esa-sceva/satcom-chunk-collection huggingface dataset:
# Propolis as a Potential Novel Histological Tissue Fixative: A Preliminary Analysis
## 3 Results
### Comparison of Fixation Ability of Different Solutions throughout Various Microscopic Structures
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**(b)**} \\ \hline \multirow{2}{*}{**Solution**} & \multicolumn{2}{c}{**Basement Membrane**} & \multicolumn{2}{c}{**Nerve Bundles**} & \multicolumn{2}{c}{**Fibrous Tissue**} \\ \cline{2-7}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
0.9\% Saline & 3.62 & 0.16 & 3.66 & 0.13 & 2.83 & 0.20 \\ \hline
10\% Formalin & 3.70 & 0.09 & 3.66 & 0.24 & 2.92 & 0.59 \\ \hline
6.6\% Propolis & 3.58 & 0.21 & 3.71 & 0.29 & 3.25 & 0.10 \\ \hline
6.6\% Propolis + 10\% Formalin & 3.87 & 0.09 & 3.54 & 0.21 & 3.16 & 0.24 \\ \hline
0.9\% Saline + 10\% Formalin & 3.54 & 0.44 & 3.46 & 0.44 & 2.79 & 0.48 \\ \hline \multicolumn{7}{c}{Pairwise comparisons by [PERSON]’s multiple post hoc procedures} \\ \hline
0.9\% Saline vs. 10\% Formalin & \(p=0.9883\) & \(p=1.0000\) & \(p=0.9973\) \\ \hline
0.9\% Saline vs. 6.6\% Propolis & \(p=0.9990\) & \(p=0.9995\) & \(p=0.5225\) \\ \hline
0.9\% Saline vs. 6.6\% Propolis + 10\% Formalin & \(p=0.5814\) & \(p=0.9697\) & \(p=0.7081\) \\ \hline
10\% Formalin vs. 6.6\% Propolis & \(p=0.9456\) & \(p=0.9995\) & \(p=0.7136\) \\ \hline
10\% Formalin vs. 6.6\% Propolis + 10\% Formalin & \(p=0.8442\) & \(p=0.9697\) & \(p=0.8724\) \\ \hline
6.6\% Propolis vs. 6.6\% Propolis + 10\% Formalin & \(p=0.4367\) & \(p=0.9157\) & \(p=0.9976\) \\ \hline \multicolumn{7}{c}{**(c)**} \\ \hline \multirow{2}{*}{**Solution**} & \multicolumn{2}{c}{**Blood Vessels**} & \multicolumn{2}{c}{**Skeletal Muscles**} & \multicolumn{2}{c}{**Adipose Tissue**} \\ \cline{2-7}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
0.9\% Saline & 3.71 & 0.29 & 3.04 & 0.29 & 3.29 & 0.25 \\ \hline
10\% Formalin & 3.79 & 0.16 & 2.75 & 0.40 & 3.29 & 0.34 \\ \hline
6.6\% Propolis & 3.87 & 0.09 & 3.16 & 0.24 & 3.08 & 0.35 \\ \hline
6.6\% Propolis + 10\% Formalin & 3.66 & 0.13 & 2.96 & 0.21 & 2.87 & 0.16 \\ \hline
0.9\% Saline + 10\% Formalin & 3.50 & 0.19 & 2.71 & 0.37 & 2.58 & 0.57 \\ \hline \multicolumn{7}{c}{Pairwise comparisons by [PERSON]’s multiple post hoc procedures} \\ \hline
0.9\% Saline vs. 10\% Formalin & \(p=0.9673\) & \(p=0.6760\) & \(p=1.0000\) \\ \hline
0.9\% Saline vs. 6.6\% Propolis & \(p=0.7040\) & \(p=0.9769\) & \(p=0.9195\) \\ \hline
0.9\% Saline vs. 6.6\% Propolis + 10\% Formalin & \(p=0.9973\) & \(p=0.9952\) & \(p=0.4988\) \\ \hline
10\% Formalin vs. 6.6\% Propolis & \(p=0.9636\) & \(p=0.3547\) & \(p=0.9226\) \\ \hline
10\% Formalin vs. 6.6\% Propolis + 10\% Formalin & \(p=0.8691\) & \(p=0.8711\) & \(p=0.5044\) \\ \hline
6.6\% Propolis vs. 6.6\% Propolis + 10\% Formalin & \(p=0.5132\) & \(p=0.8711\) & \(p=0.9226\) \\ \hline \end{tabular} \({}^{*}\) Indicates statistical significance. \({}^{\lx@sectionsign}\) SD: Standard deviation.  
\end{table} 
Table 6: _Cont._
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 4 The Proposed Method  
Figure 2 presents an overview of our proposed method. It combines a dual-branch encoder-decoder architecture with a multi-scale local planar guidance module (MLPGM) to create a unified model capable of handling both depth prediction and depth completion tasks. More specifically, we construct dual-branch encoder-decoders with one branch dedicated to depth prediction (DP) and the other focused on depth completion (DC). In the decoder of each branch, a multi-scale local planar guidance module is integrated to ensure high-quality results when the input depth is extremely sparse or missing. The details of each module will be introduced in the following sections.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 3 Materials and Methods
### Proposed Data Collection Optimization Model  
We derive the data collection problem as an Integer Quadratic Programming (IQP) model. Here, we have a network graph of G = (V,E), where V refers to the set of nodes (OBUs and RSUs), and E refers to the edges (connections) between the nodes. For the data collection problem, we use \(x_{i}\) to represent the association of node \(i\) in the data collection process as a data collection agent, as given in Equation (4).  
\[x_{i}=\left\{\begin{array}{ll}1;&if\ node\ i\ is\ an\ agent\ in\ the\ data\ collection\ process\\ 0;&otherwise\end{array}\right. \tag{4}\]  
For a given node \(i\), we represent using \(z_{i}\) the state of broadcasting of its data to immediate neighbors in the data collection process, as given in Equation (5). Note that the value of \(z_{i}\) depends on the SDVN architecture since the decision to exchange data between neighbors depends on it.  
\[z_{i}=\left\{\begin{array}{ll}1;&if\ node\ i\ is\ broadcasting\ data\ to\ its\ immediate\ neighbors\\ 0;&otherwise\end{array}\right. \tag{5}\]  
Furthermore, we use \(\mathcal{S}_{i}\) to represent the set containing all one-hop neighbors of a given node \(i\). The total number of nodes in the system is represented using \(\mathcal{N}\) and the total number of one-hop neighbors of a given node (\(i\)) is represented using \(\mathcal{N}_{i}\).
###### Abstract  
In order to solve the problems brought upon by off-shore wind-power plants, it is important to improve fault ride-through capability when an on-shore fault occurs in order to prevent DC overvoltage. In this paper, a coordinated control strategy is implemented for a doubly-fed induction generator (DFIG)-based off-shore wind farm, which connects to on-shore land by a modular multilevel converter (MMC)-based high voltage direct current (HVDC) transmission system during an on-shore fault. The proposed control strategy adjusts the DC voltage of the off-shore converter to ride through fault condition, simultaneously varying off-shore AC frequency. The grid-side converter detects the frequency difference, and the rotor-side converter curtails the output power of the DFIG. The surplus energy will be accumulated at the rotor by accelerating the rotor speed and DC link by rising DC voltage. By the time the fault ends, energy stored in the rotor and energy stored in the DC capacitor will be released to the on-shore side to restore the normal transmission state. Based on the control strategy, the off-shore wind farm will ride through an on-shore fault with minimum rotor stress. To verify the validity of the proposed control strategy, a DFIG-based wind farm connecting to the on-shore side by an MMC HVDC system is simulated by PSCAD with an on-shore Point of Common Coupling side fault scenario.  
doubly-fed induction generator (DFIG); fault ride-through (FRT); high voltage direct current (HVDC) system; off-shore wind farm; voltage sourced converter (VSC); variable frequency Article
# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 2 Related Works
### Feature Selection  
Nowadays, datasets are getting larger and larger. Some datasets have a lot of information, but not all this information is helpful. Some of it is redundant or even irrelevant in training our model. The redundant and irrelevant information can be called noise. The presence of irrelevant and redundant features in a dataset can lower the performance of predictive models due to over-fitting and the curse of dimensionality [27]. To produce a better model, we need to ensure that we use only the essential information when training our model. Therefore, we need to conduct feature selection to our dataset before feeding the data into our model.  
Research has proved that eliminating redundant or irrelevant features can improve the model's accuracy. Research from [28] shows that the accuracy of breast cancer detection is improved after the number of features is reduced using Recursive Feature Elimination (RFE). The authors of [29] also conducted research that shows that feature selection can help improve the accuracy of predicting Type-II diabetes. In the financial sector, research from [30] used the Lasso regression method to reduce the redundant features from a bank failure prediction model, while [31] used RFE to reduce the data dimensionality to obtain more efficient stock market predictions.  
RFE is different from the single-run feature selection method because of its recursive nature. A normal feature selection in a single run will fit the learning model and calculate the feature importance based on the fitted model. Then, it will show the feature importance of each feature and a threshold should be selected to decide which feature is important. Because it is only run one time, the feature's importance could be influenced by some other features that are not important. Meanwhile, RFE will select the best n features based on how many features we want. RFE also works by fitting the model and then calculating the feature importance of each feature. After that, it will prune the feature set by removing the least important feature and start another model fitting and calculation of feature importance based on the remaining features and remove the least important feature. This process will be repeated until no feature remains in the feature set. Finally, RFE will show the top-\(n\) most important features. From this, RFE removes the correlation between the remaining features and the eliminated features in each recursion.
# Robust Backstepping Control Applied to UAVs for Pest Recognition in Maize Crops
## 5 Experimental Results  
### Experimental Platform Description  
The quadrotor is constructed on a plastic frame F450. An autopilot Pixhawk, version 2.4.8, is on board the F450 structure. The Pixhawk exhibits the following performance: a primary clock at 168 MHz, and with a 32-bit processor STM32F427 Cortex M4 core with a floating processor unit. The autopilot has two accelerometers, a gyroscope, and two magnetometers for attitude measurements of the UAV. The Pixhawk is equipped with radio signal input ports compatible with Futaba 10J radio. Furthermore, it has eight pulse width modulation main outputs that are used to control the motors. To pinpoint the longitudinal positions, an external module Ublox NEO-M8N GPS (accuracy of 0.6-0.9 m) is mounted. The yaw angle is estimated via an internal magnetometer located in the GPS. As power supply of the UAV, Lipo technology batteries are employed (capacity of 5200 mAh, discharge rate of 15 C). The quadrotor has four propellers of model 1045, four 2212 920 KV brushless motors and four electronic speed controllers of model SIMONK30A. The trajectory tracking reference was programmed into the Pixhawk (Figure 2).  
Figure 2: Quadrotor prototype.
# A Few Shot Classification Methods Based on Multiscale Relational Networks
## 2 Materials and Methods  
Two datasets are selected, the Omniglot dataset and the MiniImageNet dataset, to evaluate and examine the performance and accuracy of the proposed method. The two datasets are briefly introduced below.  
By using Amazon's Mechanical Turk, [PERSON] et al. at MIT collected and published the Omniglot data set [20]. It comprises 50 international languages. The letters in each language vary widely, as shown in Figure 1. Therefore, the Omniglot dataset consists of 32,460 images from 1623 categories.  
The MiniImageNet dataset consists of 60,000 84 \(\times\) 84 \(\times\) 3 color images. This dataset produced by [PERSON] et al. has 100 categories and 600 samples in every category [1]. The dataset's distribution is different from Omniglot. The image category involves animals, goods, and remote sensing photos. This original dataset is not published. [PERSON] and [PERSON] produced and published the MiniImageNet dataset by randomly selecting 100 classes from the ImageNet. This new set is divided into training, validation, and test sets at 64:16:20 [29].  
The few-shot image classification methods based on metric learning include four types of networks. By learning similarity measurements from various tasks, metric learning guides new tasks using experience extracted from previous tasks. First, the learner learns from the training tasks by measuring the support set's distance and learns a metric. Then, a small number of support set samples can correctly classify the target set for the test set's new task.  
This paper aims to calculate two images' similarities using the support and target sets. The three types of metric learning networks (the matching, the prototype, and the relational) are selected in comparing experimental results. The network design of the proposed multi-scale relational network (MSRN) is shown below.  
Figure 1: Sample Omniglot data set.
# Coenzyme Q at the Hinge of Health and Metabolic Diseases
## 3 CoQ as Metabolic Integrative Factor for Cellular Homeostasis
### Mitochondrial Redox Reactions  
#### 3.1.1 Redox Reactions at the ETC  
The CoQ-dependent redox reactions in the mitochondria are linked directly or indirectly to oxidative phosphorylation (OXPHOS), in which electrons flow through mitochondrial complexes to generate the proton gradient necessary for ATP synthesis, until they finally reach oxygen to reduce it to water [39], additionally affecting diverse metabolic pathways [41].  
To access ETC, the energy of the electron flows clusters the mitochondrial redox reactions into three groups, depending on their redox potential [45]. The more energetic dehydrogenases operate at approximately \(-280\) mV and transfer electrons from metabolites to NAD\({}^{+}\), reducing it to NADH, and oxidizing the proper metabolite to another with lower potential energy. Within this group, enzymes, for amino acid metabolism, such as 2-oxoadipate dehydrogenase for lysine catabolism and branched-chain dehydrogenases for leucine, isoleucine and valine catabolism; hydroxyacyl-CoA-dehydrogenase for fatty-acid \(\beta\)-oxidation; pyruvate dehydrogenase for carbohydrate catabolism; and the three NAD-dehydrogenase integrated in the Krebs cycle: isocitrate dehydrogenase, 2-oxoglutate dehydrogenase, and malate dehydrogenase are included. All these dehydrogenases produce NADH to feed mitochondrial complex I, which uses the energy to generate the proton gradient necessary for ATP synthesis and transfers the electrons to ubiquinone reducing it to ubiquinol, which is the substrate for complex III.  
The second group of dehydrogenases operate at a lower redox potential of approximately +20 mV. They use FAD, a flavin-adenine dinucleotide that is reduced to FADH\({}_{2}\), and transfer electrons from metabolites directly to ubiquinone, without contribute to the proton gradient generation. This group includes succinate dehydrogenase (complex II), enzymes for amino acid metabolism, such as proline dehydrogenase, dihydrorotate dehydrogenase for pyrimidine nucleotide metabolism, acyl-CoA dehydrogenases for fatty-acid beta oxidation, and glycerol-3-phosphate dehydrogenase, which shuttles the glycolytic electrons from NADH directly to the mitochondrial respiratory chain to regenerate NAD\({}^{+}\) and avoid the blockage of the glycolysis. All these dehydrogenases use as substrate CoQ producing ubiquinol, which is reoxidized by complex III, using this energy to generate the mitochondrial proton gradient, and finally transferring the electrons to molecules of cytochrome c [41].  
The third group of dehydrogenases operate at a very low redox potential of approximately +320 mV. Here, the cytochrome c linked enzymes are included, such as the sulfite dehydrogenase necessary for cysteine catabolism, which oxidizes sulfite to sulfate and reduces cytochrome c. Finally, electrons from cytochrome c go through complex IV, contributing to the proton gradient, until they reach oxygen at +600 mV.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 4 Parabolic Off-Axis Reflective Optical System Design
### Optical System Image Quality Evaluation
Through the image quality analysis, it can be seen that the optimization results of the optical system meet the requirements of the design index, and the imaging quality of the system is good.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 3 Mathematical Model of a Differential Drive Mobile Robot  
The behavior of a differential drive mobile robot is based on two independent wheels, one on each side of the robot's body (Figure 3). Movement is achieved by applying different velocity values to each wheel. This way, the robot can move forward by making both wheels spin at the same speed, or it can turn in any direction using different speeds for each wheel. In this work, the approximated mathematical model of the differential drive mobile robot is used. [PERSON] in [17] presents the following mathematical model for a differential drive mobile robot, observing the following considerations:  
* The robot moves in a perfectly flat surface without sliding, despite wheel resistance;
* Robot position is given by \(x\), \(y\), and \(\theta\) coordinates, where \(\theta\) represents the robot rotation in relation to the coordinate system.  
The position and rotation of the robot in the plane are based on the following state equations, forming the kinematic model of the mobile robot [17]:  
\[\dot{x} =v\cos(\theta), \tag{4}\] \[\dot{y} =v\sin(\theta),\] \[\dot{\theta} =\omega.\]  
with \(v\) and \(\omega\) defined as follows:  
\[v =\frac{v_{l}+v_{r}}{2}, \tag{5}\] \[\omega =\frac{v_{r}-v_{l}}{l}.\]  
Figure 3: Graphic representation of the differential drive mobile robot model.  
where \(v\) is linear speed, \(\omega\) is the angular speed, \(\theta\) is the robot's orientation angle, \(l\) the distance between wheels, \(v_{l}\) is the right wheel speed, \(v_{l}\) the left wheel speed, and \(l=8.841\) cm (for simulation purposes, this value was taken from the distance between the two wheels present in the Khepera III mobile robot, presented in Section 7.1). A graphic representation of these variables is shown in Figure 3.  
Isolating \(v_{l}\) y \(v_{r}\) from Equation (5) we obtain:  
\[\begin{split} v_{l}&=v-(l*\omega),\\ v_{r}&=v+(l*\omega).\end{split} \tag{6}\]  
These Equations (6) will be used later for the numerical simulations.  
The goal of this work is to generate chaotic behavior in the linear \(v\) and angular \(\omega\) speed of the differential drive robot, described by Equations (4) and (5). To make this possible, an algorithm is designed using the Henon map (1) as described in the sequel.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Completeness  
Completeness refers to the extent to which all relevant and necessary data elements are included and conveyed in data exchange. It guarantees that no critical information is lost on transmission, enabling a comprehensive comprehension of the transmitted data for effective use. Completeness is an important characteristic of DQ and one of the essential DQ dimensions. For data to be considered complete, it must meet the requirements of its intended application. Completeness impacts other DQ dimensions, including accuracy, consistency, and timeliness [PERSON] et al. (2018). [PERSON] et al. (2018) proposed a transmission control mechanism that considers data completeness of large Spatial-temporal data. (STD). In their scheme, each node keeps accounts of received packet statuses and dispatches the information only when all packets have been accounted for, thereby minimising2 unnecessary transmission and optimising wireless bandwidth. STD combines geographical locations and timestamps to provide insights. It is worth noting that there is a plethora of applications in V2I interactions, ranging from traffic management applications to location-based services.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 4 Discussion
### The Influence of BRIRs on Externalization in a Visual-Only Virtual Listening Environment  
In the case of VR applications (e.g., gaming), users are usually introduced to virtual rooms that are different from the rooms in which they are physically present. Compared to the AR/MR scenarios in Experiment I, only the visual information provided to the listeners changes.  
When listening binaural sounds in visual-only virtual rooms B and C, the externalization ratings are high not only for the BRIR-A but also for the BRIR-B condition. Although the median externalization results differ slightly, there is no significant difference between them (\(p>0.5\)). Eight out of ten subjects have visited or performed activities in anechoic chambers and know the room acoustics of such special rooms, but the ratings for anechoic sounds (BRIR-C) when listening in the virtual room C are still low. The experimental results can be explained by the fact that the listeners were not aware of the acoustic information of these two visual-only virtual rooms during the experiment, and they evaluated the sounds based on their expectations or experiences with the acoustics of these two virtual rooms. For the virtual room C, listeners are familiar with the reverberant sounds in daily life and such experience is stored in human short-term memory, and they expected to hear reflections in the virtual room. Therefore, they rated the anechoic sounds as low based on their expectations and experiences [23].  
In AR/MR scenarios, listeners could learn the room-related auditory information of the local listening environment through any kind of acoustic feedback from the room, e.g., their own voice, footsteps, etc. To perceive well-externalized virtual sounds, the room-related auditory cues contained in binaural sounds should match to that provided by the room. As reported in Experiment I, the use of BRIRs (e.g., matched reverberation) for generating well-externalized binaural sounds is therefore restricted.  
According to the results in Experiment II, it seems that the use of BRIRs is not restricted in VR scenarios compared to that in AR scenarios when listening in typical virtual rooms. When no acoustic feedback provided by the virtual room, listeners' expectations are generated based on their experiences and the visual impression of the room, e.g., the size and decoration of the room. If the room-related acoustic information contained in binaural sounds meets listeners' expectations, the sounds can be perceived as externalized. However, the \"correct\" room information contained in binaural sounds may degrade perceived externalization when listening in some special virtual rooms. This is the case when listening to anechoic sounds in the virtual room C.