esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Efficiency and Fairness Signal Optimization Model
### Feasibility Analysis
#### 4.1.1 The Delay-to-Fairness Conversion Rate  
The delay-to-fairness conversion rate refers to the proportion of fairness improvement in the average delay of vehicles in each phase of the intersection that sacrifices the total delay of the intersection. When this conversion rate is maximum, it can maximize the fairness improvement obtained by sacrificing the total delay of the intersection.  
In the context discussed in this paper, it can be considered that sacrificing delay for equivalent or greater benefits is worthwhile or feasible; otherwise, it is not feasible. The paper proposes using the delay-to-fairness conversion rate to measure the feasibility of sacrificing delay for fairness. The delay-to-fairness conversion rate represents the ratio between the benefits of fairness and the sacrifice of delay. If this ratio is greater than 1, it indicates feasibility; if less than 1, it indicates infeasibility. The fairness improvement ratio is used to represent the benefits of fairness, while the delay increase ratio represents the sacrifice of delay. The baseline fairness and delay are respectively referenced to the fairness evaluation index and total delay obtained from the signal timing scheme calculated based on the Webster model.
### Non-Ribosomal Peptides  
Non-ribosomal peptides (NRPs) are low-molecular-weight peptides with extensive chemical variety [38]. As their name indicates, they are synthesized by a mechanism unrelated to protein synthesis in the ribosome. In addition to their smaller sizes compared to most proteins, they differ from these in their structures, which are frequently cyclical, and in the participation of atypical amino acids, such as hydroxylated or methylated variants, or their D forms. Unlike proteins, which undergo modifications only after their synthesis,  
Figure 1: Chemical structures of the representative SMs from the four major families described in Table 1.  
NRPs undergo chemical changes in their amino acids during their formation or by other enzymes after they have been released.  
Like PKs, NRPs are produced by gigantic multi-enzyme complexes known as NRP synthetases (NRPSs). They are usually organized as modules, each consisting of several catalytic domains that function in a coordinated fashion [39]. A basic module includes an adenylation (A) and a thiolation domain (T). Selection and activation of the substrate is carried out by the A domain. The substrate is then transferred to the T domain, where it is covalently linked by a thiodiester bond into a phosphopantethein unit. Each module contributes one aminoacyl or aryl residue to be used in the formation of the final NRP. Other enzymatic domains optionally present in the modules introduce modifications in the residues, which can lead to their epimerization (E) or methylation (M), or to other chemical changes, while the condensation domains (C) catalyze the union by peptide bonds of substrates linked to the adjacent phosphopantethein. Reactions proceed until the full NRP is generated. The NRP's release is often accompanied by a cycling reaction carried out by a thioesterase domain located at the carbon end of the multienzyme complex. In many cases, the NRP undergoes new chemical modifications by other enzymes, including oxidation, halogenation, or glycosylation reactions, among others, thereby increasing the chemical diversity of the resulting NRPs.  
A database of NRPs called Norine [40] is available to researchers, and at the time this article was written it included 1740 peptides formed by 544 different monomers. The number of monomers in each NRP varies from 2 to 26 [41]. Due to their numerous applications, different strategies have been used to improve the biotechnological production of many NRPs [38; 42; 43]. Some representative examples of fungal NRPs are described in Table 1 and Figure 1.
## Abbreviations  
IBD inflammatory bowel disease  
UC ulcerative colitis  
CD Crohn's disease  
IC indeterminate colitis  
GI gastrointestinal  
GWAS genome-wide association study  
NOD2 nucleotide-binding oligomerization domain containing 2  
MDP muramyl dipeptide  
NF-k\(\beta\) nuclear factor-kappa beta  
TLR toll-like receptor  
LPS lipopolysaccharideacharide  
IL-17 interleukin-17  
IL-23 Interleukin 23  
IL-23R IL-23 receptor  
Th17 T-helper 17 cells  
FMT fecal microbiota transplantation  
FOS fructo-oligosaccharides  
_R. bronii_  
ROS reactive oxygen species  
S. Typhimuriumurium _Salmonella enterica_ serotype Typhimurium  
SHIME simulator of the human intestinal microbial ecosystem  
_B. infants_  
_Bifidobacterium longum_ subspecies _infantis_  
GCF gingival crevicular fluid  
TAP Transit amplifying progenitor cells  
ISCs intestinal stem cells  
AMPs antimicrobial peptides  
TGF-\(\beta\)1 transforming growth factor beta-1  
Tregs T-regulatory cells  
SPF specific pathogen free  
plgA pentameric IgA  
plgR polymeric immunoglobulin receptor  
slgA secretory IgA  
SCFAs short-chain fatty acids  
PRRs Pattern recognition receptors  
NODRs NOD-like receptors  
RLRs RIG-I-like receptors  
AhR aryl hydrocarbon receptor  
_C. diff_  
_Clostridium difficile_  
OTUs operational taxonomic units  
HOMD human oral microbiome database  
EPH Ecological Plaque Hypothesis  
KPH Keystone-Pathogen Hypothesis  
_P. gingivalis_  
_Porphyromonas gingivalis_
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### The Structure of Federated Filtering Algorithm  
The structure diagram of the federated filter model used in this article is shown in Figure 1.  
Each sub-filter of the federated filtering model used in this paper is introduced as follows.
## 3 Results and Discussion
### Hydrogen Embrittlement of 316L Stainless Steel Under Different Hydrogen Storage Conditions  
The effect of hydrogen embrittlement (HE) on the mechanical properties and fracture morphology of additively manufactured (AM) 316L stainless steel was investigated under various simulated hydrogen storage conditions. Tensile and Charpy impact tests were conducted to evaluate the influence of hydrogen embrittlement on mechanical performance before and after electrochemical hydrogen charging.  
Testing was performed at room temperature, simulating compressed hydrogen gas storage conditions, and in a liquid nitrogen environment, representing cryo-compressed and liquid hydrogen storage. The alloy exhibiting the highest resistance to hydrogen embrittlement in each condition was identified.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Performance Comparison of DL Models in Cyberbullying Detection
Similarly, [PERSON] et al. [152] combined CNN and Bi-LSTM to classify cyberbullying in real-time posts on Twitter. For the experimental analysis, they employed two other combinations of deep learning models: CNN + Bi-GRU, and Bi-LSTM + Bi-GRU. Experimental results show that their proposed method outperformed the other two combinations in terms of an accuracy score of 95%. [PERSON] et al. [153] proposed a hybrid model that combines CNN and Bi-GRU to detect cyberbullying from the Kaggle Toxic comment classification dataset. The proposed model obtained the best accuracy and F1 scores of 98.39% and 79.91%, respectively. Table 5 shows the performance comparison of deep-learning-based cyberbullying-detection systems on different datasets where it is classified whether the model the study used is a hybrid model or not. In addition, the best-performing model in that experiment is reported along with the scores of performance matrices. According to the most recent studies in the area of cyberbullying detection, hybrid deep learning models show more promising results than individual deep learning models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
This decision is supported by the growing understanding that classifying cyberbullying is a challenging task that calls for a combination of methods and approaches in order to produce accurate and trustworthy results. In independent models, the methods may have some limitations, which are complemented when we use hybrid models. These models present exciting results in terms of enhancing the overall effectiveness and stability of cyberbullying classification systems.
## 4 Results
### Pilot Group 3
#### 4.3.1 Pilot Group 3 Usage of the Different Worlds  
The results of the descriptive statistics among Pilot 3 groups show a greater 'time of usage' and 'number of activities' in the primary 1 st Cycle group. The fact that the number of classes in the 1 st Cycle group was bigger than in the 2 nd Cycle classes explains this event. In relation to the 'number of activities', the maximum was obtained by the primary cycle classes in the world of Logic (N = 1000) (Table A5).  
In order to verify differences between the groups in Pilot 3, a one-way ANOVA was performed comparing the usage time per groups and activities carried out. Significance is obtained in 'time of usage' with the Literacy world along the groups, F(2) = 3.565, \(p\) = 0.032 (<0.05) (Figure 9). Pilot 3 shows a high use of the Literacy world by the 1 st Cycle of primary school, above average (mean: 56,068.24), and very high in the Special Needs group (mean: 217,563.95). This usage may have been increased in some class groups by their preference for such material, however, no significant differences in use were found in the other worlds of the platform, with a fairly homogeneous use.  
Figure 8: Pilot Group 2’s use of worlds (Arts, Emotion, Logic, Science, Literacy, Spatial, Multiplayer) at Smile and Learn’s platform. (**a**) General time of usage (seconds) with the platform for Pilot Group 3; (**b**) General number of activities played by Pilot Group 3 with the platform.  
When performing the one-way ANOVA analysis to compare the 'number of activities' carried out by the groups, significant differences were observed in the Arts world, F(2) = 6.208, \(p\) = 0.003 (<0.05); Logic F(2) = 4.844, \(p\) = 0.010 (<0.05); Science F(2) = 7.214, \(p\) = 0.001 (<0.05); Spatial F(2) = 7.065, \(p\) = 0.001 (<0.05) (Figure 10). This points out the value of fulfilling activities in the 1 st Cycle of primary school, in all of these worlds, contrasted to the use and implementation with the rest of classes, which include Special Needs and 2 nd Cycle of primary education groups.
## 2 Materials and Methods
### Radial Basis Function Networks  
We employed the RBFNs [20] to approximate \(\Delta\mathbf{F}_{b}\), and it can be expressed as:  
\[\Delta\mathbf{F}_{b}= \left[\Delta F_{b1},\Delta F_{b2}\right]^{\top}\] \[= \left[\mathbf{\Phi}_{1}(\mathbf{\chi}_{1})\mathbf{\omega}_{1}^{*}+\varepsilon _{1},\mathbf{\Phi}_{2}(\mathbf{\chi}_{2})\mathbf{\omega}_{2}^{*}+\varepsilon_{2}\right]^{ \top}, \tag{4}\]  
where \(\mathbf{\chi}_{i}\in\mathbb{R}^{n}\) (\(i=1,2\)) is the input vector of the RBFN, \(\mathbf{\omega}_{i}^{*}\in\mathbb{R}^{m}\) is the optimal weight vector, \(m\) is the node number, \(\varepsilon_{i}\) denotes the reconstruction error, and \(\mathbf{\Phi}_{i}(\mathbf{\chi}_{i})=\left[\Phi_{i1}(\mathbf{\chi}_{i}),\ldots,\Phi_{im}( \mathbf{\chi}_{i})\right]\) with:  
\[\Phi_{ij}(\mathbf{\chi}_{i})= \exp\left(\frac{-\|\mathbf{\chi}_{i}-\mathbf{\mu}_{ij}\|^{2}}{\eta_{ij}^ {2}}\right),\ j=1,2,\ldots,m. \tag{5}\]  
Here, \(\mathbf{\mu}_{ij}\in\mathbb{R}^{n}\) is the center of the receptive field and \(\eta_{ij}\) is the width of the Gaussian function.  
**Assumption 1**.: _There exists an ideal constant weight vector \(\mathbf{\omega}_{i}^{*}\) such that \(|\varepsilon_{i}|\leq\varepsilon_{M}\) with a positive constant \(\varepsilon_{M}\) for \(i=1,2\), and the optimal weight vector is defined as the value of \(\widehat{\mathbf{\omega}}_{i}\) that minimizes \(|\varepsilon_{i}|\), i.e.,_  
\[\mathbf{\omega}_{i}^{*}= \arg\min_{\widehat{\mathbf{\omega}}_{i}\in\mathbb{R}^{m}}\left\{ \sup_{\mathbf{\chi}_{i}\in\mathbb{R}^{n}}|\Delta F_{bi}-\mathbf{\Phi}_{i}(\mathbf{\chi}_{ i})\widehat{\mathbf{\omega}}_{i}|\right\}. \tag{6}\]  
**Assumption 2**.: _The optimal weight vector is bounded such that \(\|\mathbf{\omega}_{i}^{*}\|\leq\omega_{M}\) with a positive constant \(\omega_{M}\)._
## 4 Experimental Results and Analysis
### Comparison of Blockchain Framework Results  
This result is unexpected since Hyperledger Fabric is often marketed as a highly scalable blockchain [49]. In practice this means that consensus is performed by fewer peers, increasing the centralisation, and theoretically throughput as well. When we compare these blockchain solutions to more standardised cloud technologies such as Apache Kafka, the difference is enormous. Apache Kafka is on average more than 298 times faster than Quorum, 437 times faster than Hyperledger Fabric and 654 times faster than Tendermint.  
The purpose of the Apache Kafka test is to create a baseline for comparison on the same use-case, and with the same in-the-wild distributed test approach, and draw some conclusions about the efficiency and use of Apache Kafka as an ordering service in Hyper  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **\#ch** & **Max (tps)** & **Average (tps)** \\ \hline \multirow{2}{*}{2 KB} & 1 & 763 & 245 \\  & 4 & 723 & 142 \\ \hline \multirow{2}{*}{10 KB} & 1 & 714 & 137 \\  & 4 & 678 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperledger Fabric throughput results, averages and maximums for 2 KB and 10 KB transaction sizes.  
ledger Fabric. The core difference between Hyperledger Fabric and Apache Kafka is the inbuilt components as smart contracts and identity management that Hyperledger Fabric provides. It is evident that Apache Kafka is not only capable of satisfying the national B2B/B2G use-case for a country the size of Denmark, but is also capable of satisfying the requirement for the entire EU-zone using standardised cloud infrastructure, under the assumption that crash fault tolerance suffices as system trust the e-business use-case.
# An Improved Fuzzy Vector Signature with Reusability
## 1 Introduction
### Contribution  
In this paper, we propose a new fuzzy vector signature (FVS) scheme based on a subset concept in Reference [17] to deal with noise in fuzzy (biometric) data. In consequence, our scheme is reusable and can tolerate sub-linear errors without any additional requirements, such as sufficiently high min-entropy of the input source. Compared to the previous fuzzy vector signature [20], we eliminated redundant parts in both verification key and signature by trying a new approach to security proofs, which in turn improved the efficiency of the scheme. For instance, for 80-bit security with 20% error tolerance rates, we reduce the size of the signing parameter by 33%, from 48 KB to 32 KB, the signature by 50%, from 32 KB to 16 KB, and a verification key by 22%, from 1.61 GB to 1.25 GB, where the length of the fuzzy (biometric) data is \(n=512\) bits. Additionally, we reduce the number of pairing operations in verification by up to 33% from \(18.5\times 10^{6}\) to \(12.3\times 10^{6}\).  
We also provide the formal security models to reflect reusability of the privacy of the verifier and signer (i.e., VK-privacy and SIG-privacy, respectively) and the unforgeability of the signature (i.e., Reusability). And we prove these properties on the assumption that the repeating readings of fuzzy (biometric) data are arbitrarily correlated, which is more realistic than the \((T,k)\)-block source used in Reference [20]. In addition, we analyze the performance of our FVS scheme in terms of transmission and computational costs by comparing it with previous reusable biometric cryptosystems [17; 18; 20]. In particular, a signature can be generated in 155 ms on the signer's side, which is almost twice as fast as in Reference [20] (for more details, see Section 5).
# Nano-Polymers as Cas9 Inhibitors
## 1 Introduction  
Since its conceptualization, CRISPR/Cas9-mediated gene editing has been rapidly changing the paradigm of life science in the past decade owing to its ease of use and programmable gene editing capability [1, 2, 3]. However, several technical hurdles, such as difficulty in delivery and potential unwanted (e.g., off-site-on-target and off-target) editing, remain to be overcome [4, 5].  
We sought to develop an efficient CRISPR delivery system by using nano-polymer technology. A naturally occurring polymer, poly-(\(\beta\)-L-malic acid) (PMLA), derived from an acellular slime mold was employed mainly due to its biocompatibility [6, 7]. Owing to its biodegradable property and a decent capacity for covalent conjugation through various -COOH groups, PMLA polymers have been explored as nanodrug carriers in various areas [8, 9, 10]. We have previously engineered the PMLA nano-polymer to carry different payloads, including anti-sense nucleotides, antibodies, and proteins, to treat brain cancer and Alzheimer's disease in multiple preclinical models [11, 12, 10, 9]. Generally, these therapeutic agents are covalently conjugated with the PMLA backbone through a disulfide (S-S) bond that enables the cargo release into the cytoplasm due to the S-S bond breakage in response to the highly reducing subcellular environment. In addition to the therapeutics, varioustargeting mechanisms are also conjugated to the PMLA polymer backbone to allow cell- or tissue-specific drug delivery. After internalization by the target cells, to avoid degradation of payloads in the late endosome and lysosome, a short peptide consisting of three leucine resides is also conjugated to the PMLA backbone to allow endosomal escape by breaking the endosome membrane in a pH-dependent fashion [13]. Surprisingly, when we mixed the LLL-conjugated PMMA polymer (PMLA-LLL) with the Cas9 in a DNA cleavage assay, we found the PMLA-LLL was able to completely block the endonuclease activity of the Cas9 protein. Intrigued by this finding, we wanted to determine whether there is a common structural basis of nano-polymers that may affect Cas9 nuclease function and investigated four different nano-polymers that have been widely used. After conjugation with the LLL moiety, we found that two out of four different polymers can also block Cas9 function with varying efficiency.  
Multiple Cas inhibitors have been identified, and these Cas inhibitors exhibit a wide variety of inhibitory mechanisms. Generally, Cas inhibitors include naturally occurring proteins, small molecules, and synthetic peptides or nucleotides by rational design [14; 15]. For instance, the most abundant naturally occurring Cas inhibitors are anti-CRISPR (Acr) proteins [16; 17; 18]. Currently, there are more than 20 different families of Acr proteins found in bacteriophages [19; 20]. Meanwhile, significant efforts have been made to apply CRISPR/Cas inhibitors to modulate gene editing with precision, and these tools have shown great promise in the temporal control of Cas9 function.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 3 Results
### Accuracy for Different Settings of Measurement Devices  
As the closing section of the study, the relevance of the information provided by monitoring devices was analyzed. The first step was to check to what extent each device is able to differentiate between displacements due to one crack and those due to a different crack, without taking into account interpolation or measurement errors. For this purpose, the errors were analyzed for each monitoring device using the alternative methodology, for the validation levels, to compare each crack of the simulations without noise.  
It was observed that the six devices located below 18 m above the foundation of their respective block did not provide relevant data (Figure 13). In these cases, the RMSE values at these points were less than 0.03 mm for any comparison of cracks. It should be taken into account that the locations very close to the base of the dam barely experience any relevant movement, whether there are cracks or not, and, therefore, the recorded movements are nearly identical.  
It was also concluded that the devices located in blocks at a horizontal distance far from the cracks, more than 20 m, do not provide relevant data for the same reason. These are the ones located in blocks 7, 8, 9, 10, 11, 12, and 14, and the RMSE values at these points were always 0.03 mm or less (Figure 13). This way, 17 devices were eliminated from the study, leaving 12 useful monitoring points. The results for these 12 devices are shown in Table 7.  
In the next stage, the number of monitoring points was further reduced by eliminating those seven that do not exist in the real dam to check whether the number of devices in the real dam used as case study would be enough for crack detection and estimation. The results, including noise, are shown in Table 8.  
Figure 13: Maximum RMSE for all possible combinations of validation cases and training cases using alternative methodology. Left: monitoring devices more than 18 m above the foundation. Right: monitoring devices below 18 m. All devices are named based on the cantilever they are and the movement they register.
## 4 Optimization of Blasting Parameters
### Algorithm Comparison Verification  
The enhanced Gray Wolf algorithm is further assessed for its capability to explore the objective function in engineering applications. To accomplish this, we utilize a mathematical model based on the aforementioned engineering parameters as the target solution. We compare it with well-established and widely used optimization algorithms, such as simulated annealing (SA), genetic optimization (GA), and particle swarm optimization (PSO). Moreover, our emphasis lies in minimizing overall cost consumption using a low-complexity model. The experiment comprises 200 iterations, a population size of 20, and a solution dimension of 3. We observe the variation in fitness with each iteration number. As illustrated in Figure 11, the Gray Wolf Optimization algorithm demonstrates rapid convergence speed, with lower fitness values indicating attainable optimal cost solutions. In contrast, other intelligent optimization algorithms exhibit slower iteration speeds and encounter challenges escaping local optima or locating the global optimum.
## 5 Novel Proposed Method Application for Planetary Gearbox Fault Diagnosis
### Ring Gear Fault Experiment  
For this experiment, we used a ring gear wear fault mode to test the proposed method while the remaining gears were all in good condition, undamaged. Similar to the case study in Section 4, in order to ensure a stable signal, the data acquisition system is adjusted to use the data sample rate based on the tachometer signal. The adjusted sampling rate is a synchronous sampling rate and donates the resampled vibration signal. During the experiment, the mean rotating frequency of the input shaft connecting the sun gear of the planetary gearbox was 22.3889 Hz, while a load of 405 Nm was applied to the output shaft connecting the planet carrier. An accelerometer was mounted on the top of the gearbox casing (as shown in Figure 19), and the vibration signals were collected from the accelerometer at position S3. According to the planetary gearbox configuration and its running speed, the characteristic frequencies are calculated and listed in Table 5.  
Figure 26: (**a**) Squared envelope spectrum after the first DC offset cancellation; (**b**) envelope spectrum of the original signal.  
Figure 25: CK value variation vs. iteration number.  
The ring gear fault frequency is not always calculated in the same way. Theoretically, its fault frequency is \(f_{\text{mesh}}\). However, because of manufacturing error, the three planet gears are different from each other. Therefore, the ring gear fault frequency may be \(1/3f_{\text{mesh}}\). Therefore, it is very difficult to determine the parameter \(T\) in CK. In this case, we can continue to apply the first DC offset cancellation to the signal. The resulting squared envelope spectrum and the original signal envelope spectrum can be seen in Figure 27. Similarly, it is possible to find the second order ring gear fault frequency in both envelope spectrums. There is no difference, except the value of the amplitude.