esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Components of High-Speed Data Transmission for Lightweight and Small UAV Platforms  
The critical functional modules of the wireless optical communication system for small and light UAVs are shown in Figure 1. The composition of the small UAV airborne optical communication system mainly contains the following three major parts: a laser transceiver system, a tracking and aiming system, and a communication subsystem. Among them, the laser transceiver system resides in the core position and shoulders the task of transmitting and receiving laser signals, including the subsystems of signal light transmitting, beacon light sending, signal light receiving, and beacon light receiving. Signal light is used to transfer data, and beacon light is used to indicate the position and distance of the target. The tracking and aiming system is used to realize the seeking and tracking functions of the laser communication terminal. It includes a coarse- and fine-tracking detection unit and a servo system. The coarse- and fine-tracking detection units are responsible for fast and rough target tracking. At the same time, the servo system is responsible for the precise control of the aiming direction and position. The communication subsystem is responsible for processing and transmitting data for laser communication, and it includes units for signal coding, modulation, demodulation, and storage. These units process the raw data to make them suitable for laser transmission and then demodulate, decode, and store them at the receiving end to recover the raw data. Among the three subsystems, capture alignment tracking is the prerequisite, the laser transceiver system is the system's core, and the modulation and demodulation units guarantee high-speed data transmission for optical communication.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 5 Conclusions  
The purpose of this study aims to inquire into the effect of incorporating porous materials on noise and vibration reduction in wooden floor panels, as well as to analyze the vibroacoustic performance of the assembled panel under various types of excitation and boundary conditions, particularly in the lower frequency range. The research focuses on a timber floor panel assembled from glued laminated elements, such as beams, joists, and the wooden floor.  
Here are the key scientific achievements and conclusions:  
1. Experimental tests are conducted to determine the mechanical properties of the glued laminated timber, and numerical models are developed using the finite element method (FEM). The material properties obtained from the experimental tests are used as input data in the FEM model to predict the behavior of the floor panel.
2. A finite element formulation of sound radiation from an elastic structure with a poroelastic layer is presented. The poroelastic material is characterized using Biot theory, and its behavior is coupled with that of the elastic structure using a symmetrical coupling term. The porous material has two phases: solid and fluid, which are represented in the formulation by the displacement field for the solid phase and the pressure field for the fluid phase. This original approach has the benefit of lower computing costs and facilitates connectivity between all domains.
3. The dynamic analyses and parametric study of the timber floor panel are performed considering different loading conditions and the presence of the porous material. The study shows that the incorporation of the porous layer leads to a significant reduction  
Figure 9: Comparison of a radiated sound pressure in the narrow band for rigid and hinge connection for (**a**) panel submitted to load walking, (**b**) panel submitted to load walking with porous material, (**c**) panel submitted to washing machine load, and (**d**) panel with porous material submitted to washing machine load.  
in sound levels (approximately 18%) by acting as an efficient sound absorber. The porous material not only attenuates sound but also dampens structural vibrations, contributing to improved overall noise reduction. This is owing to the porous sound-absorbing material's damping effect on timber floor panels displacement, in which vibration energy is released as heat and absorbed by the porous sound-absorbing material's solid skeleton. The porous sound-absorbing material also reduces noise due to thermal and viscous effects. Further research can focus on optimizing the properties of the porous material used in the floor panel to achieve maximum noise reduction.  
Both authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.
# Modeling and Numerical Computation of the Longitudinal Non-Linear Dynamics of High-Speed Elevators
## 4 Study of the Nature and Law of Elevator Dynamics
### Effect of Car Position Height on Elevator Response  
The operational height impacts both the traction rope and compensation rope, two non-linear components of the elevator, consequently influencing the time-domain response. Similarly, to investigate the influence of the operational height on the time-domain response of a high-speed elevator, the control strategy was chosen as the parabolic-linear control approach. Given the minimal effect of no load on the time-domain response, the elevator was assumed to have no load. The operational height of the elevator was then segmented into the lowest (10 m), middle (80 m), and highest floors (140 m), and the model was computed accordingly. The comparative results are illustrated in Figure 13.  
It can be observed from Figure 13 that the magnitude of time-domain response fluctuations is significantly higher at the highest level during the acceleration, uniform speed, and deceleration sections, compared to the lower and middle levels. During the acceleration phase, the response fluctuations at the lowest and middle levels are similar in magnitude,  
Figure 12: Time-domain response of the elevator for different loads.  
Figure 13: Time-domain response of the elevator at different operating heights.  
corresponding to the frequency of the car, which is close to the middle level. However, in the uniform speed section, the response fluctuations at the middle level are smaller than those at the lowest level, attributable to the elevator's ascent to the middle floor. At this point, the distribution of the traction rope and compensation rope is more uniform, resulting in a higher intrinsic frequency. Conversely, during the deceleration phase, when the car is at the middle and lowest floors, the response magnitudes and frequencies are similar. This is because at the lowest floor, the traction rope is longest and the tensioning rope is shortest, while at the highest floor, the reverse is true.
## 3 Materials and Methods
### Regression Model  
During the process of capturing images of broiler chickens using DR images, we ensured a fixed orientation for the chicken, resulting in each DR image containing information from two chicken legs. Through the Tibia-YOLO model, we can obtain bounding boxes detecting the two tibias in the DR image. We performed statistical analysis on the height of these bounding boxes, yielding a quantity approximately equivalent to 350,000 pixels. This numerical value serves as the independent variable for the regression model. The dependent variable is the length of the broiler chicken's tibia, typically measured in units of approximately 120 mm. Given the substantial disparity between the independent and dependent variables, involving a transformation of high-dimensional data, we opted for the application of the support vector regression (SVR) model for regression learning of tibia length.  
SVR, a machine-learning paradigm, constitutes the application of support vector machine (SVM) [40] in the realm of regression. It involves the utilization of a kernel function to map data into a high-dimensional space, aiming to maximize the margin between the optimal hyperplane and the training data within this augmented dimensionality. We employ unaltered DR images processed through the Tibia-YOLO model for detection, extracting the count of pixels corresponding to the bounding box height. This original DR image encompasses a set of authentic tibia lengths corresponding to the leg region in the image, without resorting to DR images subjected to post-image enhancement procedures.  
Diverging from conventional regression approaches, the SVR model, by identifying the optimal hyperplane in the feature space, employs support vectors as the data points nearest to this hyperplane. SVR utilizes these support vectors to delineate the decision boundary. Moreover, SVR employs a loss function to gauge the disparity between predicted and actual values. In the course of minimizing this disparity, it ensures that the margin of error does not fall below a predetermined threshold. We have opted for the radial basis function (RBF) as the kernel function. This choice facilitates the adaptability of the hyperplane in a high-dimensional space to intricate linear relationships, concurrently enabling SVR to manifest more densely within a small sample set, thereby diminishing the dependence on extensive datasets.  
In the results section, we conducted a comparative analysis utilizing various regression models on the same dataset. All models were employed in a supervised learning fashion, where the independent variables (input data) maintained a one-to-one relationship with the dependent variables (output labels). The compared models encompassed Gradient-Boosting Regressor, Tikhonov Regularization (Ridge Regression), K-Nearest Neighbors Regressor (KNeighbors), Random Forest Regressor, Least Absolute Shrinkage and Selection Operator Regression (LASSO Regression), Decision Tree Regression, ElasticNet Regression, and eXtreme Gradient-Boosting Regression (XGBoost Regression).
# Adaptive Feature Fusion for Small Object Detection
## 5 Conclusions  
The factors affecting the detection accuracy of small objects mainly include the size of the object pixel, background information being highly similar to the object pixel, object overlap, and pixel blur. According to the problems of small object scale, large object scale span in the image, and similar object and background information, we proposed an improved MMF-YOLO algorithm. In order to alleviate the problem that the scale of small objects is large and the object information is highly similar to the background information, a feature fusion module capable of adaptive learning was proposed. By adding cross-scale paths and skip connections, it brings deep high-level semantic information and shallow spatial location information to fixed-size features, and filters redundant features for fusion features through adaptive fusion factors such as the improved attention mechanism M-CBAM, adding weight to the object information, filtering background information, and overcoming the problem of object overlap. Finally, by adjusting the size of the object detection head, the algorithm is adapted to the detection of objects of different scales, which increases the probability of small objects being detected. Experiments show that the proposed algorithm can significantly improve the mAP of small objects by 10%, and the size of the parameters was reduced by about 5 MB.  
Although the proposed algorithm improved the detection accuracy of small objects, the detection effect of small objects with only a few pixels in the case of being occluded was generally average, and the optimization of the prediction frame was not considered. Therefore, this will be strengthened in future work.  
Conceptualization, Q.Z. and H.Z.; Methodology, Q.Z. and X.L.; Software, Q.Z.; Validation, Q.Z.; Formal analysis, Q.Z.; Investigation, Q.Z., H.Z. and X.L.; Resources, Q.Z.; Data curation, Q.Z.; Writing--original draft preparation, Q.Z.; Writing--review and editing, X.L.; Visualization, Q.Z.; Supervision, H.Z.; Project administration, H.Z.; Funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 61872304).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Materials and Methods  
### Cell Culture and Infection  
For analysis of the cell culture medium 60 mm cell culture dishes were used. A549 cells were purchased from the American Type Culture Collection (ATCC-107, Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Heidelberg, Germany).  
For extracellular metabolome samples 0.9 \(\times\) 10\({}^{6}\) cells were seeded in 4 mL RPMI 1640 R7509 medium (obtained from Sigma Aldrich, Munich, Germany) (2 mmol/L glutamine and 10% FCS were added, both obtained from Sigma Aldrich) 24 h before infection. Control cells were cultivated for 24 h until maximum confluence was reached and infected cells were cultivated over a time period of 72 h with a medium change every 24 h to prevent nutrient limitation.  
_Staphylococcus aureus_ USA300 LAC (obtained from the strain collection of the Institute for Microbiology, Ernst-Moritz-Arndt University Greifswald, Greifswald, Germany) was cultivated in RPMI medium supplemented with 2 mmol/L glutamine and trace elements as described for other _S. aureus_ strains [60]. On the day before infection a pre-culture was inoculated from a glycerine-stock culture. The main culture was inoculated from the pre-culture with an optical density of 0.05 at 560 nm and was cultivated to an optical density of 0.5. _S. aureus_ cells were washed with PBS and resuspended in RPMI supplemented with 1% FCS to an optical density of 0.5. For infection, A549 cells were washed and 1.5 mL infection medium (2 mmol/L glutamine and 1% FCS) was added containing 450 \(\mu\)L of a _S. aureus_ cell suspension with an optical density of 0.5 corresponding to a multiplicity of infection of approximately 50:1. After 2 h under standard growth conditions (37 \({}^{\circ}\)C and 5% CO\({}_{2}\)) the cell monolayer was washed twice with PBS and 2.25 mL fresh RPMI medium supplemented with 2 mmol/L glutamine, 10% FCS, lysostaphin (20 \(\mu\)g/mL, obtained from Sigma Aldrich) and gentamicin (100 \(\mu\)g/mL) were added (incubation medium). Cells were incubated under standard conditions for 2 h to 72 h with a medium change every 24 h. For intracellular metabolome samples the infection was performed as described above, but in 150 mm cell culture dishes. For that matter 6.0 \(\times\) 10\({}^{6}\) cells were seeded in 25 mL RPMI medium 24 h before infection. For infection the medium was changed with 10 mL infection medium containing 3 mL _S. aureus_ suspension with an optical density of 0.5. After infection time, cells were washed and 15 mL of incubation-medium were added.  
To determine the cell number and viability of cells and the number of intracellular _S. aureus_ cells, cells where washed and 2 mL of accutase (A549 cells) (obtained from Sigma Aldrich) were added for 5 min to detach the cells. To elucidate the cell number, an aliquot of the cell suspension obtained was mixed with trypan blue and counted using a Countess Automated Cell Counter (Invitrogen, Carlsbad, CA, USA). An aliquot of the infected A549 cell suspension was diluted in several steps and further plated on LB-Agar plates to determine the colony forming units of living _S. aureus_ cells.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 2 Clustering Techniques
### Density-Based Clustering  
Both hierarchical clustering and partition-based clustering are sensitive to outliers. On the other hand, density-based clustering is more robust towards outlier. Density-based clustering has two notions in mind, which are \"clusters\" and \"noise\" [43]. As clusters are normally a dense region of points, this technique targets the dense regions to perform clustering. The technique will treat the less dense regions as \"noise\". However, density-based clustering has difficulty identifying the clusters if the density varies. The quality of the clusters in density-based clustering is also highly dependent on the clustering parameters, _minPts_ and _eps_. If the _minPts_ value is too low, the clusters will contain more outliers. On the other hand, if the _eps_ value is too low, valid clusters will be split into smaller and smaller clusters. In this paper, _DBSCAN_ is discussed, as it is widely used in web usage mining.  
_DBSCAN_ discovers clusters and noise in a spatial database. It uses two parameters: (1) _minPts_, which is the minimum number of points clustered together for a region to be dense, and (2) eps, which is the distance measure that will locate the points in the neighbourhood of any point. The basic steps are illustrated in Figure 7.  
Figure 6: Basic steps of bisecting _k-means_.  
Figure 7: Basic steps of _DBSCAN_.  
* Determine the value of _minPts_ and _eps_.
* Randomly select a starting data point. If there are at least _minPts_ within a radius of _eps_ to the starting data point, then the points are part of the same cluster. Otherwise the point is considered a noise.
* Repeat step 2 until all the points are visited.  
In [44; 45], the conventional _DBSCAN_ is used to perform clustering in web usage mining. The drawback of _DBSCAN_ is its reduced performance for clusters with a big difference in densities. Thus, adaptive _DBSCAN_ modifies the traditional _DBSCAN_ algorithm [46]. Instead of having fixed values for _minPts_ and _eps_, both of the values will be updated based on the data points. [PERSON] et al. [47] compared the performance of the traditional and adaptive _DBSCAN_ in web usage mining. As result, the adaptive _DBSCAN_ is seen to have a higher accuracy and recall score. [PERSON] et al. [48] combined both _k-means_ clustering and _DBSCAN_ to overcome the weaknesses in both algorithms. The _k-means_ component that is incorporated in the _DBSCAN_ is the search for a more optimal central point. On the other hand, [PERSON] et al. [49] combined both the Expectation Maximization (EM) algorithm and _DBSCAN_. The EM algorithm is first performed in the weblog data to get the number of clusters. After that, the output of the EM algorithm is inserted into a Gaussian function to calculate the minimum and the maximum number of web sessions in a cluster before performing the _DBSCAN_. The performance of this hybrid algorithm is significantly better than the standalone EM, _DBSCAN_, and _k-means_.
## 3 Envelope Detector-Aided, Machine Learning-Based Fault Type Detection Model
### Machine Learning-Based Fault Presence and Type Detection Models  
The envelope detector identifies faults' regions in the examined signal; however, this study uses ML-based models to detect and identify faults. The extracted area of interest in the algorithm's first part is fed into the ML-based fault presence and type detection model. This section of the algorithm consists of a feature extraction process and two classification layers as shown in Figure 9. Various techniques are investigated in the feature extraction process to obtain the most-effective algorithm. The extracted features are then fed into the first part of classification to recheck the presence of the fault because the envelope detector may not be able to separate the fault from the effects of the motor and switching operations on the examined signal. Therefore, the first part defines the fault presence and provides classification results directly if no fault is detected in the signal. In the case of a fault, the first classification layer triggers the second classification layer to identify the fault type in the signal. In both classification layers, various ML-based models are also evaluated to find the most effective one.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Relays or Meters** & **Type of Signal** & **Threshold (\(\tau\))** & **Percentage of Change (\(v\))** \\ \hline \multirow{2}{*}{SEL700 GT} & Voltage & \multirow{2}{*}{10\%} & 15\% \\  & Current & & 20\% \\ \hline \multirow{2}{*}{SEL351S} & Voltage & \multirow{2}{*}{10\%} & 15\% \\  & Current & & 20\% \\ \hline \multirow{2}{*}{SEL451} & Voltage & \multirow{2}{*}{2\%} & 10\% \\  & Current & & 40\% \\ \hline \multirow{2}{*}{SEL421 SV} & Voltage & \multirow{2}{*}{5\%} & 10\% \\  & Current & & 40\% \\ \hline \multirow{2}{*}{SEL735} & Voltage & \multirow{2}{*}{2\%} & 30\% \\  & Current & & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Threshold configuration for each relay and meter.  
Figure 9: The block diagram of ML-based fault presence and type detection models.
## 1 Introduction
It was observed that areal density, flexibility, and thickness of the fabric remain almost the same after STF treatment, but there is significant enhancement in the magnitude of impact energy absorption [30]. For other fibers, [PERSON] [31] studied the ballistic and stab performance of composite material based on aramid fabric and polyvinyl butyral (PVB), reinforced by adding a small concentration of inorganic nanotubes of tungsten disulfide (INT-WS2). A significant improvement in anti-ballistic performance was observed: 39.3% higher impact toughness, 8.5% higher absorbed energy of stab, and 12% smaller depth of back-face deformation after shots. [PERSON] [32] studied the numerical back-face deformation of Kevlar fiber helmet under ballistic impact at different locations and in different directions. The results showed good agreement with the experiment conducted by [PERSON] [33] using the DIC technique. To sum up, current dynamic compressive tests of fiber composites focus on the maximum stress or failure stress, absorbed energy, and damage kinetics with the increase in strain rates [34; 35]. Studies on UHMWPE fiber-reinforced composite laminate are less common than studies on other fiber composite laminates. Moreover, investigations of both out-of-plane and in-plane directions are also important. However, many studies on UHMWPE laminate mainly focused on the out-of-plane direction; the in-plane properties were not explored as carefully as for other fiber composites [17; 18]. In addition, the size and the shape of the specimen had an influence on the dynamic properties in many studies [7; 36; 37; 38]. Thus, the effect of size and shape with regard to UHMWPE laminate should also be explored [14]. Therefore, in order to explore the items mentioned above, a careful, systematic study of the dynamic compressive mechanical properties of UHMWPE fiber-reinforced composite laminate is of importance. In this study, quasi-static and dynamic compression mechanical experiments of specimens under both out-of-plane and in-plane directions were conducted using a universal test machine, SHPB, and high-speed camera. At first, for the quasi-static experiment, the stress-strain relationships under both directions were studied. Then, for the dynamic experiment, four aspects were studied. Firstly, the stress-strain curve was divided into several zones according to different characteristics. Secondly, the change in maximum stress, fracture stress, and energy absorption with the increase in strain rate were studied. Thirdly, the influences of thickness, area, and shape of the specimens on the stress-strain relationship were studied. Fourthly, the tendency of fracture stress in terms of quasi-static and dynamic properties is studied. Finally, FE models with cohesive elements were developed to support the experimental findings by comparing the maximum stress values and delamination form.