esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Seasonal and Inter-Annual Variation in Carbon Fluxes and Balance  
Across all years, the seasonal variation in GPP and RE were distinct. An increasing GPP and RE were apparent from the end of the non-growing season in winter towards spring. However, GPP and RE peaked in summer and gradually declined towards fall (Figure 3). Average daily GPP and RE during the growing season were 8.09 g C m\({}^{-2}\) d\({}^{-1}\) and 8.31 g C m\({}^{-2}\) d\({}^{-1}\), respectively. During the non-growing season, the average GPP was 2.25 g C m\({}^{-2}\) d\({}^{-1}\), and RE was 3.04 g C m\({}^{-2}\) d\({}^{-1}\). However, the seasonal variation in net ecosystem exchange was less distinct (Figure 3). During the growing season, NEE showed lower C loss (0.21 g C m\({}^{-2}\) d\({}^{-1}\)) when the vegetation was active than during the non-growing season (0.79 g C m\({}^{-2}\) d\({}^{-1}\)). Thus, photosynthesis and respiration during the growing season were higher than during the non-growing seasons, when GPP and RE were low (Figure 3, \(p<0.05\)). This pattern was fairly consistent across the study duration, except for anomalous spikes in 2010 and 2011, which we attribute to either flux system performance or random measurement errors.  
In 2009, a higher annual GPP than the annual RE was observed (Table 1). This higher GPP than RE resulted in a net C sink. However, the annual carbon balance switched to a net C source from 2010 onwards, when RE continued to exceed GPP. The weak C source varied largely from 87 g C m\({}^{-2}\) yr\({}^{-1}\) in 2010 to as high as 759 g C m\({}^{-2}\) yr\({}^{-1}\) in 2017 (Table 1). We found a 27% inter-annual variation in GPP while that of RE was 36%.  
Figure 2: Seasonal variations in (**a**) net radiation (Rn), (**b**) air temperature (Tair), (**c**) groundwater table depth (GWT), and (**d**) precipitation. Daily data from 2009–2019 obtained from the eddy covariance flux tower in Alligator River National Wildlife Refuge, Dare County, North Carolina, USA was used.
## 3 Results and Discussion
### Material Models Based on the Measured Results  
Three material models were used for the simulations. The first and most simple one is the linear elastic material model based on the Young's modulus calculated by the ISO 527-2 tensile test [34]. The input values for this material model were the Young's modulus and Poisson's ratio values from Table 2.  
The second material model was another linear elastic material model. In this case, the Young's modulus was calculated until the stress limit of the material model validated measurement. The limit was set to be 30 MPa to ensure that the specimen remains in the elastic region, as explained in Section 2.2. The values were calculated using the following equation:  
\[E=\frac{\sigma_{30}-\sigma_{0}}{\varepsilon_{30}-\varepsilon_{0}}, \tag{1}\]  
where \(E\) is the [PERSON]'s modulus, \(\sigma_{30}\) is the closest stress value to 30 MPa, \(\sigma_{0}\) is the lowest stress value of the curve, \(\varepsilon_{30}\) is the strain at the 30 MPa stress value and the \(\varepsilon_{0}\) is the lowest strain value of the curve. The results of the calculations for each specimen can be seen in Table 4.  
Because of the small standard deviation and maximum difference, the average value can be used for further calculations. The material constants for the second material model can be seen in Table 5.  
The third material model was the nonlinear elastic material model available in the NX Nastran solver. This model is a multilinear material model and is defined by creating a stress-strain function from the measured stress-strain values in the software. To define this material model, the median specimen was selected based on the measured values. The stress-strain data of this specimen were selected until the yield stress and then reduced into 30 data points to ensure a quicker and easier calculation for the software. These data points were then placed into a function in Femap, which was added to the material model along with the [PERSON]'s modulus and Poisson's ratio values, see in Table 2. The defined nonlinear curve can be seen in Figure 8, along with the other stress-strain relations of the other two used material models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specimens** & **Young’s Modulus until the Limit Stress (MPa)** \\ \hline Specim 1 & 447.757 \\ Specim 2 & 450.420 \\ Specim 3 & 461.211 \\ Specim 4 & 477.030 \\ Specim 5 & 478.626 \\ \hline Average & 463.009 \\ Std. deviation & 14.446 \\ \hline Median & 461.211 \\ Max. difference & 3.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Young’s modulus values until the limit stress value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Constants** & **Value** \\ \hline Young’s modulus (MPa) & 463.009 \\ Poisson’s ratio (-) & 0.450 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The material constants required to define the linear elastic material model with the Young’s modulus calculated until the limit stress.
# Exploring Frontiers of 4D X-ray Tomography
## 1 Introduction  
While telescopes and microscopes have been used for more than 400 years to unravel the mysteries of the universe and matter, the ability to take continuous images beyond the limits of the human eye is a relatively recent development, dating back only about 150 years. Since the advent of the charge-coupled device (CCD) image sensor in 1970 [1], digital cameras have gradually replaced film cameras, and this digitalization has dramatically accelerated the speed of continuous shooting. Recently, complementary metal-oxide-semiconductor (CMOS) cameras with a frame rate of over 1000 frames per second have been available at relatively low prices, and those with a frame rate of even over 100,000,000 frames per second have been developed in the most advanced fields [2].  
However, such high-speed CMOS cameras can only capture visible light that is reflected from the surface of an object or transmitted through a transparent object. Many objects in nature are opaque, and it is not possible to observe high-speed phenomena occurring inside them using visible light. X-ray computed tomography (CT) and magnetic resonance imaging (MRI) can visualize the interior of objects in three dimensions, but they generally cannot capture images faster than the order of seconds.  
In this paper, we will introduce X-ray CT with a temporal resolution in the order of milliseconds that we recently developed. The spatio-temporal domain described in this paper is an unexplored world, a blue ocean that is expected to be full of various non-equilibrium phenomena that have never been seen before. In 2020, we successfully demonstrated proof-of-concept X-ray CT that can capture a three-dimensional (3D) image of a sample in 1 ms without sample rotation by using a synchrotron radiation multi-beam optical system, and it is expected to be applied to various kinds of material and biological research in the future.
# An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic
## 2 The P-S Multirate Loss Model--A Review
The K-R formula is recursive and therefore leads to an efficient way for the CBP calculation in a loss system that services multirate Poisson traffic. Because of this, the interested reader can find many applications of the K-R formula in PFS and non-PPS models [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. The complexity problem of (1) can be circumvented via a recursive formula, proposed in [26], that resembles the K-R formula. The presentation of this formula, requires the following notation: \(j_{1}=\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}n_{kl}b_{l}\) refers to the occupied subcarriers, i.e., \(j_{1}=0\),..., \(M\) and \(j_{2}=\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}p_{k}n_{kl}b_{l}\) refers to the occupied cell's power, i.e., \(j_{2}=0\),..., \(P\). Furthermore, let \(q(\stackrel{{\rightarrow}}{{j}})=q(j_{1},j_{2})\) be the occupancy distribution, denoted as:  
\[q(\stackrel{{\rightarrow}}{{j}})=q(j_{1},j_{2})=\sum\limits_{n \in\Omega_{\stackrel{{\rightarrow}}{{j}}}}\pi(\boldsymbol{n}), \tag{3}\]  
where \(\Omega_{\stackrel{{\rightarrow}}{{j}}}\) refers to those states where the occupied subcarriers and power is \(j_{1}\) and \(j_{2}\), respectively. The recursive calculation of \(q(j_{1},j_{2})\)'s is based on (4), whose complexity is O(MPKL):  
\[q(j_{1},j_{2})=\left\{ \begin{array}{l}1,\quad\text{for }j_{1}=j_{2}=0\\ \frac{1}{\hbar}\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}\alpha_{kl}b_{l}q(j_{ 1}-b_{l},j_{2}-p_{k}b_{l})\\ \quad\text{for }j_{1}=1,\ldots,M\text{ and }j_{2}=1,\ldots,P\end{array}  \right.. \tag{4}\]  
Having obtained the unnormalized values of \(q(j_{1},j_{2})\), we can calculate the CBP \(B(k,l)\) of service-class (\(k,l\)) via:  
\[B(k,l)=\sum\limits_{\{(j_{1}+b_{2}>M)\}\cup(j_{2}+p_{k}b_{l}>P)\}G^{-1}q(j_{1},j_{2}), \tag{5}\]  
and the mean number of service-class (\(k,l\)) in-service calls, \(E(k,l)\), via:  
\[E(k,l)=\alpha_{kl}(1-B(k,l)), \tag{6}\]  
where \(G\) is the normalization constant, given by \(G=\sum\limits_{j_{1}=0}^{M}\sum\limits_{j_{2}=0}^{P}q(j_{1},j_{2})\). Based on \(E(k,l)\), we can determine the blocking probability (BP) of the entire system, the subcarrier and the power utilization (SU and PU, respectively) via:  
\[\text{BP}=\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}B(k,l)\lambda_{k,l}/ \Lambda,\quad\Lambda=\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}\lambda_{k,l}, \tag{7}\]  
\[\text{SU}=\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}E(k,l)b_{l}/M, \tag{8}\]  
\[\text{PU}=\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}p_{k}E(k,l)b_{l}/P. \tag{9}\]
## 6 Results and Discussion
### Optimization for TDLAS  
The cause of the error in TDLAS is the disagreement between the theory and experiment. The discrepancies between the original virtual data and the TDLAS calculation data are not so large, and it would be also improved by revising the theoretical spectroscopic data [22]. In addition, it is important to evaluate CT algorithm errors [18; 23] together with the evaluation of these spectroscopic errors. The CT algorithm error is according to the shape of the temperature and concentration distributions, and the error tends to increase when the shape is complicated. In actual use of the optical experimental apparatus, the process of reducing the error between the theory and actual experiment is important. It is directly measured by changing the temperature and pressure of the target gas to be measured, and it can be corrected by contrast with the theoretical value. Figure 7 shows the comparisons between the temperatures measured by the TDLAS and by the thermocouple. At the temperature range of 300 K to 800 K, the absorption intensities obtained by a calibration process have been used, in which the concentration of H\({}_{2}\)O, the temperature and the pressure could be controlled in a cell [24; 25]. At the temperature range higher than 800 K, the absorption intensities obtained by a flat flame burner test [22] performed under the temperature range from 1244 K to 1264 K have been used. Using these results, the HITRAN database over 800 K has been modified for the actual TDLAS calculations. It can be said that the temperature measured by the thermocouple and the temperature calculated by the TDLAS shows a good agreement, revealing high accuracy.
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
### Proposed Decryption Architecture Design  
The proposed Kyber decryption architecture to recover the input message \(m\) from ciphertext \(c=(c_{1},c_{2})\) and secret key \(sk\) is presented in Figure 7.  
Secret key \(sk\) and ciphertext \(c\) are decoded and then decompressed to obtained the values of \(\hat{\mathbf{s}}\) and \(\mathbf{u}\), respectively. In particular, \(\mathbf{u}=\mathrm{Decompress}_{q}(\mathrm{Decode}_{d_{u}}(c),d_{u})\), and \(\hat{\mathbf{s}}=\mathrm{Decode}(sk)\). In addition, the value of \(v=\mathrm{Decompress}_{q}(\mathrm{Decode}_{d_{v}}(c+d_{u}\cdot k\cdot n/8),d_ {v})\) can be reconstructed from ciphertext \(c\) through the decompression and decoding operations. The obtained value of \(\mathbf{u}\) is transformed using the proposed DDF-based NTT, and then multiplied by \(\hat{\mathbf{s}}^{T}\). The product \(\hat{\mathbf{s}}^{T}\circ\mathrm{NTT}(\mathbf{u})\) is transformed using DDF-based INTT module, and then subtracted by \(v\) to obtain the value \(v-\mathrm{INTT}(\hat{\mathbf{s}}^{T}\circ\mathrm{NTT}(\mathbf{u}))\). This result is compressed using the compression module in Figure 5, and encoded to obtain the value \(m=\mathrm{Encode}(\mathrm{Compress}(v-\mathrm{INTT}(\hat{\mathbf{s}}^{T} \circ\mathrm{NTT}(\mathbf{u})),1))\). The original message \(m\) is finally recovered.  
Figure 6: Decompression block: (a) decompression architecture, and (b) decompression element.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 2 Popular Pooling Methods
### Stochastic Pooling Method  
Overfitting is a serious problem when training CNNs with fewer data. Various forms of pooling methods have been introduced to reduce overfitting, including mixed pooling [28], stochastic pooling [36], rank-based random pooling [37], max pooling dropout, and fractional max pooling. A trained model can be viewed as a set of networks connected as a result of randomization during training, with each random pool configuration defining a unique member of the group. The idea of stochastic pooling was presented by [PERSON] and [PERSON] [38], inspired by dropouts [39]. Stochastic pooling is introduced to address the shortcomings of average and max pooling. Multinomial distribution is applied by stochastic pooling to pick the value randomly. In stochastic pooling, the probabilities' pi values are constantly defined by normalized activations within the sectors for each region \(j\). The description can be seen in the equation below.  
\[pi=\frac{a_{i}}{\sum{k}{{{\epsilon}}_{R}}{{\mu}_{k}}} \tag{6}\]By finding these probabilities, multinomial distribution will be created which determines a location and corresponds to the activation \(a_{l}\) grounded on \(p\). The location \(l\) will be selected by multinomial distribution within the region.  
\[s_{j}=a_{l}\text{ where }l\,\sim p\,\left(p_{1,\dots,p|R_{j}|}\right) \tag{7}\]  
In short, activations are selected based on probabilities and further calculated by multinomial distribution. The constraint on exclusively applying non-negative activation is stochastic pooling's primary problem. Additionally, it is clear from the equation that the technique does not work for negative activations. Only the ReLU activation function is applied with stochastic pooling because the ReLU activation function lowers negative activations to zero. Stochastic pooling is still sensitive to overfitting, particularly if the training data are few [40]. This is because stochastic pooling is significantly more likely to generate strong activation during training, raising concerns about the possibility of overfitting. Therefore, rank-based stochastic pooling [41] presents a novel approach for estimating the likelihood based on the rankings of the activations inside each pooling region. All of these stochastic pooling problems involve scale-related challenges [42]. The working of stochastic pooling is depicted in Figure 4.
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 3 Proposed Method
### Feature Extraction
#### 3.1.1 log-Mel Spectrogram Extraction  
The log-Mel spectrogram is the most commonly used input feature of ASD systems. This spectrogram can capture the global characteristics of a sound, offering a concise representation of the overall frequency distribution in the machine sound signal, and can align with human auditory perception.  
Let \(x(n)\) be the input original sound signal. Initially, the original sound signal is pre-processed by framing and windowing, and then the fast Fourier transform (FFT) is applied on each frame of data. It can be vividly seen that discerning the signal's features in the time domain turns out to be challenging; the FFT is used to convert the signal from the time domain to the frequency domain. The calculation process can be written as follows:  
\[X(k)=\sum_{n=0}^{N-1}x(n)\exp(\frac{-j2\pi kn}{N}),0\leq k\leq N-1 \tag{1}\]  
where \(N\) denotes the number of sample points for the FFT and \(X(k)\) denotes the value of the \(k\)-th frequency point. Then, it is supposed to take the modulus square of the resulting complex spectrogram to obtain the power spectrogram, and the Mel spectrogram can be obtained by passing the power spectrogram through the Mel filter bank. The calculation process can be written as follows:  
\[S(m)=\text{In}(\sum_{k=0}^{N-1}|X(k)|^{2}H_{m}(k)),0\leq m\leq M \tag{2}\]  
where \(H(k)\) denotes the transfer function of the Mel filter bank; \(M\) denotes the number of filters in the Mel filter bank. Finally, the log-Mel spectrogram can be obtained via the logarithmic energy processing of the Mel spectrogram.  
Figure 3: The extraction process of IMS spectrogram.  
Figure 2: The framework of the proposed method for anomalous sound detection.
# A Biologically Inspired Self-Organizing Underwater Sensor Network
## 5 Forwarding Guide
### Expected Wait  
In this section, we define the concept of expected wait, with which the cost to a node to relay a message packet can be measured. Considering communications among nodes in a network, the network is usually abstracted as a weighted graph model, with nodes being the vertexes and edges being built between pairs of neighbors. It is expected that a message packet can be transmitted to its destination following the shortest path, i.e., taking the least time.  
The weights of the edges are usually a metric in relation to the time spent on communication, such as the time spent on signal propagation or time spent on signal parsing. However, in our method, as we are using the TDMA framework, we set the slot long enough so that the whole process of signal propagation and packet decoding can be done within one slot. Thus, the time spent on message propagation can be treated as a fixed value, i.e., the length of the slot. Note that it does not play a main role in message transmission. A more important thing that should be taken into consideration is that if a node receives a message packet that should be relayed, how long, or how many slots, it needs to wait before sending this message packet.  
As in Section 4 with the slot distributor, finally each node will occupy several slots with different addresses (i.e., positions of slots in the frame). Nodes that occupy more slots speak more frequently and take less time to wait before sending a message packet. Even if two nodes occupy the same number of slots, the average time needed to wait is different according to the distribution of the slots.  
Thus, we define the concept of expected wait as a metric; it is the average time the slots need to wait before sending a message packet, with the requirement of sending the message packet appearing at a random time. It is defined as:  
\[t_{exp}=\frac{t_{slot}}{N}\sum_{i=1}^{N}n_{slotsWait}^{i}\]  
where \(t_{slot}\) is the length of a slot, and \(N\) is the number of nodes in the network and also the length of the frame; \(n_{slotsWait}^{i}\) means the number of slots between slot \(i\) and the next slot occupied by the current node.
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 4 Results
### Impact of Different Distances  
To evaluate the performance of hand gesture recognition at different distances, we conducted the experiment at the varied distance between the volunteer and transceiver from 0.5 m to 3 m at a step size of 1 m. For each distance, there were 50 trials of each hand gesture. As shown in Figure 8, at 0.5 m, TaGesture achieved the highest accuracy of 99.2%. As the distance increased, the accuracy decreased due to the lower SNR caused by attenuation. But even at 3 m, TaGesture still achieved an accuracy of 94.8%. The results demonstrate that TaGesture can still work effectively at a distance of 3 m.
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 3 Results
### Surface-Based Analysis  
Performance for all wind-regimes is presented in Section 3.1.1, and then a focus on wind speed events sustained above 17 m/s for three hours is shown in Section 3.1.2.
## 4 Flange Image Recognition Principle and Process
### Spatial Circle Fitting and Circle Parameter Calculation  
In the process of the automatic docking of the small crane tube, in order for the small crane tube to move to the specified space coordinates with a matching pose it is necessary to solve the problem according to the point cloud data of the target flange obtained by the sensor and obtain the pose of the target flange, including the space coordinates and the normal vector of the circle center.  
The above equation is an overdetermined equation and can be transformed into a well-posed form as:  
\[\mathbf{B}^{\mathsf{T}}\mathbf{B}\mathbf{C}=\mathbf{B}^{\mathsf{T}}\mathbf{L} \tag{8}\]  
Since the center c is on the plane of the circle:  
\[\mathbf{A}^{\mathsf{T}}\mathbf{C}=1 \tag{9}\]  
Therefore, to solve the coordinates of the circle center, Equation (7) can be solved by constructing the optimization problem under the constraints of Equation (9). Therefore:  
\[\left[\begin{array}{c}\mathbf{B}^{\mathsf{T}}\mathbf{B}\\ \mathbf{A}\end{array}\right]^{\mathsf{T}}\mathbf{C}=\left[\begin{array}{c} \mathbf{B}^{\mathsf{T}}\mathbf{L}\\ 1\end{array}\right] \tag{10}\]  
Then, the center coordinate C is:  
\[\mathbf{C}=\left(\left[\begin{array}{c}\mathbf{B}^{\mathsf{T}}\mathbf{B}\\ \mathbf{A}\end{array}\right]^{\mathsf{T}}\!\!\left[\begin{array}{c}\mathbf{B} ^{\mathsf{T}}\mathbf{B}\\ \mathbf{A}\end{array}\right]\right)^{-1}\!\left[\begin{array}{c}\mathbf{B}^{ \mathsf{T}}\mathbf{B}\\ \mathbf{A}\end{array}\right]^{\mathsf{T}}\!\!\left[\begin{array}{c}\mathbf{B} ^{\mathsf{T}}\mathbf{L}\\ 1\end{array}\right] \tag{11}\]  
The radius of the circle can be determined by the average of the distances from all points to center of the circle:  
\[\overline{r}=\frac{1}{N}\!\sum_{i=1}^{N}\sqrt{\left(x_{i}-x_{0}\right)^{2}+ \left(y_{i}-y_{0}\right)^{2}+\left(z_{i}-z_{0}\right)^{2}} \tag{12}\]
## Appendix A  
We assume that \(x1=\left(\frac{\varepsilon_{0}}{\sigma_{u}^{2}}-\gamma-1\right)\sqrt{\frac{ \tau f_{s}}{2\gamma+1}}\), \(x2=\left(\frac{\varepsilon_{1}}{\sigma_{u}^{2}}-\gamma-1\right)\sqrt{\frac{ \tau f_{s}}{2\gamma+1}}\), \(x3=\left(\frac{\varepsilon_{0}}{\sigma_{u}^{2}}-1\right)\sqrt{\tau f_{s}}\), \(x4=\left(\frac{\varepsilon_{1}}{\sigma_{u}^{2}}-1\right)\sqrt{\tau f_{s}}\). Because of \(\varepsilon_{0}<\varepsilon_{1}\), \(\gamma>0\), then \(x2>x1\), \(x4>x3\); \(x4>x2\), \(x3>x1\). According to the \(Q(x)\), we can get that \(P_{d}\big{|}\varepsilon_{0}=Q(x_{1})\), \(P_{d}\big{|}\varepsilon_{1}=Q(x_{2})\), \(P_{f}\big{|}\varepsilon_{0}=Q(x_{3})\), \(P_{f}\big{|}\varepsilon_{1}=Q(x_{4})\). \(Q(x)\) is shown in Figure A1.  
Based on (26) and (27), we can get  
\[\Delta_{1}=\frac{Q(x_{2})-Q(x_{1})}{Q(x_{4})-Q(x_{3})}=\frac{Q(x_{1})-Q(x_{2}) }{Q(x_{3})-Q(x_{4})} \tag{28}\]  
For the purpose of calculation, we assume that  
\[A=Q(x_{1})-Q(x_{2})\approx(x_{1}-x_{2})\ast Q^{\prime}\left(\frac{x_{1}+x_{2}} {2}\right) \tag{29}\]