esa-sceva/satcom-chunk-collection huggingface dataset:
# Blur Kernel Estimation by Structure Sparse Prior
## 3 Method
### Optimization Procedure
#### 3.3.2 \(x\) Update  
The \(x\) sub-problem is given by:  
\[\hat{x}=\underset{x}{argmin}\;||y-k*x||_{2}^{2}+\eta||x-D\alpha||_{2}^{2}+\lambda _{2}||d\odot\alpha||_{2}^{2}. \tag{13}\]  
The third term, \(||d\odot\alpha||_{2}^{2}\), is a regularization on \(x\), where \(d=[d_{1},d_{2},\ldots,d_{n}]\), and \(d_{i}\) is connected with \(x_{i}\) and \(D\). The element-wise multiplication of \(d\) and \(\alpha\) makes it too difficult to solve \(x\) from the equation. Therefore, to make the problem easier to solve, an approximation can be applied to this sub-problem. We adopt the same regularization on \(x\) as [PERSON] _et al._[15], and the new object function becomes  
\[\underset{x}{min}\;\|y-k*x\|_{2}^{2}+\eta\;\|x-D\alpha\|_{2}^{2}+\lambda_{2} \sum_{l=1}^{2}|e_{l}*x|^{s}, \tag{14}\]  
where \(|e_{l}*x|^{s}\) takes the place of the third regularization term \(||d\odot\alpha||_{2}^{2}\) to get the problem solved more quickly. \(e_{l}\) means a certain kind of derivative filter and typically \(0.5\leq s\leq 0.8\). A new auxiliary variables \(u_{l}(l\leq 1,2)\) and the corresponding penalty term \(\sum_{l=1}^{2}||u_{l}-e_{l}*x||_{2}^{2}\) are introduced in the function:  
\[\underset{x,u_{l}}{min}\;||y-k*x||_{2}^{2}+\eta||x-D\alpha||_{2}^{2}+\lambda_ {2}\sum_{l=1}^{2}|u_{l}|^{s}+\beta\sum_{l=1}^{2}|u_{l}-e_{l}*x|_{2}^{2}. \tag{15}\]  
Similarly, the optimization problem can be divided into two sub-problems:  
\[\hat{x}=\underset{x}{argmin}\;||y-k*x||_{2}^{2}+\eta||x-D\alpha||_{2}^{2}+ \beta\sum_{l=1}^{2}||u_{l}-e_{l}*x||_{2}^{2}, \tag{16}\]  
\[\hat{u_{l}}=\underset{u_{l}}{argmin}\;|u_{l}|^{s}+\beta||u_{l}-e_{l}*x||_{2}^ {2}. \tag{17}\]  
The \(x\) sub-problem can be solved by FFT:  
\[\hat{x}=F^{-1}(\frac{\overline{F(k)}\odot F(y)+\eta F(D\alpha)+\beta\sum_{l=1 }^{2}\overline{F(e_{l})}\odot F(u_{l})}{\overline{F(k)}\odot F(k)+\eta I+\beta \sum_{l=1}^{2}\overline{F(e_{l})}\odot F(e_{l})}), \tag{18}\]  
where \(F(\cdot)\) represents Fast Fourier Transform (FFT), \(F^{-1}(\cdot)\) means inverse FFT, \(\overline{F(\cdot)}\) is the complex conjugate of \(F(\cdot)\), and \(\odot\) denotes element-wise multiplication.  
As to \(\hat{u_{l}}\), we set \(s=0.8,e_{1}=[1,-1]\), and \(e_{2}=[1,-1]^{T}\). Equation (17) can be solved efficiently through the algorithm proposed by [PERSON] and [PERSON] [26].
## 4 Discussion
### Ablation Study
The left y-axis corresponds to the MSE, while the right y-axis corresponds to the FLOPs and parameter count. The blue bars represent the MSE relative to the original model, with the red dashed line indicating the baseline (1.00\(\times\)). The orange segments highlight the increase in error above the baseline. The green and purple lines represent the FLOPs and parameter count for each configuration, respectively. The model is based on the one used in Section 3.2 “Results of Simulating Three Types of Fibers Using a Single Model”, with only the associated parameters adjusted and all other parameters kept unchanged. FLOPs (G): Computational complexity in Giga Floating Point Operations per Second. Params (M): Number of model parameters in millions. this mechanism was removed, the MSE increased by 52%, confirming the importance of the learning rate scheduler in the optimization process. Particularly during the later stages of training, the scheduler helps the model continue optimizing in the presence of small loss gradients by gradually reducing the learning rate, thus avoiding local minima. Similar to dropout, removing the learning rate scheduler did not impact the FLOPs or model parameter count, indicating that its primary function is to guide the optimization process rather than influence the model's computational complexity. The synergy between these model components is particularly important. The combined efforts of the residual blocks and self-attention mechanism in feature extraction and relationship modeling, along with the contributions of the dropout layer and learning rate scheduler in ensuring the model's generalization ability and optimization stability, create a robust architecture. This combination allows the model to maintain a high accuracy while effectively generalizing to the complex task of fiber pulse propagation. Through these ablation experiments, we not only confirmed the necessity of each component in the model design but also highlighted how their interactions critically influence the overall performance. These findings provide valuable insights for future model optimization and design, further validating the effectiveness of the current model architecture in addressing complex nonlinear problems.
# Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability
###### Abstract  
This paper presents a study on optimal design to determine the installation position and link lengths of a robot within a designated workspace for welding, aiming to minimize singularities during the robot's motion. Bi-objective functions are formulated to minimize singularities while maximizing the volumes of linear velocity manipulability ellipsoid and angular velocity manipulability ellipsoid, respectively, ensuring isotropy. We have constructed a simulation environment incorporating PID control to account for robot tracking errors. This environment was utilized as a simulator to derive a Bi-objective function set within a genetic algorithm. Through this, we optimized four robot link length variables and two installation position variables, selecting the optimal design variables on the Pareto Front. In the standard work object, the volume average of the linear velocity manipulability ellipsoid was confirmed to have improved by 72% compared to the initial level, and the isotropy of the angular velocity manipulability ellipsoid was confirmed to have improved by 23% compared to the initial level. Furthermore, correlation analysis between design parameters identified those with a high correlation with the objective functions, and the analysis results are discussed.  
robot manipulability; singularity avoidance; optimal design; Pareto optimal; manipulability ellipsoid; genetic algorithm +
Footnote †: journal: Applied Sciences
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 1 Introduction  
Meat is considered the top quality protein source, not only due to its nutritional characteristics but also to its appreciated taste [1]. In recent years, since the addition of non-meat proteins to meat products may cause health problems, this practice has been forbidden in many countries [2]. Thus, the need for detecting added soy proteins in meat products is obvious. However, in the meat industry, ground meat is a further-processed product used for manufacturing a wide range of marketable products such as sausages, hams, bologna, salami, etc. It is of great importance to ensure meat products are healthy from the root material of ground meat.  
As a kind of vegetable protein, soy protein products, such as flour, grits, concentrates, isolates, and textures, are widely used in processed meat, poultry and seafood products [3]. Tracing its root, on the one hand, using soy proteins as meat extenders is due to the lower price to reduce the total cost. On the other hand, soy protein products could present different functional properties in meat products such as texture-forming, fat and water emulsification and gelation [4]. However, in fact, individuals, especially infants and young children that are allergic to the added non-meat proteins, can be affected greatly by the ingestion of minute amounts of the allergen [2]. Thereby, the addition to meat products, in some cases, is not allowed. The regulatory department in the United States has beenroutinely requested to determine soy contents in further-processed meat products, such as ground meat, meat patties, frankfurters and chili with meat and beans [5].  
Current analytical methods, such as microscopic methods, electrophoretic methods and chromatographic methods for determining soy proteins in meat products are time-consuming or invasive. Near-infrared spectroscopy (NIRS) is a sensitive, fast and non-destructive analytical technique with simplicity in sample preparation and has received attention for use in the quantitative and qualitative analysis of quality and adulteration in meat and meat products, such as beef [6], pork [7], poultry [8] and seafood [9]. As for the near-infrared spectroscopy technique, both the dispersive system and Fourier transform (FT) system showed potential for rapid analysis of meat quality [10; 11]. The objective of this study was to investigate if the NIR spectroscopic method could be used in the qualitative and quantitative analysis of soy contents in ground beef. A comparison between dispersive NIR and FT-NIR spectral models was also performed to determine which kind of NIR spectra performs better in the prediction.
## 4 Materials and Methods
### Design of the Introduced CAD
#### 4.2.2 DL Feature Extraction  
Building a CNN from scratch requires a huge amount of data and updating this network's enormous number of parameters, which increases the complexity of the training procedure. Instead, transfer learning (TL) can be used to solve these challenges. TL is a prevalent ML method that permits the reusability of an effectual CNN model constructed to deal with one problem using a huge dataset like ImageNet, as a preliminary step for handling some other classification issue in a relevant area. TL could significantly reduce the requirement for enormous computation power and model development time [55]. For this reason, TL was employed to reuse three CNNs that were formally trained on ImageNet to tackle the problem of a cervical cancer diagnosis. Initially, TL was utilized to alter the total sum of fully connected (FC) layers of each of the MobileNet, ShuffleNet, and ResNet-18 compact CNNs to be equal to 4 corresponding to the categories of the Mendeley LBC dataset. Afterward, these CNNs were relearned with the slides of the Mendeley LBC dataset. When the relearning process was done, TL was further applied to extract DL features from the final FC layers of each DL structure. Each CNN was made up of several deep layers; preliminary layers discovered basic components from a photo, while fairly late deep layers learned high-level detailed characteristics out of the photo. As a result, the very last FC layers before the softmax layer was chosen to retrieve feature representations. The length of features obtained from every CNN was 4.
## 1 Introduction
[PERSON] et al. [19] established an ANN model to predict the drying characteristics of shelled corn drying in a fluidized bed dryer, and the results showed that a network with the Tansig transfer function and trainrp back propagation algorithm made the best prediction performance (\(R\) = 0.9991). [PERSON] et al. [20] studied the drying characteristics of rough rice drying in a hot air dryer assisted by infrared heating, ANN methodology was also adopted to predict the bending strength of brown rice kernel, percentage of cracked kernels and moisture content, the simulation results showed that the established model has an excellent prediction performance. Based on the literature review, it can be inferred that the ANN methodology can be adopted to predict the drying behavior of the corn drying in an industrial scale drying system. Though the ANN modeling approach has been widely used predicting the drying process, few reports have taken the ambient conditions into consideration when establishing the ANN model. However, the ambient conditions are the main factors affecting prediction accuracy of the established ANN model; therefore, the ambient conditions should be fully considered especially for the outdoor installed drying system. For an industrial scale grain drying plant, the drying system is usually installed outdoors [17; 21; 22]; however, the installed method is difficult to control the ambient factors. As analysis above, though the ambient conditions are difficult to be controlled, the influences of ambient conditions on the drying performance should be taken into consideration when analyzing the drying process. The present work aims to verify that the ambient conditions should be taken into consideration when analyzing and modeling the drying process for the outdoor installed industrial scale corn drying system and, accordingly, establish an ANN model for predicting the moisture content of corn, exergy efficiency of the heat exchanger, and the specific recovered exergy--hoping to lay some theoretical basis for guiding the practical production.
# Biocompatible Dispersants for Carbon Nanomaterials
## 1 Introduction  
Carbon nanomaterials (CNMs) are a unique class of materials that have received growing interest in the past three decades. They are the most widely studied class of nanomaterials due to their unique chemical, physical, mechanical, thermal, and electrical properties [1]. CNMs are typically classified depending on their structural dimensionality. Zero-dimensional (0D) CNMs such as fullerenes and carbon nano-onions (CNOs) have all their dimensions in the nanoscale (i.e., <100 nm) [2]. In contrast, one-dimensional (1D) CNMs such as carbon nanotubes (CNTs) and two-dimensional (2D) materials such as graphene have one and two dimensions exceeding the nanoscale, respectively.  
CNMs are unique in terms of their physical and electronic structures. Their impressive mechanical strength, extremely high surface area, and thermal properties have not escaped the interest of researchers [3]. With applications in engineering [4; 5], biomedicine [6; 7], and renewable energy [8; 9], this unique class of materials, and indeed nanotechnology in general, is set to have an enormous impact on our future. In fact, nanomedicine has been recognised as a \"key enabling technology\" by the EU [10]; by 2016, the FDA had approved 51 nanomedicines [11].  
It is widely known that hydrophobicity is a major problem for pristine CNMs, particularly when using them in biomedical applications, where aqueous dispersions are often needed. This is where biocompatible dispersants have a role to play; these molecules can be attached to a CNM's surface and impart hydrophilicity to the nanomaterial. This can occur covalently; however, this process is known to damage the nanomaterial's structure [12]. Therefore, non-covalent methods of CNM functionalisation are required to preserve their unique properties.  
A range of biocompatible dispersants exist that are capable of non-covalently binding to a CNM's surface. Many of these are biomolecules (such as bile acid salts) or naturally derived (for example, nanocrystalline cellulose), but there also exist many synthetic surfactants, such as sodium dodecyl sulphate (SDS). The different classes of dispersants will be detailed and compared in this review.  
Many of these dispersants are toxic in high concentrations [13]; therefore, efficient dispersants are needed to ensure biocompatibility. Many factors affect the efficiency andefficacy of a surfactant. For example, aromatic groups on a surfactant can partake in \(\pi\)-\(\pi\) stacking interactions with a CNM, increasing the binding strength [14]. These factors will be detailed in this review, and metrics for comparing different dispersants will also be discussed.
## 4 Simulating Array Acquisitions and Array Imaging  
Grain orientation maps obtained from weld formation modelling were used in ultrasonic modelling to discover their effect on the images for chosen defect scenarios. Since our interest was to discover the effect of weld model parameters on the images, simulation was seen as the best way to achieve that. If comparing to experimental data, the main questions would be whether the material parameters assumed in simulations reflect those in the real sample, if the geometry was measured accurately, etc. Conversely, modelling allows us to retain control of the configuration and focus solely on the effect of chosen weld model parameter variations.  
Ultrasonic images were computed using a classical total focusing method (TFM) approach  
\[I(x,y)=\left|\sum_{i=1}^{M}\sum_{j=1}^{M}\Big{[}u_{i,j}\Big{(}\tau_{i\to(x,y)}+ \tau_{j\to(x,y)}\Big{)}+j\dot{u}_{i,j}\Big{(}\tau_{i\to(x,y)}+\tau_{j\to(x,y)} \Big{)}\Big{]}\right| \tag{9}\]  
where \((x,y)\) is the position in the physical domain, \(M\) is the number of elements in the array, \(u_{i,j}\) is the response of element \(i\) to excitation at element \(j\), \(\tau_{i\to(x,y)}\) is the time-of-flight between element \(i\) and point \((x,y)\) (called a delay law) and \(\dot{u}_{i,j}\) is the Hilbert transform of \(u_{i,j}\)[25]. The analytic signal \(u_{i,j}+j\dot{u}_{i,j}\) is the output of \"Hilbert\" functions in major scientific computing environments (e.g., scipy, MATLAB).  
Two modelling methods contributed to this paper, time-domain finite element simulations were used to model virtual ultrasonic array acquisitions, mimicking the experiments (outputting time traces \(u_{i,j}\)), while a shortest ray path model was used to calculate delay laws \((\tau_{(x,y)\to i})\). Conventionally, TFM imaging assumes straight ray paths and constant isotropic wave velocity to calculate delay laws. The isotropic assumption often fails dramatically in complex-structured materials, and the present paper shows how weld formation simulations can improve the legibility of the images.
# Identification of _Bradyrhizobium elkanii_ Genes Involved in Incompatibility with _Vigna radiata_
## 2 Materials and Methods
### Nucleotide Sequence Analysis of Tn5-Flanking Regions  
Genomic DNA was isolated from _B. elkanii_ strains using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA). Transposon-flanking sequences were amplified using the Y-linker method [24]. The genomic DNA was digested with NlaIII or SphI, and ligated with the Y-linker. PCR was carried out using the GoTaq Green Master Mix (Promega), Y-linker primer and Tn5 primer (Table 1). The thermal cycling conditions were as described previously [8]. The PCR fragments were sequenced  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Strain, Plasmid or** & \multicolumn{3}{c}{**Characteristics or Sequence *} & **Reference or Source** \\ \multicolumn{3}{c}{**Oligonucleotide**} \\ \hline \multicolumn{3}{c}{**Plasmids**} \\ \hline \hline pUTKm & Transposon delivery vector; \(\mathrm{Ap}^{\mathrm{r}}\), \(\mathrm{Km}^{\mathrm{r}}\) & [21] \\ pRK2013 & Helper plasmid; Coloni E1 Epilon carrying RK2 transfer genes; \(\mathrm{Km}^{\mathrm{r}}\), \(\mathit{tra}\) & [22] \\ pSUPSCAKm & Derivative of pSUPDOL28C2 [23] with a kanamycin resistance gene in the & This study \\  & _Dal_ site, _oriT_ of RP4, \(\mathrm{T}^{\mathrm{r}}\), \(\mathrm{Km}^{\mathrm{r}}\) & This study \\ pSUPSCAKmint _innB_ & pSUPSCAKm carrying a 0.5-kb DNA fragment containing internal sequence & This study \\  & of _innB_, \(\mathrm{Km}^{\mathrm{r}}\) & This study \\ \hline \multicolumn{3}{c}{**Oligonucleotide**} \\ \hline \hline Linker 1 & 5’-TTTCTCTCGCGAATTCAAGCTICAAGCTATGCGGGGACACATG-3 & [24] \\ Linker 2 & 5’-TGTCCCCGCATACGTGTAAGAACTACTGCATCCACAT-3’ & [24] \\ Y-linker primer & 5’-CTGCGCGCGAATTCAAGCTCT-3’ & [24] \\ Tn5 primer & 5’-GCGCGCGC  
directly using the Tn5 or Y-linker primers. Homology searches were carried out using BLASTN at the National Center for Biotechnology Information and an in-house database for the _B. elkanii_ USDA61 genome [25]. For the identification of _tts_ box (TCGTCCAG[CGT]TTNTCG[TA][CA]AGCTN(8)[TC]A) motif, the 2 kb upstream sequences of each gene were searched with the program \"fuzznuc\" of the EMBOSS package [26]. The consensus sequences from _Rhizobium_ sp. strain NGR234 [27] and _B. diazofficiens_ USDA110 [23] were compared to select the _tts_ box motif sequence (data not shown). In order to confirm that the observed phenotypes of the mutants were caused by transposon insertion and not by a secondary mutation independent of the transposon, \(innB\) identified in BE53 was mutated via single-crossover recombination. The internal region of \(innB\) was amplified by PCR using the primers InnBXbaI-intF and InnB EcoRI-intR (Table 1). The PCR product was cloned into the vector pSUPSCAKm and was mobilized into _B. elkanii_ USDA61 via conjugation, as described previously [12]. The mutant generated by integration of the plasmids was selected on PSY medium containing polymyxin and kanamycin. Integration of the plasmids was confirmed by PCR using the primer set Psupppol-F and Psupppol-R (Table 1).
# Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
## 5 Discussion
### The Ecological Impact of Light Pollution  
Many studies have demonstrated that light pollution could impact wildlife on foraging, reproduction, migration, and communication in natural systems. Artificial light can extend the period of foraging behavior of animals, which could introduce disturbances in the ecosystem and communities [1]. Additionally, artificial light may confuse organisms accustomed to navigating in a dark environment [62]. The sea turtles hatched from nests would be disorientated by nighttime light, which would affect their reproduction [63]. Birds can be disoriented and collided with others by lights at night [64]. Many insects, such as moth, are likely to be attracted by nighttime light [18].  
Compared to continuously polluted PAs, the short-term nighttime lights revealed from the discontinuously polluted PA (e.g., near Zhangjiajie in Figure S2) have a greater impact on the ecosystem, e.g., the wild animals need to adapt to such emerging nighttime lights. This is also an important reason for humans to prevent light pollution from spreading to PA. To identify the sprawl of light pollution in the future, we provided early warning of global light pollution for biodiversity and wildlife by mapping the light pollution source (i.e., the first polluted buffer and the high-intensity buffer) and their distances to PAs. These new insights into light pollution are not available in previous studies [14; 36].  
Light pollution continues to expand on Earth. However, for many species and ecosystems, its impacts have not been examined on a global scale. Human pressures on the natural system, commonly referred to as threats to biodiversity, are from diverse human activities [65], such as built environment, road, pasture, and nighttime light, etc. Especially, the impact of nighttime light on wildlife is complex, which can change the habits of some species (e.g., owl, insects, and migratory birds, etc.). Specially, the International Union for Conservation of Nature (IUCN) Red List will be employed to explore the impact of light pollution on threatened species on a continental scale in the future. This is helpful for biodiversity conservation, i.e., the INCU Red List was widely used to evaluate the vulnerability of PA in the face of climate, human footprint, and agricultural expansion [66; 67].  
Additionally, blue light is a new emerging source of light pollution due to the increased use of LED lighting [60; 68]. Unfortunately, such kinds of lights cannot be well-detected by DMSP/OLI and VIIRS/NDB [69], and its impact was not considered in our study. Some new remote sensing platforms have recently been used to monitor blue light pollution using advanced sensors [69] and new indices [70], which will be considered in future works of global light pollution.