esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 3 Clinical Brain Monitoring with TD-NIRS
### Brain Assessment in Death  
TD-NIRS could also be used to assess the adequacy of resuscitation. In a pilot study, [PERSON] and colleagues [94] used a NIRO-TRS1 Hamamatsu system to monitor the cerebral saturation and haemoglobin concentration in \(11\) patients with septic shock, and compared those measurements with traditional markers of perfusion adequacy. In another study using the same system, [PERSON] and colleagues [95] reported the case of a young man who suffered a pulseless electrical activity (PEA) arrest (i.e., cardiac arrest) while cortical oxygenation was monitored. The onset of cortical deoxygenation preceded the loss of palpable pulses for 15 min, despite otherwise stable measuresof perfusion, reflecting falling cortical microvascular haemoglobin concentration and oxygenation minutes before PEA arrest. This result is presented on Figure 5b. Even though it is a single subject study, this suggest that TD-NIRS might provide the means of detecting PEA arrest, by monitoring cortical oxygenation.  
Finally, [PERSON] and colleagues [96] and [PERSON] and colleagues [97] investigated the possibility to use DCE-TD-NIRS to confirm brain death. Using an in-house developed multichannel TD-NIRS systems (ID: 17 in Table 2), different stages of cerebral perfusion disturbances were compared in three groups that included controls, patients with post-traumatic cerebral oedema, and patients with brain death. The authors reported that DCE-TD-NIRS was feasible in the demanding ICU environment and had promising initial results. However, at its current stage of development, the accuracy of the method did not reach the high standards required for brain death confirmation.
## 4 Discussion
In addition, there was a 200\(\%\) increase in MP seeds grown in saline conditions as compared to MP seeds grown in non-saline conditions (data not shown). The lesser increase in ABA by MP in saline conditions is due to higher tolerance of MP seeds towards salinity stress. The levels of both the phytohormes ABA and GA were significantly altered during seed germination even under salt-stressed conditions with and without the participation of NO [72; 80; 81; 82]. In the current study, we also observed that NO exercised its control on ABA/GA, as the relative change in the ratio of ABA/GA in magnetoprimed vs. unprimed seeds reduced in absence and presence of the NO donor, but the inhibitors showed a positive change in ABA/GA under both non-saline and saline conditions. Similarly, ROS produced by NADPH oxidase enzyme in barley seeds, induced GA biosynthesis in the embryo which further activated alpha-amylase enzyme in the aleurone layer [83] to hydrolyse starch for seedling growth. In our experiments, the involvement of IAA in the NO-induced tolerance to salinity at germination stage indicates the crosstalk of auxins with the signalling molecule (NO). IAA levels increased in magnetoprimed seeds as compared to unprimed seeds in absence and presence of NO donor treatment, both under non-saline and saline conditions. An increase in IAA content along with GA was reported in magnetoprimed faba bean [84], which could be a result of hydrolysis of amide-IAA conjugates stored in the mature seed [34,85]. We also found that the trend in relative change of IAA was similar to that of GA under all the treatments. We derived the ratio of IAA/GA and noticed that the ratios were severely reduced in control and SNP treatments by magnetopriming under saline conditions. Lower ratios of IAA/GA are favourable for germination under both the growing environments, as higher IAA levels in the germinating seeds, if not checked, can inhibit seed germination in an ABA dependent manner [31]. [PERSON] et al. [31] showed that transgencies over-expressing auxin (iaaM-OX) stopped the germination of Arabidopsis seeds, while mutants with low levels of auxins improved germination rate. [PERSON] et al. [86] identified ABI3 as a pivotal regulatory component for the maintenance of auxin-associated dormancy. Recently, [PERSON] et al. [33] found that the auxin signalling repressor IAA8 accumulated during germination and down regulated the expression of ABSCISIC ACID INSENSITIVE3 (ABI3), which is a negative regulator of germination in _Arabidopsis thaliana_ leading to the enhancement in germination rate. Although we have observed an increase in IAA levels in the magnetoprimed seeds, which may be the released product from IAA conjugate breakdown, the stimulation in germination was not hampered, as the ratio of IAA/GA was low in all the treatments. Our study suggested that magnetopriming produces NO that regulates the homeostatic control over the hormones (ABA, GA and IAA) and reduces the influx of Na\({}^{+}\) in the germinating seed, thus inducing salt tolerance in soybean.
## 2 Materials and Methods  
### Transfer Function of a Single Lead  
According to the Transfer Function approach, the power deposition at the tip of the implanted lead is determined by  
\[\mathrm{P}=\mathrm{A}\biggl{(}\int_{\mathrm{L}}\mathrm{E}_{\mathrm{tan}}(\mathrm{ z})\mathrm{S}(\mathrm{z})\mathrm{d}\mathrm{z}\biggr{)}^{2}, \tag{1}\]  
where S(z) is the transfer function (TF), A is a calibration factor and E\({}_{\mathrm{tan}}\) (z) is the tangential component of the incident electric field along the lead path L inside the patient, without the lead in place [19]. Thus, the TF decouples the scattered field from the incident field and is usually assessed experimentally, using a piecewise excitation method. In the latter, a localized electric field is applied along the implant and the TF is obtained by measurements of the resultant electric field at the tip of the implant. Alternatively, the TF can be evaluated using the theorem of reciprocity by exciting the tip of the implant and monitoring the induced current distribution that directly reflects the TF [32]. The latter approach provides the advantage of requiring only two simulations for the numerical determination of the TF [33].  
In this work, the reciprocity approach was employed to determine the electrical model of a generic lead, 40 cm long, at 64 MHz. In particular, the implanted lead was modeled as a straight wire of 1.6 mm diameter, isolated by a tube of 0.9 mm thickness with \(\varepsilon_{\mathrm{r}}=3\), and with 1 cm of insulation removed at both ends (uncapped wire) [19]. The TF was numerically assessed with full-wave simulations using Sim4 Life V6.0 (SPEAG, Zurich, Switzerland), where the lead was placed along the z-axis in an infinite in all dimensions, lossy, high-permittivity, tissue-simulating material (HPM, \(\varepsilon_{\mathrm{r}}=78\), \(\sigma=0.47\) S/m). The lead was excited by a 1 A current source at the tip and the grid of the simulations was set to 0.1 mm \(\times\) 0.1 mm \(\times\) 0.3 mm. The calibration factor A was estimated using a plane wave excitation of 1 V m\({}^{-1}\) rms at the lead and matching the absorbed power numerically calculated at the lead tip through the computer simulation with the absorbed power assessed by the TF and the incident tangential electric field along the implant trajectory. The numerically calculated power was obtained via the integration of the specific absorption rate (SAR) distribution inside a volume containing the \(-30\) dB iso-contour relative to the maximum SAR value and subtracting the corresponding power deposited in the tissue in the absence of the implant.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 3 Proposed Method
### Defect Identification
#### 3.4.2 Conductive-Hole Exposing  
As shown in Figure 9a, with a small size of 5 \(\upmu\)m (3 pixels) and low contrast with the surroundings, conductive-hole exposing appears in batches, which greatly affects the chip yield. Therefore, this has always been a problem for enterprises. To this end, a defect enhancement method was designed, and the counting processes were as follows:  
\[\textit{Kernel2}=\left[\begin{array}{cccccccc}1&1&0&-1&-2&-1&0&1&1\\ 2&2&0&-2&-4&-2&0&2&2\\ 1&1&0&-1&-2&-1&0&1&1\\ \end{array}\right]\times\frac{1}{8}, \tag{21}\]  
\[\textit{Kernel3}=\textit{Kernel2}, \tag{22}\]  
\[\textit{Dst1}=R\ \otimes\ \textit{Kernel2}, \tag{23}\]  
\[\textit{Dst2}=R\ \otimes\ \textit{Kernel3}, \tag{24}\]  
\[\textit{Dst}=R-(\textit{Dst1}+\textit{Dst2}). \tag{25}\]  
where \(R\) is the red channel of color image, _Kernel2_ is the convolution kernel designed after observing characteristics such as size and its comparison with the surroundings, _Kernel2_ is the transposed matrix of _Kernel2_, and \(\otimes\) represents convolution. As the defect occurred around the chip, _Dst1_ and _Dst2_ respectively reflected the contrast between the horizontal and vertical directions with the surroundings, as shown in Figure 9b,c. By superimposing  
Figure 8: Electrode-pinhole-missing: (**a**) shallow pinhole; (**b**) extracting.  
the two filtered pictures and subtracting from \(R\), the characteristics were enhanced, and the results after marking are shown in Figure 9d.
## 4 Experiment and Results
### Datasets  
Mainly two datasets were used to conduct the experiment. First, the MICC-SRI [9] dataset is made from the Car Learning to Act [39] simulation, an open-source driving simulator. It contains 11,913 segmentation maps of 13 classes of scene elements. It includes both a static map and a dynamic map for the same scene, which helps quantitatively evaluate the inpainting segmentation result. We split the MICC-SRI dataset into 10,000 maps for training and 1913 maps for testing. For unsupervised learning, the training set was split again into 5000 maps for the dynamic map domain and 5000 maps for the static map domain. The segmentation maps were resized from \(800\times 600\) to \(512\times 256\). During the training stage, the maps were randomly cropped to a size of \(256\times 256\).  
To match both classes of the MICC-SRI and Cityscapes datasets, we removed some of the segmentation classes and defined a new set as follows:  
\[D=\{\textit{Person},\textit{Veichle}\}\]  
\[D=\left\{\begin{array}{l}Road,\textit{Sidewalk},\textit{Building},Wall, \textit{Fence},\textit{Pole},\\ Traffic\textit{Maker},\textit{Vegetation},Other,\textit{Unlabeld}\end{array} \right\},\]  
where \(D\) denotes the dynamic class and \(S\) denotes the static class. Other classes not included in the set were merged into the defined class.  
All the procedures were implemented in Tensorflow version 1.14. An Adam optimization method [40] was used for training the SSIN with a learning rate of \(10^{-6}\) and the momentum of 0.9 to minimize (10) for the SSIN. The discriminator follows the same learning rule as the SSIN. The parameters of the loss function (10) for the training-loss weighting factor of the SSIN were set as follows: \(a=1\) for the static-preserving cross-entropy term, \(b=3\) for the adversarial training, \(c=40\) for the total variation, and \(d=60\) for the self-entropy term.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 3 Algorithms
### Projection onto the Set \(\Omega_{r}\) of Rank-Constrained Structured Matrices  
Here we consider the projection of \((X_{1},X_{2})\) onto the set \(\Omega_{r}\) of rank-constrained structured matrices in (14). This projection can be written by definition as  
\[\Pi_{\Omega_{r}}(X_{1},X_{2})\in\operatorname*{arg\,min}_{(Z_{1},Z_{2})\in \Omega_{r}}\|Z_{1}-X_{1}\|^{2}+\|Z_{2}-X_{2}\|^{2}. \tag{19}\]  
We note that, since the set \(\Omega_{r}\) is closed but non-convex, multiple solutions may exist for the minimization in (19) may exist.  
For this, we propose a precise projection based on alternating direction method of multipliers (ADMM). For the minimization problem (19), we introduce the indicator function \(\mathcal{I}_{r}\) defined by  
\[\mathcal{I}_{r}(Z)\triangleq\begin{cases}0,&\text{if }\text{rank}(Z)\leq r,\\ +\infty,&\text{otherwise}.\end{cases} \tag{20}\]Then the minimization problem in (19) is equivalently described as  
\[\begin{split}\underset{(Z_{1},Z_{2})\in\mathcal{S}^{2}_{u},\tilde{Z} \in\mathcal{S}_{2u}}{\text{minimize}}&\quad\|Z_{1}-X_{1}\|^{2}+\| Z_{2}-X_{2}\|^{2}+\mathcal{I}_{r}(\tilde{Z})\\ \text{subject to}&\quad\tilde{Z}=\begin{bmatrix}Z_{1}&I\\ I&Z_{2}\end{bmatrix}\end{split} \tag{21}\]  
The convergence of the ADMM algorithm of (21) with nonconvex constraint (20) is discussed in [33].  
To solve this optimization problem, we first consider the projection \(\Pi_{\mathcal{C}_{r}}(Z)\) onto the set of rank-\(r\) matrices  
\[\mathcal{C}_{r}\triangleq\{Z\in\mathbb{R}^{2n\times 2n}:\text{rank}(Z)\leq r\}. \tag{22}\]  
The projection \(\Pi_{\mathcal{C}_{r}}\) is easily computed via the singular value decomposition \(Z=U\Sigma V^{\top}\). Define \(\Sigma_{r}\) by setting all but \(r\) largest (in magnitude) diagonal entries of \(\Sigma\) to \(0\). Then, the projection \(\Pi_{\mathcal{C}_{r}}(Z)\) is given by  
\[\Pi_{\mathcal{C}_{r}}(Z)=U\Sigma_{r}V^{\top}. \tag{23}\]  
Now, the optimization problem in (21) can be efficiently solved by adapting the alternating direction method of multipliers (ADMM) algorithm [26]. The iterative algorithm is given by  
\[Z_{1}[i+1] =\Big{(}1+\frac{\rho}{2}\Big{)}^{-1}\Big{(}X_{1}+\frac{\rho}{2}M_ {11}[i]\Big{)}, \tag{24}\] \[Z_{2}[i+1] =\Big{(}1+\frac{\rho}{2}\Big{)}^{-1}\Big{(}X_{2}+\frac{\rho}{2}M_ {22}[i]\Big{)},\] (25) \[\tilde{Z}[i+1] =\Pi_{\mathcal{C}_{r}}\bigg{(}\begin{bmatrix}Z_{1}[i+1]&I\\ I&X_{2}[i+1]\end{bmatrix}-W[i]\Big{)},\] (26) \[W[i+1] =W[i]+\tilde{Z}[i+1]-\begin{bmatrix}Z_{1}[i+1]&I\\ I&Z_{2}[i+1]\end{bmatrix},\quad i=0,1,2,\ldots, \tag{27}\]  
where \(\rho>0\) is the step size, and \(M_{11}[i],M_{22}[i]\in\mathbb{R}^{n\times n}\) are defined as  
\[\begin{bmatrix}M_{11}[i]&M_{12}[i]\\ M_{21}[i]&M_{22}[i]\end{bmatrix}\triangleq\tilde{Z}[i]+W[i]. \tag{28}\]  
We show in Appendix B how to obtain this iteration algorithm for solving (21).
###### Abstract  
Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.  
vibration signal; multiple cross-correlation; signal fusion; fault diagnosis Article  
Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation  
11
## 2 Materials and Methods
### Field Experiments for Validation  
To evaluate the performance of the atmospheric correction method for tower-based SIF observations, two field experiments were carried out on an EC site (Daman site) at Zhangye, China (100.37\({}^{\circ}\) E, 38.86\({}^{\circ}\) N). The altitude of the EC site was 1560 km. A continuous spectral observation system, AutoSIF-1 (Bergsun Inc., Beijing, China), equipped with an Ocean Optic QE Pro spectrometer was mounted on the EC tower at a height of 25 m above the ground. The full-width at half maximum (FWHM) of the spectrometer was 0.31 nm. Two fibers with a switcher were connected to the spectrometer for the measurement of downwelling irradiance and upwelling radiance. A cosine corrector was used to obtain the downwelling irradiance using a hemispherical field of view. A bare  
Figure 2: The relationship between AOD\({}_{550}\) and the ratio of the irradiance at 790 nm to that at 660 nm (E\({}_{790}\)/E\({}_{660}\)) based on the MODTRAN simulations for different solar zenith angles.  
fiber was used to observe the upwelling radiance with a conical field of view of 25\({}^{\circ}\) and a view zenith angle of 25\({}^{\circ}\) (Figure 3). The automatic spectral observation system collected the downwelling irradiance and upwelling radiance every 3 min, and the time lag between the collection of the downwelling irradiance and upwelling radiance was less than 10 s.  
The first field experiment was carried out on 27 October 2017, when the summer maize around the EC site was mature and all leaves had turned yellow. Therefore, the SIF emission was expected to be zero. The automatic spectral observation system mounted on the EC tower was used for the collection of the downwelling irradiance and upwelling radiance.  
The second field experiment was carried out on 7 September 2018. The summer maize was at the filling stage and the leaves were still green. Synchronous measurements were taken over the ground with an Ocean Optics QE Pro spectrometer with the same configuration as the continuous spectral observation system. The spectrometers used for ground and tower-based measurements were cross-calibrated. The downwelling irradiance was measured using a 40 cm \(\times\) 40 cm BaSO\({}_{4}\) reference panel (produced and calibrated by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences). The measurements on the ground were made every 10 min from 10:00 to 17:00; the weather was clear and stable during this time.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### Comparison of Energy Saving Techniques  
Many research works have tackled the matter of energy minimization [4]. Some of them have focused on the DPM technique [36; 47; 48; 49; 50; 51; 52; 53; 54]. The implementation of the DVFS in WSNs is widely applied [55; 56; 33; 57]. Only few papers combined DPM with DVFS in WSNs such as in [35; 37]. Additionally, the scheduling has been considered in [49; 58]. Table 2 describes the main techniques used to optimize the energy consumption in the WSN.  
The outcome of this comparison is both DPM and DVFS have a great impact on power saving while respecting the scheduling constraints mainly for real-time multiprocessor systems [62]. In this paper, the focus is on using strategies that vary during the execution and are adapted to any type of application whose level of consumption is likely to increase. To this end, we have chosen an on-line energy management techniques that best optimize energy consumption and maximize QoS.  
To meet the performance constraints and workload variations, DVFS tends to be combined with scheduling [33; 57]. Therefore, slowing down the speed of execution is more optimized in energy terms if the deadlines of the tasks are respected. If there is a slack in the execution time, executing as slow as possible, while just meeting the timing constraints is more dynamic-power-efficient than executing as fast as possible and then idling for the remaining time. However, the delay of a circuit also depends on the supply voltage.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & [51; 52; 53; 54] & [27; 35; 55] & [26; 49] & [59] & [28; 29; 30] & [35; 37; 38] & [58] & [23; 24; 25] & [60] & [61] & [33; 57] & **HEEPS** \\ \hline DPM & x & & x & x & & & & & & x \\ \hline DVFS & x & & & & x & & & & x & x \\ \hline Undervolting & & & & x & & & & & & & \\ \hline Scheduling & & x & & & x & & x & x & x & x \\ \hline MDP & & & x & & & x & & & & & \\ \hline Clock Gating & & & & & & & x & x & & \\ \hline Power Gating & & & & & & x & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Energy Saving Techniques.  
Thereby, by reducing the supply voltage, although we can achieve a cubic power reduction, the execution time increases. The main challenge in achieving the power reduction through voltage and frequency scaling is to overcome all timing constraints.  
In Figure 2, a power management taxonomy for CPU-level used in HEEPS like DPM, DVFS and scheduling is established. After presenting a palette of the main low-power techniques of embedded systems, we will present the power consumption reduction model adopted for the sensor nodes.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Model Performance Comparison  
After establishing the experimental relationships between soil stabilization parameters and UCS through laboratory testing, predictive models were developed to estimate strength development without the need for extensive physical testing. This section presents the performance analysis of various machine learning algorithms and the detailed evaluation of the final selected model.  
Following the experimental characterization of cement-treated soil behavior, this section presents the development and evaluation of machine learning models for predicting unconfined compressive strength. These predictive models aim to provide reliable UCS estimates based on cement content, curing period, and compaction rate parameters, reducing the need for extensive laboratory testing in future applications. The analysis begins with an initial screening of various algorithms, followed by rigorous validation of the most promising candidates, and concludes with a detailed assessment of the optimal model's performance.