esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Control of Low-Frequency Noise through a Single Top-Hung Window in a Full-Sized Room
## 2 Materials and Methods
### System Characteristics  
The impulse response of the primary paths was measured from the reference microphone at the periphery of the opened top-hung window panel (see Figure 1b) to each of the four error microphones, with the measurements shown in Figure 4a. The reverberations in the adjacent chamber, where the primary noise was placed, were reflected in the jitter of the impulse responses. There was also a prominent mode at around 300 Hz, as portrayed in the frequency response of the primary paths, shown in Figure 4b. This was despite keeping the doors open to release the modes and employing unidirectional microphones for both the reference and error microphones.  
\begin{table}
\begin{tabular}{c c} \hline \hline Reference microphones & \(J=1\) \\ Control sources and filters & \(K=4\) \\ Error microphones & \(M=4\) \\ Sampling rate & 16,000 Hz \\ Control filter length, \(L+1\) & 640 \\ Secondary path length & 256 \\ Step size & \(1\times 10^{-11}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Active control parameters.  
Figure 3: Block diagram of the active noise control system.  
The same jitter was also observed in the secondary paths. These secondary paths were measured from each of the speakers to each error microphone to obtain a total of 16 transfer functions (i.e., \(s_{mk}\)), as shown in Figure 5. It seemed that only the secondary paths at the third error microphone were adversely affected by the acoustic mode at 300 Hz, as shown in Figure 6c. Despite the jitter and acoustic mode observed, preliminary tests indicated that the acoustic conditions were satisfactory for the control filters to converge.  
Figure 4: (**a**) Impulse response of the primary paths measured from the reference microphone to each of the four error microphones, and (**b**) the corresponding frequency responses.  
Figure 5: Offline measurements of the secondary paths from the four loudspeakers to error microphones (**a**) 1, (**b**) 2, (**c**) 3, and (**d**) 4.
## 2 Methods
### Point Cloud Alignment and Parameter Optimization Point Cloud Pose Estimation and Optimization
#### 2.3.1 Coarse Registration  
Feature Description: In this study, we utilized the Fast Point Feature Histograms (FPFH) [28] descriptor to extract local features from a 3D point cloud. This method involves constructing a multidimensional point cloud by analyzing the spatial variances between the target point and its neighboring points, including the angular disparities among the normal vectors of the cloud. The resulting histogram accurately characterizes the geometric attributes of the point within its vicinity.  
The FPFH descriptor captures the surface characteristics of a point cloud by considering the spatial and geometric relationships of each point and its neighborhood. The accuracy of the FPFH feature space heavily relies on the quality of normal vector estimates for points in the neighborhood. To enhance these estimates, this study employed the mpp-MLS algorithm, previously discussed, to handle point clouds with non-cooperative targets. The refined normal vector estimation achieved through the mpp-MLS algorithm enhances the precision and performance of FPFH descriptors in local feature extraction.  
Feature Matching: The feature matching process between the source and target point clouds involves calculating the translation and rotation parameters. This is achieved by establishing one-to-one matching pairs through the calculation of point cloud FPFH features. Each point in the target point cloud is traversed to find the nearest-neighbor similar matching points in the source point cloud using the KD-tree search algorithm. By obtaining these matching pairs, we can determine the correspondence between the two sets of point clouds and subsequently calculate the necessary translation and rotation parameters.  
During the feature matching process, a small number of mismatched pairs can have a significant impact on subsequent pose estimation. To mitigate this, the RANSAC al  
Figure 5: Point cloud phase consistency detection.  
gorithm [29] was employed in this study to enhance the robustness of the initial pose estimation. The algorithm operates by treating the dataset as comprising 'inlier points' (correctly matched pairs) and 'outlier points' (mismatched pairs or noise), and iteratively optimizes the model through random sampling and computation. This process aims to acquire higher accuracy results of the final pose estimation by effectively handling outliers and optimizing model parameters.
## 3 Results and Discussion
### Final Picture in Real Space  
Figure 6 shows a schematic picture of the average structures of aqueous solutions of PE stars, linking together their characteristic lengths and the positions of the main maxima of their scattering function. In dilute solution (Figure 6a?), the PE stars are separated from each other, with a liquid-like position order. Their arms are quasi-completely extended. In semi-dilute solution (Figure 6b'), the arms interpenetrate to some extent. The average structure can then be envisioned as a \"composite\" one made of effective stars of smaller size \(\chi\) (islands) immersed in a matrix formed by the interpenetrating arm parts, with a constant correlation size \(\xi\) (sea of blobs). So, the space is partitioned into two media of distinct structure interpenetrated between each other. The effective stars become closer to each other, their size \(\chi\) is reduced at the benefit of the sea of blobs, as concentration increases. This is one of the two regimes predicted by scaling approaches [14]. We believe that the \"shrinking regime\" predicted both in [14] and in [10], is not visible. This is supported by the fact that the height of the \(q_{1}\)* peak decreases rapidly, becoming comparable to the height of the \(q_{2}\)* electrostatic peak. The shrinking would correspond to a more intense peak. As said above, more could be obtained from the analysis of the scattering profile, which is beyond the scope of this paper.  
Simultaneously, since the arms interpenetrate, the repeat unit concentration \(c_{\rm i}\) inside the star increases, so both spatial and electrostatic screenings make \(\xi\) decrease. This results in an increase of \(q_{2}\)*; strikingly, data show that \(q_{2}\)*, for \(c>c^{\star}\), varies right as the position of the PE peak of the semidilute solutions of linear PSS. This suggests that spatial distributions of both polymer units and counterions are: (i) the same as for linear in average; (ii) rather homogenous (though the peak appears wider than in linear PSS); (iii) the same whatever \(N_{\rm a}\). These three points suggest that the size \(\chi\) of the \"islands\" is limited. The same can be inferred at \(c<c^{\star}\), from the constancy of \(q_{2}\)* according to \(c\) (the inner average concentration appears constant), observed for each \(N_{\rm a}\) (the \(q_{2}\)* value depends on \(N_{\rm a}\)). Therefore the least we can say is that we have no direct evidence of the distribution being peaked on the center of the star.  
Finally, let us point out that such an average structure may be encountered only in the present range of functionality, _i.e._, \(<\!\!f\!\!>\) not too little as well as not too large, for highly charged star-branched PEs. It remains that, in this range, it is different from the picture often proposed for neutral stars in good solvent [3; 4; 52; 53; 54] where the solvent concentration and the chain average conformation depend on the distance to the center of the star.
## 3 Results and Discussion
### PLSR-Based Characteristic Aroma Analysis
Fruit wines, such as mulberry and strawberry wines, have been reported to contain this ester, which contributes to the floral aromas of these wines [33]. Figure 3: Violin plot showing the MF (%) values of sensory characteristics of the wine samples among various varieties and clones. Hexanol imparts botanical-type aromas, commonly described as green/herbaceous, and freshly cut grass, but has also been reported to provide floral aromas [36]. Hexanol is associated with nut and honey aromas in Vidal icewines [9]. This compound has also been reported to be the key aroma component in peach. Aroma analysis of flat peach juice using PLSR showed that (Z)-3-hexen-1-ol provided a floral aroma, which is consistent with the results obtained from this study [37]. The black berry properties of the Malbec and Cabernet Franc wine samples were analyzed, as shown in Figure 5A,B. The volatile compounds that correlated positively and negatively with the black berry aroma were (Z)-3-hexen-1-ol and isobutyl acetate, respectively. The corresponding correlation coefficients were 0.059 and \(-\)0.058 for Malbec, and 0.126 and \(-\)0.165 for Cabernet Franc (Table S4). Isobutyl acetate contributed'stone fruit' aromas to the natural fermentation of Chardonnay and banana aromas to the aligote wines of Burgundy [38; 39]. It was found to impart fruit aromas to beers, thus improving the flavor and aroma harmony and making the beer more pleasant [40]. In conclusion, the aroma of isobutyl acetate is biased towards bananas and drupes and opposes the expression of black berry aroma. As shown in Figure 5C, ethyl 9-decenoate, hexyl alcohol, and (Z)-3-hexen-1-ol were positively correlated with spice aroma in Malbec, with correlation coefficients of 0.057,  
Figure 4: The PLSR analysis of floral sensory and volatile compounds, standardized and estimated regression coefficients from the PLSR model showed a significant correlation between volatile compounds and floral sensory odor attributes in Merlot (**A**), Cabernet Sauvignon (**B**), Malbec (**C**), and Pinot Noir (**D**) wine samples. Bar with slash indicates a significant effect (\(p<0.05\)). 0.059, and 0.059, respectively (Table S4). Combining the previous analyses for the floral (Figure 4C) and black berry aromas (Figure 5A) of Malbec, the results of the PLSR analysis for Malbec were found to be more consistent, and ethyl 9-decenoate, hexyl alcohol, and (Z)-3-hexen-1-ol were found to correlate positively with floral, black berry, and spice aroma characteristics, respectively. The PLSR presented some characteristic volatile compounds that were useful in discerning the sensory origin of red wine samples between Hexi Corridor and other wine regions. In addition, identification of the most important wine aroma compounds and their contribution to wine aroma has potential to assist wine producers to brew and blend wines and establish region style, and guide researchers to better understand the mechanisms governing transformation of precursor compounds into contributors to the aroma of finished wines.
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 5 Performance Evaluation
In every sampling period \(T_{s}\), the plant software generates sensor output packets with state \(x(t)\) and output matrix \(C\) in the dynamics (1); then, it transmits the sensor output packets to controller computers. The plant model embedded in the plant software is defined in (1) with the following parameters:  
\[A= \begin{bmatrix}0.02&0.005\\ 0.001&0.05\end{bmatrix} ,B= \begin{bmatrix}1\\ 0\end{bmatrix} ,C= \begin{bmatrix}1&0\end{bmatrix} , \tag{12}\]  
where sampling period \(T_{s}\) is 2 s.  
The controller software embedded in the controller computers conducts observer-based feedback control, which estimates the state of the plant with the sensor output packets, and calculates control input signals \(u(t)\) as illustrated in the control logic (2). Then, the controller software generates the control input signal packets, and transmits them to the plant computer. The parameters of the observer-based state feedback controller in (2) were chosen as follows:  
\[K= \begin{bmatrix}0.9677&0.1890\end{bmatrix} ,L= \begin{bmatrix}0.8423\\ 18.7251\end{bmatrix} . \tag{13}\]  
Figure 10 shows the result of using the switching mechanism. We stopped the primary controller simulator at 250 s to check the controller switching mechanism (vertical line with a constant 250 value). We displayed a marker every 10 s. Figure 9(b) shows the results of the sensor measurements. The backup controller detected malfunctions in the controller and initiated transmission in place of the controller. However, the state of the plant increased to infinity without a controller switching mechanism after 250 s. Figure 9(b) shows the result  
of the control input signals. Unlike when the switching mechanism was used, the primary controller could no longer transmit the control input signal. Therefore, the plant performed control by using the control input value transmitted at 250 s as it is. Results indicate that the proposed mechanism could quickly recover by switching to the backup controller in the case of controller failure. Figure 10: Experimental results on plant with/without controller switching mechanism. (**a**) Sensor measurements; (**b**) control input signals.
# Damping Assessment of Lightweight Timber Floors Under Human Walking Excitations
## 1 Introduction
These floors generally had a main resistant structure composed of a series of engineered timber beams up to 12 m in length (I-joist, web-joist), oriented strand board (OSB) or plywood boards nailed or screwed on their upper edge to receive concrete topping, and gypsum boards at their bottom edge to increase fire resistance. The damping ratios obtained for the first resonant mode varied between 0.47% and 9.10%. Subsequently, with the rise of industrialized construction, other researchers concentrated on studying the damping ratios of prefabricated timber floors for eight-story timber frame residential buildings [11] and five-story multi-family apartment buildings [12]. These floors generally had a main resistant structure composed of CLT slab panels and gulam beams, which often interacted with sophisticated ceiling systems and lightweight partition walls. The damping ratios obtained for the first resonant mode varied between 3.40% and 8.00%. Previous work has tended to focus on damping ratio estimations for high-quality timber floors that were constructed in developed countries. The aim of our work is to expand current knowledge on timber floors' damping to lightweight timber floors that are typical in Latin American social housing for low-income families. To the best of the authors' knowledge, no systematic investigation on the damping mechanisms that are activated under human-walking operational conditions has been previously presented in a timber floor context, where softwood species, low-depth timber joists, and unskilled labor are usually used. Such an investigation is presented herein. Generally, this kind of timber floor has a poor vibration performance, which is reflected in the adverse results of satisfaction surveys distributed to users by government institutions. In this context, we focused on the experimental estimation of damping ratios in both laboratory and in-situ timber floors. Different floor spans, support details, types of non-structural elements, and human walking conditions were considered. All the floors were instrumented with accelerometers, and subsequently, operational modal analysis techniques were applied. Finally, some conclusions about the influence of damping ratio levels on the floor vibration performance were made. With the experimental results of this research, it is hoped that this study can contribute to the future calibration of simplified formulas for the vibratory design of these low-cost timber floors, which correlate more complex parameters(frequencies, damping, vibration dose value) with simpler ones (vertical displacements, velocities, or accelerations due to point loads).
## 5 Designs of TDMA Protocol-Based Distributed Coalition Formation Games
### Time Frame Structure  
In the multi-UAV network, time is partitioned into regular time intervals with equal lengths of \(T_{F}\), which is called the repetition period. As shown in Figure 4, the repetition period consists of two kinds of time slots: (1) control slots and (2) data slots. The repetition interval is divided into 50 N equal-length slots. The length of each time slot that is assigned to each member, which is denoted by \(t_{slot}\), can be determined by \(t_{slot}=T_{F}/N\).  
There are 10 N control slots and 40 N data slots in the repetition interval \(T_{F}\). The control slot, which is denoted by \(C_{i}\), includes the ITJ/RTJ message, neighbor discovery message and distributed coalition formation message. In the data slot \(D_{i}\), the UAV nodes included in the same coalition can transmit data simultaneously. Obviously, in this design, control slots occupy effective data transmission time, which decreases the performance of network throughput. In Figure 4, there are 10 N control slots in one repetition interval, which consists of 50 N slots. The overhead of the control slots is 20%.
## 4 Conclusions  
The paper presents a comprehensive and extensive literature review on the VSA estimation: 119 works have been considered in total. They have been divided into two main categories depending on the approach: observer-based, and neural network-based. A recap of the methods is reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Method Detail** & **Robustness** & **Model** & **Simulations** & **Experiments** & **Rel.** \\ \hline
**LO** & **VSA and other state variables estimated using this observer in its linear or non-linear form, according to the type of vehicle model adopted & High robustness to model uncertainty and system noise & Dynamic—both linear and non-linear & yes & yes & [976, 101, 111, 112] \\ \hline
**SMO** & **VSA and other state variables estimated using this observer in its linear or non-linear form, according to the type of vehicle model adopted, & High robustness to model uncertainty and system noise & Dynamic—both linear and non-linear & yes  
The observer-based approach has been found to be the most used (71 works out of 120). It heavily depends on the kind of vehicle model, its accuracy, and the completeness of its set of parameters. The computational burden can also be an issue; nevertheless, the method can lead to very accurate results. GPS-aided methods are often combined to an observer, using the GPS data to overcome any estimation gaps or inaccuracy. However, GPS sampling frequencies, availability, and cost should also be considered during the design of a VSA estimation system.  
The neural network-based approach has been specifically designed to overcome the need of a vehicle model and be suitable for a real-time environment, since it does not require much computational effort. In this case, the poor capability of the ANN to deal with any environmental or vehicle physical changes (for example, tyre wear or road friction) cannot be neglected.  
In all cases, the cost-to-accuracy ratio plays an important role in choosing the correct estimation method.  
[PERSON] conceived this work as a review. [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 Results
### Results and Discussions Regarding the Batter Boards Layout Methods
#### 3.2.3 Survey of the Batter Boards and Computer-Aided Design (CAD) Implementation  
This method is based on the same methodology and principle as the previous one, with the survey of the batter boards, in order to determine the coordinates of each side. The difference is that instead of a manual calculus of the intersecting coordinate, the survey data is exported inside CAD software, the same one used for establishing the site layout plan, and the batter boards are represented as polylines. Using the geometry functions, the construction line (axes) is extended until they intersect the new polyline (batter board), and the intersecting coordinate is extracted and inserted in the total station (Figure 12). The advantages of this method are that it can be performed with older models of total stations that do not have special functions (e.g., reference line/layout line) or with total stations where these functions are blocked and it is necessary to purchase additional software packages. Also, the actual layout on the batter board is easy and accessible to anyone, as it is a regular stake-out based on the polar coordinates method (horizontal angle and distance). The disadvantages of this method are numerous, because it has a low efficiency in terms of time. Also, numerous errors can occur when measuring the wood planks and the insert-export in CAD software, as well as the need to bring a laptop on-site. Another disadvantage is the need for qualitative batter boards, because if the wood planks are bent and the survey is at each side, the resulting intersection of coordinates will be outside the wood section.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 3 Network-on-Chip Architecture  
Our proposed NoC architecture is based on a hierarchical organisation: small rings serve more frequent local communications among a restricted group of PEs, while a global 2D-mesh interconnection provides connectivity for distant PEs. This hierarchy shows a better performance-to-power ratio and scalability when compared to the flattened designs (i.e., a single type of interconnection serving all the PEs in the chip). The introduction of reconfiguration capability in the proposed NoC architecture requires some additional component that we describe in the following. To limit the complexity of the router and ring-switch micro-architectures, we carefully designed these components, as well as adopting a communication protocol that takes advantage of less frequent transmission of a specific data packet payload, thus reducing the amount of used bandwidth.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Machine Learning Classifiers
#### 3.1.5 Random Forest  
The RF model is ensemble learning and tree-based, which are employed to construct predictive models. In line with its name, the classifier creates a forest that is made up of trees; more trees mean a more robust forest. RF uses the data samples to create decision trees to calculate each tree and select the best result using the voting approach [46; 47]. The algorithm can best identify the significance of features from a set of datasets. The parameters were set to default and later optimised to _n_estimators_ -10 and _criterion_ -_entropy_.