esa-sceva/satcom-chunk-collection huggingface dataset:
# Isolation and Characterization of a Novel Cyanophage Encoding Multiple Auxiliary Metabolic Genes
## 3 Results and Discussion
### AMGs Encoded by S-SZBM1 Re-Program Phase-Host Interactions
According to previous studies, 2 OG-Fe(II) oxygenase encoded in phages were proposed to be functioning in DNA repair, because they are always located adjacent to DNA repair genes, such as _uvSX_, _uvSY_, and _uvSW_[44; 73]. Unlike early findings, 2 OG-Fe(II) oxygenase in cyanophage S-SZBM1 are scattered throughout the genome and are not present between _uvSY_ and _uvSW_. Previous study showed that the 2 OG-Fe(II) oxygenase genes phylogenetically divided into several groups, even where the genes came from the same genome, indicating gene duplication followed by divergence or multiple origins [74]. A cobalt chelatase gene _cobS_, which was predicted to catalyze the final step of vitamin B\({}_{12}\) biosynthesis, is also possessed by cyanophage S-SZBM1 [75]. According to the function of bacterial _cobS_, phage-encoded _cobS_ was presumed to increase the activity of ribonucleotide reductases for DNA synthesis [76]. However, the cyanophage _cobS_, including the one found in S-SZBM1 genome, is phylogenetically distinct from the host counterpart,suggesting that it might not originate from its host and probably has different functional properties [77]. Finally, we also identified a gene encoding ctidylyltransferase-like protein, which was found in various phages, such as S-WAM1, P-TIM40, P-RSM6, etc. [18; 78]. The homologous genes have been reported to encode glycerol-3-phosphate ctidylyltransferase, which may be involved in biosynthesis of lipoteichoic acid and is required for bacterial cell wall biogenesis [79; 80; 81]. However, the function of cyanophage S-SZBM1-encoded ctidylyltransferase remains unclear.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 3 Theoretical Basis of UV-Vis Spectrum Data Processing
### Method of Establishing Data Model  
According to [PERSON]'s law, the amount of spectral absorption has a strong correlation with the concentration of water quality parameters, such that a high-precision prediction model between water quality spectral data and water quality parameters can be established [28]. The steps of establishing the prediction model of water quality parameters was shown in Figure 4. Currently, the main methods of model analysis include partial least squares regression (PLSR) and support vector machine regression (SVR). In linear analysis modeling, PLSR is a commonly used multivariate analysis method in chemometrics. The principle of regression modeling is to project prediction variables and observation variables into a new space and establish a linear correlation between these variables to achieve the purpose of prediction [29]. The PLSR is a combination of PCA, canonical correlation analysis, and multiple linear regression analysis. Both PCA and PLSR attempt to extract the maximum information reflecting data variation; however, PCA only considers an independent variable matrix, while PLSR has a \"response\" matrix, so it incorporates a prediction function [30]. SVR is a machine learning algorithm based on structural risk minimization, which can be used for classification and regression. It specializes in the problem of statistical estimation and prediction in the case of small samples and discusses how to obtain the best solution in the case of existing limited samples. This method embodies the idea of compromising empirical risk and confidence range. Practical problems such as small samples, nonlinearity, and high dimensionality can be solved [31; 32]. Many studies have focused on the use of other models to establish the linear relationship between absorbance and water quality parameter. The modeling algorithms with more notable capabilities in simulating such a relationship are the extreme learning machine (ELM) model, principal component regression (PCR), and stepwise regression (SR) (Table 4). Figure 4: The steps of establishing a prediction model of water quality.
## 2 Preliminaries
### Hub Labels  
Hub labeling is a scheme to answer shortest path distance queries which differs fundamentally from graph search based methods. Here, the idea is to compute for every \(v\in V\) a _label_\(L(v)\) such that for given \(s,t\in V\) the distance between \(s\) and \(t\) can be determined by just inspecting the labels \(L(s)\) and \(L(t)\). All the labels are determined in a preprocessing step (based on the graph \(G\)), later on, the graph \(G\) can even be discarded. There have been different approaches to compute such labels (even in theory); we will be concerned with hub labels that work well for road networks, following the ideas in [8]. To be more concrete, the labels we are interested in have the following form:  
\[L(v)=\{(w,d(v,w)):w\in H(v)\}\]  
Here, we call \(H(v)\) a set of _hubs_--important nodes--for \(v\). The hubs must be chosen such that for any \(s,t\), the shortest path from \(s\) to \(t\) intersects \(H(s)\cap H(t)\).  
If such label sets are computed, the computation of the shortest path distance between \(s\) and \(t\) boils down to determining the node \(w\in H(s)\cap H(t)\) minimizing the summed distance. If the labels \(L(.)\) are stored lexicographically sorted, this can be done in a cache-efficient manner in time \(O(|L(s)|+|L(t)|)\).  
Knowing about CH, there is a natural way of computing such labels [8]: simply run an upward Dijkstra from each node \(v\) and let the label \(L(v)\) be the settled nodes with their respective distances. Clearly, this yields valid labels as CH answers queries exactly. The drawback is that the space requirement is quite large; depending on the metric and the CH construction, one can expect labels consisting of several hundreds to thousands node-distance pairs. It turns out, though, that many of the labels created in such a manner are useless as they do not represent shortest-path distance (as we restricted ourselves to a search in the upgraph only); pruning out those (which means reducing them to the direct search space of the respective node) typically reduces the label size by a factor of approximately 4. A source target distance query can then be answered in the _microseconds_ range. This is not the only method to generate hub labels but our lower bound applies to any hub labeling scheme.
# Terrain Irregularity Sensing by Evaluating Feet Coordinate Standard Deviation
## 4 Robot Description  
For our experiments, we used a hexapod robot with 18 Dynamixel AX-12+ servomotors; each leg has three servomotors (giving each leg 3 DoFs). AX-12+ actuators support a maximum 12 V input voltage and a 900 mA maximum current. The robot is controlled with an ATmega16 microcontroller with a 16 MHz external oscillator. The robot is relatively small: body length \(L1\) = 160 mm and width \(L2\) = 90 mm. The leg dimensions are as follows: femur length \(l1\) = 80 mm, tibia length \(l2\) = 106 mm, and coxa length \(l3\) = 68 mm (see Figure 5). The total weight of the robot is around 1.5 kg, consisting mostly of 18 actuators, each weighing 55 g. The robot's control system function diagram is shown in Figure 6. Each leg is also equipped with limit switches (Figure 7) to indicate when the foot touches the ground. An I2C accelerometer is used to measure body inclinations. External EEPROM memory is used to store all the foot coordinates.  
Figure 5: Hexapod robot model.  
Gauts and Adaptive Walking Trajectory  
Four main hexapod gaits were chosen for the experiments: tripod, tetrapod, wave, and ripple. (Figure 8). The tripod gait is the most common hexapod gait. During this gait, two legs on one side and one leg on the other are transferred while the other three support the robot's body. During the tetrapod gait, only two legs are repositioned simultaneously while the four remaining legs support the body. The wave gait is when only one leg is transferred. First, all the legs on one side are transferred and then the legs on the other side are transferred sequentially. The ripple gait is a wave gait modification when the leg transfer sequence is altered between both robot sides; first, one leg on one side is transferred, then one leg on the other is transferred, and so on. Figure 8 shows all the leg transfer sequences. In this figure, the black lines represent the leg support phase, and space represents the leg transfer phase. \(T\) is the whole gait period.  
The leg trajectory for adaptive walking is shown in Figure 9. It shows the leg transfer trajectory in the \(yz\) coordinate plane. \(h\) is the step height; \(l\) is the step length. They are set to 60 mm and 30 mm during the experiments, respectively. To simplify adaptive walking, all the robot's legs are lowered simultaneously while the supporting legs remain  
Figure 6: Function diagram of robot’s control system.  
Figure 7: Robot’s foot with limit switch for indicating when the ground is touched.  
stationary--and are not moving backward. The beginning of the gait is normal, but when the corresponding legs reach the highest point during the step (are at maximum step height), the supporting legs stop moving, and the transfer legs begin to move down. They are lowered until feedback is received from the limit switches, meaning the foot is on the ground. This cycle is repeated until all the legs are on the ground. Afterwards, the gait continues from the starting point; thus, the sequence remains unchanged.
# AVSS: Airborne Video Surveillance System
## 6 Evaluation
### Simulations
#### 6.1.2 Time Constraint  
Figure 6c shows the time consumed to find each step of placement. Because each score calculation has to consider the UAVs that has been deployed so far, the time consumed to find a placement location and altitude linearly increases. Thus, the time consumed to deploy \(n\) UAVs has time constraint of \(n^{2}\). However, the case of deploying 45 UAVs is extremely exaggerated, especially when the purpose of the algorithm is to find the sub-optimal solution for a small number of UAV deployment. Even under such extreme condition, time to place a UAV takes less than 5 s.
# Grafting of (3-Chloropropyl)-Trimethoxy Silane on Halloysite Nanotubes Surface
## 3 Results and Discussion  
In principle, halloysite has four distinguished surfaces: (i) an internal lumen surface, composed of hydrophilic aluminol (Al-OH) groups; (ii) an external surface, composed of hydrophobic siloxane (Si-O-Si) groups; (iii) an interlayer surface that is chemically the same as the internal lumen surface; and (iv) edge surfaces, terminated by Al-OH and Si-OH groups. These Si-OH groups can condense with the surface hydroxyl groups of inorganic materials by eliminating water molecules under proper conditions. In the current work, the expected mechanism for the grafting is a two-step process: first, the hydrolysis of methoxy groups of CPTMS to form intermediate species; second, the creation of siloxane bonds as described in Figure 1. Similar mechanisms were reported for the reaction of silanes with silica gel [29]. In the following, the effect of reaction conditions on the modification efficacy are highlighted.
###### Abstract
In contrast, communicative competence refers to the ability of a speaker to communicate effectively with other native or nonnative speakers [38; 42; 43; 44; 45]. With the purpose of strengthening both competencies of their German language students, the authors of the present study decided to digitize a learning task called _Terminkalender_ that was initially designed to be performed in paper format (students had to use a pen and paper to perform the given learning task). _Terminkalender_ is based on a students' exchange of written messages to negotiate and agree on various appointments regarding different leisure activities they would like to do together. Although _Terminkalender_ had been successfully implemented in its paper format for several years with German-language learners, its paper format made it difficult to analyze in detail students' interaction and task performance, especially when the number of course participants and, hence, messages generated was very high. Apart from the difficulties in terms of learning analytics (LA), the authors detected other drawbacks that affected students' task performance more directly: (1) the exchange of messages amongst students was, in general, very tedious and convoluted due to the continuous delivery of papers to arrange the different appointments, and (2) it was not easy to ensure that communication between students took place just by means of written messages and solely using the target language since they knew each other and were physically grouped together. To address such drawbacks of _Terminkalender_ in its original paper-based format, the authors decided to design a multiuser system that allows performing the learning task through mobile devices, allowing students to synchronously communicate with each other by sending instant messages anonymously while at the same time facilitating teachers' the access and, hence, the analysis of students' interaction. On the aforementioned background, the current study aims to analyze the pros and cons of digitizing a concrete learning task (_Terminkalender_) that intends to help students in developing their linguistic and communicative competencies. The knowledge gathered during the study can serve to inform the design and, specifically, the digitization of effective learning tasks that focus on the development of such competencies. For their analysis, the authors first compare the results obtained from the implementation of both the paper and the digital versions of _Terminkalender_ and then examine the advantages and disadvantages of each tool for developing students' linguistic and communicative competencies.
# A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones
## 2 Drone Charging Station Design and Development
### Energy Harvester
\[B(r,t)=\text{B}_{sat}\frac{2}{\pi}\text{arctan}\bigg{(}\frac{H(r,t)}{\alpha} \bigg{)}=\text{B}_{sat}\frac{2}{\pi}\text{arctan}\bigg{(}\frac{I_{p}\text{ sin}(wt)-N_{s}I_{s}(t)}{2\pi\text{r}\alpha}\bigg{)} \tag{3}\]  
Simplifying the core shape in the induction equation for a toroidal core and denoting the inner and outer radius and length of the toroidal core as \(r_{i}\), \(r_{o}\) and l, respectively, the induced voltage \(V(t)\) is expressed as:  
\[ \begin{split} V(t)=\int_{r_{i}}^{r_{o}}N_{s}\text{l}\frac{9 \delta(r,t)}{\delta t}\partial r=\\ \frac{N_{s}\text{l}\text{B}_{sat}}{2\pi^{2}\alpha}.\Big{(}wI_{p} \text{cos}\left(wt\right)-N_{s}\frac{\partial I_{s}(t)}{\partial t}\Big{)}.I \cdot n\bigg{(}\frac{r_{o}^{2}+\frac{\left(I_{p}\text{sin}(wt)-N_{s}I_{s}(t) \right)^{2}}{4\pi^{2}\alpha^{2}}}{r_{i}^{2}+\frac{\left(I_{p}\text{sin}(wt)-N_ {s}I_{s}(t)\right)^{2}}{4\pi^{2}\alpha^{2}}}\bigg{)}\end{split}  \tag{4}\]  
where \(I_{s}(t)\) is assumed to be sinusoidal and dependent on the value of the load impedance. A window area was defined in the core to place the coils and the grooved pads. The window utilization factor was maximized to bring the ferromagnetic core as close as possible to the power line. Finite element simulation was conducted to determine the magnetic field within the ferromagnetic core. The core creates a magnetic path with low reluctance, directing the existing magnetic field around the power line generated by the current flowing through the conductor. The magnetic field is subsequently confined within the core, creating an electromotive force in the secondary coil. When a charge is connected,the current induced in the secondary coil opposes the magnetic field in the primary coil, resulting in a reduction in the magnetic field through the core, as shown in Figure 3.  
In contrast, without a load, the secondary coil generates a high peak voltage, and the ferromagnetic core may be saturated by the primary magnetic field. The overvoltage stage mitigates the mechanical stress on the core and prevents overvoltage of the electronics by short-circuiting the secondary coil. As a result, no voltage is induced, and the magnetic field through the ferromagnetic core is minimized.
# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
## 3 Proposed Method
### Direct Sum Loss  
The direct sum of subspaces is a relationship between two linear spaces in higher algebra as a special case of the sum of subspaces.  
**Definition 1**.: _Let \(\mathcal{W}_{1}\) and \(\mathcal{W}_{2}\) be two subspaces of linear space \(\mathcal{V}\). Let \((\alpha_{1},\alpha_{2}\dots,\alpha_{s})\) be basis vectors of \(\mathcal{W}_{1}\) and \((\beta_{1},\beta_{2}\dots,\beta_{t})\) be basis vectors of \(\mathcal{W}_{2}\). If \((\alpha_{1},\alpha_{2}\dots,\alpha_{s},\beta_{1},\beta_{2}\dots,\beta_{t})\) are basis vectors of \(\mathcal{V}\), then we say \(\mathcal{W}_{1}+\mathcal{W}_{2}\) meets the direct sum condition._  
If two subspaces meet the direct sum condition, the redundant components between the two subspaces can be effectively removed. Therefore, the direct sum loss constraint is designed to reduce the redundant components between the identity-related features and age-related features. The details on how to implement direct sum loss are as follows.  
Let \(x_{id}\) denote the identity-related feature and \(x_{age}\) denote the age-related feature. The identity-related feature space and age-related feature space are marked as \(V_{I}\) and \(V_{A}\), respectively.  
The basis vectors \((v_{id1},v_{id2}\dots,v_{idK})\) are obtained from a fully connected layer. The space formed by the basis vectors \((v_{id1},v_{id2}\dots,v_{idK})\) is marked as \(V_{II}\). In the same way, the space \(V_{AA}\) is obtained by the basis vectors \((v_{agel},v_{ag2}\dots,v_{agK})\). To make the space \(V_{II}+V_{AA}\) meet the direct sum condition, \((v_{id1},v_{id2}\dots,v_{idK},v_{agel},v_{ag2}\dots,v_{agK})\) must be linearly independent. Therefore, the direct sum loss is represented in Equation (3).  
\[L_{direct\_sum}=\frac{1}{K}\sum_{i=1}^{K}|cos(v_{id_{i}},v_{ag_{i}})| \tag{3}\]  
where \(K\) denotes the number of basis vectors. The cosine similarity is as close to 0 as possible, making the two basis vectors linearly independent.  
In the training process, the identity-related feature space \(V_{I}\) and age-related feature space \(V_{A}\) and the subspaces \(V_{II}\) and \(V_{AA}\) are updated continually. As illustrated in Figure 4, the updated spaces are marked as \(V^{\prime}_{I}\), \(V^{\prime}_{A}\), \(V^{\prime}_{II}\), and \(V^{\prime}_{AA}\). By applying the direct sum constraint to the two feature subspaces \(V_{II}\) and \(V_{AA}\), the redundant components between the identity feature space \(V_{I}\) and the age feature space \(V_{A}\) are optimally separated. Making the identity-related features and age-related features linearly independent ultimately allows the facial identity features to be extracted. The process is summarized in in Figure 5.  
Figure 4: Direct sum. (**a**) Before applying the subspace direct sum constraint. (**b**) After applying the subspace direct sum constraint.
## 2 Design of the Mode Converters for the Sum and Difference Patterns
The peak observed around 36.5 GHz is precisely related to the cutoff frequency of \(TM_{11y}{}^{circ}\) mode. Any possible asymmetry in the structure could unintentionally increment this level. Mode Converter from the \(TE_{10}{}^{rec}\) Mode in the Rectangular Waveguide to the \(TE_{01}{}^{circ}\) Mode in the Circular Waveguide  
The proposed \(TE_{10}{}^{rec}\) - \(TE_{01}{}^{circ}\) mode converter is shown in Figure 5. This topology, as presented in Figure 5a, follows the design principles described in [60]. It has two parts. The first part is a four-way power divider network, starting at the standard WR-28 waveguide. It allocates the dedicated port for the difference pattern (\(port\) 1, \(\Delta\)). The second part is the junction combining the four signals coming from the first part, exciting the \(TE_{01}{}^{circ}\) circular waveguide mode at the output (\(port\) 2). The operation is depicted in Figure 5b, with the electric field pattern for the modes involved in the mode conversion. The four-way power divider is made up of three sections. Section 1 is the standard WR-28 rectangular waveguide for \(port\) 1, allocating the fundamental \(TE_{10}{}^{rec}\) mode associated to the system difference pattern (\(\Delta\)). Section 2 is a power divider, including stepped rectangular waveguides and a septum to divide the power evenly toward two outputs. Section 3 is a double power divider, again including an adapter and a septum, to achieve the four ways. Due to the symmetry of the four-way power divider network, all four outputs have the same phases and amplitudes. This leads to an azimuthal E-field pattern that easily couples to the \(TE_{01}{}^{circ}\) mode in port 2. It should also be noticed that the locations and symmetries of the outputs in the four power dividers are crucial for preventing the generation of higher order modes. The return loss for the \(TE_{10}{}^{rec}\) (\(S_{11}\) (\(TE_{10}{}^{rec}\))) in the rectangular waveguide is shown in Figure 6a. Its performance, as expected, is worse than for the mode converter in Figure 4, since it involves a more complex structure. Nevertheless, the return loss is better than 20 dB in the operating bandwidth from 37.5 to 39 GHz. The generation of higher order modes at the circular waveguide when the mode converter is excited by the \(TE_{10}{}^{rec}\) is  
Figure 4: Simulated S-parameters of the mode converter in Figure 1, from \(TE_{10}{}^{rec}\) mode in the rectangular waveguide to the \(TE_{11}{}^{circ}\) mode in the circular waveguide. (**a**) Return loss at the rectangular waveguide for the \(TE_{10}{}^{rec}\). (**b**) Generation of higher order modes at the circular waveguide when the mode converter is excited by the \(TE_{10}{}^{rec}\) mode at the rectangular waveguide. investigated in Figure 6b. It shows that all higher order modes, other than the \(TE_{01}{}^{cir}\), are deeply attenuated when the \(TE_{10}{}^{rec}\) fundamental mode of the rectangular waveguide is injected into the network.