esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper mainly studies the heat transfer performance of backplane micro-channel heat pipes by establishing a steady-state numerical model. Compared with the experimental data, the heat transfer characteristics under different structure parameters and operating parameters were studied, and the change of heat transfer coefficient inside the system, the air outlet temperature of the back plate and the influence of different environmental factors on the heat transfer performance of the system were analyzed. The results show that the overall error between simulation results and experimental data is less than 10%. In the range of the optimal filling rate (FR = 64.40%-73.60%), the outlet temperature at the lowest point and the highest point of the evaporation section is 22.46 \({}^{\circ}\)C and 19.60 \({}^{\circ}\)C, the temperature difference does not exceed 3 \({}^{\circ}\)C, and the distribution gradient in vertical height is small and the air outlet temperature is uniform. The heat transfer coefficient between the evaporator and the condenser is larger than the heat transfer coefficient under the conditions of low and high liquid charge rate. It increases gradually along the flow direction, and decreases gradually with the flow rate of the condenser. When the width of the flat tube of the evaporator increases from 20 mm to 28 mm, the internal pressure drop of the evaporator decreases by 45.83% and the heat exchange increases by 18.34%. When the number of evaporator slices increases from 16 to 24, the heat transfer increases first and then decreases, with an overall decrease of 2.86% and an increase of 87.67% in the internal pressure drop of the evaporator. The inclination angle of the corrugation changes from 30\({}^{\circ}\) to 60\({}^{\circ}\), and the heat transfer capacity and pressure drop increase. After the inclination angle is greater than 60\({}^{\circ}\), the heat transfer capacity and resistance decrease. The results are of great significance to system optimization design and engineering practical application.  
micro-channel; backplane heat pipe; steady-state model; optimal filling rate; heat transfer characteristics +
Footnote †: journal: Applied Sciences
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 3 Robust Visual Localization
### Bayesian Regression
Notice that the third image at pose \(C\) presents newer feature points (in blue), in addition to previous feature points already seen in \(A\) and \(B\) (green). That is the reason why the probability data for \(C\) differ substantially. This is a key aspect in order to apply specific weighting factors within the matching, with the purpose of encoding this visual change on the scene and also avoiding possible false positive. Figure 6: Navigation example along three different poses within an office-like scenario. The robot sequentially traverses poses \(A\), \(B\) and \(C\), respectively. Omnidirectional images are acquired at each pose, and feature matching and Bayesian regression are computed as per Equations (10)–(13). Motion transformations at both the pose reference and image reference are depicted in Figure 7a,b. Overall, this strategy stores the information of the points' distribution, which is then transferred as a KL value in its accumulative format. Then, this information is used for obtaining new weighting coefficients for the matching:  
\[\gamma=\sum_{t}KL(F_{t-1}\parallel F_{t}) \tag{13}\]  
thus evaluating this metric in an accumulative manner allows one to obtain a global data distribution for the feature points along the navigation, in terms of probability. Figure 7: Description of the example presented in Figure 6, where the following subfigures correspond all to traversed poses \(A\), \(B\) and \(C\). (**a**) motion transformation between poses, on the robot reference system; (**b**) motion transformation on the image reference system. The projection of \(P(x,y,z)\) is indicated as \(p_{A}(u,v)\), \(p_{B}(u,v)\) and \(p_{C}(u,v)\) respectively on each image. Repeated matches and new matches are indicated in green and blue, respectively.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 3 Description of the Proposed Method
### Chromosome Encoding  
As the S-WTA problem has many similar characteristics to the WTA problem, the traditional encoding method of WTA [13] can be used as a reference. Based on the WTA model, [PERSON] [27] proposed a chromosome encoding method for S-WTA. However, because the S-WTA model used in their paper is more difficult than the one we proposed, chromosome encoding needs to be further customized for better adaptation of the proposed S-WTA model. The chromosome encoding is defined as \(C=\left\{t_{m1},\cdots,t_{mi},\cdots t_{mM},t_{w1},\cdots,t_{wj},\cdots t_{wW}\right\}\), where \(t_{mi}\) is the target that is detected or tracked by sensor \(i\) and \(t_{wj}\) is the target which is attacked by weapon \(j\). Figure 5 is the illustration of the chromosome encoding.  
As depicted in Figure 5, the chromosome comprises two parts: The first part represents the assignment of sensors. The second part is the assignment of weapons. Based on the definition of chromosome encoding, the code can handle some basic constraints of the S-WTA. First, the dimension of the chromosome is \(M+W\), which is the sum of the number of sensors and weapons. Therefore, the consumption of sensors and weapons can be ensured within the predefined amount.  
Figure 5: The illustration of the chromosome encoding.
## 4 Conclusions  
In conclusion, we studied the diode-pumped Kerr-lens mode-locked Yb:GYSO oscillator for the first time. Based on the good spectral properties of the alloyed Yb:GYSO crystal, stable KLM operation was achieved with 141 fs pulse duration and 237 mW average power at 5 W diode pump power. The laser emission was centered at 1094 nm with a 10.1 nm bandwidth. Shorter pulses of 55 fs duration were obtained by adjusting the cavity to operate at 1054 nm. The mode-locking bandwidth was as broad as 23.5 nm with an output power of 27 mW. Higher output powers and shorter pulse durations down to sub-50 fs with the Yb:GYSO crystal may be obtained by reducing the cavity loss and optimizing chirp compensation.  
Figure 6: **(a)** Spectrum of the femtosecond pulses centered at 1054 nm; **(b)** Autocorrelation trace showing the pulse duration was 55 fs assuming a sech\({}^{2}\) pulse shape. Black curve, measured data; red dashed line, sech\({}^{2}\) fitted curve.
# Prototypical Graph Contrastive Learning for Recommendation
## 3 Methodology
### Algorithm Complexity  
In this section, ProtoRec is decomposed into several modules to discuss complexity. Let \(E\) denote the edge numbers. \(M\), \(N\), and \(d\) represent user and item numbers and embedding size, respectively. In each epoch, K-means is executed to extract \(k\) prototypes with \(I\) iterations costs \(\mathcal{O}(I(M+N)kd)\). The complexities for constructing the interaction graph and executing \(L\) layers of LightGCN in ProtoRec are \(\mathcal{O}(2|E|)\) and \(\mathcal{O}(2|E|Ld)\), respectively. The time complexities for constructing the relationship graph between nodes and prototypes, as well as for reorganizing prototypes based on different weights, are \(\mathcal{O}((M+N)k)\) and \(\mathcal{O}((M+N)kd)\), respectively. Deploying the linear transformation function in the PFN module requires \(\mathcal{O}(2d^{2})\). Let \(\mathcal{B}\) and \(H\) denote the batch size and the node numbers per batch, respectively. Therefore, the time complexities for BPR loss and CL loss are \(\mathcal{O}(2 Bd)\) and \(\mathcal{O}(Bd+BHd)\), respectively. To better understand the training process of ProtoRec and the implementation of each module, Algorithm 1 presents the pseudocode of ProtoRec. It clearly demonstrates the execution steps of the K-means algorithm and graph-based recommendation, making it easier to comprehend the complexity of ProtoRec.  
```
1:Input: adjacency matrix \(\overline{\boldsymbol{\Lambda}}\), training dataset \(\mathcal{X}\), number of clusters \(k\)
2:Output: user and item embeddings \(\{e^{(u)},e^{(i)}\}\) for recommendation
3: Initialize: Random initialize user and item embeddings matrix \(\mathbf{E}^{(0)}\)
4:while Not Convergence do
5: Extract \(k\) cluster prototypes \(\mathbf{P}\) using the K-means algorithm in Equation (6)
6:for batch in Dataloader(\(\mathcal{X}\)) do
7: Obtain user \(u\), positive item \(i^{+}\), and negative item \(i^{-}\) from batch
8: Compute embedding \(\mathbf{E}\) using GNNs by Equations (3) and (4)
9: Refine prototypes \(\mathbf{P_{r}}\) and augment features \(\mathbf{G}\) using Equations (7) and (8)
10: Compute the total loss \(\mathcal{L}\) by Equation (10) and back propagation
11:endfor
12:endwhile
```  
**Algorithm 1** Prototypical Graph Contrastive Learning for Recommendation (ProtoRec)
## 5 Discussion
### Effects for Different Land Cover Types
Drought and heat conditions influence yields negatively as physical and physiological damages and biochemical changes are induced, leading to reductions in plant growth and grain development, e.g., in cereal [56]. In addition, oil, starch, and protein content is negatively affected by high temperatures and water shortage in oilseed crops and cereals [56]. A typical reaction of plants to drought conditions is a shift in water use efficiency (WUE), which is defined as the ratio between dry matter and evapotranspiration [22; 56]. In temperate regions, where vegetation shows poor adaption towards drought conditions [23], plants show an increase in WUE under dry conditions, which results in reduced biomass accumulation [56]. This is in accordance with a study analyzing the WUE with satellite-based GPP and evapotranspiration data, revealing a decreasing trend in WUE in the Northern Hemisphere [20]. An additional important drought characteristic influencing crop yields is the timing of water shortage. In a study from the Iberian Peninsula, it was found that cereal yield is especially reduced with limited soil moisture in early spring and high temperatures during harvesting time [57].
# Unique Features of Nonlocally Nonlinear Systems with Oscillatory Responses
## 6 Optical Beams in NLC with Negative Dielectric Anisotropy
### Optical Nonlinearities of NLC with Negative Dielectric Anisotropy  
By substitution of Equation (33) and \(E_{rf}\approx V_{rf}/L\), the nonlinear characteristic length \(W_{mL}\) given by Equation (41) for the NLC with negative dielectric anisotropy is reduced to the following:  
\[W_{mL}=\frac{L}{\pi^{4/3}}\sqrt{\frac{\gamma^{5/3}}{\sin\gamma-\gamma\cos \gamma}}, \tag{43}\]  
where \(\gamma=\pi^{4}K^{3/2}/[\varepsilon_{0}(-\epsilon_{a}^{rf})]^{3/2}V_{rf}^{3}\). \(W_{mL}\) is, obviously, the function of the cell thickness \(L\), the bias voltage \(V_{rf}\) and the material parameters of the NLC. By setting \(dW_{mL}(V_{rf})/dV_{rf}=0\), we obtain \(\gamma_{min}=2.35\) at which \(W_{mL}/L\) takes the minimum \(0.29\), which does not depend on the material parameters of the NLC and is confirmed in Figure 16. Since model Equations (42) and (38) are obtained with the assumption that the beam width in the lab coordinate system, \(W_{0}\), is far smaller than NLC thickness \(L\), we can assume that \(W_{0}=0.1L\) so that the weakest degree of nonlocality \(\sigma_{min}\left[=\left(W_{mL}\right)_{min}/W_{0}\right]\) is \(2.9\)and the degree of nonlocality \(\sigma_{0}\)\((=W_{mL}/W_{0})\) is always higher than \(\sigma_{c}\) (for an example, \(\sigma_{c}|_{z=1}=0.63\) for the \((1+2)\)-dimensional case). Therefore, the NLC with negative dielectric anisotropy can only behave with self-focusing nonlinearity, and the transition from the self-focusing to the self-defocusing when \(\sigma_{0}\) decreases across the critical point \(\sigma_{c}\), which was predicted by the system of Equations (1) and (2) with \(s=-1\), cannot be observed in the system of Equations (38) and (42) for the negative dielectric anisotropy, because the condition \(\sigma_{0}<\sigma_{c}\) cannot be realized in such a NLC. We also numerically investigated the propagation of the optical beam in the NLC with negative dielectric anisotropy described by Equations (34) and (35) without simplification and found that the beam can always sample the self-focusing nonlinearity indeed [53]. Consequently, the nonlinear refractive index due to reorientation in the pure NLC is always self-focusing, despite the sign of the anisotropy in the low-frequency domain. It is a well-known fact [54; 55; 56] that only dark solitons can exist in nonlocally nonlinear media with the positively defining attenuating response and the negative Kerr coefficient (\(n_{2}<0\)) described by Equations (1) and (3) for \(s=-1\), no matter how much the degree of nonlocality is. However, as discussed above, the nonlocally nonlinear system of Equations (1) and (2) with \(s=-1\), that is, Equations (42) and (38) for the NLC with negative dielectric anisotropy, can exhibit self-focusing nonlinearity in high ranges of the degree of nonlocality (\(\sigma_{0}>\sigma_{c}\)) and support the bright solitons. It is the unique feature of a nonlocally nonlinear system with oscillatory responses.
# New Advances, Challenges, and Illustrations in Applied Geochemistry
## 3 Environmental Geochemistry
### Methods for Heavy Metal Risk Assessments  
Methods for assessing the pollution risk of heavy metals in soils have been used and discussed for several decades and can be classified into two types: those with standards and those without standards.  
Assessment methods without standards for heavy metal soil pollution were reviewed and summarized by [PERSON] et al. [16] and are currently widely used, albeit with some new revised versions. For example, [PERSON] et al. (contribution 4) adopted the indices of the Pollution Load Index (PLI) [17], the Geoscumulation index (Igoo) [18], and the Nemero index [19] to assess the pollution risk of heavy metals in the soils of an industrial park in Kunming, China.  
Methods with standards are also commonly used to assess the pollution risk of soils and other geological materials, and are based on promulgated standards, such as GB15618-2018 [20] in China, among others. [PERSON] et al. (contribution 5) proposed single and integrated indices for the assessment of the pollution risk of heavy metals in soils and crops based on the Chinese standards GB15618-2018 and GB2762-2022 [21], respectively. They found two inconsistent assessments, indicating a new challenge in maintaining consistency between the assessment standards for soils and crops. This inconsistent assessment was also found by [PERSON] et al. (contribution 3). In contrast, concerning atmospheric deposition, [PERSON] et al. (contribution 6) adopted the criteria of non-carcinogenic and carcinogenic risk assessment methods used by the United States Environmental Protection Agency (USEPA) in order to assess the risk of potential toxic elements (PTE) in a ion-adsorption rare earth mining area in Ganzhou City, Southeast China.
## 2 Materials and Methods
### Quantitation of Phenolic Compounds by HPLC-Qq-Ms/Ms  
Phenolic compounds were identified and quantified using HPLC-QqQ-MS/MS based on our previously established methods with slight modifications [10; 11]. The HPLC was Agilent 1200 series, combined with a 6410B triple tandem quadrupole mass spectrometer. The column was a Poroshell 120 EC-C18 (150 \(\times\) 2.1 mm, 2.7 \(\upmu\)m). The samples were filtered through a 0.22 \(\upmu\)m filter before analysis. The mobile phases used for elution were A (0.1% formic acid aqueous solution) and B (50/50 methanol acetonitrile solution with 0.1% formic acid). The elution procedure of phenolic compounds was 0-10 min, 10% B; 10-15 min, 100% B. The flow rate of the mobile phase was 0.4 mL/min, the column temperature was maintained at 55 \({}^{\circ}\)C, and the injection volume was 5 \(\upmu\)L. The elution program of anthocyanin derivatives was 1 min, 100% A; 3 min, 25% B; 15 min, 30% B; and 20 min, 100% B. The posting time was for 5 min. The flow rate of the mobile phase was 0.3 mL/min, the column temperature was controlled at 55 \({}^{\circ}\)C, and the injection volume was 10 \(\upmu\)L. An electrospray ionization source was used with a spray voltage of 4 kV in negative and positive modes for the non-anthocyanins and anthocyanins, respectively. The temperatures of the ion source and drying gas (N2) were 150 \({}^{\circ}\)C and 350 \({}^{\circ}\)C, respectively. The gas flow rate was 12 L/h and the nebulizer pressure was 35 psi. Multiple reaction monitoring (MRM) was selected as a scanning mode for both identification and quantification.  
The qualitative of phenolic compounds was achieved based on their mass information and retention time. Monomeric anthocyanins and their derivatives were quantified based on the calibration curve of malvidin-3-\(O\)-glucose and non-anthocyanins were quantified according to the calibration curves of their reference standards. Qualitative and quantitative information for the phenolic compounds were listed in Table S4.
###### Abstract  
This paper presents a scratch detection system based on a cubic Bezier curve fitting using a linear scan camera. The objective was to detect the scratch defects of an aluminum flat tube stably in real-time under complex uncertain background noise. To that end, according to the features of the input image of the linear scan camera and the scratch defects, the proposed method first segmented the input image to ten equal sections in a longitudinal direction, and for every section, OTSU thresholding and morphological filtering were used to reduce the background noise. After the image preprocessing, every section image was projected along a vertical direction to form a vertical histogram. After that, for each point of every vertical histogram, a novel curve fitting method based on the Monte Carlo method was employed to calculate the best fitted Bezier curve. All the curvatures of the middle point of the best fitted Bezier curves then formed a curvature curve, and the scratches were located by finding the peaks of the curvature curve. Next, the result of the ten sections were fused to find the final positions of the scratches. The experimental results based on the linear scan camera that captured the image of flat tubes on a moving speed of 2m/s showed that the proposed method can detect the scratch defects under complex background noise with a high success rate in real-time.  
scratch defects detection; cubic bezier curve; monte carlo sampling; image processing 2022
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 2 Principles of Light Polarization and PS-OCT
### Recent Advances in PS-OCT Technology
In image processing, speckle noise can be reduced by image averaging and dedicated algorithms [119; 120]. PS-OCT also enables the segmentation of structures based on common polarization properties and the determination of interfaces between different tissue segments based on changing polarization properties. Segmentation and image feature assessment was developed based on depolarization [20; 103; 121; 122; 123; 124; 125] and birefringence [98; 116; 126; 127; 128; 129]. Practical examples of PS-OCT applications will be shown in the following sections.
## 3 Results and Discussion
### Apoptosis Induction Effect  
#### 3.6.1 Apoptosis Induction According to Flow Cytometric Analysis  
Apoptosis is the programmed cell death that is a hallmark of the anticancer action of the tested compound [32; 37]. BGR at two different concentrations according to the cell viability assay (0.5 \(\times\) IC\({}_{50}\) or 100 \(\upmu\)g/mL and 1 \(\times\) IC\({}_{50}\) or 200 \(\upmu\)g/mL) were used in this assay. At 100 \(\upmu\)g/mL, BGR at 3 different ages (5-7, 10-15 and 20-25 days) induced apoptosis (12.8 \(\pm\) 1.4%, 11.3 \(\pm\) 2.3% and 17.7 \(\pm\) 6.1%, respectively) compared to the untreated cells (15.9 \(\pm\) 1.2%) (Figure 3a). At higher concentration (200 \(\upmu\)g/mL),  
Figure 2: (a) Flow cytometry analysis of intracellular ROS (H\({}_{2}\)O\({}_{2}\)) by DCFH-DA assay. H\({}_{2}\)O\({}_{2}\) (0.1 \(\upmu\)g/mL) was used as a positive control. Data are expressed as mean \(\pm\) SD of four replicates. (b) Total endogenous glutathione (GSH) level in the Jurkat cells treated with BGR. Data are expressed as mean \(\pm\) SD of triplicates. (c) Value is significantly different (\(p\) \(<\) 0.05). (\(\#\)\(p\)\(<\) 0.05 when compared the same extract at different concentrations.  
BGR at 10-15 and 20-15 days significantly caused apoptotic cell death for \(30.5\pm 3.9\) and \(31.2\pm 4.9\%\), respectively (Figure 3a). Increasing concentration of BGR increased apoptotic cell death. The standard anticancer drug, docetaxel at 100 \(\upmu\)M was used as a positive control and was found to strongly induce apoptosis (\(56.5\pm 3.1\%\)) in Jurkat cells. Our results suggested that BGR at different harvest ages induced Jurkat cells to die via apoptosis.
## 3 Results  
### General Observations  
The first step was to validate if the Hermitian symmetry and the juxtaposition methods are comparable in terms of performance. As only OFDM is able to perform both techniques, Figure 10 represents the result for OFDM with a simple back to back communication. As expected, both techniques behave the same way, and the juxtaposition method can indeed replace the Hermitian symmetry.  
Even though the use of a constant bias is a necessity to have a real and positive valued signal on the LED for both OFDM and UFMC, it is, however, an extra source of power consumed. Figure 11 shows a UFMC communication taking place with a direct current (DC) bias of 0.5 and without a DC bias with BPSK modulation per subcarrier. A bias of 0.5 normalized unit is chosen, as the UFMC waveform is generated in a normalized way in the simulator. Thus, adding 0.5 on all the values necessarily makes the signal positive.  
It can be seen that there is a shift between the two curves, introducing a penalty of approximately 4 dB in SNR for the use of the bias. The advantage with VLC is that this  
Figure 11: Comparison between UFMC/BPSK modulations with or without DC Bias.  
Figure 10: The use of juxtaposition or Hermitian Symmetry for OFDM.  
extra power is not wasted but essential for the room to be lit. On the other hand, when infrared lighting devices are used, the question sometimes arises as to the relevance of Li-Fi in infrared, as this extra DC bias is not used to light the room and could be considered wasted power. However, there are other methods of making a signal positive in OFDM, from clip-OFDM, which uses only the odd carrier, to other more sophisticated forms [31]. To the best of our knowledge, these have not yet been explored in the scientific literature for UFMC. In terms of computational complexity, as UFMC requires more computational steps compared to OFDM due to the filtering stages, a physical implementation of a UFMC communication on an electronic board would consume more power than OFDM. Indeed, the power consumed by programming boards is proportional to the number of computations it is required to do. Furthermore, it is easy to understand that the use of optical OFDM or UFMC is less spectrally efficient due to the Hermitian symmetry condition or the juxtaposition of real and imaginary data. Indeed, both techniques require at least twice the resources to send the desired information compared to the RF version. Nevertheless, VLC still is interesting where RF cannot reach.