esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Semi-Synthetic and Molecular Activation of Silent BGCs
### The Mutation Approach: Deleting the Suppressors  
While positive regulation by transcriptional proteins is the dominant phenotype, repression is also a feature of many, whether intrinsic or state dependent. In this case, deletion or deactivation of these suppressor proteins has the potential to free silent BGCs from their locked in state and result in production of the natural compounds. Members of the LysR and TetR families of transcriptional regulator are known to possess suppressive activity and are one potential target for this approach. Null mutation of the _hexA_ LysR from _Photorhabus luminescens_ led to a dramatic increase in the biosynthesis of small molecules [102]. _Bth_II1681_ encodes for a LysR-type TF associated with the previously characterized thailandamide biosynthetic cluster of _B. thailandensis_[53,103], with mutations in this regulator resulting in the production of a novel thailandamide lactone variant [53,54]. An example TetR repression was shown by the null mutation of repressors within two silent gene clusters in _Streptomyces_ sp. PGA64 and _S. ambofaciens_, which induced expression of the BGCs and resulted in the detectable production of the corresponding natural products--novel angucylinone and already described kinamycins, respectively [55,56]. In another study, deleting a presumed pathway-specific regulatory gene (_scbR2_) that encodes a member of the c-butyrolactone receptor family of proteins and which lies in the _cpk_ gene cluster of _S. coelicolor_ A3(2) led to production of novel antibacterial activity (abCPK) and a yellow-pigmented secondary metabolite (yCPK) [57]. In some cases, repression of biosynthetic clusters involves both transcriptional and post-translational control, as seen with production of the cryptic orsellinic acid gene cluster in _A. nidulans_. Genetic assessment of _mv1A_ mutants revealed the role of both itself and VeA (but not the VeA partner LaeA) in the suppression of the cryptic ors gene cluster producing orsellinic acid and its F9775 derivatives [104]. Adding to the complexity of this regulatory system was the involvement of histone 3 acetylation, suggesting the involvement of epigenetic control [104].  
In a broader sense, deletion of cellular systems also has potential for activation of silent BGCs. An example of this is where protein destabilization has been targeted for deletion resulting in recovery of novel bioactive compounds from _A. nidulans_. Specifically, deleting the conserved eukaryotic _csnE_/CSN5 demethylylase subunit of the COP9 signalosome results in the activation of a previously silenced gene cluster comprising a polykteide synthase gene producing the antibiotic 2,4-dihydroxy-3-methyl-6-(2-oxopropyl)benzaldehyde (DHMBA) [105]. The highly conserved nature of this system among eukaryotic organisms underpins its potential as a broad spectrum approach for the activation of silent BGCs.
## 1 Introduction
Our simulations revealed that the performance ranking of the three channels would be reversed when switching from ensemble-average SNR to short-term SNR. After an in-depth study, we found that the method used to calculate the SNR value significantly affects the BER performance in the mentioned ETSI channel models. Previously, a portion of the results was published in [4]. In this paper, we aim to share our newest findings on this issue and provide recommendations on the use of SNR in simulations. The rest of the paper is organized as follows: Section 2 introduces the channel models and the two different definitions of SNR, namely ensemble-average SNR and short-term  
Figure 1: A simple simulated communication system. SNR. Section 3 describes the LTE DL physical layer. Section 4 outlines the simulation method that uses ensemble-average SNR as the basis for comparison and analyzes the distributions of channel frequency responses. Section 5 details the simulation method that uses short-term SNR as the basis for comparison and discusses the differences in BER performances based on the two SNR calculation methods. Finally, Section 6 summarizes the conclusions.
## 3 Network Anomaly Detection
### Data Acquisition and Process
#### 3.1.1 Feature Library Construction Framework  
The need and significance of establishing a 5G vertical application network demand and traffic feature library based on the scenarios lies in the utilization of network data from dedicated network platforms. This seeks to establish a comprehensive feature evaluation method based on 5G private network business, network demands, and network traffic. Afterwards, based on the proposed feature evaluation method, this will facilitate the categorization of the network data features of 5G vertical applications in industry-specific scenarios. Ultimately, based on the 5G vertical application network demand and traffic feature library, recommendations for the deployment and planning of the 5G private network platform can be given. This not only guarantees network performance but also enhances the deployment efficiency and reduces the network planning costs.  
In conclusion, the analysis of network demand and traffic features for contextual 5G vertical applications includes two requirements: (1) establish a multi-dimensional feature extraction and fusion method based on 5G private network business, network demand, and network traffic; (2) design a classification method based on multi-modal features of 5G private networks, and construct a 5G private network demand and traffic feature library model.  
The industry-specific 5G private network traffic feature analysis model performs traffic feature analysis based on network-level data, and primarily consists of two parts. These are: a method for multi-dimensional feature extraction and fusion based on 5G private network business, network demands, and network traffic; and a method for constructing a 5G private network demand, business, and traffic feature library.  
The multi-dimensional feature extraction and fusion scheme based on 5G private network business, network demand, and network traffic, processes the original data collected from the private network platform through a series of data-processing operations. This yields index data representing business features, network traffic features, and network demand. The 5G private network demand, business, and traffic feature library model, on the other hand, build a feature library and perform the related result analysis based on the dataset after the fusion of features, in accordance with the specific scenarios.
# Effect of Postharvest UVB Irradiation on the Fruit of cv. Dottato (_Ficus carica_ L.)
## 3 Results and Discussion
### Chemical Analysis  
To characterize the effects of UVB treatment on fig fruits, changes in sugars and secondary metabolites (carotenoides and polyphenols) in both flesh and skin tissue exposed to 1.4 and 8.4 kJ m\({}^{-2}\) UVB (+UVB10 and +UVB60, respectively) were evaluated.
## 3 Applications and Limits of ATPs in Water and Wastewater Treatment
### Energy Demand and Cost-Effectiveness of ATPs
Although a direct estimate of the current cost of an equivalent facility is quite difficult, the industrial diffusion of this technology and new accelerator construction technology can now offer better economic and technical characteristics than those available in the late 1990s. An analysis of EB irradiation of municipal sewage sludge showed that operational irradiation costs were poorly sensitive to delivered doses: At 6.7 kGy irradiation, the energy cost for treatment was estimated at 1.10 US$/kg, increasing to 1.26 US$/kg (+14.5%) when raising the irradiation dose to 25.7 kGy (+283.6%) [110]. This indicates that once the accelerator is in place, increasing the irradiation doses (in response to new contaminants' presence or increased removal requirements) has minimal impact on the cost of treatment, resulting in an unparalleled adaptation flexibility and operability of this specific process. Other AOPs' (e.g., ozone and UV) cost are, on the contrary, directly proportional to the oxidant or irradiation doses, as shown in Table 9.
# Burst Detection in Water Distribution Systems:
## 3 Application  
The scope of Section 3 is to test the performance of the SHtsG. To guide the reader into the proposed methodology, a summary of all the steps involved is hereafter described. The first step regards the water request generation. Hence, a suitable request time series is generated according to the formulation proposed in Section 2.1. This involves the modelling of both the deterministic and the random component through Equation (1). Therefore, the second step concerns the water consumption generation including the water leakages. This means that the previously generated water requests are used as input in the hydraulic simulations based on DFPDM. Specifically, the background losses in Equation (13) are defined through a calibration procedure given pressure and flow rate measurements in some points of the network. The bursts are indeed generated randomly according to the Poisson distribution in Equation (18) with a random hole shape selected among the three formulations proposed in Section 2.2. In the following two applications, the entire methodology is deeply described by practical applications.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 4 Discussion
### GAN Variants  
The conventional GAN model suffers from mode collapse, where the optimal discriminator does not provide enough information for the generator, leading to poor image generation by the generator. Many modifications are suggested and implemented in the basic GAN structure. The most popular models, such as CGAN, WGAN, cycleGAN, starGAN, and SRGAN are more robust and are used in image synthesis, translation, and super-resolution applications.  
IN CGAN, a condition is set for the generator and the discriminator that acts as the controlling mode, giving a better presentation for image synthesis [163; 164]. WGAN [164] provides stable training in conventional GAN by solving the network coverage problem and accelerating the training speed. The loss function is the distance between the two probability distributions known as the Wasserstein distance or Earth Mover's (EM distance) [11]. CycleGAN is suitable for unpaired image translation and training stabilization. It has two generator networks mastering the two different mappings- source to target and target to source. It also has two discriminators that differentiate the synthesized image from the original image belonging to each domain. Evaluation of the resultant image is based on cycle-consistent losses to find the similarity between synthetic and original images [165]. StarGAN is suitable for cross-domain image translation using a single model. Multiple generators are not required to produce images from different domains. The mask vector method allows image translation between multiple datasets [87]. Figure 11 shows major GAN variants and their extensions.
# DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT
## 3 Proposed Dynamic Cross-Domain Authentication Scheme
### Our Scheme
#### 3.3.1 System Initialization  
System setup: Randomly select security parameters, GM runs the algorithm to generate public parameters, group public key, signature key, and tracking key, initialize internal State L and Registry Reg. Then, the public key gpk of the group is published to the blockchain, and anyone can find the public key of the group from the blockchain, and send ok to the tracking administrator TM, which will be used to regulate the daily behavior of users in the future.  
The specific process is as follows:  
1. Select \(n=\mathcal{O}(\lambda)\), and \(n\) is a power of 2. The modulus \(q=\widetilde{\mathcal{O}}\big{(}n^{4}\big{)}\), \(R=\mathbb{Z}[X]/\big{(}X^{n}+1\big{)}\), \(R_{q}=R/qR\), where \(q=3k\), \(k\) is a positive integer). Then, set \(\ell=\log\left\lfloor\frac{q-1}{2}\right\rfloor+1\), \(m\geq 2\left\lceil\log q\right\rceil\), \(\overline{m}=m+k\).
2. Choose an integer \(d\geq\log_{c}(\omega(\log n))\) and a strictly increasing sequence of integers, \(\{c_{0},c_{1},\ldots,c_{d}\}\), where \(c_{0}=0\), \(c_{i}=\left\lfloor\alpha_{0}c\right\rfloor\), \(i\in[d]\).
3. Choose an integer \(\beta=\widetilde{\mathcal{O}}(n)\), \(B=\widetilde{\mathcal{O}}\Big{(}n^{5/4}\big{)}\), \(\chi\) for the bounded distribution of \(B\) on \(R\).
4. \(\mathcal{H}_{FS}:\{0,1\}^{*}\to\{1,2,3\}^{\mathcal{K}}\), where \(\mathcal{K}=\omega(\log\lambda)\) is an anti-collision hash function.
5. COM is a statistical hidden and computationally bound commitment scheme.
6. Uniform random matrix \(B\in R_{q}^{1\times m}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline GM & Group manager \\ TM & Track manager \\ U & User \\ gpk & Group public key \\ gsk & Group signing key \\ pp & Public parameter \\ \(\lambda\) & Safety parameters \\ Reg & Registration list \\ ik & Issue key \\ ok & Open key \\ upk & Public key \\ usk & Private key \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the symbols.  
Figure 2: The user update algorithm.
## 2 Materials and Methods  
### Experimental Nets and Set-Up  
The experimental gillnets used to conduct the sea trials were based on the blackfin flounder tie-down gillnets used in the waters off Gangwon Province and had the same area and composition. Experimental nets with four mesh sizes (84, 90, 105, and 120 mm) were used to investigate the selectivity of tie-down gillnets with differing mesh sizes. The height of the support line was 2.47 m, and the ratio of the height of the vertical dimension of the net to the height of the support line was approximately 55%. The length of the float line in the experimental net was 75.4 m, and that of the sink line was 93.3 m. A monofilament net was used, and the diameter of the netting thread was \(\mathcal{O}\)0.286 mm. The support line was made of polyethylene (PE) with a thread diameter of \(\mathcal{O}\)1.4 mm. Two strands of the support line were vertically hung from the float line to the sink line, and the middle portion of the support line was knotted with netting once. The internal diameter of the experimental net in each group was measured 20 times, and the average values were 83.8 \(\pm\) 4, 90.2 \(\pm\) 2, 104.3 \(\pm\) 2, and 120.3 \(\pm\) 4 mm, respectively. The float and sink lines had different lengths, and the hanging ratios were 40.0% and 49.5%, respectively. For each experimental net, the number of horizontal meshes was adjusted to maintain a constant hanging ratio. The compositions of each experimental net are listed in Table 1, and the basic structure of the experimental nets is shown in Figure 1. The experimental nets were arranged in groups of four in order of the mesh size, and 16 panels were assigned to one set for use in the experiment, as shown in Figure 2.  
Figure 1: Construction of the experimental tie-down gillnets used in the sea trials.  
Figure 2: Arrangement of the experimental tie-down gillnets for the mesh size selectivity tests.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mesh Size** & **Vertical Mesh** & **Horizontal Mesh** & **Hanging Ratio** & **Hanging Ratio** & **Length of** & **Length of** & **Net Height** \\ (**mm)** & **(mesh)** & **(mesh)** & **(\%, Float Line)** & **(\%, Sink Line)** & **Float Line (m)** & **Sink Line (m)** & **(m)** \\ \hline
84 & 50 & 2244 & 40.0 & 49.5 & & & 3.07 \\
90 & 50 & 2094 & 40.0 & 49.5 & & & 3.07 \\
105 & 50 & 1795 & 40.0 & 49.5 & & & (support \\
120 & 40 & 1571 & 40.0 & 49.5 & & & line 2.47) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the experimental gears to estimate mesh size selectivity in the sea trials.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Ablation Study  
We conducted an ablation study to analyze the effects of different activation functions on the accuracy of our SEEN-B4 model. For this, the first EfficientNet-B4 model contains a Swish activation function with layer freezing; however, we employ different activation functions in our second EfficientNet-B4 model. Specifically, we conducted experiments with Swish, SeLU, ReLU, and ELU activation functions for the second EfficientNet-B4 model in our fused method. The models are then trained and tested on the AZH dataset for 3-class classification. The results of the ablation study are presented in Table 5. The results indicate that our proposed model with the Swish-ELU activation function combination outperforms all the other variants and achieved an overall accuracy gain of 4.69%. SEEN-B4 with Swish-Swish activation is the second-best performer, while the model with the Swish-SeLU variant attained the lowest accuracy. So, Table 5 indicates that our proposed model with Swish-ELU activation function can effectively classify the different types of wounds. ELU is the non-saturated activation function that smooths slowly, is robust towards noise, and allows the activations' mean to become closer to the natural gradient. Thus, it generates accurate outputs and improves the model's performance.
## 2 Nanomaterials in Endodontics
### Metallic Nanoparticles
#### 2.4.1 Nanosilver  
Silver ions have powerful antibacterial properties that have been thoroughly researched by scientists. The process of inactivating enzymes that are key to DNA replication is one of the mechanisms by which silver ions can kill microorganisms. The main mode of action of silver ions on microorganisms is to disrupt metabolic and structural processes inside bacterial cells. Silver ions can combine with enzymatic proteins, leading to their denaturation and loss of function. As a result, the bacterial cell loses its ability to replicate DNA and reproduce, which leads to its death. Due to its strong antibacterial properties, silver has been widely used in various fields of medicine, including dentistry. In dental materials, such as fillings, dressings or anesthetic impregnations, the addition of silver ions can provide additional protection against bacterial infections, supporting the treatment process and preventing the recurrence of infections [132; 133; 134].  
[PERSON] et al. examined the properties of silver nanoparticles, which they had previously coated with polyvinyl alcohol and farnesol. The aim of the study was to verify their antimicrobial properties against _E. faecalis_, _C. albicans_ and _Pseudomonas aeruginosa_. The results they obtained confirmed that silver nanoparticles support the disinfection of root canals and inhibit the formation of biofilm [135]. [PERSON] et al. added nanosilver to edetic acid, used for rinsing root canals. As a result, in addition to the antibacterial effect, they also observed an increased effectiveness in removing the smear layer from the canals [135].  
The effectiveness of silver nanoparticles was also observed in studies conducted by [PERSON] et al. In this case, a preparation containing nanosilver at a concentration of 0.02% was applied to a biofilm of E. faecalis created in laboratory conditions [136]. These observations were used by [PERSON] et al., who compared a preparation of the same concentration with calcium hydroxide at a concentration of 0.1%. Both substances had a gel formula and were used for seven days. After this time, an almost complete destruction of the biofilm was observed on samples covered with the preparation containing nanosilver [137]. Although silver nanoparticles have undeniable advantages, they also have side effects--they stain the surrounding tissues. Looking for a solution to this problem, scientists proposed combining silver nanoparticles with potassium iodide. However, this method has been questioned due to the short-term effect and reduced bond strength of the resin composite with dentin [138].  
Metallic nanoparticles used in dentistry also include gold and copper nanoparticles. Their unique properties, such as osteoinductivity and antibacterial properties, are used to improve the effectiveness and stability of titanium implants [139].
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 2 Related Work
### Kinematic Model  
Mobile robots can be divided into omnidirectional and non-omnidirectional robots according to the kinematics of casters, and the robot used in this paper is a non-omnidirectional mobile robot. In Figure 1, XOY represents the global coordinate system. \(x_{1}Py_{1}\) is the local coordinate system, \(x_{1}\) is the linear movement direction of the robot, \(y_{1}\) is perpendicular to the \(x_{1}\) axis, P is the center of the robot as the origin of the local coordinate system, and \(\theta\) is the heading angle. Thus, the robot's global coordinates, position \(\varepsilon\), can be obtained, as shown in Equation (1). To map the global coordinate system to the local coordinate system, the rotation matrix \(R_{\theta}\) is used. Equation (3) is converted from global coordinates to local coordinates \(R\varepsilon\).  
\[\varepsilon=\left[\begin{array}{c}x\\ y\\ \theta\end{array}\right] \tag{1}\]  
\[R_{\theta}=\left[\begin{array}{ccc}\cos\theta&\sin\theta&0\\ -\sin\theta&\cos\theta&0\\ 0&0&1\end{array}\right] \tag{2}\]  
\[R_{\varepsilon}=R_{\theta}\times\varepsilon \tag{3}\]  
Figure 1: Reference system coordinates.  
In a non-omnidirectional moving robot, the robot only has a linear velocity \(V\) in the \(x_{1}\) direction and an angular velocity \(\omega\) of rotation. Suppose the sampling time is \(\Delta T\), the distance the robot travels is \(\Delta S\), and the coordinates of the robot are \(\Delta X,\Delta Y\).  
\[\Delta S=V*\Delta T \tag{4}\]  
\[\left\{\begin{array}{l}\Delta X=V*\Delta T\cos\theta_{T}\\ \Delta Y=V*\Delta T\sin\theta_{T}\end{array}\right. \tag{5}\]  
According to the coordinates of the robot, \(X_{n}Y_{n}\), the current sampling time can be obtained by summing up its state and increment.  
\[\left\{\begin{array}{l}X_{n}=X_{n-1}+V*\Delta T\cos\theta_{T}\\ Y_{n}=Y_{n-1}+V*\Delta T\sin\theta_{T}\end{array}\right. \tag{6}\]  
The DWA algorithm [25; 26; 27], in Equation (6), has three evaluation metrics. They are the azimuth evaluation function, \(heading(v,w)\), which denotes the angular difference between the robot and the target point. \(dist(v,w)\), which denotes the distance between the robot and the nearest detected obstacle, is the distance function. \(velocity(v,w)\) denotes the relative velocity magnitude of the robot trajectory. \(\alpha\), \(\beta\), and \(\gamma\) denote the weight coefficients of the function and \(\sigma\) denotes the normalization required to obtain the evaluation function \(G(v,w)\).  
\[\gamma=1-\beta-\alpha \tag{7}\]  
\[G(\
u,\omega)=\sigma(\alpha*heading(\
u,\omega)+\beta*dist(\
u,\omega)+\gamma* velocity(\
u,\omega)) \tag{8}\]