esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Model Building and Analysis
### Fuzzy Logic Modelling
#### 6.3.3 Fuzzy Inference  
The Mamdani type fuzzy model is selected in the fuzzy logic toolbox of Matlab R2015 with the maximum and minimum rule. The Mamdani is easier to understand and work with the consequent of the systems. The Mamdani method uses a simple structure of the Minimum operator, as shown in Equation (6).  
\[R^{j}=If\ x_{1}\ is\ A_{1}^{j}\ \ and\ x_{2}\ is\ A_{2}^{j}\ \ and\ x_{3}\ is\ A_{3}^{j}\ \ \ldots\ldots\ldots\textit{and}\ x_{n}\ is\ A_{n}^{j}\ \ \forall\ \text{is}\ B^{j} \tag{6}\]  
where \(R^{j}\) is the \(j\)-th rule, \(A_{i}^{j}\ (j=1,2,\)\(N,\)\(i=1,\)\(2,\)\(n)\), and \(B^{j}\) is the fuzzy subsets of the inputs and outputs, respectively.  
The minimum operator is used to calculate the firing strength of each fuzzy rule. The firing strength is directly proportional to the impact on the output. The rules are setup up in the if-then format.  
Figure 11: Membership functions assigned to the input variable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Rule** & **Clear** & **Competitive Availability** & **Business** & **Employee** & **Training** & **Market Conds/** & **Equivalent** & **Combined** \\
**No.** & **Vision** & **Strategy** & **Knowledge** & **Exp** & **Culture** & **Employees** & **Customer Eng** & **Impact** & **Impact** \\ \hline  & 15.21 & 15.30 & 15.21 & 8.35 & 16.49 & 12.69 & 16.74 & & \\
1 & 0.75 & 0.50 & 0.75 & 0.50 & 0.75 & 0.33 & 0.75 & 6.38 & High \\
2 & 0.75 & 0.50 & 0.75 & 0.04 & 1.00 & 0.67 & 1.00 & 7.25 & High \\
3 & 0.75 & 0.75 & 1.00 & 0.56 & 1.00 & 1.00 & 1.00 & 8.87 & Very high \\
4 & 0.75 & 0.50 & 0.75 & 0.51 & 1.00 & 0.67 & 1.00 & 7.64 & High \\
5 & 0.75 & 1.00 & 0.75 & 0.10 & 0.75 & 0.67 & 0.75 & 7.24 & High \\
6 & 1.00 & 0.75 & 1.00 & 0.10 & 1.00 & 0.67 & 1.00 & 8.44 & Very high \\
7 & 1.00 & 1.00 & 0.75 & 0.43 & 0.75 & 0.67 & 1.00 & 8.31 & Very high \\
8 & 1.00 & 1.00 & 1.00 & 0.43 & 0.50 & 1.00 & 1.00 & 8.70 & Very high \\
9 & 0.50 & 0.50 & 0.50 & 0.28 & 0.50 & 0.33 & 0.50 & 4.61 & Moderate \\
10 & 1.00 & 1.00 & 1.00 & 0.28 & 1.00 & 1.00 & 1.00 & 9.39 & Very high \\
11 & 0.75 & 0.75 & 0.50 & 0.28 & 0.50 & 0.33 & 1.00 & 6.20 & High \\
12 & 1.00 & 0.75 & 0.50 & 0.11 & 1.00 & 0.33 & 1.00 & 7.27 & High \\
13 & 0.25 & 1.00 & 0.50 & 0.04 & 0.25 & 0.33 & 0.75 & 4.80 & Moderate \\
14 & 0.50 & 0.50 & 1.00 & 0.46 & 0.50 & 0.67 & 0.75 & 6.36 & High \\
15 & 1.00 & 0.75 & 1.00 & 0.50 & 1.00 & 1.00 & 0.75 & 8.78 & Very high \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sub factor weights and equivalent impact.
## 7 Mechanisms of PGPB (Plant Growth Promoting Bacteria) towards Salinity Tolerance
### Indirect Mechanisms
#### Ethylene  
Plants, against various stress factors [253], produce ethylene. Ethylene ensures a fundamental function in plant development in a plethora of ways. Ethylene is associated with seed germination, root development (beginning and extension repression), leaf abscission, inactivation of mycorrhiza and plant relationship, and fruit ripening [254]. Ethylene additionally serves as a signal transducer for the detection of salt stress [255]. Accumulation of ethylene in reaction to abiotic stress prevents root development and, eventually, plant growth [256]. Salinity-induced ethylene accumulation caused by high production of 1-aminocyclopropane-1-carboxylic acid (ACC) hinders rice plant growth and development. Nevertheless, ACC deaminase may alleviate salt stress and high ethylene production in rice cultivars under salinity stress [257]. Another crucial enzyme in ethylene biosynthesis is ACC-oxidase (ACO). Maize plants under salt stress have demonstrated enhanced ACO activity as compared to plants under normal condition. Application of PGPR resulted in a significant reduction in ACO activity; _Bacillus safensis_ NBRI 12 M-treated maize plants have exhibited maximum reduction which is consistent with the low ethylene production under salt stress [258].
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 1 Introduction  
One method of joining steel structures of modern buildings is the high-strength bolted joint in Japan. Multiple steel materials are fastened with high-strength bolts and joined via friction force. In this paper, we focused on the method that uses a torque share-type high-tension bolt. With this method, prior to final tightening, a practicing engineer marks each tightening point by drawing a straight line across the bolt, nut, washer, and base material. Next, the practicing engineer conducts final tightening and confirms that the correct tightening is completed by visually confirming the misalignment of the marking at the tightening point [1]. When final tightening is performed properly, only the nut rotates, so only the nut marking moves along the markings on the bolt, nut, washer, and base metal. The pin tail of the bolt head receives the reaction force when the nut is tightened and the bolt is tightened. The pin tail breaks when the prescribed bolt axial force is applied. Therefore, there is no need to check the torque with a torque wrench. A torque check with a torque wrench is also unnecessary. If final tightening is not performed properly, the markings on the bolts and washers will also shift. By checking the shift of the markings, the worker can determine if the tightening was performed correctly. Workers perform this check on every bolt to ensure that no bolt tightening errors exist in the structure.  
Two possible problems with this process of checking the tightening of high-strength bolts are the length of time involved and the lack of an objective record of the total number of bolts checked. This confirmation work is time-consuming because the workers have to move to all the tightening points and visually check the marking results of each bolt to determine if it is tightened properly. From [PERSON]'s study [2], it is considered importantto keep records of individual bolts. However, due to the large number of target locations, the workers' records of the confirmation results were only a partial sampling, making it difficult to keep a record of the entire confirmation process.  
To solve these problems, we propose a bolt tightening judgment system using im-age-processing technology. The proposed system takes video footage of the bolt tightening locations as an input, extracts each bolt from the footage, and judges whether the bolts are correctly tightened from the marking results. The videos used as input for the system can be recorded instead of having practicing engineers visually confirm the bolt tightening, which reduces the work time and provides an objective record of the tightening confirmation results.  
In this paper, we present an overview of the proposed judgment system, as well as an examination of the results of actual bolt videos.
# Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations
## 4 Simulation of a Complete U-WSN
### Scenarios under Test
#### 4.3.1 Point to Point  
The first scenario under study is a two-node, point-to-point setting, where one node transmits and the other listens (see Figure 8).  
We study the probability of successfully receiving a packet for different distances between five and 30 m. Figure 9 shows results for three different transmit powers: 10 dBm (red dotted), 25 dBm (black dashed) and 40 dBm (blue solid); and for four different receivers: noncoherent with no channel coding (square), coherent with no channel coding (circle), noncoherent with channel coding (triangle) and coherent with channel coding (no marker). As expected, including a coherent receiver and some channel coding improves the rate. We conclude that with the current radio system, the maximum reliable distance (with 99% success packet reception rate) is around 14 m for 10 dBm, 18 m for 25 dBm and 23 m for 40 dBm.  
Figure 8: Point to point scenario with two nodes.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Carboxylic Groups  
The monomer structure of alignates, \(\alpha\)-L-guluronic acid (G), and \(\beta\)-D-mannuronic acid (M) consist of a 1:2 ratio of the carboxylic acid group to the hydroxyl one. The carboxylic acid group in the alginate's backbone is the one that mainly dictates the solubility. Thus, it is essential to set the pH above a particular critical value in an aqueous environment, so the carboxylic acid groups are deprotonated.
## 4 Conclusions and Future Works  
This paper presents a variant of CoSaMP algorithm, which can tackle a special sparse signal recovery problem \(v=\mathcal{A}(Uw+s)\). The algorithm could separate the sparse signal and low-rank background signal from their sum given very few CS measurements. From experiments we show that the recovery performance is similar to ordinary CoSaMP and performs better if the sparse signal also corrupted by Gaussian noise not only the measurement noise.  
This proposed algorithm assumes that the low-rank subspace is known as a prior. However, what if the subspace is not known or even the subspace is time-varying? It is a challenging problem, to simultaneously estimate the subspace and recover the sparse signal only from a few CS measurements, which is attracting the attention of CS community [22, 23, 24]. We are very interested in incorporating the recent online subspace learning technique [18, 25] into this CS framework. We put this endeavor for future work.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 3 Proposed System
### Direction Estimation  
This section describes the method for vehicle trajectory estimation. To check the direction of a tracked vehicle we calculate the difference in the pixels in the F\({}_{N}\)-frames range, where \(N\)- is the number of frames to track a unique vehicle. First, we calculate the centroid from a detected car's bounding box and set it to be the initial points (\(X_{0}\), \(Y_{0}\)) in the frame (\(F_{0}\)). Then, as the car moves, the position of the centroid changes from frame to frame. Thus, we calculate the pixel difference in the range (\(X_{1}\), \(Y_{1}\)) to (\(X_{N}\), \(Y_{N}\)) for each frame, where \(X_{1}\), \(Y_{1}\) indicates the car's centroid position in the frame (\(F_{1}\)), along with \(X_{N}\) and \(Y_{N}\) in frame (\(F_{N}\)). For example, let us assume we use six frames for tracking. The pixel position difference would be \(\Delta X=\)\(X_{5}-X_{1}\). Basically, we take the second frame as an initial starting point and the sixth frame as the ending point. This is due to the ID assignment step that is done on the first frame as soon as a car is detected. To find the direction, we do simple math: if \(\Delta X\) is negative and \(X_{5}\) is smaller than \(X_{1}\), the car is moving from right to left; vice versa, then it is moving left to right.  
**Algorithm 2**Pseudo-code for the direction check.
```  if (length of centroids \(>0\)) for (iterate \(\geq\) number of detected centroids): for (iterate \(\geq\) N, where N number of tracking frames for unique cars) take \(X_{1}\), \(Y_{1}\) positions from tracker take ID for unique car \(\Delta X=\)\(X_{\text{N}}-X_{1}\) if (check \(\Delta X\) sign and \(X_{1}\) with \(X_{2}\)) assign direction ```  
**Algorithm 3**Pseudo-code for Kalman filter tracker.  
Figure 6 visually explains the changing vehicle's position within \(N\) = 6 frames. Green dots show the displacement of the car through each frame. The displayed text \"West\" in this case shows that the car in the video is moving from right to left.
# Prediction of Power Fluctuations of Gaussian Beams After Transmission Through Turbulent Atmosphere
## 5 Conclusions  
A novel Time Series Long Short-Term Memory Fully Connected Processing Network (TSLSTMFCPN) is proposed to predict future laser power fluctuations caused by atmospheric turbulence based on measured laser power and meteorological parameters. This neural network model is composed of two long short-term memory (LSTM) network layers and a fully connected layer. The power of a laser beam traveling through atmospheric turbulence and some meteorological parameters were collected simultaneously in time steps to form time-series data, which serve as the input to the model, and the laser power of the subsequent time steps was the output for model training. We found that the successfully trained TSLSTMFCPN is able to predict future laser power fluctuations based on current measurements of laser power and meteorological parameters. The experimental results indicate that a high accuracy with a mean absolute percentage error as low as 1.2% in prediction was achieved by the TSLSTMFCPN. The results presented in this paper hold promising application prospects in various fields, including free-space optical communication and imaging laser radar system.  
Figure 9: Laser power measured and predicted by TSLSTMFCPN using data obtained in outdoor experiment (**a**) using all collected meteorological parameters as model input and (**b**) using meteorological parameters excluding atmospheric pressure and ambient illuminance as model input.  
**Author Contributions:** Conceptualization, Z.W. and H.L.; methodology, H.L., Z.H. and J.Z. ([PERSON]); software, Z.W., C.H., F.C. and C.Y.; validation, Y.L., H.C. and Y.Z.; investigation, [PERSON] and J.Z. ([PERSON]); writing--original draft preparation, [PERSON] and J.Z. ([PERSON]); writing--review and editing, H.L. and J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was supported by the National Natural Science Foundation of China (NSFC), grant number 12174173. This research was funded by the Natural Science Foundation of Fujian province, grant numbers 2022H0023, 2022J02047, _ZZ2023_J20, and MSPY2022010; the Industry-University-Research Collaboration Foundation of Fujian Province, grant numbers 2024H6014 and 2022G02006; and the Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents (China), grant number 201927879.  
**Data Availability Statement:** The original data has not been used to create an additional dataset. For any inquiries regarding the research data used, please contact the corresponding author.  
**Conflicts of Interest:** The authors declare no competing interests. The authors [PERSON] were employed by the company Dongguan Guangdong Intelligent Technology Co., Ltd. There is no conflict of interest between any of the authors and the company Dongguan Guangdong Intelligent Technology Co., Ltd.
## 4 Role of sRNA in Antibiotic Resistance
### Activation of Efflux Pumps
#### 4.3.1 CsiR  
Strong evidence indicates that antibiotics are a major driving force behind bacterial resistance [206]. Ciprofloxacinacin, a third-generation synthetic quinolone, is widely used in clinical and agricultural settings, exerting its antibacterial effects by inhibiting bacterial DNA gyrase and topoisomerase IV and effectively targeting _P. aeruginosa, E. coli, Proteus vulgaris,_ and other Enterobacteriaceaeae [207]. CsiR, a sRNA, functions as a negative regulator of _emrB_, a multi-drug efflux pump gene that plays a crucial role in ciprofloxacin resistance in _P. vulgaris_ strain P3M. Gene expression analysis revealed that EmrB was significantly upregulated under ciprofloxacin treatment. Interestingly EmrB expression was even higher in a _csiR_ mutant strain, confirming that CsiR inhibits EmrB expression, thereby decreasing ciprofloxacin efflux and reducing bacterial resistance. Bioinformatics predictions identified a specific binding site for CsiR in 5' UTR, near the SD sequence of EmrB mRNA, suggesting a direct posttranscriptional regulatory interaction. This was experimentally validated through microscale thermophoresis (MST) assays, which showed a strong binding affinity between wild-type CsiR and EmrB mRNA, with a dissociation constant of 11.5 \(\pm\) 3.56 \(\upmu\)M [208]. Mutation of the predicted EmrB binding site in CsiR resulted in abolishing its interaction with EmrB. Furthermore, in vivo studies confirmed that mutating this binding site in CsiR led to a loss of repression, restoring EmrB expression to levels observed in the _csiR_ strain and significantly increasing bacterial survival in the presence of ciprofloxacin. These findings strongly suggest that the CsiR-mediated repression of EmrB represents a key regulatory mechanism controlling antibiotic resistance in _P. vulgaris_, highlighting the potential of targeting sRNA-efflux pump interactions for antimicrobial therapy [208]. The absence of CsiR also affected resistance to polymyxin B and erythromycincin, indicating its multifunctional role in antibiotic resistance.
###### Abstract  
The combination of biocompatible polymers and ceramics shows great promise in the development of composites with suitable mechanical properties for dental applications. In an attempt to further expand this research line, Al\({}_{2}\)O\({}_{3}\) commercial powders (Vitro-ceram, Alglass, In-ceram) were sintered at 1400 \({}^{\circ}\)C for 2 h and infiltrated with poly(methyl methacrylate) for potential use in dental prostheses. The infiltration was performed using a homemade apparatus under a pressure of 7 bar for 6 and 12 h. The microstructure (studied using a scanning electron microscope), Archimedes density, 3-point bending flexural strength and Vickers hardness of the prepared composites were assessed and quantitatively compared. In general, microstructural analyses showed ceramic- and polymer-based interpenetrating network in all materials. The preforms infiltrated for 12 h showed superior properties; among them, the Vitro-ceram-based composite also demonstrated a near-zero open porosity and optimum mechanical characteristics. Specifically, its density, strength and hardness were 2.6 \(\pm\) 0.07 g/cm\({}^{3}\), 119.3 \(\pm\) 5.0 MPa and 1055.1 \(\pm\) 111.0 HV, respectively, passing the acceptance criteria of ISO 6872 and making it suitable for consideration as a metal-free structure for dental crowns and fixed partial prostheses until three anterior units.  
Keywords:composite; PMMA; alumina; dental prosthesis; infiltration; mechanical properties +
Footnote †: journal: Applied Sci.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 3 Group Secret and Private Key Generation Scheme
### Key Agreement
#### 3.2.1 The First Step of Public Discussion  
The i.i.d. \(n\)-dimensional sequences \(\mathbf{\tilde{Y}}_{R}^{(1)}\) and \(\mathbf{\tilde{Y}}_{R}^{(2)}\) at the relay are transformed into quantized vectors \(\mathbf{\tilde{Y}}_{R}^{(1),\Delta}=\left[\tilde{Y}_{R}^{(1),\Delta}(1),\ldots,\tilde{Y}_{R}^{(1),\Delta}(n)\right]^{\prime}\) and \(\mathbf{\tilde{Y}}_{R}^{(2),\Delta}=\left[\tilde{Y}_{R}^{(2),\Delta}(1),\ldots,\tilde{Y}_{R}^{(2),\Delta}(n)\right]^{\prime}\), where \(\tilde{Y}_{R}^{(1),\Delta}(i)\) and \(\tilde{Y}_{R}^{(2),\Delta}(i)\) are quantized versions of \(\tilde{Y}_{R}^{(1)}(i)\) and \(\tilde{Y}_{R}^{(2)}(i)\) with quantization interval \(\Delta\), respectively. Similarly, [PERSON] and [PERSON] transform \(\mathbf{\tilde{Y}}_{B}\) and \(\mathbf{\tilde{Y}}_{C}\) into \(\mathbf{\tilde{Y}}_{B}^{\Delta}=\left[\tilde{Y}_{B}^{\Delta}(1),\ldots,\tilde{Y}_{B }^{\Delta}(n)\right]^{\prime}\) and \(\mathbf{\tilde{Y}}_{C}^{\Delta}=\left[\tilde{Y}_{C}^{\Delta}(1),\ldots,\tilde{Y }_{C}^{\Delta}(n)\right]^{\prime}\), respectively. The relay randomly divides the typical \(\mathbf{\tilde{Y}}_{R}^{(1),\Delta}\) and \(\mathbf{\tilde{Y}}_{R}^{(2),\Delta}\) sequences into non-overlapping bins and then sends the bin number as helper data to [PERSON] and [PERSON]. Based on Slepian-Wolf coding, the relay needs to, respectively, send \(H\left(\mathbf{\tilde{Y}}_{R}^{(1),\Delta}\middle|\mathbf{\tilde{Y}}_{B}^{ \Delta}\right)\) and \(H\left(\mathbf{\tilde{Y}}_{R}^{(2),\Delta}\middle|\mathbf{\tilde{Y}}_{C}^{ \Delta}\right)\) bits of information through the public channel to [PERSON] and [PERSON], where \(H(X|Y)\) represents the conditional entropy of a random variable \(X\) given \(Y\). By combining the bin number transmitted over the public channel with their own observations, [PERSON] and [PERSON] can recover \(\mathbf{\tilde{Y}}_{R}^{(1)}\) and \(\mathbf{\tilde{Y}}_{R}^{(2)}\) with the probability being arbitrarily close to \(1\). After that, combining \(\mathbf{\tilde{Y}}_{B}\) and \(\mathbf{\tilde{Y}}_{R}^{(1)}\), [PERSON] can obtain an estimation sequence of \(G_{1}\), denoted by \(\mathbf{\hat{G}}_{1,B}\). Combining \(\mathbf{\tilde{Y}}_{C}\) and \(\mathbf{\tilde{Y}}_{R}^{(2)}\), [PERSON] can obtain an estimation sequence of \(G_{1}\), denoted by \(\mathbf{\hat{G}}_{1,C}\).
# Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach
## 1 Introduction
The total yield of a photovoltaic (PV) system refers to the total amount of electrical energy produced by the system over a specific period. It is typically measured in kilowatt-hours (kWh) and represents the cumulative energy output generated by the entire solar array. This metric is influenced by factors such as the size of the PV system (installed capacity in kilowatt's peak, kWp), the amount of sunlight (solar irradiance) received, and the system's efficiency. Total yield provides an overall measure of the system's performance and is crucial for assessing its contribution to the energy supply. Figure 1: Single-line diagram of the 5 kW rooftop PV system in a residential house. The specific PV yield, on the other hand, is a normalized measure of the PV system's performance. It is calculated by dividing the total energy produced by the system (total yield) by its installed capacity (in kWp). The specific yield is typically expressed in kWh/kWp and indicates how efficiently the system converts sunlight into electricity. It allows for a more meaningful comparison of performance between different PV systems, regardless of their size or location. By normalizing the energy output, the specific yield helps in evaluating how well the PV system is performing relative to its potential, taking into account factors like system orientation, shading, and local weather conditions. The present study aims to investigate the effect of the energy produced by a grid-connected rooftop solar photovoltaic system for small households on overall energy consumption patterns and grid stability. By employing the ARIMA method to forecast the monthly energy yield of the system, this research seeks to provide valuable insights into how such renewable energy sources can contribute to more efficient energy management and reduced reliance on conventional power sources. Additionally, the study analyzes the system's performance to identify potential improvements in forecasting accuracy, thereby supporting more reliable integration of solar power into existing energy grids. The findings are expected to inform homeowners and utility providers of the benefits and challenges of adopting rooftop solar photovoltaics, ultimately promoting the broader adoption of renewable energy solutions for sustainable development. The paper continues as follows: In Section 2, the design of the photovoltaic system is explained, and the historical data are presented. Furthermore, the ARIMA approach is given in detail. In Sections 3 and 4, the resulting forecasts for the specific PV system yield and the total yield are thoroughly presented and discussed, respectively. The Section 5 concludes the study.