esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
Overexpression of _srrA_, _marA_, _ycel_ and _yfgM_ _Restore_ LpxC_ _Amounts in lapC Mutant Bacteria  
To address the molecular basis of the suppression of growth defects at elevated temperatures by overexpression of the above-mentioned multicopy suppressor-encoding genes, experiments were undertaken to examine the amount of LpxC upon their mild induction. The rationale for these studies is to verify if the major defect reflected by the reduction in LpxC amounts associated with the absence of the periplasmic domain of LapC can be overcome by overexpression of any of these genes. Thus, total cell extracts were obtained from the wild type and panels of isogenic _Alap190_ derivatives either with an empty vector or when the overexpression plasmid was present under conditions described in the legend to Figure 5. The equivalent amount of proteins was resolved on a 12% SDS-PAGE, and LpxC was detected by immunoblotting with LpxC-specific antibodies. The estimation of relative amounts of LpxC in such experiments prominently shows that induction of expression of _marA_, _ycel_ and _srrA_ restore LpxC to near wild-type levels (Figure 5A, lanes 4, 5 and 8). We applied the same amounts of total proteins from the _\(\Delta\)ftsH_ _sfhC21_ strain since, in the absence of FtsH, LpxC amounts are increased to serve as a marker for LpxC and as a control. In parallel, the same samples were immunoblotted with anti-TxA antibodies serving as an additional loading control. Among the remaining genes, whose overexpression also suppresses the Ts phenotype of _lapC190_ bacteria, a modest increase in LpxC abundance can be observed in strains with induced expression of either _yfgM_ or _dksA_ or _artl_ genes in the _lapC190_ background (Figure 5B, lanes 4, 5 and 6). These results suggest that the induction of _marA_, _ycel_ and _srrA_ genes and, to a certain extent, of _yfgM_, _dksA_ and _artl_ can suppress the growth defect of _lapC190_ bacteria by restoring LpxC amounts. Surprisingly, the overexpression of _pldA_, _gnsA_, _ymgG_ and _acpT_ genes (Figure 5A) as well as _lapD_, _acpP_ and _acCB_ (Figure 5B) did not cause any noticeable increase in LpxC amounts and remained similar to when extracts from _lapC190_ with the vector alone were applied. It is possible that in the case of expression of such multicopy-encoding suppressor genes, a mild  
Figure 4: Overexpression of the _lapD_ gene can restore the growth of _lap190_ bacteria at 43 \({}^{\circ}\)C. Growth of isogenic cultures of _lap190_ bacteria transformed with either plasmid DNA of the vector alone or when the _lapD_ gene is present on the plasmid was quantified by spot dilution on LA 30 \({}^{\circ}\)C and 43 \({}^{\circ}\)C. The relevant genotype and temperature of incubation are indicated. induction of their expression may not be sufficient to significantly increase LpxC amounts, although Ts of _lapC190_ bacteria can be suppressed. Overall, we can conclude that a mild overproduction of SrrA, MarA, YceJ and YfgM suppress the Ts phenotype and also lead to the restoration of LpxC amounts, explaining the molecular basis of their contribution in the regulation of LpxC.
## 5 Experimental Results
### Analysis of the Proposed Scheme  
As mentioned in Section 4, the width of a character is useful information for lexicon analysis. Here, we assume that the bounding box of each key character is near a square. Some key characters are analyzed manually. After sorting the area values of these key characters, the median value of these sorted area values is 2200 pixels as the estimated area value of a character, and then the estimated width of a character is about 46 pixels, i.e., \(N_{W}^{\text{CM}}=46\), as the prior information in the lexicon analysis.  
Figure 9 shows the results of ROI localization by using the proposed scheme. The left and right columns of Figure 9 show the original document image and their corresponding ROIs. As we can see in the right column of Figure 9, these ROIs contain the keywords for further analysis. The experimental results demonstrate that the proposed scheme can effectively localize the ROIs in the digitized document images.  
Figure 9: \(Cont.\)
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 3 Methodology
### Model Performance Evaluation
#### 3.3.1 Cox Models Comparison According to Metrics  
First, the three Cox-based models, the simple Cox model, the Cox model with latent classes, and the Cox model-based machine learning model, are trained and evaluated using both an 80/20 train-test split and K-fold cross-validation. Performance is assessed according to the Concordance Index (C-index), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), ensuring robust evaluation across different scenarios.  
Additionally, the two traditional Cox models are also compared using the Akaike Information Criterion (AIC) and the Watanabe-Akaike Information Criterion (WAIC). These criteria assess model fit while penalizing complexity: AIC is standard for conventional models, while WAIC is more suitable for Bayesian models. Since the Cox model with frailty incorporates Bayesian elements, calculating AIC directly may not be ideal, but significant differences in magnitude can still offer insight. It is important to note, however, that AIC and WAIC are not appropriate for evaluating machine learning models, particularly machine learning approaches, due to differences in model structure, regularization techniques, and optimization objectives. These comparisons help highlight the benefit of accounting for unobserved heterogeneity in generating realistic synthetic data. Lower values in AIC, WAIC, and error metrics indicate better model performance.
# Thurstonian Scaling for Sensory Discrimination Methods
## 4 Discussion
### Application of Thurstonian Scaling for Food Quality and Safety  
There are several review articles and research papers that apply Thurstonian scaling in the context of food quality and safety. These articles discuss the use of the scaling in sensory discrimination, consumer testing, and quality control. Recent studies have increasingly applied Thurstonian scaling to monitor and detect food quality and safety, particularly in the fields of sensory analysis, food contamination, and adulteration.  
Moreover, other recent studies have emphasized using the scaling to explore consumer perceptions of food safety and quality. These approaches help better understand how sensory attributes contribute to perceived quality, influencing consumer acceptance and purchase decisions. The scaling has also been utilized to improve the accuracy of sensory panels in discriminating between small differences in product formulations, crucial for maintaining consistent food quality standards. Below are some specific review articles, studies, and book chapter content that discuss sensory discrimination methods in this field that are essential references for understanding how analyses and Thurstonian scaling are applied for quality control in the food industry.  
[PERSON] and [PERSON] [68] deliver a review of practical applications to accurately quantify sensory differences/similarities in food; [PERSON] and [PERSON] [69] and [PERSON] [26] provide detailed overviews of Thurstonian models in sensory and consumer science, offering examples specific to food products; [PERSON] [70] gives a review of sensory discrimination testing methods and includes practical applications of Thurstonian models for food quality testing; [PERSON] [66] offers statistical insights and a broader context for Thurstonian models in the evaluation of food quality and safety, providing a framework for interpreting sensory differences using statistical models; [PERSON] and [PERSON] [27], [PERSON] [29] (pp. 171-177), and [PERSON] [71] extend the collection of Thurstonian models with the degree of differencemethod and other modified discrimination tests, including double discrimination tests by [PERSON] [29] (pp. 177-186).
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 4 Simulation and Result
### Narrow Passages Scene  
The ability of our proposed Fast-RRT algorithm is also evaluated to find a feasible path in an environment with narrow passages. The algorithm needs to find an obstacle-free path from \(x_{start}\) to \(x_{goal}\). Due to the requirement of a large number of sampling, the RRT algorithm does not perform well in making the random tree pass through these narrow  
Figure 12: Comparison of the search time between RRT algorithm (black) and Improved-RRT algorithm (red) with different step sizes.  
Figure 13: Comparison of the node number of the random tree in the Fast-RRT algorithm and the RRT algorithm with different step sizes.  
channels. The environment, in this case, is shown in Figure 9. The length and width of the environment are 1200 and 900, respectively. There are three narrow passages in the environment, and the width of the channel is set to 80. \(x_{start}\) is the start state and \(x_{goal}\) is the goal state. \(d_{goal}\) is the distance from \(x_{start}\) to \(x_{goal}\), which is set to 1000.  
The average and variance of the search time are used to evaluate the efficiency of the Improved RRT algorithm, and the RRT algorithm is used as the benchmark algorithm. Several groups of experiments were performed with the step sizes set to 10, 20, 30, 40, and 50, respectively. For each experiment, we ran the RRT algorithm and the Improved-RRT algorithm 100 times, respectively. Then the average and variance of the search time of these algorithms were calculated, and obtained results are shown in Figure 14.  
The results show that the average and variance of the search time within the Improved-RRT algorithm are significantly smaller than those of the RRT algorithm. When the step size is set to 10, 20, 30, 40, and 50, the average search time of the Improved-RRT algorithm is 0.567 s, 0.146 s, 0.076 s, 0.047 s, and 0.035 s, respectively. The values are 15.3%, 15.1%, 17.1%, 17.0%, 16.8% of the RRT algorithm, respectively. Moreover, the variance of search time of the Improved-RRT algorithm is 0.480 s, 0.101 s, 0.056 s, 0.050 s, and 0.032 s, which are 16.5%, 15.9%, 10.5%, 11.5%, 11.7% of the RRT algorithm, respectively. Therefore, the Improved-RRT algorithm can find a feasible path faster than the RRT algorithm and has better stability. Compared with the results of Section 3.1, the difference between the average and variance of the search time of these two algorithms is further increased, indicating that the random expansion strategy proposed in this paper has a significant effect on the pathfinding problem of narrow passage environment.
# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 3 Resynchronization Algorithm Based on ASIFT  
In general, the image can be attacked by different kinds of factors, such as common image processing and geometric attacks. Most robust image watermarking can resist the common processing, but geometric attacks can change the properties of an image, which can result in the destruction of watermark synchronization. To overcome this problem, an ASIFT-based resynchronization algorithm is used to extract the feature points as reference points for matching establishment between watermarked and received images. Then, the distortion parameter will be calculated and inverse processing can be done before watermark extraction to get the corrected image.
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 3 Experimental Test of Output Torque for a PMSM
### Principles of Experimentation
\tag{9}\]  
where \(i=1,2,\ldots,n\), \(\theta\), \(\gamma\), and \(\psi\) are Euler angles shown in Figure 9.  
Based on the definition of linear velocity, the components of unit linear velocity vector \(V_{x}(i)\), \(V_{y}(i)\), and \(V_{z}(i)\) during the \(i\)-th sampling time are obtained by taking the first derivative of \(S_{x}(i)\), \(S_{y}(i)\), and \(S_{z}(i)\) as follows:  
\[\left\{ \begin{array}{l}V_{x}(i)=\frac{S_{x}(i)-S_{x}(i-1)}{T_{s}}\\ V_{y}(i)=\frac{S_{y}(i)-S_{y}(i-1)}{T_{s}}\\ V_{z}(i)=\frac{S_{z}(i)-S_{z}(i-1)}{T_{s}}\end{array} \right. \tag{10}\]  
where \(i=2,3,\ldots,n\). Similarly, based on the definition of linear acceleration, the components of unit linear acceleration vector \(a_{x}(i)\), \(a_{y}(i)\), and \(a_{z}(i)\) during the \(i\)-th sampling time are obtained by taking the first derivative of \(V_{x}(i)\), \(V_{y}(i)\), and \(V_{z}(i)\) as follows:  
\[\left\{ \begin{array}{l}a_{x}(i)=\frac{V_{x}(i)-V_{x}(i-1)}{T_{s}}\\ a_{y}(i)=\frac{V_{y}(i)-\tilde{V}_{y}(i-1)}{T_{s}}\\ a_{z}(i)=\frac{V_{z}(i)-V_{z}(i-1)}{T_{s}}\end{array} \right. \tag{11}\]  
where \(i=3,4,\ldots,n\). In the three-dimensional space, angular acceleration vector in the fixed coordinate system OXYZ is expressed according to the rigid body kinematics as follows:  
\[\tilde{\mathbf{Q}}=\frac{\mathbf{r}\times\mathbf{A}}{\left|\mathbf{r}\right|^{ 2}} \tag{12}\]  
where \(\mathbf{r}\in\mathbf{R}^{3}\) is the rotor position vector, \(\mathbf{A}\in\mathbf{R}^{3}\) is the rotor linear acceleration, \(\times\) represents cross product. According to the definition of the unit position vector and unit linear acceleration vector in Equation (9) and Equation (11), it can be obtained that:  
\[\mathbf{S}=\frac{\mathbf{r}}{\left|\mathbf{r}\right|^{2}},\ \mathbf{a}=\frac{\mathbf{A}}{\left|\mathbf{r}\right|} \tag{13}\]where \(\mathbf{S}=\left[S_{x},S_{y},S_{z}\right]^{T}\), \(\mathbf{a}=\left[a_{x},a_{y},a_{z}\right]^{T}\). By taking Equation (13) into Equation (12), the angular acceleration is rewritten as follows:  
\[\ddot{\mathbf{Q}}=\mathbf{S}\times\mathbf{a} \tag{14}\]  
Furthermore, the components of \(\ddot{\mathbf{Q}}\) during the \(i\)-th sampling time are derived as:  
\[\left\{ \begin{array}{l}\alpha_{x}(i)=S_{y}(i)a_{z}(i)-S_{z}(i)a_{y}(i)\\ a_{y}(i)=S_{z}(i)a_{x}(i)-S_{x}(i)a_{z}(i)\\ a_{z}(i)=S_{x}(i)a_{y}(i)-S_{y}(i)a_{x}(i)\end{array} \right. \tag{15}\]  
By taking Equation (9), (10), and (11) into Equation (15), the functional relation of the angular acceleration vector and Euler angles can be established as Equation (16). \[\ddot{\mathbf{Q}}=\ddot{\mathbf{Q}}(\theta,\gamma,\psi,i) \tag{16}\]  
The flow chart for calculating the output torque is shown in Figure 10.
## 5 Machine Learning Application for Conventional and Universal Decoding Schemes
Table 6 summarizes different learning-based models for conventional coding techniques along with the corresponding applications in 5G/6G networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Algorithm Ref** & **Coding Technique/ Decoder** & **Description** & **Advantage** \\ \hline \multirow{8}{*}{Reinforcement learning [59]} & \(\bullet\) & BCH (63, 45) and Reed & Bit-flipping decoding mapped to Markov decision process & LBF improves error correction by employing RL to learn \\  & \(\bullet\) & Learned bit flipping & for data-driven learning of \\  & (LBF) & Bit-flipping decoding & optimal bit-flipping \\  & \(\bullet\) & with Q-learning & decision strategies & error rate \\ \hline \multirow{4}{*}{Deep learning neural network (DNN) [60]} & \(\bullet\) & Polar (256, 128) & Polar code construction by application of iterative loop & Improved error correction and faster convergence to a stable BER through neural \\  & \(\bullet\) & Belief propagation (BP) & unrolling (deep unrolling) to \\  & & decoding as trainable & replace frozen/unfrozen bit \\  & & neural network (NN) & positions by trainable weights & network training \\ \hline \multirow{4}{*}{Recurrent neural network (RNN) [61]} & \(\bullet\) & Short LDPC Rate 1/2 & \multirow{4}{*}{Investigation of decoder  diversity architecture} & Improved decoding accuracy \\  & & code length = 64, 128 & & \\ \cline{1-1}  & \(\bullet\) & BP-RNN decoding and & & \\ \cline{1-1}  & & OSD Post-Processing & & \\ \hline \multirow{4}{*}{Convolutional neural network (CNN) [62]} & \multirow{4}{*}{Deep neural network decoder (NND)} & \multirow{4}{*}{Performance evaluation of  NND built upon CNN and  proposed saturation length for NND} & \multirow{4}{*}{Investigation of decoder  as different BP-RNN decoders are trained to decode errors in absorbing sets in bipartite graph} \\ \cline{1-1}  & & & \\ \cline{1-1} \cline{2-5}  & \(\bullet\) & Maximum a posteriori  probability (MAP) & Data-driven symbol detector  exploring channel diversity & More robust in scenarios with diverse channel conditions  where fading is uncertain \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Application of ML models in conventional coding techniques.
## 3 Results
### Grouping by Cropland and Crop Rotation  
Using the environmental variables of scenario 6 which had the highest prediction accuracy, we fitted the soil pH prediction models for different grouping strategies, i.e., grouping by cropland type and crop rotation systems. The models were also validated by the 10-fold cross-validation method, and the results are shown in Table 5 and Figure 5. The two different strategies led to quite distinct predictions. The strategy based on cropland type obtained similar results to scenario 6, with an RMSE of 0.67 and an R\({}^{2}\) of 0.35. On the other hand, grouping by crop rotation systems further enhanced the predictability of soil pH compared to scenario 6, with a decrease in the RMSE of 15% and an increase in the R\({}^{2}\) of 53%. The individual statistical accuracy of the sub-models fitted by subsamples also varied markedly. For cropland type, the irrigated subsample had a higher accuracy, with an RMSE of 0.65 and an R\({}^{2}\) of 0.41, while the rainfed one only had an R\({}^{2}\) of 0.28. For the crop rotation systems, the orchard subsample performed the best out of the three systems, with an RMSE of 0.46 and an R\({}^{2}\) of 0.55, followed by the vegetable subsample, with an RMSE of 0.68 and an R\({}^{2}\) of 0.45. Despite having the lowest R\({}^{2}\), the paddy subsample performed the best regarding the RMSE (0.44).  
Figure 5: Scatter plots of predicted versus observed soil pH from the 10-fold cross-validation for the two different grouping strategies, namely cropland type (**a**) and crop rotation systems (**b**). The fitting lines with colors are fitted individually by different subsamples. The dashed line represents the 1:1 line, and the solid line represents the fitted line.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Subsample Determination**} & \multicolumn{1}{c}{**Number of Selected Variables**} & \multicolumn{1}{c}{**ME**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \hline \multirow{3}{*}{Cropland type} & Rainfed & 13 & 0.01 & 0.68 & 0.28 \\  & Irrigated & 5 & 0.00 & 0.65 & 0.41 \\  & Total & - & 0.00 & 0.67 & 0.35 \\ \hline \multirow{3}{*}{Crop rotation systems} & Paddy & 4 & \(-\)0.05 & 0.44 & 0.25 \\  & Vegetable & 8 & 0.00 & 0.68 & 0.45 \\ \cline{1-1}  & Orchard & 5 & 0.02 & 0.46 & 0.55 \\ \cline{1-1}  & Total & - & \(-\)0.01 & 0.56 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Soil pH prediction accuracies for different soil sample grouping strategies.
## 5 Modeling Details
### Advanced Preprocessing and Hybrid DI Approach
encode them, and the second half is the BLSTM, which is used to analyze the extracted features from the 1-D CNN and to decode them for load prediction. The process of feature extraction in the 1-D CNN can sequentially capture the dynamics of the complex dataset and can determine complicated time series events. The BLSTM model was formerly designed for text processing, which is difficult to analyse without using sequential inputs and outputs. The BLSTM consists of forward training and backward training to analyze and store information of sequential data. Therefore, training of the proposed hybrid DL model utilizes the 1-D CNN as a feature extraction of time series data and the BLSTM model for feature analysis. The training of the model is forward training in the feature extraction and the first part of the extracted feature analysis of BLSTM, where backward training is performed in the second stage of extracted feature analysis. The training process includes two layers of the 1-D CNN, with 64 filter size and 3 kernel size, to improve extracting the input features, and two layers of the BLSTM, with 50 hidden neurons and 25 hidden neurons, respectively, to analyze the extracted features and to predict the output. The activation function used in our model is rectified linear unit ( _ReLU_), and the total number of training epochs is 100. The hyperparameters, including the number of hidden layers, the number of hidden neurons, activation function, loss function, and number of epochs, create an nondeterministic polynomial (NP) optimization problem for DL fine-tuning and complex stochastic process. In our modeling, the hyperparameters were selected based on a trial and error approach. The parameter settings of the proposed hybrid DL framework are presented in Figure 4:2  
The development environment of our system is MATLAB, where preprocessing techniques were implemented, and Python 2.7, where the DL models were implemented with the Keras deep learning framework, and machine learning models were developed with the scikit-learn framework.