esa-sceva/satcom-chunk-collection huggingface dataset:
# Current-Sensing Topology with Multi Resistors in Parallel and Its Protection Circuit
## 2 Materials and Methods
### Parallel or Series Multi Resistor Current-Sensing Topology
#### 2.1.2 Current-Sensing Topology with Multi Resistors in Parallel
\(V_{O1}\) and \(V_{O2}\) are sent to magnitude-detection circuits and then compared with voltage threshold \(V_{T1}\) and \(V_{T2}\), respectively. These two overcurrent indication signals \(PT_{1}\) and \(PT_{2}\) are used to carry out an OR operation with \(CTR_{1}\) and then to control \(S_{1}\). Switch \(S_{1}\) is closed while any one of these three signals is high level. The working principle in detail is as follows:  
1. The initial condition: A lower current range is selected. \(CTR_{1}\) is low level. \(S_{2}\) is closed and \(S_{1}\) is open. 2. When \(I_{R2}=I_{IN}>I_{T2}\), the overcurrent indication signal \(PT_{2}\) changes from low to high level. The relation between \(I_{IN}\) and \(I_{R1}\) is given by: \[I_{IN}=I_{R1}\times(R_{S2}+R_{2}+R_{S1}+R_{1})/(R_{S2}+R_{2})\] (6) When protection occurs, define the input threshold current \(I_{T1}{}^{\prime}\) for \(PT_{1}\) transformation as: \[I_{T1}{}^{\prime}=I_{T1}\times(R_{S2}+R_{2}+R_{S1}+R_{1})/(R_{S2}+R_{2})\] (7)
3. Let \(I_{T1}{}^{\prime}\) be slightly lower than \(I_{T2}\). Figure 5 shows the working flow of overcurrent protection. As shown in Figure 5, the protection state is maintained while \(I_{IN}>I_{T2}\) and exits automatically whenever \(I_{IN}<I_{T1}{}^{\prime}\). The protection hiccup mentioned before is eliminated. Figure 4: Protection circuit with dual threshold detection for parallel current-sensing topology. The amplifier gains \(G_{1}\) and \(G_{2}\) and compare thresholds \(V_{T1}\) and \(V_{T2}\) should be carefully designed to ensure \({I_{T1}}^{\prime}<{I_{T2}}\). The work flow in Figure 5 is based on the condition that the delay of the magnitude-detection circuit and the residual ripple of the detection output are not considered. They may also cause protection hiccups. So the magnitude-detection circuit and its dynamic response to various waveforms are further studied in the next subsection. The complete condition for eliminating protection hiccups is shown in the results section. To sum up, if the number of sensing resistors is higher than 2, the parallel sensing circuit requires a smaller layout space and lower heat dissipation than the series counterpart. But the protection circuit is much more complicated and needs to be carefully considered in design.
## 4 Results and Discussion
### Mechanical Properties  
A strong cross linked bond between polymers in HFs will produce a high tensile strength value and an optimum elongation at break [13]. To investigate the effect of \(\alpha\)-M in mechanical properties of HFs, tensile strength and elongation at break of HFs were studied. HFs were held between two clips and pulled with the upper clip at a speed of 0.5 mm/s. Then, the results were calculated by tensile strength and elongation at break equations. Figure 4A shows that tensile strength of Ch HF, Alg HF, ChAlg HF, and ChAlg/\(\alpha\)-M were 11.88, 4.92, 5.06, and 8.75 MPa, respectively. For the elongation at break study (Figure 4B), Ch HF, Alg HF, ChAlg HF, and ChAlg/\(\alpha\)-M HF were 95.57%, 83.82%, 90.98%, and 85.52%, respectively. The tensile strength value is inversely proportional to the value of elongation at break where high tensile resistance will reduce the elasticity [34]. In this study, Ch strengthened the Alg structure and provided the rigid structure of ChAlg/\(\alpha\)-M HF, suggesting that a physical interaction occurred between Alg and Ch. The results also imply that \(\alpha\)-M increased the tensile strength and decreased the elongation at break of ChAlg HF.
## 3 Experimental Schemes for a SLM Laser and Its Frequency Correction  
We constructed an all-solid-state Nd:YVO\({}_{4}\) SLM laser. The laser's experimental scheme is shown in Figure 1a. The laser included a pumping system, an optical oscillator, a gain medium, and an SLM-selection element.  
The laser's pumping system comprised a pump source, a coupling fiber, and a pair of convex lenses. The pump source is an 808 nm laser diode (P4-050-0808-3-0.5-A-R01-S0119, nLIGHT) with the highest pump power of 50 W. The pump light was coupled into a fiber with a numerical aperture of 0.22 and a diameter of 400 \(\upmu\)m. Two convex lenses collimated and expanded the pumping beam from the fiber. The focal lengths of these two convex lenses are 30 mm (Lens 1) and 60 mm (Lens 2). The distance between the two lenses was 220 mm. The fiber pigtail stood near Lens 1's focus. The pump spot at the focus of Lens 2 was 0.76 mm in diameter.  
The laser had a plane oscillator. The oscillator's first cavity mirror (Mirror 1) was coated with an 808 nm antireflection (AR) and 1064 nm high-reflectivity (HR) film. The oscillator's output coupler was coated with an 808 nm antireflection (AR) and 1064 nm 90%-reflectivity film. Both mirrors were 12.7 mm in diameter. The optical length of the laser's oscillator was 61 mm, and the laser's frequency separation between two consecutive longitudinal modes was 2.46 GHz.  
The gain medium of the SLM laser was a 10 \(\times\) 3 \(\times\) 3 mm\({}^{3}\) Nd:YVO\({}_{4}\) crystal, with 8 mm doped of 0.3 at% Nd\({}^{3+}\) and 2 mm undoped. The crystal's end faces were coated with 1064 nm and 808 nm antireflection coatings. This crystal was wrapped in Indium foil and mounted in a copper heat sink. A thermoelectric cooler (TEC) controlled the heat sink's temperature to 25 \({}^{\circ}\)C.  
Three FP etalons were inserted into the laser oscillator separately as the SLM-selection element. Two FP etalons were made of K9 glass, 2.5 mm in thickness and 12 mm in diameter. The reflectivity of these two FP etalons at 1064 nm wavelength was 4% and 80%.  
The third FP etalon was made of an LT crystal. Details about the LT etalon are presented in Figure 1b. The LT crystal's EO coefficient \(\gamma_{33}\) is 30.4 pm/V. The LT crystal was 2.5 \(\times\) 3 \(\times\) 3 mm\({}^{3}\), 2.5 mm in lightpath direction. Its reflectivity was also 4%. The two sides of the LT etalon were plated with gold electrodes (painted in yellow in Figure 1b). Those two sides were perpendicular to the z-axis, and the distance between the two gold electrodes was 3 mm. The electric field applied on the LT etalon was in the direction of the z-axis. The laser's polarization was also in the direction of the z-axis. Figure 1c represents the voltage application on the LT etalon. A signal generator provided a direct-voltage signal. A high-voltage amplifier amplified the voltage signal. The amplified voltage was then loaded onto the LT etalon through gold-plated probes.  
Figure 1: The experimental schemes include (**a**) all-solid-state Nd:YVO\({}_{4}\) SLM laser, (**b**) details of LT etalon, and (**c**) voltage application for LT etalon.
# Effect of Chitosan on the Removal of Different Types of Tannins from Red Wines
## 3 Results and Discussion
### The Effect of Chitosan on Wines Rich in Tannins of Different Origin
Looking at the individual anthocyanins (Table 2), only in W the addition of C determined a loss of almost all anthocyanins suggesting that in wines enriched with enological tannins, a possible competition for the adsorption on C occurred. The effect of tannins and C on acetaldehyde content of wines was not significant. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Total Native Anthocyaninas**} & \multicolumn{2}{c}{**Polymeric Pigments**} & \multicolumn{2}{c}{**Acetaldehyde**} \\ \multicolumn{2}{c}{**(mg/L)**} & & **(mg/L)** & & **(mg/L)** \\ \hline W & 1330.18 \(\pm\) 20.44 & * A & 137.30 \(\pm\) 9.39 & C & 11.74 \(\pm\) 0.10 & AB \\ W-C & 1231.73 \(\pm\) 13.07 & a & 136.74 \(\pm\) 24.49 & c & 11.81 \(\pm\) 0.23 & a \\ CT & 1235.17 \(\pm\) 27.14 & B & 204.92 \(\pm\) 34.33 & B & 11.75 \(\pm\) 0.07 & B \\ CT-C & 1137.24 \(\pm\) 74.98 & a & 207.01 \(\pm\) 18.26 & b & 11.83 \(\pm\) 0.05 & a \\ ET & 1173.72 \(\pm\) 44.15 & C & 216.44 \(\pm\) 2.40 & * B & 11.90 \(\pm\) 0.09 & AB \\ ET-C & 1147.02 \(\pm\) 29.01 & a & 199.27 \(\pm\) 3.60 & b & 11.76 \(\pm\) 0.06 & a \\ GT & 1185.37 \(\pm\) 16.59 & * BC & 264.72 \(\pm\) 6.49 & * A & 11.92 \(\pm\) 0.02 & A \\ GT-C & 1132.69 \(\pm\) 25.32 & a & 246.25 \(\pm\) 4.40 & a & 11.88 \(\pm\) 0.04 & a \\ \hline \hline \end{tabular} Different letters indicate a statistically significant difference among treated wines, according to [PERSON]’s (HSD) analysis (\(p<0.05\)). The effect of tannins addition is expressed with a capital letter on wines W, CT, ET, and GT before the addition of C (A, B, C), and the effect of chitosan addition on wines W, CT, ET, and GT is expressed with a lowercase letter on wines W-C, CT-C, ET-C, and GT-C (a, b, c). The asterisk (*) indicates a statistical difference between each kind of wine before and after the treatment with chitosan. All data are expressed as means \(\pm\) standard deviation.  
\end{table} 
Table 1: Effect of tannins and C on total anthocyanin, polymeric pigments, and acetaldehyde determined by HPLC methods.
## 4 Validation of Proposed Method
### Impact of the Number of Layers on the Results  
In order to supplement the analysis of the impact of the number of layers on the proposed method, a calculation model for dividing the method from 3 layers to 19 layers is established. The average radius under different dividing methods is shown in Table 4, and the results are organized and analyzed using the aforementioned method. Taking the amplitude of the fundamental wave of the back electromotive force and the average electromagnetic torque as examples, the results are shown in Figure 17. It can be seen that as the number of layers increases, the calculation results of the 2D method are closer to those of the 3D method; that is, the more layers there are, the more accurate the calculation results are. In the current example size, when the number of layers exceeds nine, the impact of the number of layers is not significant. Therefore, in the subsequent practical application process, it is necessary to select the appropriate number of layers based on the actual size and accuracy requirements of the calculation model. It should be noted that due to the inability of the 2D method to consider the impact of end leakage flux on axial flux permanent magnet synchronous motors, the calculation errors caused by these factors cannot be solved by increasing the number of layers. One of our future key tasks is to quickly and accurately consider the impact of magnetic flux leakage at the end of this three-dimensional model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **3-Layer** & **5-Layer** & **7-Layer** & **9-Layer** & **11-Layer** & **13-Layer** & **15-Layer** & **17-Layer** & **19-Layer** \\ \hline
1 st & 58.33333 & 55 & 53.57143 & 52.77778 & 52.27273 & 51.92308 & 51.66667 & 51.47059 & 51.31579 \\
2 nd & 75 & 65 & 60.71429 & 58.33333 & 56.81818 & 55.76923 & 55 & 54.41176 & 53.94737 \\
3 rd & 91.66667 & 75 & 67.85714 & 63.88889 & 61.36364 & 59.61538 & 58.33333
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 3 DAA Algorithm Design  
### Access Selection Mechanism  
The DAA algorithm calculates the first time access satellite of ES through the following steps.  
1. The communication domain of ES is calculated by Equation (1);
2. ES calculates which satellites in the constellation are located in the communication domain by Equation (2).
3. ES calculates the satellite coverage time in the communication domain by Equation (3).
4. ES compares the relationship between the satellites' coverage time and \(t_{u}\). If there is a satellite covering time greater than \(t_{u}\), then \(\mathbf{t}_{V}^{con}\
eq\emptyset\) in Equation (6). The first time access satellite is calculated directly through Equation (6). If the coverage time of all satellites is less than \(t_{u}\), then \(\mathbf{t}_{V}^{con}=\emptyset\) in Equation (6). The access satellite of ES cannot be calculated by Equation (6).  
Next, we will introduce the access satellite selection strategy of the DAA algorithm when \(\mathbf{t}_{V}^{con}=\emptyset\).  
As shown in Figure 4, if the traffic volume transmitted by the ES at time \(t_{1}\) is \(t_{u}\). When \((t_{6}-t_{1})>t_{u}>(t_{5}-t_{1})\), then \(\mathbf{t}_{V}^{con}=\emptyset\). Choosing any satellite cannot complete the data transmission without switching ESL. To solve the access problem for \(\mathbf{t}_{V}^{out}=\emptyset\) and to achieve the minimum number of switching and maximum \(P_{u}\) value. This paper defines the set of ES's access satellites at time \(t\) as \(\mathbf{Q}\). The satellites are stored in \(\mathbf{Q}\) according to the access sequence of ES. The flow chart of calculation \(\mathbf{Q}\) is shown in Figure 5.  
In Figure 5, \(t^{\prime}\) and \(t^{\prime\prime}\) are the time variables. \(\mathbf{Q}^{\prime}\) and \(\mathbf{Q}^{\prime\prime}\) are the set variables. \(\text{pop}(\mathbf{Q}^{\prime})\) denotes removing the last element from \(\mathbf{Q}^{\prime}\). \(\mathbf{Q}^{\prime\prime}\)(end) represents the last element in \(\mathbf{Q}^{\prime\prime}\). \(t_{e}^{\prime}\) is the end moment of the maximum coverage time of the satellites in \(\mathbf{V}_{t^{\prime}}\). \(t_{e}^{\prime\prime}\) is the minimum positive value obtained by subtracting \(t^{\prime\prime}\) from the end time of satellite coverage in \(\mathbf{V}_{t}\). \(S^{\prime}\) and \(S^{\prime\prime}\) are satellite variables. \(S^{\prime}\).\(t_{e}\) denotes the end moment of \(S^{\prime}\) coverage and \(S^{\prime\prime}\).\(t_{s}\) denotes the start moment of \(S^{\prime\prime}\) coverage. \(\mathbf{Q}=\text{flipr}(\mathbf{Q}^{\prime\prime})\) denotes that the elements in the set \(\mathbf{Q}^{\prime\prime}\) are assigned to \(\mathbf{Q}\) after reverse order.  
Figure 4: Satellites coverage time at different moments.  
In Figure 4, the access satellite set \(\mathbf{Q}\) of the ES at time \(t_{1}\) is calculated according to the above process. When \(t_{u}\) satisfies \((t_{5}-t_{1})<t_{u}\leq(t_{6}-t_{1})\), the set of access satellites is \(\mathbf{Q}=\{v_{2},v_{6}\}\).
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 2 Method
### Evaluation Metrics  
Evaluating the performance of the classification models is a critical step in maneuver detection. We used precision, recall, and the F1 score to assess the model performance [36]. Precision, which is of particular interest in target analysis and collision avoidance, is defined as the proportion of correctly predicted anomalies out of all samples predicted as anomalies. [37]. Recall, which is critical for ballistic coefficient estimation and orbit improvement, is the proportion of actual anomalies that are correctly identified as such. [38]. Given the trade-off between precision and recall, we incorporate the \(F1\) score as a comprehensive metric to evaluate the effectiveness of the model. The precision, recall, and \(F1\) score were calculated as follows:  
\[P=\frac{TP}{TP+FP} \tag{15}\]  
\[R=\frac{TP}{TP+FN} \tag{16}\]  
\[F1=2\cdot\frac{P\cdot R}{P+R} \tag{17}\]  
where \(P\) is the precision rate, \(R\) is the recall rate, and \(F1\) is the \(F1\) score. \(TP\) denotes true positive samples, i.e., the model correctly predicts the positive class. \(FP\) denotes false positive samples, i.e., the model predicts the negative class as a positive class. \(FN\) denotes false negative samples, i.e., the model predicts the positive class as a negative class.
# A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation
## 6 Comparative Analysis
The speed performance of the algorithm implemented on four and eight sections exceeds the acceleration factor obtained by the algorithm [40] in all cases--5.41 and 8.08 versus 4.25 at the maximum working frequency of the GPU and, respectively, 12.1 and 14.41 versus 6.12 at the minimum operating frequency of the GPU.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 6 Our Approach: Proposed Model
### Layer 1: FT-OLSR Protocol
While various consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are prevalent in the literature, neither PoW, reliant on computational capacity, nor PoS, favoring the wealthiest node, is ideally suited for OppNets with their inherent limitations in computational resources. In our work, we introduce the Proof-of-Trust (PoT) consensus algorithm tailored specifically for highly dynamic and resource-constrained environments. In the PoT algorithm, the node with the highest trust value assumes the role of the validator. For a node to be eligible as a validator, its trust value must surpass a predetermined threshold, the smallest value determined through multiple simulations, signifying the minimum level of trustworthiness. **Block Generation Process**  
In the event of detecting a malicious node via FT-OLSR, it is permanently isolated from communication by incorporating its details into the shared blockchain. The detection process relies on potential attacker messages sent by nodes to validators following an attack incident. However, there exists a possibility of erroneous transmission, where a node might mistakenly include the information of a reliable node in its potential attacker message. To address this concern, an attack claim transaction necessitates confirmation by neighbors before a validator can generate a block based on the claim. To prevent potential attackers from isolating trustworthy nodes by maliciously sending potential attacker messages, a validation mechanism is in place. If the majority of neighboring nodes include the same node's information in their HELLO messages, the transaction is considered validated. Once a transaction is validated, determined by the number of nodes voting for it, the validator generates and disseminates an encrypted block containing the attacker's information.
## 1 Introduction
Fuzzy control algorithms, with their strong ability to handle uncertainty and fuzzy information, can effectively plan and design path results in agricultural machinery autonomous driving. The innovation of this research lies in two aspects. One is the combination of fuzzy logic and adaptive control, which enables the controller to flexibly adjust control parameters in the face of changing environments, improving computational efficiency while ensuring system adaptability and responsiveness. The second is to study the overall performance and integrated design of unmanned agricultural machinery operation systems, which can provide reference and guidance for the development of intelligent agricultural machinery and the enrichment of mechanical control theory. This research contribution lies in the fact that the proposed method can better consider the uncertainty of agricultural machinery parameters and changes in road interference, and the proposed nonlinear control method based on multi-deviation feedback can ensure overshoot during autonomous driving of unmanned agricultural machinery. The entire design of the driving and operation control system can achieve full automation of operations. This research is divided into four parts in total: the first part is a content review of the current technical literature related to unmanned agricultural machines, the second part is a methodological discussion of both system architecture and algorithm design, the third part is an experimental validation of the improved fuzzy adaptive control algorithm proposed by this research, and the last part is a summary of the whole unmanned agricultural machine system in the farmland.
## 3 Findings and Discussion
### Evaluating Health Concerns  
Ensuring the well-being of individuals in the context of high-frequency deployment within a smart environment necessitates a thorough examination of potential health concerns [43]. While current scientific knowledge does not provide conclusive evidence linking RF-EMF exposure from user equipment and base stations to cognitive functions, it is imperative for ongoing research to understand any possible risks comprehensively. An extensive review of the available literature reveals a compelling finding: approximately 57% of the studies indicate that exposure to RF-EMF emitted from the UE or base stations has been associated with one or more detrimental effects on cognitive functions.  
These findings raise significant concerns about the potential impact of RF-EMF exposure on cognitive abilities and underscore the need for further investigation and precautionary measures. Figure 5 shows the relative impact on human cognitive functions from the current accessible research works.  
It is important to acknowledge that there is some inconsistency in the findings, with around 24% of the studies yielding mixed or inconclusive results regarding the impact on cognitive functions. These variations in findings highlight the complex nature of the subject and the importance of conducting additional high-quality research to gain a comprehensive understanding of the relationship between RF-EMF exposure and cognitive effects. While a portion of the literature reports no significant impact on cognitive functions, accounting for approximately 19% of the studies, it is crucial to interpret these findings with caution. Research in this area is ongoing, and it is essential to consider the overall body of evidence and the potential long-term effects of RF-EMF exposure. Regular updates on scientific findings and risk assessments are essential to inform the public about the latest developments in this field. It is important to emphasize that while health concerns are carefully evaluated, the integration of high-frequency deployments in smart environments has brought numerous benefits to society. From improved connectivity and enhanced data transmission to more efficient services and greater convenience, these deployments have revolutionized various sectors, including healthcare, transportation, and energy management [44]. Therefore, it is crucial to strike a balance between innovation and safety, leveraging advancements while proactively addressing any potential risks through evidence-based research, regulation, and responsible practices. While there is currently no definitive evidence linking cognitive functions to RF-EMF exposure, ongoing research, adherence to safety standards, and open dialogue are essential for addressing  
Figure 5: The overall impact of RF-EMF in accordance with existing literature.  
any potential risks. By prioritizing public health and safety, while fostering technological advancements, we can create smart environments that are both innovative and safe for everyone.