esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Recent Advances in Biotransformation of Terpenes: Study Case of Limonene and Alpha Pinene  
Currently, the primary production of aroma compounds relies on chemical methods due to the challenges associated with direct extraction from natural sources. Natural extraction often results in complex mixtures with low concentrations and limited availability based on geography and seasonality [17]. Essential oils typically yield quantities ranging from 0.005% to 10%, with many falling below 1% [18]. The composition of essential oils varies, and they can contain over 100 different components, with major compounds often comprising more than 85% of the oil. The increasing focus on health, nutrition, and the desire to avoid synthetic chemicals in food has spurred the development of biotechnological processes to produce flavour compounds that can be classified as \"natural\" [19]. Some companies are actively working on biotechnological methods for aroma production. For instance, the Amyris Company utilises a genetically modified strain of Saccharomyces  
Figure 1: Some examples of monoterpenoides.  
cerevisiae to produce \(\beta\)-farnesene from sugarcane. This sesquiterpene can also serve as a precursor to produce other aroma compounds in the fragrance industry [8]. Evolva and Isobionics companies have reported the biotechnological production of valencene, which can be further oxidised to nootkatone. These companies' market nootkatone as a flavouring agent and a potential natural insect repellent. Isobionics has developed efficient microbial strains for terpene biotransformation. Based on the above examples, bioprocessing of limonene and pinenes, mainly found in many essential oils and citrus fruits, offers significant potential for biotransformation [20].
## 2 Literature Survey
### QoS Routing  
Quality-of-Service (QoS) is a major issue that can be divided into several factors including the reliability, mean delay, delay jitter, and bandwidth of packet transmissions [15]. Controlling QoS is a challenge faced by the routing of multihop wireless sensor networks with interference [16]. In multichannel wireless sensor networks, transmitted packets may collide in an interference environment if the nodes and neighbors within transmission range use the same channel. This adversely affects system performance and the QoS. Different types of applications have different QoS standards. As such, the QoS routing problems differ by the QoS types requirements of end users and therefore require careful control of channel assignment within an interference environment. By using different and non-overlapping channels, each node can freely communicate with its neighboring nodes (_i.e._, the nodes within transmission range) without interference. As noted in [15], using multiple channels instead of a single channel in multihop wireless sensor networks has been shown to not only reduce packet contention probability in non-overlapping channels, but to also dramatically improve the network throughput. A channel assignment strategy is necessary to efficiently use multiple channels with commodity hardware; besides, the channel assignment can be changed with significant changes in traffic load or network topology [7, 8].
## 2 Prior Work
### Binocular Stereo Vision Location Theory
#### 2.1.2 Binocular Stereo Vision System Imaging Model  
Binocular stereo vision [13] refers to using two cameras to film the same object from different angles; then, based on triangulation principle, the three-dimensional space position of the object is obtained by calculating the parallax, as shown in Figure 3.  
In the parallel binocular vision model, the projection of any point \(P(X,Y,Z)\) in the camera coordinate system on the left and right camera planes is \(p_{i}(x_{i},y_{i})\), \(p_{r}(x_{r},y_{r})\), respectively. When \(y_{l}=y_{r}\), then according to the triangulation principle:  
\[\frac{Z}{f}=\frac{X}{x_{l}}=\frac{X-B}{y_{r}}=\frac{Y}{y_{l}}=\frac{Y}{y_{r}} \tag{2}\]  
Furthermore:  
\[\left\{\begin{array}{l}X=\frac{B\cdot x_{l}}{B\cdot x_{l}}\\ Y=\frac{B\cdot y_{l}}{B\cdot x_{l}}\\ Z=\frac{B\cdot f}{d}\end{array}\right. \tag{3}\]  
where \(f\) is the focal length of the camera, and \(B\) is the baseline distance between the left and right cameras, both of which are fixed parameters of the camera. \(d=x_{l}-x_{r}\) is the  
Figure 2: Four coordinate systems in the pinhole imaging model.  
unknown parameter of the parallax. The object position in three-dimensional space can be restored only by calculating the parallax.
## 2 Materials and Methods
### Systems for Hand Tool Tracking
#### 2.1.1 ArUco-Based SYSTEM  
The system based on ArUco marker considers the possibility of applying a 2D planar marker on the hand tool that must be always visible by a RGB 2D camera. The main benefit of this approach is that a single marker provides enough correspondences to obtain the camera pose and, from that, a tracked object pose can be derived. Once the position of the tracked tool's point within the marker reference frame is known, the path of such a point can be reconstructed by the marker frame pose. Such an approach is the most invasive one since it considers the introduction of a new object, i.e., the marker, within the scenario. The strength of this approach is that it can determine a robust 3D pose estimation using a simple 2D camera and some printed markers, keeping computational costs low and therefore saving hardware resources for other essential tasks. On the other hand, it is an invasive way to track an object because markers have to be installed on it. Furthermore, if the marker is not visible, it is not possible to detect the object and, as a consequence, to establish the correct pose.  
ArUco [21; 22] is a popular opensource library for detection of square fiducial markers and camera pose estimation mostly used in augmented reality applications. An ArUcomarker is a synthetic square marker composed of a wide black border and an inner binary matrix that determines its identifier (id). The black border facilitates its fast detection in the image, and the binary codification allows for its identification and the application of error detection and correction techniques. The marker size determines the size of the internal matrix. For instance, a marker size of \(4\times 4\) is composed of 16 bits. The ArUco decoding algorithm can locate, decode and estimate the pose in realtime of any ArUco markers in the camera's field of view, as shown in Figure 2. It is based on the knowledge of the matrix encoded in the square. Multiple matrices are encoded in a group of markers, identified in dictionaries. It is possible to choose among several predefined dictionaries or by generating one yourself.  
For the sake of simplicity, in this study, a wooden support for the power drill was realized and three ArUco square markers with 5 cm side were stuck on it. Usage of a group of markers was preferred to a single marker in order to provide a more robust pose estimation: a single marker being visible but not recognized quickly in the scene, or not recognized at all because of an occlusion, could occur. When more markers of the group are detected in the same frame, only one marker's pose needs to be chosen as reference for the power drill chuck pose estimation. The area in pixels of a marker was used as selection criteria for the best marker because it is a matter of fact that AruCo algorithms have better performance the bigger the detected marker appears in the frame.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 3 Results  
We process data from all of the test stations during two selected test periods in five multi-GNSS solutions using the proposed weighting schemes, as well as in one GPS only solution G_L10 using the same weights as in GREC_L10 scheme. Exemplary coordinate time series and respective formal errors are presented in Figure 5. Centimeter level differences between solutions can be noticed and the impact of weighting on convergence time is also found.  
We evaluate the results by analyzing formal errors of estimated coordinates, day-by-day repeatability of coordinates, and solution convergence time. The formal errors of coordinates are understood here as the a posteriori errors of each coordinate component obtained at the last epoch of each daily solution. Day-by-day repeatability was calculated as a standard deviation of coordinates obtained at the end of each daily solution. We assume that the convergence is reached in a specific epoch if the formal error of the coordinate component remains below the 0.01 m for at least 15 min afterward.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Learning-Based Methods
#### 2.3.2 Deep Learning-Based Methods  
An increasing number of related deep learning methods are applied to image sharpness evaluation with the continuous improvement of deep learning methods [73]. The deep learning-based methods do not need to extract features manually but directly build a deep learning model and obtain the evaluation score of the image after training. These types of methods include a variety of network models, and nowadays, there are convolutional neural network (CNN), deep convolutional neural network (DCNN), generative adversarial network (GAN), etc. Such methods enable the learning of image quality prediction networks.  
[PERSON] et al. [74] evaluated image quality by a method based on an optimized convolutional neural network structure, aiming to automatically extract distinctive image quality features, to improve the network learning ability and to predict the evaluation score through normalization and packet loss. [PERSON] et al. [75] divided the entire image into blocks and then used a deep convolutional neural network (DCNN) to extract their advanced features. They then aggregated information from different blocks and fed these aggregated features into a least-squares regression model to obtain sharpness evaluation values. [PERSON] et al. [76] proposed the generation of a pseudo-reference image from the distorted image first, then paired the information of the pseudo-reference image with the distorted images and input them into the quality regression network to obtain the quality prediction result. [PERSON] et al. [77] proposed a deep bilinear model for blind image quality assessment (BIQA) that works for both synthetically and authentically distorted images and is able to predict image quality values. [PERSON] et al. [78] used the features extracted by the pre-trained convolutional neural network (CNN) as the general image description and estimated the overall image score by averaging the predicted scores in multiple sub-regions of the original image. [PERSON] et al. [79] extracted multi-level representations of images from VGGNet (Visual Geometry Group Net), calculated a feature on each layer, then estimated the quality score of each feature vector and finally obtained the final evaluation result by averaging.  
Deep learning-based methods can automatically learn multi-layer representations of image features from large amounts of data to obtain image feature information for image quality assessment, but their network models are only applicable to large data sets.
## 2 Experimental Section
### Chloride Penetration Measurements  
Cylindrical mortar specimens \(\Phi 100\)\(\times\) 50 mm cured for 120 days were used for chloride transport studies. These cylinders were formulated with different contents of slag. Before preconditioning, the mortar specimens of each mixture underwent a vacuum-saturation procedure. The saturated mortar specimens were then preconditioned at 50 \({}^{\circ}\)C to different degrees of saturation, i.e., 60% and 80%. The partially saturated mortar specimens after the preconditioning were homogeneous in relative humidity. More details of the specimen treatment can be referred to in a previous report [22]. The degree of saturation of the mortar specimen can be determined using Equation (1).  
\[S=\frac{m-m_{\mathrm{d}}}{m_{\mathrm{s}}-m_{\mathrm{d}}}, \tag{1}\]  
where \(S\) is the degree of saturation, \(m\) (g) is the mass of the sample at an unsaturated state, \(m_{\mathrm{s}}\) (g) is the mass of the sample at a saturated state, and \(m_{\mathrm{d}}\) (g) is the mass of the sample dried at 105 \({}^{\circ}\)C.  
Chloride transport studies were carried out on mortar specimens exposed to cyclic drying-wetting conditions. All surfaces of the specimens were sealed except the tops, which were designed as the testing surfaces. The specimens at saturation levels of 100, 80, and 60% were placed in a climatic chamber maintained at 25 \(\pm\) 1 \({}^{\circ}\)C. The top surfaces of the mortar specimens were subjected to cyclic exposure of 6 h drying and 6 h wetting with a NaCl solution (165 g/L). Such a drying-wetting regime was adopted since it coincides with the exposure conditions for marine structures located in tidal zones. At the exposure periods of 2, 5, 10, 30, 50, and 70 drying-wetting cycles, the chloride profiles of the specimens were obtained by grinding powders layer-by-layer, together with potentiometric titration [23]. The (acid-soluble) total chloride profiles and (water-soluble) free chloride profiles were both measured [2; 23; 24]. The difference between the total and free chloride profiles represents the bound chloride profile. The chloride-binding isotherm for specimens of different slag content was analyzed. In the case of the diffusion of chloride ions in the specimen, the chloride transport can be quantitatively described based on the curve fittings of the chloride profile using the solution of [PERSON]'s second law, as shown in Equation (2).  
\[c(x,t)=C_{\mathrm{s}}-(C_{\mathrm{s}}-C_{\mathrm{i}})\mathrm{erf}\left(\frac {x}{2\sqrt{D_{\mathrm{a}}t}}\right), \tag{2}\]  
where \(c(x,t)\) is the total chloride content at depth \(x\) (m) after exposure time \(t\) (s); \(C_{\mathrm{s}}\) is the surface chloride content; \(C_{\mathrm{i}}\) is the initial chloride content of the material, which is zero in this work; \(D_{\mathrm{a}}\) is the apparent chloride diffusion coefficient (m\({}^{2}\)/s).
# Iterative Oblique Decision Trees Deliver Explainable RL Models
## Appendix B Effect of Observation Space Normalization on the Distances to Hyperplane  
In a \(D\)-dimensional observation space every point is given by \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{D})\). Every decision hyperplane is defined by  
\[\mathbf{x}\cdot\mathbf{w}=x_{1}\cdot w_{1}+x_{2}\cdot w_{2}+\ldots+x_{D}\cdot w _{D}=b \tag{1}\]  
with weight vector \(\mathbf{w^{T}}=(w_{1},w_{2},\cdots,w_{D})\) and bias \(b\).  
The distance \(d\) of a point \(\mathbf{x}\) to a given hyperplane is computed by  
\[d=\frac{|\sum_{i=1}^{D}x_{i}\cdot w_{i}-b|}{\sqrt{\sum_{i=1}^{D}w_{i}^{2}}} \tag{2}\]  
Now we perform an anisotropic scaling of the observables by subtracting a dimension-specific constant \(\mu_{i}\) and by dividing with another dimension-specific constant \(\sigma_{i}\) for \(i=1,2,\ldots,D\):  
\[x_{i}^{\prime}=\frac{x_{i}-\mu_{i}}{\sigma_{i}} \tag{3}\]  
The hyperplane in the scaled space is defined by  
\[\mathbf{x}^{\prime}\cdot\mathbf{w}^{\prime}=b^{\prime} \tag{4}\]  
Substituting (3) in (4) leads to  
\[\frac{x_{1}-\mu_{1}}{\sigma_{1}}\cdot w_{1}^{\prime}+\frac{x_{2}- \mu_{2}}{\sigma_{2}}\cdot w_{2}^{\prime}+\ldots+\frac{x_{n}-\mu_{D}}{\sigma_{ D}}\cdot w_{D}^{\prime} =b^{\prime} \tag{5}\] \[x_{1}\frac{w_{1}^{\prime}}{\sigma_{1}}-\frac{\mu_{1}}{\sigma_{1 }}w_{1}^{\prime}+x_{2}\frac{w_{2}^{\prime}}{\sigma_{2}}-\frac{\mu_{2}}{\sigma _{2}}w_{2}^{\prime}+\ldots+x_{D}\frac{w_{D}^{\prime}}{\sigma_{D}}-\frac{\mu_ {D}}{\sigma_{D}}w_{D}^{\prime} =b^{\prime} \tag{6}\]  
Comparison of coefficients with (1) results in  
\[w_{i} =\frac{w_{i}^{\prime}}{\sigma_{i}} \tag{7}\] \[b =b^{\prime}+\sum_{i=1}^{D}\frac{\mu_{i}}{\sigma_{i}}w_{i}^{\prime} \tag{8}\]or, respectively,  
\[w^{\prime}_{i} =w_{i}\cdot\sigma_{i} \tag{111}\] \[b^{\prime} =b-\sum_{i=1}^{D}\frac{\mu_{i}}{\sigma_{i}}w_{i}\sigma_{i}=b-\sum_{ i=1}^{D}\mu_{i}w_{i} \tag{112}\]  
The distance in the scaled space is  
\[d^{\prime}=\frac{|\sum_{i=1}^{D}x^{\prime}_{i}\cdot w^{\prime}_{i}-b^{\prime}| }{\sqrt{\sum_{i=1}^{D}w^{\prime 2}_{i}}} \tag{113}\]  
Substituting (10), (111), and (112)  
\[d^{\prime} =\frac{|\sum_{i=1}^{D}\frac{x_{i}-\mu_{i}}{\sigma_{i}}\cdot w_{i} \sigma_{i}-b+\sum_{i=1}^{D}\mu_{i}w_{i}|}{\sqrt{\sum_{i=1}^{D}(w_{i}\sigma_{i} )^{2}}} \tag{114}\] \[=\frac{|\sum_{i=1}^{D}(x_{i}-\mu_{i})w_{i}-b+\sum_{i=1}^{D}\mu_{i }w_{i}|}{\sqrt{\sum_{i=1}^{D}(w_{i}\sigma_{i})^{2}}}\] (115) \[=\frac{|\sum_{i=1}^{D}x_{i}w_{i}-w_{i}|-b+\sum_{i=1}^{D}\mu_{i}w _{i}|}{\sqrt{\sum_{i=1}^{D}(w_{i}\sigma_{i})^{2}}} \tag{116}\]  
Finally a comparison with (10) results in  
\[d^{\prime}=d\frac{\sqrt{\sum_{i=1}^{D}w^{2}_{i}}}{\sqrt{\sum_{i=1}^{D}(w_{i} \sigma_{i})^{2}}}=d\frac{\sqrt{\sum_{i=1}^{D}w^{2}_{i}}}{\sqrt{\sum_{i=1}^{D} w^{\prime 2}_{i}}}=d\frac{||\mathbf{w}||}{||\mathbf{w^{\prime}}||} \tag{117}\]  
Thus, we have shown that anisotropic scaling changes the distances of all points to a given hyperplane only by a common factor (unlike point-to-point distances, which usually change by different factors depending on the direction of the vector connecting these points).
# The Role of Buoyancy Induced Instability in Transpirational Cooling Applications
## 2 Mathematical Model
### Base State  
The basic steady state energy equation subjected to the aforementioned boundary conditions with a constant through-flow given by \(w_{b}=-\lambda\) is given by Equation (5),  
\[C\frac{dT_{b}}{dz}=\frac{dT_{b}^{2}}{dz^{2}} \tag{5}\]  
where \(C=\frac{-\lambda}{\sigma\gamma}\) and the subscript 'b' denotes the base state. The basic solution promotes a thermal boundary layer of the exponential form given in Equation (6):  
\[T_{b}(z)=\frac{1-e^{C(1-z)}}{1-e^{C}}. \tag{6}\]  
This solution is very similar to the one dimensional solution used to model transpirational cooling experimental data given in Ref. [6], which used constant temperatures for both the top and bottom boundary to match experimental conditions. It is worth noting that the non-linearity in the temperature boundary layer is driven by the velocity magnitude, \(\lambda\). In the limit of the velocity magnitude approaching zero, the temperature boundary layer becomes linear and approaches the same basic solution described in Ref. [17], where a porous material with a horizontal rather than vertical through-flow undergoes heating at the bottom boundary with a fixed top boundary temperature.  
The basic density profile can be solved for by inserting Equation (6) into Equation (2) (shown later in Section 5). Accordingly, non-linearity in the density is caused from two independent sources: (1) The through-flow velocity and its effect on the temperature profile and (2) inherent non-linearity in the equation of state. Together, these effects manifest large density gradients at the top of the domain and small gradients at the bottom. The stability of this configuration is analyzed in the next section.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Property** & \(\mathbf{w_{b}}\) * & **K** & \(\mathbf{\beta}\) & \(\mathbf{\Delta}\) **T** & \(\mathbf{\
u}\) & **L** & \(\mathbf{c_{F}}\) \\ \hline
**Units** & \(\frac{\mathrm{m}}{\mathrm{s}}\) & \(\mu\mathrm{m}^{2}\) & \(\frac{1}{\mathrm{mK}}\) & K & \(\frac{\mathrm{cm}^{2}}{\mathrm{s}}\) & mm & - \\
**Value** & 0.05–0.15 & 500–1000 & 1–5 & 650 & 0.003 & 5–10 & 5–20 \\
**Reference** & [6] & [6] & current work & [25] & [24] & [6] & [6] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value of properties used in this work.
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 2 Methods
### Retro-Reflective Marker Design
However, the surface finish of the tool was quite rough, and the manufacturing tolerances reduced the sharpness of the corners to a curvature with a diameter of approximately 2.0 mm. Furthermore, when pressing the tool onto the metal to create the retro-reflective pattern, the excess material tends to accumulate between the borders of the corner-cube indentation, leading to relatively large non-retro-reflective flat regions. An alternative option would be to create a single-shape corner-cube inverse tool, which would have to be stamped into the piece multiple times to create an array-like structure. Such a tool can be manufactured with a different technique that allows sharper corners. However, this brings additional practical issues related to the alignment of the tool that diminish the usability of this approach. We concluded that the most viable and cost-effective solution for the application is the use of the bead-array retro-reflective marker array. The experiments presented further on in this paper were conducted using this design. We create a marker constellation design for the cassette piece, where we aim to maximize the number of markers while keeping the drilling into the piece at an acceptable level. We partially utilize holes that were already in the design of the cassette surface for other purposes. There is no possibility to mount markers at different depths, so all markers are at the same depth plane. Furthermore, we opt not to use coded markers in order to optimize the available bright marker area and due to the fact that the low resolution makes it hard to detect features of smaller dimensions.
# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 3 Case Study
### Hardware Parameters of TuSeSy  
The key hardware parameters selected for TuSeSy are as follows:  
1. The model of the tracking camera: PointGray (GS3-U3-41C6C-C), resolution: 2048 \(\times\) 2048, pixel size: 5.5 \(\upmu\)m;
2. The model of the tracking camera lens: AF-Smicro NIKKOR 105 MM1:2.8 g, focal length: 50 mm;
3. The model of the high-speed recording camera: IO industries (Flare 2M360 CCL), frame speed: up to 375 FPS, resolution: 1088 \(\times\) 2088;
4. The model of the high-speed recording camera lens: VR 500/4G;
5. The model of the Kr Morgan motor used for azimuth axis: KBMS-25H01-A00; the model of the corresponding driver: AKD-P01206-NBEC-0000;
6. The model of the Kr Morgan motor used for the pitching axis: KBMS-17H01-A00; the model of the corresponding driver: AKD-P00606-NBEC-0000.
7. The multi-axis controller: Pyeon, model: Beckhoff (CX5130-0125);
8. The image workstation (CPU: Intel 7700 K, memory: 32 GB, graphics card: GV-N1080 Ti, operating system: Linux).
# Correspondence between Multilevel Graph Partitions and Tree Decompositions
## 3 Graph Partitioning  
The graph partitioning paradigm consists of dividing a graph into two or more smaller parts that are loosely connected. This is a very useful tool to extend divide-and-conquer algorithms to graphs. A complex problem, such as graph coloring [15], can be solved by reasoning in isolation about each part. From the parts' solutions, a global solution for the whole graph can be constructed. This approach can be applied recursively over many levels. In this case, a _multilevel_ partition is used.  
In the literature, many different graph partitioning variants exist [1]. Many papers divide the graph along edge cuts. This means that the input graph is disconnected by removing edges. We say that this form of graph partitioning is _edge-based_. A slightly less frequently used graph partitioning variant uses node separator, i.e., the input graph is disconnected by removing nodes. We say that this graph partitioning variant is _node-based_.  
In this section, we present a formalization of both edge-based and node-based graph partitions and highlight the differences. Our theorem, in the next section, establishes a connection between node-based multilevel graph partitions and tree decompositions.