esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Template-Monomer Interaction  
A study has shown that physical properties and recognition of MIP depend on the successful interaction between template and monomer. This process occurs in the prepolymerization stage [55]. The selected functional monomer will interact with the template, producing a stable template-monomer complex [17]. Therefore, functional monomers and templates must complement each other [59]. There are currently two strategies used in MIP technology based on the nature of the template-monomer interaction. Two types of molecular imprinting strategies have been set by covalent or non-covalent interactions between the template and functional monomer [74]. An example of interaction can be seen in Figure 3. These two strategies are:1. Self-assembly approach, which uses non-covalent bonds between monomer and template, such as hydrogen bonds, Van der Waals forces, ionic or hydrophobic interactions, and others [11]. The functional monomers are regularly positioned around the template molecules during the self-assembling process due to different exchanges [75];  
2. Pre-organized approach, which uses reversible covalent bonds between the functional monomer and template. This strategy will reduce non-specific sites on MIP [11; 76]. The technique most widely used in the manufacture of MIP is the self-assembly approach. In this technique, template-monomer complexes are formed in situ by non-covalent interaction [77]. Hydrogen bond, hydrophobic, and electrostatic interactions are the most widely used bonds for manufacturing MIP due to their excellent adaptability [65]. MIP manufacturing techniques using covalent and non-covalent bonds have their respective advantages and disadvantages, as listed in Table 1.  
The interaction between the template molecules and functional monomer and the formation of the polymerization mixture can lead to three different results [81]:  
1. If the template is added in the pre-polymerization step, at the same time as the monomer, solvent, cross-linker, and initiator, the reaction medium will be very rich in functional monomers. The polymerization process will occur rapidly due to the dominant role of the cross-linker. A weak complex will be formed between the template and the functional monomer, producing MIP with a relatively low imprinting effect;  
2. If the template is added after the start of the polymerization process, a few minutes after adding the monomer, cross-linker, initiator, and solvent, the reaction medium will contain functional macromonomers and a few monomers. Due to the nature of functional macromonomers, namely their high flexibility, they will freely re-arrange around the template to produce a MIP with a better imprinting effect;  
3. If the template is added too late in the polymerization process, minutes after the optimum time of adding the template (point b), the reaction medium will be rich in pre-formed nanogel particles, which will further form a cross-linked macrogel. The condition is particularly unfavorable because the cross-linked nanogel particles are more rigid than the functional macromonomers. The MIP formed is predicted to have a low imprinting effect.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 4 Experimental Results  
Due to the systematic error and random error in the calculation of distance, systematic error is inevitable, while random error is caused by external environmental factors. In order to ensure the accuracy of the algorithm; this paper opted for an indoor experimental platform and conducted an algorithm stability test. The stability of the algorithm was tested by using the selected algorithm to process images of the pole tower.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 3 Proposed Method  
A new feature selection method was proposed to address two issues in feature selection: relevance and computational complexity.  
The MIC cannot only measure the relevance between features and features, but also measure the relevance between features and categories. The method is divided into two parallel steps to select features from both aspects mentioned above. First, strong irrelevance features can be selected based on the MIC between features and features. Second, strong relevance features can be selected based on the MIC between features and fault categories. This frame has the advantage of a comprehensive consideration of relevance to eliminate redundant and irrelevant features from a feature set.  
In term of computational complexity, an intersection operation is employed to merge feature subsets which are selected by the relevance between features and features, and by the relevance between featuresand fault categories. The intersection operation has the advantage of obtaining a lower-dimension final subset with a lower computational complexity, containing as much as information.  
Based on the above consideration, MIC is applied to measure the relevance among features and the relevance between features and fault categories. In addition, to avoid computational complexity caused by wrapper methods, an intersection operation is applied, instead of wrapper methods, to merge subsets.  
The detailed implementation steps are as follows. First, MIC among features and MIC between features and fault categories are calculated to obtain two MIC matrixes. MIC among features is called FF-MIC and MIC between features and categories is named FC-MIC. Second, strong relevance values in FF-MIC and in FC-MIC are calculated to distinguish strong and weak relevance features. Third, strong irrelevance features selected by FF-MIC and strong relevance features selected by FC-MIC are merged through an intersection operation to form a final feature subset. Finally, PCA is applied to further reduce dimensions of the final feature set. The whole process of feature selection is shown in Figure 1.
## 3 Results and Discussion
### Fabrication of Multi-Gas Temperature-Controllable Plasma Jet  
The CFD analysis showed that the gas temperature could be controlled by the TCF temperature. Therefore, a multi-gas temperature-controllable plasma jet containing a TCF channel and a gas channel was fabricated. This device is difficult to fabricate by general machining due to the complex flow channels inside the plasma jet body. The plasma jet body was fabricated using a direct metal-printing 3D printer (Prox300 from 3 DSystems Inc., San Diego, CA, USA) that sintered the metal powder with a laser. The plasma jet body was fabricated by SOLIZE Corporation, Tokyo, Japan. The metal powder used was aluminum-silicon alloy (AlSi12), which has excellent thermal conductivity. In the particle size distribution of metal powder, D10, D50, D90 were 9 \(\upmu\)m, 20 \(\upmu\)m, and 36 \(\upmu\)m, respectively. When the metal powder is reused, it is sieved to remove foreign matter. The parameters of the 3D printer are 40 \(\upmu\)m thickness per layer. The standard tolerance of the built object is \(\pm\)0.3 mm. The parts that required high precision, such as screws and parts that needed to be airtight, were fabricated using machine tools. The fabricated plasma jet is shown in Figure 4a. The plasma was generated by applying a sinusoidal wave of 9 kV and 16 kHz to the hot electrode of the fabricated plasma jet. Figure 4b shows the generated plasmas of carbon dioxide, argon, nitrogen, and oxygen. Stable plasma generation was observed in the plasma jet body with multiple channels formed by a metal 3D printer.
# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 4 Gradiant Team
### Methodology  
The choice of Cascade R-CNN [42] architecture with a 101 ResNeXt as backbone was made from previous experience with Cascade and its performance in COCO benchmark. Furthermore, this model uses a FPN which adjusts to small drone detection, since its base level is constructed over a stride four with respect to the original image input size. This fact increases the capabilities of the network for detecting smaller drones compared to other popular architectures, as for example, RetinaNet [43] which uses stride 8 for the base level, at the cost of increasing computational complexity.  
The main alteration made by [PERSON] team was to design a reduced set of anchors that fit train data bounding box distribution so that the model can detect drones at very different scales with high accuracy. In localization based on bounding box regression, ground truth boxes only influence training loss when their maximum overlap with predefined anchors is greater than a specific threshold, usually predefined at 0.5. For this to happen, scales and aspect ratios of anchors have to be representative of the ground truth data distribution. In Figure 6 a visualization of training bounding box distribution in absolute pixels with respect to crop dimensions is shown. Two main facts can be observed from this plot: most boxes have small dimensions with respect to the crop size, and they tend to have larger width than height.  
A new method that adjusts these anchors dynamically given the input image size, the number of feature pyramid levels and their correspondent strides, is proposed. The idea is to maximize the overlap between ground truth bounding boxes and anchors, trying to match all boxes with at least one anchor by making the overlap between them greater than the established threshold. The authors have open sourced all code and made it publicly available in Github [[https://github.com/Gradient/pyodi](https://github.com/Gradient/pyodi)]([https://github.com/Gradient/pyodi](https://github.com/Gradient/pyodi)), accessed on: 14 April 2021.  
Figure 7 shows four different plots that summarize the result of this matching. In the upper left corner it can be observed a cumulative overlap distribution over the matching results. Less than a 10% of the total bounding boxes have maximum overlap values below 0.5, this is, only less than a 10% ground truth bounding boxes will not influence the training (when the augmentation effects are not taken into account). Upper right corners show the distribution of the bounding boxes dimension, color intensity represents the maximum overlap that each of them obtain after matching with generated anchors. Most low matches, represented with dark colors, are produced for extreme scale ratios. Finally, two bottom plots represent an histogram with relation between bounding scale and mean overlap (left) and log scaled scale ratios.  
Figure 6: Dimension distribution for bounding boxes in the training set.
# In Search of Lightweight Deployable Tensegrity Columns
## 5 Results and Discussion
### Simplex Column  
In the first deployment scenario, there are 27 cables, and all of them are active to change the height of the structure, keeping the tensegrity form all the time. The number of actuators is 16, which is less than the number of active cables because cables that have the same function of length can share the actuator. In this case, it is assumed that diagonal cables are grouped into pairs (one actuator per pair), and two out of three base cables are also grouped (one must be controlled separately due to their odd number). The column is planar in a stored form; it might be bundled after full relaxation of the cables, but this would cause major cable entanglement and trouble during the following deployment. Since there are 16 actuators that must be operated at the same time, it is assumed that this cannot be done by hand, but one person can operate the synchronized powered actuators. The column is prestressed all the time so it is assumed to be insensitive to additional forces (they change the deflection, but do not destabilize the structure).  
In the second deployment scenario, there are more cables, but fewer actuators. In this approach, the separate neighboring simplex modules have one common cable between the bases, which coincides with the axis of rotation. Other base cables are separated, and the third joint is also split. During the deployment, this split joint is reconnected by an additional active cable. This is valid for three intermodular bases and the lowest base that touches the ground. Initially, the individual modules must be prestressed using at least one active cable. That gives a total of eight active cables. The natural storing form for this layout is a bundle. The prestressing and column section can be performed by hand and by one person because the active cables can be adjusted one-by-one. The structure is still stable vertical-wise, but it can be susceptible to horizontal forces (depending on the direction).  
In the first scheme, the structure can be deployed directly on the ground since all the supports are in plane. In the second scheme, the structure should rather be placed on a platform that assures proper support for the bottom base nodes--especially for the one that is displaced during rising of the column.  
In Figure 9, both deployment scenarios are presented at the beginning of the deployment. A vast difference in space demand can be observed.  
What can be seen in Figure 9a is a structure in its folded form, ready to be deployed. In reality, it can be even flatter (here, it is higher due to the computer model limitations). In Figure 9b, the structure is preset for deployment i.e., it is stretched over the area and ready for the self-stress to be applied.  
Figure 8: Deflection of the columns.
# DNA Barcoding as a Plant Identification Method
## 2 DNA Barcodes  
Standardization, minimalism, and scalability are the three oilers of DNA barcoding. This technique has been successfully used for species identification in animals; a 648-base pair (bp) fragment near the 5\({}^{\prime}\)-end of the mitochondrial gene cytochrome c oxidase subunit I (_COI_) has been selected as the standard barcode [26]: (a) there is a large copy number per cell resulting in easier amplification from smaller or degraded samples, (b) it is naturally inherited, (c) there is no possibility of recombination with paternal copies, and (d) it rapidly accumulates mutations [27; 28]. While _COI_ is a suitable target for animals, it does not discriminate most plants because of a much slower mutation rate. This has led to the search of alternative barcoding regions [29; 30].  
The fundamental concept underpinning DNA barcoding is rooted in the notion that throughout the evolution of species, certain DNA lengths within both coding and non-coding regions remain highly conserved, undergoing minor changes. Sequences found in cytoplasmic mitochondrial DNA, chloroplast DNA, and selected segments from nuclear DNA embody these characteristics, making them suitable candidates for DNA barcoding. The utilization of these sequences enables the differentiation of species, providing a molecular signature that facilitates accurate and efficient species identification in the field of DNA barcoding [31]. The suitability of such loci or the combination thereof is under discussion for plant species where there is not one easily applied solution. The design of universal primers could enable efficient PCR amplification that following sequencing and bioinformatic analysis and would ideally identify all the known species. Unfortunately, so far, the ideal DNA barcode does not exist in plants [20; 21; 32]. Several barcodes, single or multiple, have been used and are presented below.
## 4 Probabilistic Model for Path Length Distribution
The model provides the following bounds:  
* Minimum path length: \(PL_{min}(T)=2\) (root plus single branch);
* Maximum path length: \(PL_{max}(T)\leq 41\) (full address path);
* Variance bound: \(Var(PL)\leq\log_{16}(N)\). These bounds are tight and achieved under specific address distributions, as demonstrated in Section 5.
## 4 Results and Discussion
### The Side Mode Suppression Ratio  
To characterize the relationship between the SMSR and injection current, the SMSRs of the ECDL system at a function of different wavelengths are measured at injection currents of 150 nm and 200 nm, respectively. The statistical result is illustrated in Figure 6. It can be seen from Figure 6 that the tunable range is basically unchanged as the current increases. This is because the bandwidth of interference filter is 40 nm. Additionally, under the low current condition of 150 mA, it still has a relatively high gain in the tunable band of 1525 nm to 1565 nm. As shown in Figure 6, the SMSR is increased significantly when the injection current increases from 150 mA to 200 mA. The maximum SMSR is 47 dB at an injection current of 150 mA. Additionally, the maximum SMSR reaches 57 dB at the wavelength of 1555 nm with an injection current of 200 mA. Furthermore, the gain of the external cavity laser within the passband of interference is relatively high when the injection current is higher than 150 mA, and the SMSR changes gently throughout the tuning range when the injection current is higher than 150 mA.  
Figure 5: The threshold current at a wavelength of 1550 nm is measured.  
Figure 6: The side mode suppression ratio (SMSR) versus tuning wavelength at injection currents of 150 mA and 200 mA, respectively.
## 1 Introduction  
The planning, design, and facilities management processes in large, dynamic, and complex installation facilities, such as healthcare facilities, are continually evolving [1; 2; 3]. The emergency department (ED) is the most vital and complex sector of any healthcare facility [1; 4], representing a \"front door\" for primary care hospitals, through which most inpatients enter the hospital [1; 2]. Inadequacy in the flow within the ED has the potential to impact the hospital system's functions and hamper the overall delivery of the health services [1; 2; 4]. Thus, it is important to have the means to carry out building performance evaluations and interventions based on evidence and inventiveness.  
Evidence-based design (EBD) allows for the improvement of the design decisions for planned or existing environments [3; 5; 6]. Research that is oriented toward design and facility management (FM) has also focused on mapping, modeling, and simulating the building performance [7], behavior [8; 9], and organization [10], the allocation of resources [4; 11], the flow of people and activities [9; 11], the operational efficiency [12; 13], the space and layout [14; 15], and the optimal paths [16]. Among the existing modeling, simulation, and evaluation methods, there is a lack of straightforward and logical resources that can be used to assess whether the allocation of activities in a built environment is congruent. Mapping the flow of activities in order to support the allocation of services in a healthcare facility can provide a source of robust information in order to support the facility's management and design. In this study, we present the application of a software program that offers the means to accomplish this task.  
In this study, an activity flow and allocation problem, which was observed in a real-life case in the form of a single pilot case study, guided the development of a method for mapping allocated activity flows. This method provided the basis for an open-source mapping software that is currently under development, which is called Multidomain Congruence Mapping and Simulation (MC-MS), the fundamentals of which are presented in Section 2, below. The results of this study focus on the functionality of the software's methods and procedures, and do not cover aspects of the software's usability. We present the matrices that have been derived from responses to a survey and examine how the congruence between the flow and the environments was analyzed.
## 2 CR-SOA
### Numerical Analysis
To ensure robustness, we performed additional simulations by varying the pulse energy between 0.5 pJ and 1 pJ, observing only minor variations in the QF (<5%) while maintaining error-free operation (BER < 10\({}^{-9}\)), indicating the resilience of the design. Similarly, the wavelengths of signal A (1550.7 nm) and signal B (1549.3 nm) were carefully chosen to match the gain bandwidth of the SOA and to enable efficient four-wave mixing and cross-phase modulation within the interferometer arms. These choices are aligned with values reported in recent CR-SOA-based logic gate studies[15; 16; 17; 18; 19; 20], where close channel spacing (\(\sim\)1-2 nm) within the C-band is employed to minimize dispersion and achieve high-speed operation. We also tested the impact of a \(\pm\) 0.5 nm shift in input wavelengths and confirmed that the performance remained stable without significant degradation in the QF. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definition** & **Value** & **Unit** \\ \hline E\({}_{0}\) & Pulse energy & 0.7 & fJ \\ \(\tau_{\text{FWHM}}\) & Pulse width & 1 & ps \\ T & Bit period & 8.33 & ps \\ N & PRBS length & 127 & - \\ \(\lambda_{\text{A}}\) & Wavelength of signal A & 1550.7 & nm \\ \(\lambda_{\text{B}}\) & Wavelength of signal B & 1549.3 & nm \\ \(\lambda_{\text{CW}}\) & Wavelength of signal CW & 1557.3 & nm \\ I & Injection current & 250 & mA \\ \(\text{P}_{\text{sat}}\) & Saturation power & 30 & mW \\ \(\tau_{\text{c}}\) & Carrier lifetime & 200 & ps \\ \(\tau_{\text{t}}\) & Transition lifetime from CR to AR & 5 & ps \\ \(\eta\) & Population inversion factor & 0.3 & - \\ \(\alpha\) & \(\alpha\)-factor & 5 & - \\ \(\alpha_{\text{CH}}\) & Linewidth enhancement factor due to CH & 1 & - \\ \(\alpha_{\text{SHB}}\) & Linewidth enhancement factor due to SHB & 0 & - \\ \(\varepsilon_{\text{CH}}\) & Nonlinear gain suppression factor due to CH & 0.2 & W\({}^{-1}\) \\ \(\varepsilon_{\text{SHB}}\) & Nonlinear gain suppression factor due to SHB & 0.2 & W\({}^{-1}\) \\ \(\tau_{\text{CH}}\) & Temperature relaxation rate & 0.3 & ps \\ \(\tau_{\text{SHB}}\) & Carrier–carrier scattering rate & 0.1 & ps \\ \(\Gamma\) & Optical confinement factor & 0.3 & - \\ \(\alpha\) & Differential gain & 10\({}^{-20}\) & m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Default numerical parameters[11; 12; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26].