esa-sceva/satcom-chunk-collection huggingface dataset:
# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 3 Theoretical Concepts
### Quaternion-Based Complementary Filter
#### 3.5.2 Adaptive Gain  
The current orientation estimation relies on the static condition assumed by the accelerometer measurements, but if the body is being accelerated by other forces than gravity, the estimations are evaluated using a false reference. However, the gyroscope measurements are not affected by linear acceleration; thus, they can still be trusted to compute an accurate orientation estimation.  
The current constant gain fusion algorithm struggles to accurately estimate orientation in these conditions because it is optimized to static conditions. To tackle this problem, an adaptive gain algorithm can be used.  
First the relative error \(e_{m}\) is computed:  
\[e_{m}=\frac{\|\,\|\,a\|-g\|}{g} \tag{17}\]  
where \(a\) is the measured acceleration vector before normalization, and \(g=9.81\) ms\({}^{-2}\).  
From the LERP and SLERP definitions, the filtering gain \(\alpha\) becomes dependent on the magnitude error \(e_{m}\) through the gain factor function \(f\):  
\[\alpha=\overline{\alpha}f(e_{m}) \tag{18}\]  
where \(\overline{\alpha}\) is the optimal constant gain evaluated for static conditions, and \(f(e_{m})\) is the gain factor that is a continuous function of the magnitude error.  
\[f(e_{m})=\left\{\begin{array}{ll}1&\text{if }e_{m}\leq t_{1}\\ \frac{t_{2}-e_{m}}{t_{1}}&\text{if }t_{1}<e_{m}<t_{2}\\ 0&\text{if }e_{m}\geq t_{2}\end{array}\right. \tag{19}\]  
When the magnitude error does not exceed a threshold \(t_{1}\), the gain factor is equal to one. This happens when the non-gravitational acceleration is not high enough to overcome the acceleration gravity. If the magnitude error is above the threshold, the gain factor decreases linearly until it reaches 0 at the second threshold \(t_{2}\) and over.  
The values for the thresholds \(t_{1}\) and \(t_{2}\) were 0.1 and 0.2, suggested empirically by [PERSON] et al. [24].
## 4 Prediction of Water Inrush Risk Based on PMGIM
### Hazard Prediction for the 182602 Working Face  
Firstly, the 182602 working face is meshed and coded, and a total of 90 units are set. The local [PERSON]'s index was calculated to determine the specific location of the anomaly area of PMGIM events, and the frequency value of PMGIM events was assigned to each unit, as shown in Figure 11.  
In the initial phase of the working face grouting project (January 2018), a higher concentration of HH units was observed, clustered in the 400-800 m section and adjacent to the return air roadway, densely. PMGIM events were predominantly distributed across the working face (with minimal LL units), indicating a poor grouting effect and a significant risk of water inrush.  
By the intermediate phase of the working face grouting project (April 2018), HH units remained densely distributed in the 400-800 m section near the return air roadway, but their numbers had significantly decreased. HL and LH elements appeared randomly, while the discrete elements of PMGIM events gradually decreased. This indicates that the grouting project successfully sealed some of the cracks in the working face, improving the overall grouting effect.  
Figure 10: Analysis of microseismic events based on Moran’s index.  
In the final phase of the working face area treatment project (October 2018), HH units significantly decreased, although their aggregation location remained unchanged. Basic PMGIM event distribution (LL units) covered most areas of both the inner and outer sections of the working face. At this stage, LL units without microseismic events largely covered the working face, though some HH units persisted, indicating that while grouting reinforcement was largely complete, there remained a risk of water irnush in the HH regions.  
According to the PMGIM distribution results using the local [PERSON]'s index, regions at high risk of water irnush were identified. One area includes units 81, 82, 83, 64, and 65 HH, located 400-550 m along the return air lane (near the F602-4 fault area). The other area includes units 85, 86, 87, 68, 69, 70, and 51, located near the Zhong Lishun syncline axis, as illustrated in Figure 11.
###### Abstract  
The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.  
UAV; multispectral; VI; agriculture; carbon export; soil landscape +
Footnote †: journal: Sensors
## 4 Identification Method for SRRV Deviation Using Wheel-Rail Force Trend Component
### The Mapping Relationship Between SRRV Deviation and Wheel-Rail Force Trend Component
#### 4.2.1 Positive SRRV Deviation  
Figure 10 presents the 7 th-order trend component of wheel-rail vertical force and lateral force on the switch rail side when the vehicle passes through turnouts with varying degrees of positive SRRV deviation. As observed from the graph, with an increase in the positive SRRV deviation, the trough value of the wheel-rail vertical force trend component gradually decreases. When the positive SRRV deviation exceeds 1 mm, the trough value position gradually shifts backward with the increasing deviation. When the positive SRRV deviation is within 1 mm, the peak value and position of the wheel-rail lateral force trend component are less influenced by the deviation amount. However, once the positive SRRV deviation exceeds 1 mm, the peak value of the lateral force trend component gradually increases, and its position shifts backward.  
Figure 9: DWT results of wheel–rail force: (**a**) wheel–rail vertical force; (**b**) wheel–rail lateral force.
## 3 Data Processing
### Band Power Calculation  
The band power (\(BP^{*}\)) represents the broadband vibrational energy of the sample corresponding to velocity component \(V_{Z}^{*}\). The \(BP^{*}\) is defined as [9]:  
\[BP^{*}(x,y,f(k_{1}),f(k_{2}))=\frac{1}{k_{2}-k_{1}}\sum\limits_{k=k_{1}}^{k_{2 }}V_{Z}^{*}(x,y,f(k))^{2} \tag{6}\]  
The \(BP^{*}\) is calculated between two frequencies: \(f(k_{1})\) and \(f(k_{2})\) with \(1\ \leq k_{1}<k_{2}\leq\frac{N}{2}\) (Nyquist). Note again that the \(BP^{*}\) can be calculated for each filtered velocity component (as represented by the asterisk \(\star\)). For instance, the band power of the second harmonic (\(V_{Z}^{HH2}\)) is denoted as \(BP^{HH2}\). The BP as calculated using Equation (6) gives the vibrational power over a certain frequency band. A damaged area is typically characterized by a local reduction in bending stiffness combined with nonlinear contact conditions. As a result, an increased fundamental and higher harmonic vibrational amplitude could be expected (especially at an LDR frequency). This would result in an increased \(BP^{lin}\), \(BP^{HH2}\) and \(BP^{HH3}\) value at the location of damage.
## 3 Spatial Phase Coding  
This section explains the two types of the SPC scheme. The two types of the SPC scheme are described as the constructive and destructive. The constructive and destructive SPC schemey differ in the phase modification method and the pre-coding vectors used by these schemes. When the constructive SPC is applied, the magnitude of the superimposed channel coefficient is increased in the MISO system. Additionally, when the destructive SPC is applied, the magnitude of the superimposed channel coefficient is reduced in the MISO system. Therefore, the proposed CoMP scheme uses the constructive SPC to increase the SNR of the desired signal and the destructive SPC to reduce the SNR of the interference signal.
## 3 Results
### Artificial Neural Network  
The artificial neural network (ANN) is a nonlinear model used in this research to model the SEMG signal with three loads 3 kg, 5 kg and 7 kg, since the SEMG signal is nonlinear. A multilayer perceptron (MLP) is a feed forward neural network consists of multiple layers with back-propagation algorithm technique to minimize the errors and supervised learning [19; 20; 21]. This research designed the ANN with MATLAB R2015a, and has chosen MLP with a Levenberg-Marquardt training back-propagation algorithm, which can be used to enhance different area of research as in [22; 23; 24]. Several designs were tested in this research to choose the good neural network design, with the number of neurons from 10 to 60 in steps of 10. The neural network was trained on the data size of 7000 samples by dividing the data into three sets (i.e., 70% for training, 15% for validation and 15% for testing).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Segment**} & **Segment Mass/Total** & \multicolumn{2}{c}{**Center of Mass/Segment Length**} & \multirow{2}{*}{**Density (kg/L)**} \\  & **Body Mass** & **Proximal** & **Distal** & \\ \hline
**Hand** & 0.006 & 0.506 & 0.494 & 1.16 \\
**Forearm** & 0.016 & 0.430 & 0.570 & 1.13 \\
**Upper Arm** & 0.028 & 0.436 & 0.564 & 1.07 \\
**Forearm and Hand** & 0.022 & 0.682 & 0.318 & 1.14 \\
**Total Arm** & 0.050 & 0.530 & 0.470 & 1.11 \\
**Head and Neck** & 0.081 & 1.000 & — & 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized mass and length of body segments [18].  
The MLP for modeling the nerve signal for the bicep muscle has three layers. The input layer consists of two nodes, which is denoted as A that represents the angle of movement of the forearm and represents the torque. Hidden layers consist of one layer with a number of neurons from 10 to 60 in steps of 10 to get the best performance. The output layer consists of one node, which is denoted as \(S\) to represent the SEMG signal. The mathematical representation of the neural network is:  
\[y=f\Big{(}w^{l}x+b^{l}\Big{)} \tag{16}\]  
\[h=f\Big{(}w^{1}T+b^{1}\Big{)}\]  
\[[h_{i}]=f\left(\left[\begin{array}{cccc}A_{1}&A_{2}&\dots&A_{N}\\ \tau_{1}&\tau_{2}&\dots&\tau_{N}\end{array}\right]\times\left[\begin{array}{ ccc}w_{11}&w_{12}\\ w_{21}&w_{22}\\ \vdots&\vdots\\ w_{N1}&w_{N2}\end{array}\right]+\left[\begin{array}{c}b_{1}\\ b_{2}\\ \vdots\\ b_{N}\end{array}\right]\right)\]  
\[S=f\Big{(}w^{2}h+b^{2}\Big{)}\]  
where \(l\) is the number layer, the input (\(\tau\), \(A\)) represented with an \(m\times n\) matrix \(T\),\(f\) used to represent an activation function in hidden layer of network is tan-sigmoid transfer function \(\frac{e^{l}-e^{-n}}{e^{n}+e^{-n}}\) and in output layer is linear transfer function \(S=h\). N used to represent the length signal which is equal 7000 samples, and b is bias in neural network. The network performed better with more neurons in the hidden layer as shown in Table 5.
## 5 Discussion on Relevant Issues
### Derivation of Calculation Formulas for Agricultural Production Potential on Roofs and Urban Spaces
The planting method was selected with half facility cultivation and half soil cultivation and substituted into Formulas (1) and (4). Therefore, the available roof area in urban built-up areas in China can produce 52.39 million tons of vegetables, meeting the annual vegetable consumption of approximately 370 million people.
## 4 Masking Threshold Constrained Time-Varying Filter
### Pass Region with Different Time Frequency Analysis Windows  
The pass region is generated according to the signal's time frequency distribution, of which time frequency concentration and resolution are affected by the analysis window. In this section, the influences of the window type and the window length on the pass region are discussed. Figure 12 shows the pass region obtained with different analysis windows, where the time frequency distribution method is CWD and the masking threshold \(\gamma\) is 0.26. It indicates that the time frequency concentration and resolution with the rectangular window are both bad, where the energies of the signal are dispersive on the time frequency plane, and the pass region contains more time frequency areas. If the time-varying filter is designed according to the pass region in Figure 12a, more interferences will be collected, which makes the interference suppression performance bad. In contrast, the concentration and resolution of the signal's time frequency distributions with the Gaussian window, the Blackman window and the Hanning window are similar, and the generated pass regions are also similar. When \(\gamma=0.26\), the high energy areas of the interferences are excluded from the pass region, and the interference suppression ability is stronger than the pass region generated with the rectangular window.  
Besides the window type, the analysis window length also affects the scope of the pass region. Figure 13 shows the pass regions with different window lengths, where the analysis window is the Hamming window. It indicates that when the window length is short, the time resolution is high but the frequency resolution is low. Accordingly, the low energy characteristic at the edge of each symbol period is obvious, but the bandwidth at the center of each symbol period is spreaded. As the window length grows, the time resolution is reduced whereas the frequency resolution is increased, and the low energy characteristic at the edge of each symbol period is not so obvious, but the bandwidth of each symbol becomes narrower. Hence the designer should choose an appropriate window length according to the energy distributions of the desired signal and the interference.  
Figure 11: BER varies with different masking thresholds. (**a**) \(E_{b}/N_{0}\) = 8 dB, SIR = 5 dB; (**b**) \(E_{b}/N_{0}\) = 8 dB, SIR = 8 dB.  
Figure 12: The pass regions with different analysis windows. (**a**) The pass region with the rectangular window; (**b**) The pass region with the Gaussian window; (**c**) The pass region with the Blackman window; (**d**) The pass region with the Hanning window.  
Figure 13: _Cont._
## 3 Method
### Vibration-System Simulator  
#### 3.2.1. FIR Filter  
An adaptive FIR filter was used for building the simulator. As shown in Figure 4, in order to implement an FIR filter, the input signal (\(\mathbf{x}(n)\)) and desired output signal (\(\mathbf{d}(n)\)) should be given. Note that the \(\mathbf{x}(n)\) and \(\mathbf{d}(n)\) are both vectors:  
\[\mathbf{x}(n)=[x(n)\;\;x(n-1)\;\;\cdots\;\;\;x(x-N+1)]^{T} \tag{1}\]  
\[\mathbf{d}(n)=[d(n)\;\;d(n-1)\;\;\cdots\;\;d(x-N+1)]^{T} \tag{2}\]  
where \(N\) is the length of the filter. The given input signal (\(\mathbf{x}(n)\)) of the filter is known, and the filter output signal (\(\mathbf{y}(n)\)) is formulated as:  
\[\mathbf{y}(n)=\mathbf{\omega}^{T}(n)\mathbf{x}(n) \tag{3}\]  
where \(n\) indicates the time sequence, and \(\mathbf{\omega}(n)\) is the vector of the filter weights.  
The output signal (\(\mathbf{y}(n)\)) is the estimate of the desired signal (\(d(n)\)). The error (\(e(n)\)) between the output signal (\(\mathbf{y}(n)\)) and desired output signal (\(d(n)\)) is defined as:  
\[e(n)=d(n)-y(n) \tag{4}\]  
We estimate the filter weights (\(\mathbf{\omega}(n)\)) to minimize the error (\(e(n)\)) by using the least-mean-square (LMS) algorithm [37]:  
\[\mathbf{\omega}(n+1)=\mathbf{\omega}(n)+2\mu e(n)\mathbf{x}(n) \tag{5}\]  
where \(\mu\) is the step size.  
The filter weights (\(\mathbf{\omega}(n)\)) can be easily well estimated if the appropriate input signal (\(\mathbf{x}(n)\)) and desired output signal (\(\mathbf{d}(n)\)) are given. In this work, the input signal and desired output signal are the experimental data.
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 3 BACQ Proposal  
In the context of concurrent queues, boundary conditions typically arise when the queue is either full or empty. These issues are primarily associated with memory alignment, buffer overflows, or underflows in the design of bounded queues. Since an unbounded queue has no fixed size, it will keep allocating memory as elements are enqueued, but they  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **GQ** & **TZQ** & **CGQ** & **LCRQ** & **CBQ** & **SFQ** & **Broker** & **BACQ** \\ \hline Highly Scalable & \(\checkmark\) & X & X & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ Fair Ordering & X & X & X & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ Linearizability & X & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ Low Memory Footprint & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ Boundary Aware Design & X & X & X & X & X & X & X & \(\checkmark\) \\ High Throughput on Skewed Distribution & X & X & X & X & X & X & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of queue designsbased on various parameters (\(\checkmark\) = Satisfy the property, X = Dissatisfy the property).  
are not well suited to the limited memory models of GPU architecture. On a highly parallel architecture like a GPU, boundary conditions occur more frequently, leading to stalls as the system handles these edge cases. Consequently, our objective is to design a queue that minimizes the occurrence of boundary conditions while maintaining linearizability and ensuring scalability.  
The Boundary-Aware Concurrent Queue (BACQ) is a bounded concurrent queue specifically designed for GPUs. The core idea of this design is to enhance the queue's throughput by minimizing the impact of boundary conditions. This is achieved by selectively opting out of enqueue or dequeue requests based on the current state of the queue. Moreover, each request to the queue is paired with a ticket, ensuring fair ordering and safe transactions during concurrent enqueue or dequeue operations.
# Angle-Resolved Fluorescence of a Dye Coupled to a Plasmonic Nanohole Array
## 2 Materials and Methods
### Experimental Optical Setups  
A custom setup was realized to perform angle-resolved photoluminescence measurements for four different pumping and collection configurations. To better distinguish the different measurement configurations, two sides of the PEF platform are defined in panel (a) of Figure 2, i.e., the front and back sides. A schematic of the setup is shown in panel (b) of Figure 2.  
This setup includes two independent custom pumps and sample holders that allow for the autonomous selection of either the pumping or collection angles relative to the optical axis indicated as \(\alpha\) and \(\beta\), respectively, in panel (b) of Figure 2.  
Specifically, a 632 nm continuous-wave (CW) He-Ne laser coupled to a standard multimode optical fiber was employed as the source. The laser beam was focused on the sample's rotation axis using a BK7 lens with a focal length of 5 cm, resulting in a 1 mm\({}^{2}\) spot diameter with a measured power of 10 mW.  
Two BK7 lenses (Lens 1 and 2 in Figure 2) having the same focal length of 10 cm were used to collect the fluorescence signal over an angle of 15 degrees.  
An Oriel Multispec spectrograph equipped with a 600 lines/mm grating and a Thorlabs LC-100/M line camera was exploited for detection. To filter out the pumping laser line, a FELH0650 Thorlabs high-pass filter was placed before the 200 \(\upmu\)m spectrograph slit.  
The fluorescence signal was collected following two different measurement schemes, where the pumping angle alpha (\(\alpha\)) and the collection angle beta (\(\beta\)) were changed. In the first one, \(\alpha\) was varied relative to the plasmonic surface normal axis, while \(\beta\) was kept fixed. In the second scheme, \(\beta\) was spanned while maintaining \(\alpha\) constant. In both configurations, the angle variation was set to 5\({}^{\circ}\). To improve the collection resolution, measurement at a fixed pumping angle was performed by using a single lens arrangement for the collection with an acceptance angle of 5\({}^{\circ}\).  
The reflectance (R) and transmittance (T) data were collected using a commercial Fourier transform spectrometer Bruker IFS665 coupled to a custom micro reflectometer. Broadband (500 nm to 1000 nm) variable-incidence-angle R and T measurements were performed, illuminating the sample from both front and back sides with a resolution of 16 cm\({}^{-1}\). Glan-Taylor polarization was used to select the TE or TM spectral components.
## 4 Evaluation Methodology
### Performance Assessment  
The sensitivity analysis described in the previous sections for the performance assessment is based on two parameters: the _Elapsed Time_ and _Time Gain_. The Elapsed Time is computed for each functional block \(B\), for each context \(C\) (i.e., combination of parameters based on which the sensitivity analysis is performed), and for each grid combination \(G\) as the average of \(N=500\) repeated executions, as follows:  
\[\text{Elapsed Time}(B,C,G)=\sum_{n=1}^{N}\frac{t(B,C,G,n)}{N}\]  
and the Time Gain is defined as the ratio between the sequential grid combination (1B1T) and the most advantageous grid combination, as  
\[\text{Time Gain}(B,C,G^{best})=\frac{ElapsedTime(B,C,G^{1B1T})}{ElapsedTime(B,C,G^{best})}\]  
The _Elapsed Time_ quantity is expressed in milliseconds ([ms]), while the Time Gain is scalar.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dependency** & **Threshold** \\ \hline \(N_{p}\) & 4096 \\ \hline \(N_{l}\) & 4096 \\ \hline \(N_{o}\) & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dependency thresholds.