances and needs. Rigorous support and evaluation of the results of the recognition system are crucial to minimize misunderstandings and errors.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Correlation Coefficient Generation
The correlation coefficients \(\rho_{1,k}\)and \(\rho_{2,k}\) in (9) were computed either using a large correlation window of length \(K_{\text{COR}}=960\) samples (corresponding to 15 s) with an overlap of 959 samples or using a small correlation window of length \(K_{\text{COR}}=16\) samples (corresponding to 0.25 s) with no overlap. In Reference [4; 7; 9; 18], it has been shown that the performance of AAD algorithms is affected by fluctuations of the correlation coefficients. In this paper, we propose two methods (GLM and SSM) to translate the fluctuating correlation coefficients into more reliable probabilistic attention measures. 3. _Auditory Attention Decoding Using Generalized Linear Model:_ The AAD algorithm using the GLM consists of a training and a decoding step. The training step takes place during the calibration phase, whereas the decoding step takes place during the open-loop and the closed-loop AAD phase. (1) _Training step_: The correlation coefficients of speaker 1 and speaker 2 in (9) are first segmented into non-overlapping (NOL) windows of length \(K_{\text{NOL}}\), i.e., \[\hat{e}_{a,k}=\mathbf{g}^{T}\mathbf{r}_{k}, \tag{1}\]1 (11) \[\hat{e}_{a,k}=\mathbf{g}^{T}\mathbf{r}_{k}, \tag{1}\]2 (12) with \(i\) the window index for \(i=1\ldots I\). The mean differential correlation coefficient between speaker 1 and speaker 2 in window \(i\) is computed as \[\hat{e}_{a,k}=\mathbf{g}^{T}\mathbf{r}_{k}, \tag{1}\]3 (13) We model the attention state \(\bar{d}_{i}\) in window \(i\) as a binary random variable [30], i.e., \[\left\{ \begin{array}{ll}\bar{d}_{i}=1,&\text{atending to speaker 1 in window }i\\ \bar{d}_{i}=2,&\text{atending to speaker 2 in window }i\end{array} \right.,\] (14) which is assumed to follow a Bernoulli distribution with probability \(\bar{p}_{i}\), i.e., \[P\big{(}\bar{d}_{i}\big{)}=\bar{p}_{i}\ ^{2-\bar{d}_{i}}(1-\bar{p}_{i})^{\bar{d}_{ i}-1}=\left\{ \begin{array}{ll}\bar{p}_{i},&\text{if }\bar{d}_{i} \text{=}1\\ 1-\bar{p}_{i},&\text{if }\bar{d}_{i}\text{=}2\end{array} \right..\] (15) Using a GLM, the probability of attending to speaker 1 is then given by [31] \[\bar{p}_{i}=P\big{(}\bar{d}_{i}=1\big{)}=1-P\big{(}\bar{d}_{i}=2\big{)}=\frac {1}{1+e^{-\bar{z}_{i}}},\] (16) with the linear predictor \(\bar{z}_{i}\), i.e., \[\bar{z}_{i}=\mathbf{x}_{i}^{T}\boldsymbol{\alpha},\] (17) \[\mathbf{x}_{i}=[1\ \ \bar{\Delta}\rho_{i}]^{T},\] (18) \[\boldsymbol{\alpha}=[\alpha_{0}\ \ \alpha_{1}]^{T},\] (19) where \(\alpha_{0}\) and \(\alpha_{1}\) denote the GLM parameters. Obviously, the probability of attending to speaker 1 monotonically increases from 0 to 1 for \(\bar{z}_{i}\in(-\infty,\infty)\).