esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] [7] studied the relationship between wavelet coefficients and energy input using wavelet transform and obtained the simulated ground motion accelerogram using the inverse transform of wavelet coefficients. [PERSON] and [PERSON] [24] proposed a method to generate artificial wind speed fluctuation using inverse wavelet transform. [PERSON] and [PERSON] [25] proposed a wavelet-based method to generate acceleration records. At the same time, integrating the harmonic wavelet (HW) or GHW proposed by [PERSON] [26; 27], [PERSON], [PERSON], and [PERSON] [28; 29] derived a general expression for estimating EPSD using HW and GHW. Based on the work of [PERSON] and [PERSON] [29], [PERSON] et al. [30] derived a GHW-based formula for simulating non-stationary stochastic processes. The expressions of the random function obtained by numerical simulation are generally infinite series. In practical engineering applications, series truncation is usually performed to facilitate analysis. However, series truncation will cause the stochastic process to only retain part of the probability information of the target stochastic process. To ensure the required accuracy, hundreds of components must be retained and too many components causes a proliferation of random variables, which lead to the increase in the number of calculations. In order to reduce the number of random variables in stochastic process simulation methods, many dimensionality reduction methods have been proposed and widely used. [PERSON] and [PERSON] [31] proposed an SRM with randomness in both frequencies and phases, which was later extended by many scholars [32; 33]. [PERSON] et al. [34; 35] applied the dimensionality reduction method based on proper orthogonal decomposition (POD) and endowed the random phase angles with orthogonality, proving that using only two random variables is enough to simulate a non-stationary stochastic process. [PERSON] et al. [36] proposed an SHF expression for random processes, and then extended this method to non-stationary random processes [37]. In SHF, besides the random phases, the discretized frequencies are also treated as random variables, and after some special conditions are satisfied by the probability density function of the frequencies, accurate target PSD or EPSD can be obtained with few components. In this paper, the classical SRM and the GHW method [30] are first reviewed. In the following section, the SHF representation of non-stationary random processes obtained by extending the SRM is briefly introduced. Then, the idea of SHF is combined with the GHW method, that is, the frequencies and time translation factors in the GHW method are defined as random variables. The proposed stochastic process simulation method is called the stochastic generalized harmonic wavelet (SGHW) method. The SGHW method is then applied to simulate the random process; it is realized that retaining only fewer components is enough to obtain accurate probability information when describing the original random process, which greatly reduces the number of random variables and reduces the difficulty of random process analysis.
# Whole-Body Keypoint and Skeleton Augmented RGB Networks for Video Action Recognition
## 3 Proposed Method
### Confidence-Weighting Aggregation Function
In the temporal domain, we augmented data by random sample of 16 consecutive frames of videos. These tricks are applied for training both the RGB and WKS networks. We have trained the network with a batch size of 64 for 250 epochs at both pretraining stage and training stage. OpenCV-Python is applied for converting video to image sequences at 25 fps. We used the AdamW optimizer to train the network parameters, with the weight decay set to \(1\times 10^{-3}\), and initial learning rate set to 0.001. The proposed method was implemented on PyTorch 1.2 with Python 3.6, and all experiments were executed on a Tesla K40 GPU and an E5-2620 CPU. For training the confidence scoring network, we introduced a new dataset \(\mathcal{D}\), which consists of **f(\(c^{(i)}\))**. Note that all clips \(c^{(i)}\) come from the training dataset, and are not introduced by any testing dataset information. On the training dataset, the classifier is able to obtain remarkable classification accuracy, so that the negative samples of dataset \(\mathcal{D}\) are quite scarce. To address this issue, we adopted data augmentation techniques to enlarge our negative samples. Tricks that were applied are similar to tricks of the RGB network, and we also deliberately reduced the number of positive samples to balance these two categories. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Output Size** & **Swin-S** & **Swin-B** \\ \hline \multirow{3}{*}{3D\_ConvNet} & \multirow{3}{*}{56 \(\times\) 56} & conv, \(7^{3}\), 64-d & conv, \(7^{3}\), 64-d \\  & & conv, \(3^{3}\), 96-d & conv, \(3^{3}\), 128-d \\  & & concat & concat \\ \hline \multirow{2}{*}{Swin\_layer1} & \multirow{2}{*}{28 \(\times\) 28} & concat \(2\times 2\), 192-d, LN & concat \(2\times 2\), 256-d, LN \\  & & \(\left[\begin{array}{c}\text{win.sz.}7\times 7,\\ \text{dim 192, head 6}\end{array}\right]\times 2\) & \(\left[\begin{array}{c}\text{win.sz.}7\times 7,\\ \text{dim 256, head 8}\end{array}\right]\times 2\) \\ \hline \multirow{2}{*}{Swin\_layer2} & \multirow{2}{*}{\(14\times 14\)} & concat \(2\times 2\), 384-d, LN & concat \(2\times 2\), 512-d, LN \\  & & \(\left[\begin{array}{c}\text{win.sz.}7\times 7,\\ \text{dim 384, head 12}\end{array}\right]\times 18\) & \(\left[\begin{array}{c}\text{win.sz.}7\times 7,\\ \text{dim 512, head 16}\end{array}\right]\times 18\) \\ \hline \multirow{2}{*}{Swin\_layer3} & \multirow{2}{*}{\(7\times 7\)} & concat \(2\times 2\), 768-d, LN & concat \(2\times 2\), 1024-d, LN \\  & & \(\left[\begin{array}{c}\text{win.sz.}7\times 7,\\ \text{dim 768, head 24}\end{array}\right]\times 2\) & \(\left[\begin{array}{c}\text{win.sz.}7\times 7,\\ \text{dim 1024, head 32}\end{array}\right]\times 2\) \\ \hline \multirow{2}{*}{Output} & \multirow{2}{*}{n\_class} & average pooling & average pooling \\  & flatten & flatten \\  & & fc \(\times\) 2 & fc \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed architecture of the proposed network.
# Shake Table Testing of Standard Cold-Formed Steel Storage Rack
## 1 Introduction
attempt, [PERSON] et al. [27] characterized the seismic performance of the rack frames under earthquake excitation and identified the pallet shedding fragility under a series of ground motions through shake table testing. In addition to the classical design of rack frames against seismic loads, the isolation systems, such as fuse elements, can also be adopted for a rack frame, which brings benefits in terms of a reduction in the structural damage and improving the safety of the stored items [28; 29; 30; 31; 32; 33]. Shake table tests performed by [PERSON] et al. [34] also revealed that the eccentric bracing configurations could lead to a significant torsional response. [PERSON] and [PERSON] [35] evaluated a complete drive-in rack system under loaded and empty conditions using a full-scale shake table, and provided the relative stiffness under different ground motion conditions. Recently, new shaking table tests were conducted on storage racks in the cross-aisle direction in order to obtain the seismic characteristics of the system in this direction [36; 37]. Considering the current increasing demand on rack storage systems as well as the improvements of storage rack regulations, the need for understanding the dynamic response and seismic characteristics of the standard rack frames, i.e., damping of the system, under actual strong ground motions has been intensified. Therefore, the objective of this paper is to provide the results of shake table tests on a New Zealand standard rack frame with two bays and two levels under increasing seismic base excitation in the down-aisle direction, aiming to reveal the dynamic features of a standard steel storage rack under actual seismic actions. The experimental program was conducted in three stages. In the first stage, identification tests were carried out to determine the fundamental period of vibration and the damping ratio of the system. In the second stage, the frame was tested by 20-80% intensities of El-Centro earthquakes in an actual time domain. In the third stage, the tests were repeated with a wide range of intensities, 40-220% of El-Centro earthquakes, with scaled records in the time domain to consider the performance of taller rack systems. The damping of the system is also determined and the results are presented for both the scaled and non-scaled time-domain conditions. The test results of this study can be valuable data for the calibration of finite element models as well as seismic provisions for the design of racks.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 4 Experiments
### Feature Extraction  
The TUM RGB-D dataset is an open-source dataset used to validate the robustness of algorithms and their positioning accuracy in dynamic environments. This dataset includes 39 image sequences that describe various indoor environments, encompassing both RGB images and depth images. In this section, we selected four indoor static datasets from the TUM RGB-D dataset--fr1/xyz, fr1/desk, fr1/room, and fr1/360--to test the feature extraction performance of the proposed algorithm and ORB-SLAM2. Among these sequences, the fr1/360 sequence involves large and rapid camera movements, while the fr1/room sequence features an indoor scene with sparse object textures. We employed the controlled variable method to compare the feature extraction performance of the improved SLAM system and the ORB-SLAM2 system. We used four static sequences to prevent the adverse effects of dynamic objects on the ORB-SLAM2 system, which could lead to inaccuracies in the experiments. Furthermore, these four static sequences involve varying degrees of camera movement. After conducting the experiments, we compared the absolute trajectory errors of the improved SLAM system and the ORB-SLAM2 system, allowing for an effective comparison of the robustness of the two algorithms.  
Figure 5 shows the feature extraction results of ORB-SLAM2 compared to the improved SLAM algorithm. As illustrated, ORB feature points were overly concentrated and even overlapped, which hindered feature matching. In contrast, even in areas with significant changes in viewpoint (as shown in Figure 5d), GCNv2 still extracted feature information uniformly, thereby avoiding overlaps and losses.  
Table 3 compares the absolute trajectory errors of the improved SLAM method with ORB-SLAM2 across the four static sequences. The results indicate that, compared to ORB-SLAM2, the improved SLAM algorithm achieved a smaller RMSE and SD in all sequences. Notably, the improved SLAM's RMSE was reduced by 19.9%, and the SD was reduced by 43.8% on the fr1/room sequence. Given that there were no dynamic objects in these static sequences, the experimental results effectively demonstrated that the improved SLAM algorithm exhibited greater stability compared to the ORB-SLAM2 algorithm.
# High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm
## 5 Numerical Examples and Results
### Wind Turbine Airfoil Geometry Optimization  
The geometry of a typical GT compressor blade airfoil [33] is optimized in this example using the suggested algorithm. In both design and off-design scenarios, the objective is to lower the total pressure loss coefficient. NURBS curves were used to parameterize the geometry input used in the optimization process. This method treats the positions of the non-uniform rational basis spline (NURBS) control points as design variables. Four NURBS curves for each of the four segments of the compressor airfoil, each having nine control points, make up the airfoil. Because each control point has two coordinates, x and y, there are a total of 72 design variables. The junction points of the segments are one of 16 known and set parameters. The remaining 16 parameters are found by enforcing C2-continuity (which requires the second derivative to be zero at the endpoints) at the intersections of the segments. As a result, 40 variables will remain as design variables in the optimization procedure. However, to limit the number of design factors while maintaining a high level of geometric flexibility, the geometry of the leading edge (LE) and trailing edge (TE) is maintained constant to save CPU time.  
The results of the airfoil optimization procedure strongly depend on the formulation of the objective function. In order to simulate 2-D fluid flow, the geometry code creates parameterized profiles that are then imported into the computational fluid dynamics program COMSOL CFD. The acceptable profiles' post-processed outputs are then fed into the fitness calculation portion, where the airfoil loss values, \(L\), should be minimized with respect to any shape. The single objective function is shown in Figure 2 as follows:  
\[\begin{array}{c}Minimize~{}L_{1}\%=(a_{1}L_{s})*100\\ Minimize~{}L_{2}\%=(a_{2}L_{d})*100\\ Minimize~{}L_{3}\%=(a_{3}L_{c})*100\\ \end{array} \tag{14}\]  
Subject to:  
\[\begin{array}{c}(|y_{3}-y_{7}|~{}and~{}|y_{3}-y_{8}|)\geq 15\%\text{ of the {{chord}}}\\ 0\leq x_{i}/{{chord}}\leq 100\%\\ 40\%\leq y_{i}/{{chord}}\leq 55\%\end{array} \tag{15}\]  
where \(a_{i}\) are weighting factors. The reduction of total pressure loss on the right (\(L_{c}\)) and left (\(L_{s}\)) sides of the design point significantly expands the operating range. The quantities \(y_{i}\) and \(x_{i}\) are design variables as shown in Figure 2.  
The following are the weighting factors for the optimization process: \(a_{1}\) = 0.20, \(a_{2}\) = 0.70, and \(a_{3}\) = 0.10.  
\[\begin{array}{c}L_{s}=(P_{o1}-P_{o2})_{stall}/0.5\rho V_{1}^{2},\quad L_{d}= (P_{o1}-P_{o2})_{des}/0.5\rho V_{1}^{2},\\ L_{c}=(P_{o1}-P_{o2})_{Choke}/0.5\rho V_{1}^{2},\end{array} \tag{16}\]  
where \(P_{o1}\) is intake total pressure, \(P_{o2}\) is outlet total pressure, and \(V_{1}\) is inlet velocity.  
Figure 2: Schematic diagram of airfoil shape with twenty shape design variables.
## 2 Preliminaries
### Bit Error Rate  
The error performance of the defined FSO system is dependent on two different potential events: the first is that the jammer is active (\(J=1\)), making the jamming signal the foremost noise, and the second event is the idle jammer (\(J=0\)), resulting in Gaussian noise being prominent. As is shown in Figure 2, we can calculate the bit error events for jamming and Gaussian noise through their respective distribution. The probability of occurrence of two events is \(\rho\) and \((1-\rho)\), respectively, which is the same as the activity of the jammer. Now, for the event of the jammer being ON, the probability of error can be obtained by using the PDF of the received signal \(y\) when \(x\) is transmitted in the jamming environment. The generalized expression for \(BER\) for an active jammer can be mathematically written as  
\[BER_{\text{J}_{\text{active}}}=P(x=1)P(e|x=1)+P(x=0)P(e|x=0), \tag{9}\]  
where \(e\) implies bit error events. Taking up the case of the jammer being in the OFF state, the \(BER\) contributed by Gaussian noise for OOK signaling is [33]  
\[BER_{\text{J}_{\text{J}_{\text{J}_{\text{J}_{\text{e}}}}}}=Q\bigg{(}\frac{h_{ m}\sqrt{\kappa\gamma}}{2}\bigg{)}, \tag{10}\]  
where \(Q(\cdot)\) is the \(Q\)-function [34]. Using (9) and (10) along with the respective probability of the jammer's activity, we attain the overall \(BER\) for the given system:  
\[BER = P(J=1)[P(x=1)P(e|x=1)+P(x=0)P(e|x=0)] \tag{11}\] \[+P(J=0)Q\bigg{(}\frac{h_{m}\sqrt{\kappa\gamma}}{2}\bigg{)}.\]  
The (11) is a generalized expression for the BER of the given FSO system employing aperture selection.  
Figure 2: Probability of error events for (**a**) jamming noise and (**b**) Gaussian noise.
## 4 Results and Discussions
The most obvious finding to emerge from the comparison of phi and 1/Phi NWTs is that the 1/Phi NWT has better I\({}_{\rm on}\) and therefore could be faster; however, the most important limitation lies in the fact that its footprint is wider than the Phi NWT, which has a negative effect on CMOS device density. Recent developments in doping technology have exceeded 10\({}^{21}\) cm [30], and such heavy S/D doping could also enhance the drive current. Nevertheless, contact resistance remains one of the greatest challenges for 5 nm technology. Figure 13 compares the I\({}_{\rm on}\) of Phi NWTs with S/D doping up to 1.2 \(\times\) 10\({}^{21}\) cm\({}^{-3}\), with contact resistance in the range (500-2500) \(\Omega\), and gate length (10-22) nm. At the predictive Lg = 12 nm and R = 2314 \(\Omega\) of the 5 nm node [26; 31; 32], Figure 13 shows the I\({}_{\rm on}\) as 1.4 mA/\(\mu\)m.  
Vertically stacked lateral NWT nanowires are included in the ITRS roadmap to reduce the contacted gate pitch and to enhance the current per footprint. Figure 14 compares both Phi and  
Figure 12: V\({}_{\rm T}\), I\({}_{\rm on}\), DIBL, and I\({}_{\rm off}\) distributions subject to combined SV (RDD, WER, and MGG), all of which are reflective of the gate-first technology of the NWTs given in Table 2.  
Figure 13: Correlation between NWT performance and source/drain peak doping, gate length, and S/D contact resistance. NWT cross-section is Phi (5 nm \(\times\) 8.1 nm) and channel orientation is <110>. I\({}_{\rm on}\) extracted at high drain voltage V\({}_{\rm D}\) = 0.7 V.  
\(1/\)Phi NWTs in vertically stacked structures. The I\({}_{\rm D}\)-V\({}_{\rm G}\) characteristics of both NWTs are shown in Figure 15.
###### Abstract  
Image adjustment methods are one of the most widely used post-processing techniques for enhancing image quality and improving the visual preference of the human visual system (HVS). However, the assessment of the adjusted images has been mainly dependent on subjective evaluations. Also, most recently developed automatic assessment methods have mainly focused on evaluating distorted images degraded by compression or noise. The effects of the colorfulness, contrast, and sharpness adjustments on images have been overlooked. In this study, we propose a fully automatic assessment method that evaluates colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images while considering HVS preferences. The proposed method does not require a reference image and automatically calculates quantitative scores, visual preference, and quality assessment with respect to the level of colorfulness, contrast, and sharpness adjustment. The proposed method evaluates adjusted images based on the features extracted from high dynamic range images, which have higher colorfulness, contrast, and sharpness than that of low dynamic range images. Through experimentation, we demonstrate that our proposed method achieves a higher correlation with subjective evaluations than that of conventional assessment methods.  
image adjustment; colorfulness; contrast; sharpness; high dynamic range +
Footnote †: journal: Applied Sciences
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 5 Commercial Systems for Fruit Number, Size and External Quality
FruitSpec (Tel Aviv, Israel) ([[https://www.fruitspec.com/](https://www.fruitspec.com/)]([https://www.fruitspec.com/](https://www.fruitspec.com/))) (accessed on 14 April 2021) offers a vehicle-mounted multispectral imaging system allowing the display of vegetation index ratios, which are claimed to allow for the better detection of green fruit. Use with grapes, citrus, stone and pome fruit for fruit count and fruit sizing is claimed. Plantai (Sao Paulo, Brazil) ([[https://plantai.net/](https://plantai.net/)]([https://plantai.net/](https://plantai.net/))) (accessed on 8 January 2021) offers a smartphone app for citrus yield estimation from images of citrus trees. PixFruit (Montpellier, France) is being developed by [PERSON] et al. ([PERSON] et al., 2013) as a commercial outcome of CIRAD research. A smart phone image is processed to identify the cultivar of a mango and count fruit visible in the image, with a factor used to convert to an estimate of the total fruit load of the tree. A yield gap is calculated to the achievable yield, as defined by canopy structure, tree age and variety. Crop Tracker (Kingston, ON, Canada) (croptracker.com/product/harvest-quality-vision.html) (accessed on 1 March 2021) offers a system in which photogrammetry is used to create a 3D model of fruit in a harvest field bin from images collected with a smart tablet. Distribution data of fruit size and color are produced. Hectre ([[https://www.hectre.com/spectre/](https://www.hectre.com/spectre/)]([https://www.hectre.com/spectre/](https://www.hectre.com/spectre/))) (accessed on 1 May 2021) also targets the sizing of fruit in field bins.
###### Abstract  
Copper-based substances have historically been shown to exhibit antimicrobial properties, but the mechanisms by which they interact with bacterial biofilms in pipeline systems remain unclear. This study investigates the effects of copper sulfate and copper nitrate on the growth, biofilm formation, and antibiotic resistance profiles of _Escherichia coli_ and _Enterococcus faecalis_. Across a wide concentration range (0.00005-100 mg/mL), both salts demonstrated strong antimicrobial activity at higher concentrations, while sublethal levels produced more nuanced effects. Higher concentrations exhibited potent antimicrobial activity, while sublethal concentrations paradoxically enhanced biofilm resistance, particularly in _E. faecalis_. Growth kinetic assays and spectroscopic analysis were used to better understand how copper interacts with microbes on a biochemical and physical level. Surprisingly, prolonged exposure to sublethal copper concentrations altered the antibiogram profiles of _E. faecalis_, developing resistance to ceftazidime. The findings confirm the bimodal activity of copper salts as antimicrobial and biofilm-controlling agents, highlighting the critical need for precise concentration optimization in wastewater treatment. This current study contributes to the collective knowledge pool of metal-microb interactions, shedding light on selecting materials for industrial and environmental applications.  
copper sulfate; copper nitrate; biofilm modulation; wastewater management; bacterial resistance; pipeline microbiology Article