esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous Studies on LSS Prediction Models  
Prediction models are developed through medical informatics for assessment, diagnosis, and treatment via methods such as logistic regression (LGR), a common and traditional predictive tool, and artificial neural networks, a computational model based on the functioning of biological neural networks that model complex relationships between inputs and outputs.  
For example, [PERSON] [19] compared the prediction ability by LGR vs. artificial neural network the satisfaction level of 168 LSS patients at two years post-surgery, as well as the adoption of surgical or conservative treatment in 346 LSS patients--in both cases, neural networks were found to have a higher accuracy rate than LGR.  
A second example in prediction models is demonstrated by [PERSON] et al. [20], in which 47 potential patient-reported predictive factors from the hospital records of 4,987 patients with chronic low back pain were used to predict referrals for spinal surgery via LGR--the results showed that being female, having previous back surgery, high-intensity leg pain, somatization, and positive treatment expectations increased the likelihood of being referred for spinal surgery, whereas obesity, comorbidities, pain in the thoracic spine, increased walking distance, and the consultation location reduced the likelihood.  
A third example of prediction models can be seen in [PERSON] et al. [21], using imaging and physical examination data from 36 LSS patients who underwent surgery, via chi-square automatic interaction detection decision tree, to predict the success of the surgery as defined by improvement in three out of four of: visual analog pain scale, lumbar functioning, limping and leg pain--the accuracy rate of said decision tree reached 90.1%.  
In the above studies of subjects with LSS or low back pain, the outcome predicted by the constructed models included satisfaction level, opting for surgery vs. conservative treatment, referral for surgery, as well as success for surgery. In a similar vein, this study aims to explore the predictive factors in LSS patients who initially opted for conservative treatment who subsequently opted for surgical treatment using imaging data and medical records.
## 5 Discussion
### Selection of Time Series Images  
In this study, time series images for a whole year were used for aquaculture mapping in order to eliminate the influence of accidental factors, such as water-dominated paddy fields and dry ponds during harvest period. Time series SAR images also are effective in suppressing speckle noise of radar image. Although there are many advantages, two problems should be noted.  
First, the seasonal characteristics of study areas may affect the selection of time series images. For example, this study area is located in southern China, in which the temperature is usually high, and the water does not freeze; thus, the images with good quality acquired at any time can be used. However, in northern China, the pond water freezes in winter and its optical properties will change. Therefore, the time series images should be selected according to the specific seasonal characteristics of study areas.  
Second, the assumption and basis of using time series images are that ground entities are not changed suddenly. If some aquaculture ponds are converted into agricultural land or built-up areas in winter, they may still be recognized as aquaculture areas. To improve the ability to respond to such sudden changes, it is necessary to shorten the time interval of time series images.
## 2 Requirements for Overall Structure and Beam Adjustment Accuracy of the Golay3 System  
### Structure and the Optical Path of the Golay3 System  
In order to meet the actual engineering applications, a three-aperture imaging system (Golay3 system) is developed using a modular design scheme. The side view of the light path structure and the three-dimensional model (in CATIA software) are shown in Figure 1a,b, respectively. The system is divided into three main structural modules, namely the subaperture system, the beam steering and adjustment structure, and the synthetic imaging system [12]. The fast steering mirror (FSM) is the core component of the beam steering and adjustment structure. The main system parameters and experimental requirements of the Golay3 system are shown in Table 1.  
Figure 1: Golay3 system: (**a**) side view of the light path structure; (**b**) three-dimensional model.  
As shown in Figure 1a, if there is position error between the subapertures, then the phase of the beams with different passing light paths will be different. It will influence the imaging quality directly. We define the phase error of the beams passing through different subapertures as the cophasing error. The key to the high-resolution observation of the Golay3 sparse-aperture imaging system is the ability to control the cophasing error on the imaging surface within the allowable value range after steering the incident light field between several discrete subapertures. Therefore, the beam steering and adjustment structure must meet the stability of light path steering. Meanwhile, the high accuracy and flexibility of light field adjustment should be achieved. This puts forward higher requirements for the integrated design of the steering and adjusting of the beam steering and adjustment structure [13].
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 4 Results and Discussion
### Simulation Setting  
In the simulation, each of the algorithms were simulated under similar conditions, with 15 independent runs consisting of 30 search agents and 200 iteration numbers. These independent runs were performed to manage the effects generated from random parameters in the methods used. The size of the environment was 100 m \(\ast\) 100 m. the simulation parameters are presented in Table 2. Time is assumed to be discrete (\(t\) = 1, 2,... ) and at each time step, every ant moves toward a neighbor node at a constant speed (m/s).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Parameter** & **Value** \\ \hline
**SCSO** & Sensitivity range (\(\mathrm{r_{G}}\)) & [2, 0] \\  & Phase control (R) & [\(-2\mathrm{r_{G}}\), \(2\mathrm{r_{G}}\)] \\ \hline \multirow{3}{*}{**GWO**} & \(a\) & [2, 0] \\  & \(A\) & [2, 0] \\  & \(C\) & 2.rand (0, 1) \\ \hline \multirow{3}{*}{**MFO**} & b & 1 \\  & t & [\(-1\), 1] \\ \hline \multirow{3}{*}{**PSO**} & C1 and C2 acceleration constants & 1.7 \\  & Maximum inertia weight (\(\mathrm{W_{max}}\)) & 0.9 \\  & Minimum inertia weight (\(\mathrm{W_{min}}\)) & 0.2 \\  & Maximum velocity (\(\mathrm{Vmax}\)) & 6 \\ \hline \multirow{3}{*}{**RACO**} & \(\alpha\) & 0.5 \\  & \(\beta\) & 0.5 \\  & \(\tau_{ij}\) (0) & 0.008 \\  & \(\rho\) & 0.05 \\  & C1, C2, C3 & 0.47, 0.34, 0.19 \\ \hline
**CS** & \(p_{a}\) & 0.25 \\ \hline
**FA** & \(\lambda\) & 2 \\ \hline
**GA** & Crossover rate & 0.1 \\ \hline \multirow{3}{*}{**GSO**} & \(p\) & 0.4 \\  & \(\gamma\) & 0.6 \\ \cline{2-2}  & \(\beta\) & 0.08 \\ \cline{2-2}  & \(\mathrm{n_{t}}\) & 5 \\ \cline{2-2}  & s & 0.03 \\ \cline{2-2}  & \(L_{0}\) & 5 \\ \hline \multirow{3}{*}{**MAP-ACO**} & \(\alpha\) & 0.5 \\  & \(\beta\) & 0.5 \\ \cline{1-1}  & \(\tau_{ij}\) (0) & 0.008 \\ \cline{1-1}  & \(\rho\) & 0.05 \\ \cline{1-1}  & C1, C2, C3, C4 & 0.15, 0.2, 0.25, 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The simulation parameters.
# Research on Improving Optical Field Distribution of TOSA Based on 3D Defocusing Coupling
## 2 Analysis of the Influence of Optical Field Distribution in TOSA
### Factors Affecting the Optical Field
For the fundamental mode Gaussian beam:  
\[\omega_{0}\theta_{0}=\frac{4\lambda}{\pi} \tag{5}\]  
\[M^{2}=\frac{\pi}{4\lambda}\cdot\omega\theta\approx\frac{1}{1.27\lambda}\cdot \omega\theta \tag{6}\]  
In the formula, \(\omega\) and \(\omega_{0}\) are the waist widths of the actual beam and the fundamental mode Gaussian beam, respectively, and \(\theta\) and \(\theta_{0}\) are the far field divergence angles of the actual beam and the fundamental mode Gaussian beam, respectively. Since the beam of the actual LD is not circular, there are large asymmetric far field divergence angles in transverse and lateral directions. The active layer of LD is relatively thin, so there is a larger divergence angle \(\theta_{\perp}\) in the lateral direction, while there is a smaller divergence angle \(\theta_{\parallel}\) in the transverse direction because of the wide active layer. Therefore, the \(M^{2}\) factor can be decomposed into transverse and lateral factors. \[\left\{ \begin{array}{l}M_{\perp}^{2}=\frac{\pi}{4\lambda}\cdot\omega_{\perp} \theta_{\perp}\approx\frac{1}{1.27\lambda}\cdot\omega_{\perp}\theta_{\perp}\\ \bar{M}_{\parallel}^{2}=\frac{\pi}{4\lambda}\cdot\omega_{\parallel}\theta_{ \parallel}\approx\frac{1}{1.27\lambda}\cdot\omega_{\parallel}\theta_{ \parallel}\end{array} \right. \tag{7}\]  
\(\omega_{\perp}\) and \(\omega_{\parallel}\) are the transverse and lateral beam waist widths, respectively. The theoretical basis of the beam quality factor is the invariant principle of the product of beam waist width and far-field divergence angle. The beam waist width is determined by light intensity distribution on beam waist section, and the far-field divergence angle is determined by phase distribution. Therefore, \(M^{2}\), as a beam quality factor, can reflect the characteristics of intensity distribution and phase distribution of light field [25; 26]. In order to achieve the coupling between LD and optical fiber, it is necessary to use an optical conversion element to transform the beam waist width and far-field divergence angle of LD, so that \(\omega_{f}\) can match the intrinsic mode field of single-mode fiber sufficiently.
# The Biosynthesis and Medicinal Properties of Taraxerol
## 3 Medicinal Properties of Taraxerol
### Anti-Inflammatory Properties  
Perhaps the most potent pharmacological properties actively shown by taraxerol is as an anti-inflammatory agent. [PERSON] et al. (2002) investigated the anti-inflammatory activity of taraxerol on carrageenanan-induced paw edema on rats and found that applying the triterpen extract at a dosage of 20 mg/kg led to edema reduction by 49.66% after 7 h [13]. [PERSON] et al. (2004) further uncovered the anti-inflammatory effects of taraxerol on TPA-induced local inflammation in Swiss Albino mice, in which development of ear edema in rat model was suppressed following its application. A dosage of 1 mg/ear showed the best suppressive effects with a 25.7 mm difference in ear thickness 4 h following the injection [31]. Apart from paw and ear inflammation, taraxerol was also found to be beneficial in inflammatory pulmonary diseases. By directly acting on airway epithelial cells, taraxerol regulates the expression of the _Muc5a_ gene in the cells, thus regulating mucus production in the inflamed airway [45].  
Other than in the treatment of edema, taraxerol's neuroinflammation amelioration effect has also been studied. [PERSON] et al. (2008) examined the effect of taraxerol on the production of nitric oxide (NO) and reactive oxygen species (ROS) by activated microglial cells, which play a number of deleterious roles in central nervous system mediation [24]. The NO and ROS are produced by activated microglial cells through the induction of NADPH oxidase (NOX) and nitric oxide synthase (NOS), which the authors noted to have been inhibited by 11.6% at 50 \(\upmu\)M concentration and 50% at 24.2 \(\upmu\)M concentration, respectively. The mechanism through which taraxerol functions as an anti-inflammatory agent was further elucidated by [PERSON] et al. (2013) who showed that taraxerol downregulates the expression of proinflammatory mediators in macrophages through the interference of TAK1 and Akt protein activation, thus preventing NF-\(\kappa\)B activation from producing various proinflammatory mediators through a cascade effect [110]. Cellular redox reactions have a critical role in the regulation of immune response, which directly suggested that taraxerol could also mediate inflammatory responses [110].
# Collision Avoidance Resource Allocation for LoRaWAN
## 3 Technical Overview of LoRa/LoRaWAN
The communication between end-devices and gateways consists of single-hop LoRa or FSK connections, whereas gateways make use of standard IP connections to the network server. All the communications are bi-directional. The communication between end-nodes and gateways takes place using different channels and spreading factors. The transmission channel is chosen in every transmission by the end-device in a pseudo-random way and the SF (or DR) is selected taking into account the communication range and message duration through an Adaptive Data Rate (ADR) scheme [14]. The DR for each transmission is chosen taken taking into account the link budget (the higher the link budget, the faster DR or SF selected). Simultaneous transmissions in the same channel but in different SFs will never collided due to the orthogonality of the SFs. So that, this feature can be used to improve the network capacity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Rate** & **Spreading Factor** & **Bit Rate (bps)** & **ToA for a 24 billion Packet (ms)** \\ \hline
0 & 12 & 293 & 1482.75 \\
1 & 11 & 540 & 823.30 \\
2 & 10 & 980 & 411.65 \\
3 & 9 & 1757 & 205.82 \\
4 & 8 & 3125 & 113.15 \\
5 & 7 & 5470 & 61.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data Rate configuration for a 125 kHz bandwidth in the EU868 ISM band. The LoRaWAN standard defines three classes of LoRaWAN end-devices. The implementation of Class A is mandatory for all devices. This class is the most employed due to its low energy consumption. In Class A devices, two receiving windows are opened after an uplink transmission in order to receive a downlink transmission. The uplink transmissions in this class are scheduled using an ALOHA scheme, that is, the end-device transmits every time it has pending data without using any listen-before-talk mechanism. Class A devices are typically used for sensor nodes which are battery operated. Class B devices work similar to Class A devices, but extra receive windows are opened at scheduled times using beacon frames. This class is typically used for battery operated actuators. Class C devices are continuously listening except when transmitting, that is, the receive window is not closed. Class C is intended for mains powered actuators since continuous reception requires more power. In Europe, LoRaWAN networks operates in the EU863-870 ISM band which allows up to eight channels with a 125 kHz bandwidth each, being three of them mandatory (868.1, 868.3, and 868.5 MHz). In order to respect ETSI regulations, LoRaWAN devices are limited to a duty-cycle lower than 1%. Moreover, there is not dwell time limitation in this band.
# A Green AI Methodology Based on Persistent Homology for Compressing BERT
## Appendix A Homology Theory: Notation and Mathematical Background
For a \(p-\)chain, \(c=\sum a_{i}\sigma_{i}\), the boundary is \(\partial_{p}c=\sum a_{i}\partial_{p}\sigma_{i}\). Hence, the boundary maps a \(p-\)chain to a \((p-1)-\)chain  
\[\partial_{p}:C_{p}(K)\mapsto C_{p-1}(K).\]  
Note that \(\partial_{p}(c+c^{\prime})=\partial_{p}c+\partial_{p}c^{\prime}\); in other words, the boundary is a homomorphism. The chain complex is the sequence of chain groups connected by boundary homomorphisms,  
\[\ldots\xrightarrow{\partial_{p+2}}C_{p+1}(K)\xrightarrow{\partial_{p+1}}C_{ p}(K)\xrightarrow{\partial_{p}}C_{p-1}(K)\ldots\]  
The elements of \(Z_{p}(K)=\ker(\partial_{p})\) are called \(p-\)cycles and those of \(B_{p}(K)=\operatorname{im}(\partial_{p+1})\) are called \(p-\)boundaries. **Definition A6** (Homology group). : _The \(p-\)th homology group is defined as_  
\[H_{p}=Z_{p}/B_{p}.\]  
The \(p-\)th Betti number is the rank of this group, \(\beta_{p}=rank\ H_{p}\). Finally, persistent homology is presented, which measures the scale of a topological feature combining geometry and algebra. Consider a simplicial complex, \(K\), and a \(f:K\mapsto\mathbb{R}\) monotonic, which implies that the sublevel set; \(K(a)=f^{-1}(-\infty,a]\) is a subcomplex of \(K\ \forall a\in\mathbb{R}\). Letting \(m\) be the number of simplices in \(K\), \(n+1\leq m+1\) different subcomplexes appear,  
\[\emptyset=K_{0}\subseteq K_{1}\subseteq\cdots\subseteq K_{n}=K.\]  
In other words, if \(-\infty=a_{0}<a_{1}<a_{2}<\cdots<a_{n}\) are the function values of the simplices in \(K\), then \(K_{i}=K(a_{i})\). This sequence of complexes is known as the filtration of \(f\). For every \(i\leq j\), there is an inclusion map from \(K_{i}\) to \(K_{j}\) and, therefore, an induced homomorphism  
\[f_{p}^{i,j}:H_{p}(K_{i})\mapsto H_{p}(K_{j}).\]  
The filtration corresponds to a sequence of homology groups connected by homomorphisms  
\[0=H_{p}(K_{0})\mapsto H_{p}(K_{1})\mapsto\cdots\mapsto H_{p}(K_{n})=H_{p}(K).\]  
for each dimension \(p\). In the transition from \(Ki-1\) to \(K_{i}\), new homology classes are gained, and other are lost when they merge with each other. Classes that are born are collected at a given threshold and die after another threshold in groups. **Definition A7** (Persistent homology). : _The \(p-\)th persistent homology groups are the images of the homomorphisms induced by inclusion, \(H_{p}^{i,j}=in\ f_{p}^{i,j}\), for \(0\leq i\leq j\leq n\). The corresponding \(p-\)the persistent Betti numbers are the rank of these groups._  
The collection of persistent Betti numbers can be visualized by drawing points in the extended real plane \(\overline{\mathbb{R}^{2}}\). Letting \(\mu_{p}^{i,j}\) be the number of \(p-\)dimensional classes born at \(K_{i}\) and dying entering \(K_{j}\),  
\[\mu_{p}^{i,j}=(\beta_{p}^{i,j-1}-\beta_{p}^{i,j})-(\beta_{p}^{i-1,j-1}-\beta_{ p}^{i-1,j}),\]  
\(\forall i<j\), \(\forall p\). Drawing each point \((a_{i},a_{j})\) with multiplicity \(\mu_{p}^{i,j}\), the \(p-\)th persistence diagram of the filtration is defined.
# Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar
## 3 Results
### Computation Process  
The recorded data were processed using MATLAB R2015a (MathWorks Inc., Natick, MA, USA). \(M(t,r)\) and \(P(t,r)\) were calculated using the built-in fast Fourier transform  
Figure 4: SPC and extracted vital-sign signals. (**a,b**) \(M(t,r)\) and \(P(t,r)\) for respiration and heartbeat, (**c,d**) SPCs, for respiration and heartbeat, and (**e,f**) extracted respiration and heartbeat.  
function, and the Hamming window was applied to \(x(t,n)\) to prevent spectral distortion. To reconstruct the continuous phase variation, phase unwrapping was applied to \(P(t,r)\). To reduce the time complexity, the boundary of the human target was set by reference to the variance of \(M(t,r)\); this process suppresses stationary clutter [50]. Then, considering that normal adults' respiratory rate is 14\(\sim\)20 respiration per minute (RPM), and heart rate is 60\(\sim\)100 beats per minute (BPM) [51; 52], \(P(t,r)\) inside the boundary, was filtered at 0.1\(\sim\)0.4 Hz for respiration, and 0.8\(\sim\)1.7 Hz for heartbeat. For each pair of range-bins, the SPC was calculated and \(r_{T}(t)\) was selected. Then, \(P(t,r)\) in the selected range-bins was averaged to calculate vital rates using the zero-crossing detection [53]. The vital rates were represented as RPM for respiration and BPM for heartbeat. For continuous monitoring of vital rates, they were extracted every second using \(t_{0}=15\) s of time-windowed data. For comparison with conventional methods, the vital rates of conventional methods were calculated in the same way as the proposed method, except for the range-bin selection. Conventional methods were implemented as followed equations. The method in [37] selects a range-bin, which has the highest coherency between magnitude and phase, as  
\[r_{T}(t)=\arg\max_{r}\frac{\sum_{s=t-t_{0}}^{t}M(s,r)\cdot P(s,r)}{\sigma_{M}( r)\cdot\sigma_{P}(r)}, \tag{7}\]  
where \(\sigma_{M}(r)\) is the standard deviation of \(M(t,r)\).  
The method in [38] selects a range-bin, which has the maximum magnitude of range-profile considering the reflected power from the target as in (8). The range profile, \(X(t,r_{k})\) is calculated by Fourier transform as in (9)  
\[r_{T}(t)=\arg\max_{r}\left\|\sum_{s=t-t_{0}}^{t}X(s,r)\right\| \tag{8}\]  
\[X(t,r_{k})=\sum_{n=0}^{N-1}x(t,n)\cdot exp\left(-\frac{j\cdot 2\pi\cdot k\cdot n }{N}\right), \tag{9}\]  
where \(r_{k}=\frac{c}{2 BW}\cdot k\) for \(k=0,...,N-1\), and \(BW\) is bandwidth and \(N\) is the number of samples in a chirp.  
The method in [39] selects a range-bin, which has the maximum phase fluctuation as  
\[r_{T}(t)=\arg\max_{r}\sum_{s=t-t_{0}}^{t}P^{2}(s,r), \tag{10}\]  
where DC offset of P(t,r) is removed in advance.  
The method in [40] uses a range-integrated phase, \(\hat{P}(t)\) for VS measurements as  
\[\hat{P}(t)=\angle\sum_{r}X(t,r) \tag{11}\]
# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
## 5 Conclusions  
In this study, experiments were conducted on a laser microwelding machine installed in the industry to investigate the capability of applying an AE signal to monitor the joint quality in lap microwelding of stainless sheets for the manufacturing of the metal mask. The correlation between AE signals and joint bonding strength resulting from laser lap microwelding was analyzed and verified by applying the traditional feature extraction method and the developed HMM classifier. The results show that the RMS and gradient of signals obtained during the first millisecond of welding and the 300 kHz frequency signal feature are promising features for identifying low-strength joints based on the conditions in this study. By applying an HMM classifier with the gradient of the signal from the first 1 ms interval, 100% classification accuracy was achieved. Furthermore, the combination of the 300 kHz frequency feature and the RMS of the signal from the first 1 ms resulted in 100% classification accuracy as well for the welding parameters and material setup in this study. Based on the results obtained in this study, the AE signal is confirmed to be capable of monitoring the joint quality in laser lap microwelding. Along with the work done by [PERSON] and [PERSON] [12] for audible sound signal analysis to monitor welding quality, the sound/AE multi-sensor joint quality monitoring system is expected to be developed in the future to apply to the production line to increase the reliability of the joint quality monitoring system.  
M.-[PERSON] contributes to the conceptualization, methodology, development, signal analysis, and writing. [PERSON] contributes to the conceptualization, methodology, and writing. [PERSON] contributes to the conduction of experiments. [PERSON] contributes to the signal analysis. All authors have read and agreed to the published version of the manuscript.  
This study was supported by GeniRay Technology Corporation, New Taipei, Taiwan. This research was partially funded by the Ministry of Science and Technology, Taiwan, grant number MOST 104-2221-E-005-019.  
Not applicable.  
This study was supported by GeniRay Technology Corporation, New Taipei, Taiwan. This research received partial financial support from the Ministry of Science and Technology, Taiwan. Grant number: MOST 104-2221-E-005-019.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.