esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### OrtT is a Proteic Toxin that Increases Persistence  
OrtT is the same size (57 aa) and shares 63% protein identity with toxin GhoT (Figure 1A). Hence, we investigated whether OrtT is a toxin. We found that production of OrtT from pCA24N-_ortT_ caused severe growth inhibition (Figure 1B,C); hence, OrtT is a toxin. To determine if OrtT works as a protein toxin, the start codon was mutated from A_T_G (Met) to A_CG (Thr) to generate pCA24N-_ortT_ACG. The wild-type strain harboring pCA24N-_ortT_ACG with inactivated protein OrtT was able to grow with full induction (Figure 1B). These results demonstrate that OrtT is a toxin that functions as a protein.  
Ectopic expression of toxins such as MqsR increases persistence [27] since cells have reduced metabolism [38]. Hence, we tested if OrtT production influences persister cell formation by producing OrtT from pCA24N-_ortT_. As expected, persistence increased 430 fold with ampicillin treatment and 27 fold with ciprofloxacin treatment relative to cells with an empty vector (Figure 1D). Note that, generally persistence fold changes are lower by one to two orders of magnitude for ciprofloxacin treatment compared to ampicillin treatment [39] and that the induction conditions of _ortT_ are different for the experiments in Figure 1B,D. Together, these results corroborate that OrtT is a bona-fide toxin that helps the cells to enter a dormant state.  
Figure 1: OrtT is a proteic toxin that increases persistence. (**A**) Sequence homology between GhoT and OrtT is shown, and the conserved Phe38 residue is boxed; (**B**) Growth of BW25113 wild-type cells harboring different plasmids in LB medium with chloramphenicol (30 \(\upmu\)g/mL) with 1 mM IPTG introduced at 1h. Two independent cultures were evaluated. Error bar represent standard deviation; (**C**) Growth of BW25113 wild-type cells harboring different plasmids on LB plates with chloramphenicol (30 \(\upmu\)g/mL) with or without 1 mM IPTG. Two independent cultures were evaluated; (**D**) Persistence assay with BW25113 wild-type cells after 3 h ampicillin (100 \(\upmu\)g/mL) or ciprofloxacin (5 \(\upmu\)g/mL) treatment with or without OrtT production. Two independent cultures were evaluated. Error bar represent standard deviation. Plasmid abbreviations: Empty, pCA24N; OrtT, pCA24N-_ortT_; OrtTACG, pCA24N-_ortT_ACG which lacks a start codon.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 7 Current Scope and Limitations
### Future Work  
Although the proposed random forest-based event detection and classification approach demonstrates promising results, there are several areas for future research and improvement:  
* Integration with Real-Time Systems: Extending the current offline analysis to a real-time system capable of processing vibration and acceleration data on embedded hardware. This involves optimizing the computational pipeline to meet the constraints of latency and power consumption.  
* Advanced Machine Learning Models: Exploring advanced machine learning techniques, such as deep learning models, to capture more complex patterns in the data. Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs) could be particularly effective in identifying spatio-temporal dependencies.
* Extended Event Categories: Expanding the scope of event detection to include additional road event detection, such as lane changes, overtaking, and sharp turns, to enhance the utility of the model in multiple environments.
* Multimodal Data Fusion: Incorporating additional sensors, such as GPS, gyroscopes, or cameras, to create a multimodal system. This would improve the accuracy of the classification and provide a richer context for detected events.
* Generalization Across Locations: Testing and adapting the model for different geographical regions to ensure robustness against variations in road conditions, vehicle types, and driving behaviors.
* Explainability and Interpretability: Develop methods to improve the interpretability of the model's decisions, particularly in scenarios where safety-critical decisions are required. Feature attribution techniques and visualization tools could provide valuable insights into model behavior.
* Scalability for Large Datasets: Investigating scalable training techniques to handle larger datasets with higher-dimensional feature spaces, ensuring that the model remains efficient and applicable to industrial-scale deployments.  
These directions aim to address both technical challenges and practical considerations, paving the way for a more comprehensive and deployable solution for road event detection in urban traffic.
## 3 Results and Discussion
The findings of this study also indicate the need to standardize and optimize the process parameters for SLM Ni-Cr-Mo alloys in order to reduce the formation of pores and thereby enhance the microstructure and mechanical properties of the resultant alloys [32]. Furthermore, when fabricating Ni-Cr-Mo frameworks, SMM would seem to be a viable and inexpensive alternative to conventional cast frameworks [14]. Although the current findings imply that SLM and SMM Ni-Cr-Mo alloys both have potential as new dental base-metal alloys, comprehensive in vitro and in vivo studies are still needed to verify their clinical performance and potential health risks [13]. In addition, commercial Ni-Cr-Mo alloy systems (materials and equipment) produced by digital manufacturing (AM and SM) need to be further developed for clinical use.
## 5 Experimental Setup and Results
### Experiment 2--Processing Individual Hand Gestures  
In this section, hand gesture detection results are presented. In Figure 10, three letters from the PSL dataset are randomly selected for this experiment and processed through the proposed system. The hand gesture detection algorithm acquires the hand gesture and compares it with the images from the dataset stored in the database. The results show that the detection can exceed 96%. Depending on the input hand gesture, more than one image from the database can be matched, and the one with the highest percentage is selected.  
Figure 11 shows the accuracy and loss graphs for the PSL dataset. The graphs include both actual and test results.  
Figure 12 shows the hand gesture detection results for the SSL dataset. The results are encouraging and show high accuracy. In this figure, three random gestures are selected from SSL, and the detection accuracy is shown. It is observed that the accuracy of the captured hand gesture is high when it is unique among the stored hand gesture images. As an example, if two different hand gesture images look similar, then the detection accuracy of these will be slightly lower.  
Figure 11: Hand gesture detection results for PSL dataset.  
Figure 12: Processing hand gestures (SSL dataset).  
Figure 10: Processing hand gestures (PSL dataset).
## 3 Results
### ROC Analysis of RF and GBDT Classifiers  
The performance of this study's proposed approach was measured using the receiver operating curve (ROC) analysis and AUC metric. A higher AUC designates better performance. Figure 3 displays a comparison between ROC curves when the entire set of features is used. The AUC for each fold is shown in the caption of each figure, and the mean of the AUC was computed. Figure 3 illustrates the ranking performance of 100 iterations of 10-fold cross-validation. The ROC curve summarizes the results and shows that performance was good for all folds, giving an average AUC equal to 95% for RF and 94% for GBDT. The  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Classifiers** & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**K-NN**} \\ \hline
**Features** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** \\ \hline Complexity & 87 \(\pm\) 02 & 88.65 & 86.00 & 83 \(\pm\) 03 & 87.13 & 84.52 & 81 \(\pm\) 03 & 73.09 & 75.07 & 84 \(\pm\) 02 & 88.80 & 87.47 \\ \hline Oscillatory & 94 \(\pm\) 01 & 90.07 & 89.60 & 93 \(\pm\) 01 & 89.56 & 89.21 & 91 \(\pm\) 01 & 81.73 & 82.96 & 88 \(\pm\) 02 & 89.50 & 88.43 \\ \hline Complexity and power & 94 \(\pm\) 01 & 90.76 & 90.30 & 94 \(\pm\) 01 & 89.76 & 89.43 & 90 \(\pm\) 01 & 80.63 & 81.92 & 88 \(\pm\) 02 & 90.04 & 89.07 \\ \hline \end{tabular}
\end{table}
Table 6: Classification performance for the complexity measures, oscillatory power, and a combination of the two with the univariate feature selection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**GBDT**} \\ \hline
**Channel** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** \\ \hline FP1 & 91 \(\pm\) 01 & 87.27 & 86.41 & 88 \(\pm\) 01 & 85.63 & 84.85 \\ \hline FP2 & 94 \(\pm\) 01 & 89.78 & 89.38 & 92 \(\pm\) 01 & 88.09 & 87.70 \\ \hline F3 & 79 \(\pm\) 03 & 88.15 & 83.86 & 73 \(\pm\) 03 & 87.50 & 82.78 \\ \hline F4 & 80 \(\pm\) 02 & 87.82 RF classifier yielded better performance for each of the 10 folds compared to GBDT. It is remarkable for both classifiers that the fluctuations of the AUC were smaller among the different folds, indicating the stability of the feature set.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 2 Related Works
### Correlation Study of Gradient and Variance  
The gradient of an image measures the change rate of pixels in the horizontal and vertical directions and is commonly used to provide more detailed information for edge detection and reconstruction [34; 35; 36; 37; 38; 39]. The variance reflects the high-frequency component. The larger the variance, the better the visual effect is, and the easier different objects are to distinguish.  
Figure 2 shows two images, either of which is divided into 36 small regions of the same size. The number in Figure 2b is the corresponding number of each region. Figure 2c illustrates the regions with the maximum gradient (_Max_g_) and the minimum gradient (_Min_g_) and the maximum (_Max_v_) and minimum variance (_Min_v_). From Figure 2, it can be seen that the regions with _Max_g_ and _Max_v_ contain rich information, while the regions with _Min_g_ and _Min_v_ only have plain contents. So, the regions with _Max_g_ and _Max_v_ are vital for detail processing.
## 3 Proposed Model
### Improved Yolov5
#### 3.3.2 Proposed Model Improvement Comparison with the Existing Model  
* Multi-Head Self-Attention: Convolutional layers (CSP bottleneck module) are effective for feature extraction, but they do not capture fine-grained details and subtle differences required for accurate fine-grained recognition. The fixed receptive field size and limited context modeling of convolutional layers hinder their ability to capture fine-grained visual cues. We replaced these convolutional layers with multi-head self-attention layers which enabled the model to capture both local and global dependencies, as it allowed the model to attend to specific fine-grained details and captured the broader context.
* Feed-Forward Neural Network: The residual connection in the CSP bottleneck module helps propagate information through skip connections. However, it is not sufficient to capture the intricate relationships and dependencies present in fine-grained recognition tasks, where subtle details and local patterns play a crucial role. We replaced it with feed-forward networks, which allowed the model to refine the feature representation in a non-linear manner and enabled the model to learn complex patterns and capture subtle differences between visually similar categories.
* Layer Normalization: An additional layer normalization step helped with stabilized training and improved gradient flow. The first normalization layer enhanced the model's ability to learn discriminative features through reducing the impact of variations in scales and intensities across fine-grained images, while another layer of normalization further enhanced the stability and convergence of the model.
* Position-wise Feed-Forward Network: Additional convolutional layers in the CSP bottleneck module have limited context modeling capabilities, which limits their ability to capture the intricate details necessary for accurate fine-grained recognition. Our position-wise feed-forward network added additional non-linearity, allowing the model to capture more intricate relationships between tokens/regions.  
Figure 2: Transformer encoder block performs the additive operation through a learnable vector and has the same input and output size.  
* In our experiment, we also applied transformer heads at the last stage of the Yolov5 network because the feature maps at that stage have low resolutions, and using transformer heads on low-resolution feature maps reduces high computing costs and memory consumption. The proposed architecture can be seen in Figure 3, where transformer encoder blocks containing a multi-head self-attention module with a feed-forward neural network and layer normalization are integrated at the end of the feature extracting stage and the transformer encoder blocks included in the model's neck are used to form prediction heads.
## 4 A Deep Learning-Based CSI Feedback for New Radio
### The Channel Estimation Task  
When considering massive MIMO systems, the channel estimation task is fundamental and many studies have addressed the issue [19; 20] since beam-forming techniques strongly depend on the outcome of this phase. In [12], the main focus is the compression task, i.e, the model in [12] takes as input a perfect channel matrix and learns efficient encoding and decoding algorithms to compress and decompress it with minimum error. Our model, instead, aims to deal also with the challenging downlink channel estimation task. This means that the compression and decompression process is not performed on the ideal channel matrix generated by the CDL channel module, but on a noisy estimation of it, coming from the CSI-RS compensation. The model is expected to learn efficient encoding and decoding algorithms that, besides compressing and decompressing the input matrices, must be able to remove the noisy contributions and reconstruct the clean channel matrices with minimum error. It should be noted that, to simplify the experiments, we decided to consider only the noise affecting the downlink transmission to avoid simulating the uplink channel.  
To include this channel estimation task, we need to define two different instances of the channel matrix \(\mathbf{H}\):  
* an ideal channel matrix \(\mathbf{H}^{id}\), generated by the CDL module of the software simulator. It represents the desired output of the NR-CsiNet model;
* a noisy version of the perfect channel matrix \(\mathbf{H}^{\text{CSi-RS}}\), estimated from the CSI-RS pilots by means of a simple interpolation. It represents the input of the NR-CsiNet model.  
To obtain a first raw estimation of the complete downlink complex channel matrix for each transmitting/receiving antenna pair, we performed a cubic radial basis function (RBF) interpolation on the real and the imaginary parts separately, using a MATLAB predefined library.  
An example of the result obtained is shown in Figure 2, where \(n\) and \(m\) are the transmitting and receiving antenna indexes, respectively. The estimated \(\mathbf{H}^{\text{CSi-RS}}\) matrices are collected by simulating different conditions of the channel; practically, we select a couple of different SNR values, 10 dB and 20 dB, and train a different NR-CsiNet model for each of these values. Then, we test the behavior of these models at different levels of channel quality; to be precise, simulations are performed with SNR values varying between \(-5\) dB and \(35\) dB, with a \(5\) dB granularity. To simplify the notation, in the following subsections, we refer to the two channel instances \(\mathbf{H}^{id}\) and \(\mathbf{H}^{\text{CSi-RS}}\) as a single matrix \(\mathbf{H}\), meaning that all the processing steps described have to be performed on both.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 2 Methodology  
The study's flowchart is depicted in Figure 1. Its primary aim is to analyze error patterns in Kompsat-3A data across experimental cases based on varying GCP conditions. This investigation necessitates a substantial quantity of GCPs, making the dense generation of GCPs the initial step. This process employs automation based on aerial image chips and robust image-matching techniques to generate GCPs. The study involves scrutinizing the geometric errors across the entire image area of Kompsat-3A using the abundance of generated GCPs. Following this, GCPs are allocated zone numbers based on their distribution across the target Kompsat-3A images. Experimental cases are then established to examine the impact of the number of GCP zones on sensor modeling errors. Finally, the study aims to determine the minimum GCP distribution requirement for achieving high-precision geometric correction at the one-pixel level.  
Compared to other studies [12; 15; 16; 19], our methodology has innovative aspects in that the Kompsat-3A local geometric error pattern is investigated within the image space. This is possible because dense but accurate GCPs are automatically generated using image matching with the outlier removal technique. Firstly, this study shows that Kompst-3A has large geometric error changes within the local image space. Note that most research has focused on overall georeferencing error analysis, not a local error change pattern within the image space. Secondly, this study tested the local GCP distribution cases from local error pattern analysis to avoid hazardous GCP distribution.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
###### Abstract
To design such device, we first created a database with information from real scenarios to train the deep learning model (Section 2.2). Audios were recorded in real urban scenarios using an affordable microphone. Then, we developed a strategy to train a classifier based on temporal and frequency data analysis (Section 2.3) and deep convolutional neural networks (Section 2.4). Through experimental results, we show that the convolutional neural network model outperforms methods that have been previously used in audio distress signal recognition (Section 3). Finally, we deployed the trained neural network model on a Raspberry Pi and evaluated the computational performance of the real-time recognition system (Section 3.4). Currently, the Raspberry Pi represent a useful element to construct inexpensive portable devices that compute complex tasks in real time [13,14]. We end this paper with some conclusions and future work (Section 4). The collected dataset and the implemented scripts are publicly available at the github repository in the link above.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method
### Improved Haze Color Correction Based on the Gray World Hypothesis  
The existing color restoration methods are based on the gray world hypothesis, and color consistency and white balance are performed by adjusting the color average. However, depending on the scene, original images without additional hues may show a biased histogram. This can be observed through histograms and color means, as shown in Figure 5.  
In Figure 5, when colorful objects dominate a wide range, the image exhibits a biased histogram. In such cases, the gray world hypothesis, as shown in Figure 5c, can perceive incorrect colors and perform excessive white balance [32]. This can be improved through the saturation of \(A(\infty)\), as shown in Figure 5d.  
The saturation of \(A(\infty)\) shows a value very similar to the hue added in the image. Accordingly, the proposed method restores the color-cast image using the saturation value of \(A(\infty)\) to the gray world hypothesis. Given that the \(a\) and \(b\) channel values of the airlight matrix extracted in Section 3.1 are \(a_{A}\) and \(b_{A}\), respectively, the proposed method calculates the corrected colors \(\widehat{a_{A}}(x)\) and \(\widehat{b_{A}}(x)\) based on Equation (6).  
\[\begin{array}{l}\widehat{a_{A}}(x)=a(x)-wa_{A}\\ \widehat{b_{A}}(x)=b(x)-wb_{A}\end{array} \tag{13}\]  
In Equation (13), \(w\) is the constant that shows the level of white balance and is set to \(0.95\sim 1\). Weight \(w\) has a value of [0 1], and as \(w\) increases, the image becomes closer to grayscale. In this paper, we use a \(w\) of 0.95 for all datasets to carry out white balance.  
Figure 5: Veil color in various types of hazy images: (**a**) original images, (**b**) histogram of the original images, (**c**) average colors of the original images, and (**d**) airlight colors of the original images.
## 4 Microencapsulation Techniques
### Chemical Process
#### 4.1.3 Microencapsulation by Interfacial Polycondensation
For example, the use of chitosan, a bio-based polymer, as a complementary monomer to isocyanates has been developed in recent years, as described in the work of [PERSON] et al. for the encapsulation of PCMs [133]. Commonly shell materials used in chemical processes and the properties of developed mPCMs are illustrated in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Shell** & **Core** & **T\({}_{m}\) mPCMs (\({}^{\circ}\)C)** & **LC (\%)** & **\(\Delta\)H\({}_{f}\) (\(\mathbb{J}\cdot\mathbf{g}^{-1}\))** & **Size (\(\mu\)m)** & **Ref.** \\ \hline \multirow{4}{*}{Suspension polymerization} & St/MMA & Paraffin & 42 & 43 & 88 & - & [84] \\ \cline{2-8}  & PMMA & Caprylic acid & 14 & 66 & 98.7 & 2–10 & [85] \\ \cline{2-8}  & TiO\({}_{2}\)-PMMA & _n_-Octadecane & 25 & 73 & 84–142 & 10–20 & [134] \\ \cline{2-8}  & OMA/MMA & _n_-Octadecane & 26 & 21 & 93 & 1.6–1.7 & [135] \\ \hline \multirow{4}{*}{Emulsion polymerization} & PMMA & Paraffin & 24–33 & & 101 & 0.5–2 & [93] \\ \cline{2-8}  & PMMA & SA-Eicosanoic & 57 & 69 & 126 & 0.046 & [94] \\ \cline{2-8}  & PMMA & SA & 55 & 52 & 102 & 0.29 & [95] \\ \cline{2-8}  & PMMA & Paraffin wax & 52–55 & 88 & 115 & 18 & [96] \\ \cline{2-8}  & Polystyrene & LA & 44 & & 167 & 1.32 & [97] \\ \hline Dispersion polymerization & MF & Hexadecane & - & 79 & 162 & 3.9 & [102] \\ \hline \multirow{4}{*}{In situ polymerization} & MF & _n_-Octadecane & - & 40 & 120 & 4 & [104] \\ \cline{2-8}  & MF & _n_-Tetracosane & 53 & 25 & 135 & 2–20 & [105] \\ \cline{2-8}  & MF & _n_-Hexadecanol & 51 & 79 & 171 & 10–60 & [106] \\ \cline{2-8}  & MF & 1-Dodecanol & 20 & 41 & 79 & 0.49 & [107] \\ \cline{2-8}  & PUF & CA & 30 & - & 129 & 0.66 & [112] \\ \cline{2-8}  & PMF & CA & 29 & - & 34 & 0.32 & [112] \\ \cline{2-8}  & PMUF & CA & 29 & 54 & 61 & 0.29 & [112] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Commonly used shell materials in chemical process and mPCMs properties.