esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 3 An Active Noise Control System with Dynamically Adapted Reconstruction and Anti-Aliasing Filters
### Construction of a Digital Filter Library in Regions with a High Sampling Rate
When constructing a digital filter library, the stored content includes the filter coefficients corresponding to different filter groups \(T_{xd}(z)\), \(T_{ud}(z)\), and \(T_{of}(z)\) and the squares of the amplitudes of the frequency responses \(|T_{xd}(\omega)|^{2}\), \(|T_{ud}(\omega)|^{2}\), and \(|T_{ed}(\omega)|^{2}\). The average group delays within the control band from \(\omega_{1}\) to \(\omega_{2}\) are calculated with  
\[\tau=\frac{1}{\omega_{2}-\omega_{1}}\int_{\omega_{1}}^{\omega_{2}}\tau(\omega) \tag{13}\]  
with (\(\tau_{xd}\), \(\tau_{ud}\), and \(\tau_{ed}\)) and \(\hat{G}(z)\). The storage of these data as the frequency response, group delay, and \(\hat{G}(z)\) when constructing a filter library is beneficial for reducing the computational cost of dynamic filter adaptation in the ANC process. In addition to Chebyshev-I-type low-pass filters, other models can also be used to construct digital filters with different features. In addition, we can also use the impulse response invariance method to convert IIR filters into FIR filters. As digital filter libraries are constructed offline, filter design tools can be used in filter design and optimization.
# A New Method for Finding the Shape of the Main Cable in the Special Cable Plane
## 1 Introduction
The main cable and suspender of the spatial special-shaped cable plane are in the spatial stress state, and the structural stress is more complex than that of the spatial slant cable plane. At present, although some achievements have been made in the research of spatial special-shaped suspension bridges, there are still many problems that need to be further studied, such as the new calculation method of finding the shape of the main cable on the spatial special-shaped cable plane, the optimization of the results of spatial special-shaped suspension bridges, etc. In order to solve the problem of finding the shape of the main cable of the spatial special-shaped cable plane suspension bridge, this paper first analyzes the force of the spatial special-shaped cable plane suspension bridge and puts forward a new calculation model for finding the shape of the main cable of the spatial special-shaped cable plane suspension bridge--the four-coordinate calculation model. The new calculation model takes into account the transverse direction error of the midspan, which is more in line with the engineering practice. Secondly, this paper proposes a new form-finding method for space main cable--the hybrid quantum genetic algorithm. The algorithm first performs interval search by the modified quantum genetic algorithm [19] to obtain a high-precision iterative initial value, and then performs iterative calculation by the modified least squares method [20] until it meets the requirements. Finally, a suspension bridge with space special-shaped cable plane is taken as an example to study. The results show that the calculation model proposed in this paper is more stable, and the algorithm proposed has the characteristics of fast calculation speed, high calculation accuracy and strong adaptability.
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 3 Mathematic Modeling  
The location deployment of the base station as well as the resource allocation between and within user groups affect the overall transmission rate of the system. Therefore, it is important to study how to jointly deploy UAVs for rate optimization of RSMA downlink multi-user group systems under the uncertainty of UAV as a base station location. This section focuses on modeling and analyzing the UAV-assisted downlink RSMA system.
## 3 Minimum Nonlinearity PL Design Guidelines  
The advanced design of parasitic layers, and reconfigurable antennas in general, heavily relies on optimization techniques to find an adequate set of switching states to achieve a target set of figures of merit. These figures of merit are usually specified in terms of fundamental antenna parameters for example, port parameters, such as match and coupling, and pattern features such as direction of maximum, gain and polarization.  
Although nothing prevents the inclusion of system-related performance metrics impacted by the antenna, such as coverage, throughput, BER or EVM, or any other parameter of interest in particular applications, the use of basic antenna properties is often the most efficient option, as the computational cost of calculating these derived quantities make them impractical when evaluating beyond a few thousands of switch states. This complexity is exacerbated if structural variations of the basic radiator are to be considered in a co-optimization process as for example in [48].  
Therefore, the use of basic antenna parameters and system-level performance estimators directly derived through simple calculations may be used whenever possible to predict the better performing configuration when comparing among several different alternatives.  
As the main interest of this work is the design of reconfigurable antennas and parasitic layers with minimum nonlinearities and impact on related system-level metrics such as EVM, a partitioning of the design process into three stages is proposed aiming to reduce the computation time and achieving controllable results. The stages of the design methodology are:  
* Choose an optimal trade-off starting point for the antenna geometry.
* Determine the optimum switch configurations using the performance metrics vector defined in terms of fundamental antenna parameters derived from small-signal formulations.
* Based on nonlinear load models, accurately calculate the nonlinearities and system-related parameters for the optimum configurations.  
A high level flowchart diagram illustrating the main elements of the proposed design methodology is presented in Figure 10.  
Figure 9: Error Vector Magnitude (EVM) of test reconfigurable antenna.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 4 Results and Discussion  
### BER Simulation Results  
Figure 3 presents the average BER performance as a function of the SNR parameter. The figure includes the closed-form approximate expression in addition to the exact form. Moreover, the Monte Carlo simulation is a benchmark for validating the exact and approximate models. As observed, the approximate BER expression in the closed form closely aligns with the exact results across the entire SNR range, confirming the reliability of the derived expression.  
An important performance threshold in modern communication systems is the forward error correction (FEC) limit, typically set at a BER of \(10^{-3}\). The FEC limit represents the maximum tolerable BER beyond which FEC decoders can no longer reliably correct errors. As observed in Figure 3, the FEC limit is achieved at approximately \(\mathrm{SNR}\approx 10.5\,\mathrm{dB}\). This implies that the system should operate with an SNR no lower than \(10.5\,\mathrm{dB}\) to ensure error rates are acceptable for FEC-assisted decoding.  
The approximate BER closed form expression's robustness across varying SNR conditions and its alignment with both exact and simulated results reinforce its applicability in system optimization, link budget analysis, and adaptive transmission schemes tailored for dynamic dusty environments.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Results Based on Parameter Tuning
#### 6.2.1 Route Reliability Results Based on the Probability of Mating Rate \(M_{R}\) Parameter  
The results of significant route reliability scores aid delivery of efficacy of the proposed system by varying the probability of mating rate \(M_{R}\) values, as shown in the figure insert. The obtained results are measured in three categories, namely best, mean, and worst based on 1000 Monte Carlo trails.  
Figure 4 insert presents the mean values of the route reliability scores for varying \(M_{R}\) values. The \(y\)-axis denotes the route reliability values based on the \(x\)-axis number of fitness evaluations. We categorized the number of fitness evaluations into three sections, namely fast-run (within range of 1-10,000 fitness evaluations), mean run range (within the range of 10,000-20,000 fitness evaluations), and long-run range (within the range of 20,000-30,000 fitness evaluations). In Figure 4, it can be seen that using higher \(M_{R}\) values (i.e., \(M_{R}\geq 0.5\)) converges the proposed JMA algorithm towards the best routing path compared with minimum \(M_{R}\) values. However, in the mid-run range category it can be seen that \(M_{R}=0.2\) outperforms the larger values when finding route reliability. On the other hand, the aid of smaller \(M_{R}\) values in the long-run range outperforms than the larger values when determining the best routes. The results specify that the use of small \(M_{R}\) values achieves better exploration in the JMA, whereas it avoids local optima and premature convergence problems. The outcome of the result concludes that the use of probability \(M_{R}=0.2\) obtains satisfactory results compared with \(M_{R}\) values. The best and worst route stability scores obtained over varying \(M_{R}\) values are satisfactory in JMA. We noticed that again the \(M_{R}=0.2\) value drastically provides the best route compared with \(M_{R}\) values. Figure 4 shows that the best mean route is achieved late in the fast run and stays the same in the long-run range. Interestingly, Figure 4 shows that using \(M_{R}=0.2\) achieves a better reliability score compared to the other \(M_{R}\) values. The overall result shows that the use of \(M_{R}=0.2\) outperforms the other values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline \(\kappa_{T}\) & 0 dBW \\ \(\Omega\) & 5.925 GHz \\ \(A\) & 1 km \(\times\) 1 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings for experimentation.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 6 Parametric Analysis Model of Ultrasonic P-Wave Velocity
### Establishment of the Predictive Model  
According to relationships of the ultrasonic p-wave velocity with various factors, a synthetic forecasting model for characteristic parameters of the material was constructed combining with the principle for developing multiple linear models, as expressed by Formula (1).  
\[\{v=a_{1}v_{1}+a_{2}v_{2}+a_{3}v_{3}+a_{4}v_{4}+a_{5} \tag{1}\]  
According to numerous test results pertaining to each of the characteristic parameters, Equation (1) is subjected to multiple linear regression, thus acquiring the relationships of the ultrasonic p-wave velocity with various factors, as expressed by Formula (2).  
\[\{v=0.97093v_{1}+1.007324v_{2}+0.95037v_{3}+0.99147v_{4}-15.43465 \tag{2}\]  
The independent variable interval of the fitted wave velocity prediction equation is \(0.2875<v_{1}<0.4000\); \(0.0300<v_{2}<0.0450\); \(0.04<v_{3}<0.12\); \(0.04<v_{4}<0.12\). In this interval, the predicted equation fits the actual measured wave velocity to a high degree.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.6 Summary of Results
For CPU, memory, and latency, the location-based approach performs better than the best-fit algorithm, while for disk usage and power consumption, the latter is superior. These results not only confirm the usefulness of these algorithms in particular scenarios but also offer practical suggestions for enhancing resource management in edge cloud setups. The outcomes provide a solid basis for additional research and real-world applications, as they are in good agreement with the original goals of optimizing resource use, limiting energy consumption, and lowering latency. Building on these findings, future studies might investigate sophisticated computational methods, optimize tactics, and develop dynamic adaptation mechanisms to further optimize resource management in edge cloud systems.
# Improving Non-Autoregressive Machine Translation Using Sentence-Level Semantic Agreement
## 2 Background
### Glancing Transformer  
Although the vanilla NAT model can significantly reduce the decoding latency, the translation accuracy is much lower than that of the autoregressive translation. To improve the performance, [PERSON] et al. [7] introduced a _Glancing Transformer_ (GLAT). With GLAT, the gap between the NAT and AT models is narrowed.  
The training process of GLAT can be divided into two steps. Firstly, GLAT copies the input from the source embeddings, feeds it into the decoder, and generates the target sentence. Secondly, GLAT computes the distance between the generated target sentence and the reference sentence. According to the distance, GLAT samples some words from the reference sentence:  
\[\mathbb{GS}=S(Y,D(Y,\hat{Y})). \tag{4}\]  
Then the sampled words are combined with the input at the first step. The new input is then fed into the decoder to perform the second pass. Different from the vanilla NAT model, GLAT does not compute the loss at each position, but it computes the loss at the position that is not sampled:  
\[\mathcal{L}_{GT}(\theta)=\sum_{y\
eq\mathbb{GS}}\text{log}p(y_{t}|\mathbb{GS },X;\theta) \tag{5}\]  
Although the Glancing Transformer can capture the latent target dependency and improve the performance of the NAT model, it still uses cross-entropy to train the whole model and cannot guarantee the semantic agreement between the source and target sentences. In other words, GLAT also faces the same problem: the semantics of the generated sentence are not consistent with the source sentence.
# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
## 2 Materials and Methods
### Analysis  
The predicted muscle forces from the three models were then used to calculate joint reaction forces at the GH joint, as well as the ratio of shear to compressive joint contact force. The root mean square difference (RMSd) between the inverse dynamics joint moments and those predicted by the EMG-assisted method as well as the Pearson correlation coefficient (R) were calculated to validate its application. The residual actuator moments obtained from SO and SOcal were used for comparison. The activations predicted from SO, SOcal and EMGA were compared with experimental EMGs using RMSd and R. Their respective empirical cumulative distribution functions [33] were also compared using a Kolmogorov-Smirnov dissimilarity index [34]. This index evaluates the probability that the compared samples are drawn from the same distribution. The larger its statistic, the less likely the samples have similar distributions. Coactivation patterns of the rotator cuff muscles between both loads were compared with a Jennrich test on the correlation matrices at each load [35]. Furthermore, the co-contraction was evaluated for muscle pairs known to have synergies: latissimus dorsi and teres major, anterior deltoid and coracobrachialis, median deltoid and supraspinatus, posterior deltoid and infraspinatus [36], using a co-contraction coefficient defined in Equation (1), where \(E_{1}\) and \(E_{2}\) are the activation of the less and more activated muscle at each time frame for each muscle pair, respectively [37].  
\[CC=\frac{E_{1}}{E_{2}}(E_{1}+E_{2}) \tag{1}\]  
Finally, the GH joint reaction force was expressed in the glenoid local frame. Within this frame, the GH joint reaction ratio, defined as the ratio of the shear to compressive contact forces in the glenoid, was assessed in regard to the reported non-dislocation thresholds [1].
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 2 USV Thruster Fault Feature
### Analysis of Thruster Fault Features
#### 2.2.1 Vibration  
When the thruster blade breaks or floating objects, such as ropes and nets, become entangled with the thruster, the geometric center and the center of mass of the thruster do not coincide with each other, resulting in mass unbalance. When a rotor operates in this state of unbalance, vibration is generated, as shown in Figure 3, and the formulas for the relationship are presented in Equations (1) and (2) [27].  
\[F_{c}=em\omega^{2} \tag{1}\]  
\[X_{vib}=\frac{F_{c}}{K_{DS}} \tag{2}\]  
where \(F_{c}\) denotes the centrifugal force, \(m\) represents the mass unbalance caused by breakage of the thruster blade and entanglement, \(e\) indicates the distance between the center of mass and the geometric centerline, \(\omega\) is the angular velocity, \(X_{vib}\) is the vibration caused by the rotor unbalance and \(K_{DS}\) represents the dynamic stiffness. Vibration is proportional to \(m\) and \(e\); therefore, if there is mass unbalance owing to the breakage of the thruster blade or entanglement of floating objects, \(m\) and \(e\) will increase, which, in turn, will increase vibration. Hence, vibration was selected as the fault feature.
## 3 Partition-Based Clustering
### K-Modes  
K-modes clustering [3,4] is a variant of k-means that is specifically designed for datasets with categorical attributes, such as customer demographic data, product preferences, survey responses, etc. The k-modes algorithm partitions the data into a specified number of clusters based on the similarity of the categorical instances. Therefore, it allows for the discovery of natural groupings among categorical instances based on their shared attribute values.  
Similar to k-means, k-modes starts by selecting initial cluster centroids. These centroids are initially chosen from the dataset. Then, each instance is assigned to the nearest centroid according to a distance metric for categorical data. The most commonly used distance metric for k-modes clustering is the Hamming distance, which calculates the percentage of attributes that differ between two instances. The Hamming distance is suitable for comparing categorical values, as it captures the proportion of attributes that differ between two instances. After assigning all instances to clusters, the centroids are updated. The new centroid of a cluster is determined as the mode (most frequent value) of each attribute within the cluster. Like k-means, the aforementioned steps are repeated iteratively until convergence. In each iteration, the instances are reassigned to the clusters based on the new centroids. The centroids are recalculated based on the instances within each cluster. The algorithm continues iterating until the assignments no longer change, indicating convergence.  
Like k-means, the number of clusters is a parameter (k) that needs to be specified by the user before running the k-modes clustering. Similar to k-means, the selection of an appropriate value can be determined by the elbow method.  
Let us assume the following dataset of categorical attributes representing people's preferences for colors and animals: {red, dog}, {blue, cat}, {green, dog}, {green, dog}, and {blue, cat}. Let as also assume that k-modes performs clustering with k = 2, and the k-modes starts with the initial centroids: {red, dog} and {blue, cat}. The instance {green, dog} is closer to the centroid {red, dog}, while the instance {blue, cat} is closer to the centroid {blue, cat}. Therefore, after the assignment step, the two clusters are C1 with {red, dog}, {green, dog}, and {green, dog}, and C2, with {blue, cat} and {blue, cat}. In C1, \"green\" is the most common preference for the color, and \"dog\" is the most frequent preference for the animal. In C2, \"blue\" and \"cat\" are the most common values. Thus, the new centroids (modes) are {green, dog} and {blue, cat}. The assignment step is repeated. The instances {red,dog} and {green, dog} are closer to the centroid {green, dog}. Also, {blue, cat} is closer to {blue, cat}. The two clusters are not modified. Thus, the algorithm stops. The final clusters are C1 with {red, dog}, {green, dog}, and {green, dog}, and C2 with {blue, cat} and {blue, cat}.
# Bioinspired Divide-and-Conquer Design Methodology for a Multifunctional Contour of a Curved Lever
## 2 Bioinspired Curved Lever Contour Design Methodology
### Concept of Bioinspired Design Methodology  
As shown in Figure 2, the divide-and-conquer contour design methodology for a multifunctional lever was developed using bioinspired insights. This methodology fundamentally does not treat a lever contour as a single curve that should satisfy multiple functions. Rather, a lever contour is regarded as a combination of partial contours responsible for each subfunction. The procedure for designing a lever with this methodology is as follows: At first, subfunctions should be generated by dividing the overall function. Second, the design space of the lever is also divided into subareas that affect each sub  
Figure 1: Schematic drawing of the lever mechanism in a _Salvia pratensis_ flower and its interaction with a flower-visiting bee as depicted by [PERSON] et al. [17]: (**a**) Longitudinal section of a flower and motions of the curved staminal lever; (**b**) a hypothetical straight-line staminal lever; (**c**) a hypothetical staminal lever where the theca has the same curvature as the anther lobe; (**d**) a real staminal lever composed of two partial contours. The partial contours 1 and 2 prevent interference with the bee’s head and avoid sharp contact between the theca and the bee’s abdomen, respectively.  
function. Third, a partial contour is derived by selecting an appropriate curve expression method and design method in consideration of the assigned subfunctions. Finally, one lever is determined by combining the derived partial contours. The partial contours only consider achieving their own functional goal. Therefore, in this design methodology, the designer can choose the most appropriate mathematical model to represent each partial contour and proper determination method for the design variables of each model. The divide-and-conquer lever design methodology has several advantages as follows: First, it can simplify complex multifunctional design problems.  
A designer can concentrate on designing each partial contour to satisfy relatively simple functional goals. Next, this methodology makes it possible to construct efficient design processes in terms of mathematical modeling and the parameter determination method for each contour. Although some lever design problems may not suit this methodology because the multifunctionality cannot be divided well, the methodology can give a view to simplify the design problem.