esa-sceva/satcom-chunk-collection huggingface dataset:
# Split-Based Algorithm for Weighted Context-Free Grammar Induction
## 2 Weighted Grammar-Based Classifier System
### Experimental Protocol and Complexity Analysis  
WCFG is learned using WGCS according to the experimental protocol described in Algorithm 3. The run-time complexity for the worst-case scenario of the given algorithm can be evaluated as follows. Say that \(k\) is the number of iterations of the WGCS algorithm, \(|G|\)--the size of the grammar, \(z\)--the number of sentences in the training set, \(n\)--the number of IOCE iterations, \(|N|\)--the number of nonterminal symbols, \(L\)--the length of the sentence, and \(y\)--the number of sentences in the validation set.  
```
1:Load the initial grammar and datasets
2:for\(i\gets 1\) to iterations do\(\triangleright\) iterations = 20
3: Run the split algorithm
4: Run IOCE on the training set \(\triangleright\) 200
5: Remove rules with low weights
6: Test grammar with CKY on the training set
7:endfor
8:Test best grammar with CKY on the validation set
```  
**Algorithm 3** Experimental protocol  
In the algorithm above, for a worst-case evaluation it should be assumed that run-time of the split algorithm (line 3) is bounded by \(|G|\), IOCE algorithm (line 4) is bounded by \(z\cdot n\cdot|N|^{3}L^{3}\), removing rules (line 5) by \(|G|\), testing grammar (line 6) by \(z\cdot L^{3}|G|\), and testing the best grammar (step 8) by \(y\cdot L^{3}|G|\). Thus the total amount of time to run lines 1-8 is:  
\[k\cdot(|G|+z\cdot n\cdot|N|^{3}L^{3}+|G|+z\cdot L^{3}|G|)+y\cdot L^{3}|G| \tag{10}\]  
Note that \(|G|=k\cdot|T|+k^{3}\) and \(|N|=k^{3}\), and the total amount of time can be calculated as follows  
\[k\cdot(k\cdot|T|+k^{3}+z\cdot n\cdot k^{3}L^{3}+k\cdot|T|+k^{3}+z\cdot L^{3}( k\cdot|T|+k^{3}))+y\cdot L^{3}(k\cdot|T|+k^{3}) \tag{11}\]  
which can be factored as:  
\[k^{2}\cdot|T|+k^{4}+z\cdot n\cdot k^{4}L^{3}+k^{2}\cdot|T|+k^{4}+z\cdot L^{3} (k^{2}\cdot|T|+k^{4})+y\cdot L^{3}(k\cdot|T|+k^{3}) \tag{12}\]  
Therefore, the total running time for this algorithm is estimated as:  
\[O(z\cdot n\cdot|T|\cdot k^{4}L^{3}+y\cdot|T|\cdot k^{3}L^{3}) \tag{13}\]  
Note that the maximum number of terminals \(|T|\) can be replaced by \((y+z)L\)  
\[O(z\cdot n\cdot(y+z)\cdot L\cdot k^{4}L^{3}+y\cdot(y+z)\cdot L\cdot k^{3}L^{ 3}) \tag{14}\]which reduces to  
\[O((y+z)\cdot k^{3}L^{4}(z\cdot n\cdot k+y)) \tag{15}\]  
As we can see the complexity of the proposed method is polynomially bounded with respect to the input size.
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Sensor-Based Systems  
Recent years have seen the size, cost, and energy consumption of small wireless sensors decrease by several orders of magnitude [92]. Indeed, today, low-power wireless sensors can be bought for an affordable price. These improvements have made it possible to connect everyday objects to the Internet, resulting in the visionary concept of the IoT [93; 94]. While there are a wealth of possible uses of the IoT for security, industry, and environment, measuring everyday activity to monitor and improve the health and well-being of persons is rapidly becoming an active area of research [95]. Furthermore, the integration of CPS and the IoT's main components, Wireless Sensor Networks (WSNs) [96], into healthcare is playing an important role in developing new IoT-enabled CPS-based healthcare solutions [97].  
Sensor networks and its subsets have been proven to be one of the outstanding tools for bridging the gap between the physical world and the computers by continuously enabling new applications in various walks of life [98]. In the context of human health, sensor systems allow us to collect data on daily activities in a free-living environment and possibly over long time periods, seamlessly [99]. One promising application in that field is the monitoring and assessment of subject for medication intake [100]. In fact, sensor-based approaches are the most widely used among other approaches these days for adherence monitoring. Utilizing sensor networks into medicine intake and adherence monitoring systems comes with features and benefits. The regularity in measurements, remote monitoring capability, and context awareness are a few examples [100]. In general, wireless sensors in this area of monitoring can be put into two main categories based on the form of deployment: fixed and wearable. Fixed sensors are tied to minimally mobile objects such as pillboxes or pill bottles, and home apparatuses. Meanwhile, wearable sensors are lightweight, have high data fidelity, and mobile devices that are attached to the user's body. Hence, wearable technology is becoming more dominant in enabling different healthcare applications [101], including medication adherence assessment.  
In vivo or intra body communication and networking [102] is another emerging sensor-based communication and network technology within the IoT family, which is enabling a new set of healthcare applications. In vivo biosensors can be integrated with ingestible dose forms for wireless and real-time medicine ingestion events retrieval [103].  
In this part, we describe the recent work on medication adherence monitoring using different forms of wireless sensing.
## 4 Case of Parallel Polarization of Source Radiation
\begin{split} E^{\prime}_{x\parallel}&=-i\frac{k^{ 2}}{\omega\varepsilon}\cdot\frac{[\left(x+x_{0}\right)\sin\alpha_{0}+y_{0}\cos \alpha_{0}]y_{0}+z^{2}\cos\alpha_{0}}{\left(r^{\prime}\right)^{3}}e^{ikr^{ \prime}};\\ E^{\prime}_{z\parallel}&=i\frac{k^{2}}{\omega \varepsilon}\cdot\frac{[\left(x+x_{0}\right)\cos\alpha_{0}-y_{0}\sin\alpha_{0} ]z}{\left(r^{\prime}\right)^{3}}e^{ikr^{\prime}},\end{split}  \tag{44}\]  
where \(r^{\prime}=\sqrt{\left(x+x_{0}\right)^{2}+y_{0}{}^{2}+z^{2}}\). Similarly, one can obtain expressions for the tangent field components from the imaginary source:  
\[ \begin{split} E^{\prime\prime}_{x//}&=-i\frac{k^{ 2}e^{ikr^{\prime\prime}}}{\omega\varepsilon}\left[\frac{-\left(x+x^{\prime }_{0}\right)\sin\left(\alpha_{0}+2\alpha\right)y^{\prime\prime}_{0}}{\left(r^ {\prime}\right)^{3}}+\frac{y^{\prime\prime}_{0}{}^{2}\cos\left(\alpha_{0}+2 \alpha\right)+z^{2}\cos\left(\alpha_{0}+2\alpha\right)}{\left(r^{\prime} \right)^{3}}\right];\\ E^{\prime\prime}_{z//}&=i\frac{k^{2}e^{ikr^{ \prime\prime}}}{\omega\varepsilon}\left[\frac{\left(x+x^{\prime\prime}_{0} \right)\cos\left(\alpha_{0}+2\alpha\right)z}{\left(r^{\prime\prime}\right)^{3 }}+\frac{y^{\prime\prime}_{0}\sin\left(\alpha_{0}+2\alpha\right)z}{\left(r^{ \prime\prime}\right)^{3}}\right],\end{split}  \tag{45}\]  
where \(r^{\prime\prime}=\sqrt{\left(x+x^{\prime\prime}{}_{0}\right)^{2}+\left(y^{ \prime\prime}{}_{0}\right)^{2}+z^{2}}\). By using the Expressions (29), (30), (44), and (45), it is possible to determine the components of the electric field intensity vector of the incident or reflected wave tangential to the beam splitter surface for a given polarization of the source radiation. The total field on the beam splitter surface for each polarization can be written, using the expressions:  
\[E_{x}=E^{\prime}_{x}+T_{x}(x,z)E^{\prime\prime}_{x};\;E_{z}=E^{\prime}_{z}+T_{ z}(x,z)E^{\prime\prime}_{z}. \tag{46}\]  
Here \(T(x,z)\) is the amplitude transmission of the beam splitter, it can be different for different polarizations of the irradiating light [13]. Thus, it can be noted that the relationships are obtained for determining the field distribution on the beam splitter surface of the laser interferometer under consideration, which is represented by the tangential components of the electric field intensity vector \(E_{x}\) and \(E_{y}\) expressed in the Cartesian coordinates of the basic system \(Oxyz\), taking into account the polarization of the radiation source and the transmittance of the beam splitter.
# Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving
## 2 Related work  
### Traditional Methods  
Histogram equalization (HE) is one of the most common methods used to improve image contrast [10]. The main idea is to count the histogram of grayscale pixels in an image, and then, adjust the distribution characteristics of the grayscale pixels to improve the image contrast. This method treats each pixel point in the image individually without considering the relationship between its domains. To solve this problem, many scholars have proposed improved methods for HE. [PERSON] et al. [11] proposed a two-dimensional HE algorithm that uses the contextual information around each pixel to enhance the image contrast. In addition, many scholars also transform the image from the spatial domain to the frequency domain via fast Fourier transform or wavelet transform, and process the relevant frequencies to adjust the image contrast. [PERSON] et al. [12] combined lifting discrete wavelet transform and singular value decomposition for low-contrast image enhancement. [PERSON] et al. [13] conducted a gradient-domain-based visualization method for high-dynamic-range compression and detail enhancement of infrared images. Since then, researchers have proposed filtering framework algorithms based on this technique. [PERSON] et al. [14] proposed a detail enhancement algorithm for infrared images based on local edge-preserving filtering, which divides the image into base and detail layers. Then, the base layer and detail layer are processed separately to obtain the respective enhanced images, and finally, a better ratio is selected to fuse the enhanced images of the base and the detail layer components. As a result, it takes a long time to enhance images with a larger resolution using the filtering framework algorithm.
## 3 Results and Discussion
It is also more efficient than the solar sintering method, which depends on the outpost's location, regolith optical properties, the position maintenance of the focal spots, and dust removal from the printing machines' lenses [96]. Hence, a precise trade-off between the available manufacturing technology and regolith sintering methods must be realized to select the reliable 3D printing technique with optimal energy and time required for the fabrication process. To test the reliability of extra-terrestrial outposts, as well as the experimental and numerical investigations on meteoroid impacts [126], evaluating the outpost's response to the moonquakes and marsquakes is also recommended. Moonquakes and marsquakes can have significant implications for lunar and Martian outposts [127; 128]. Understanding the frequency, intensity, and patterns of these seismic activities is crucial for designing any robust and resilient infrastructure. Moonquakes, although generally mild, can still pose a risk to surface structures and equipment [127]. Lunar habitats and resource extraction facilities must be engineered to withstand the occasional shaking and vibrations. Similarly, Martian outposts need to account for the potential impact of marsquakes. These seismic events can vary in magnitude and duration, potentially affecting the stability of structures, underground habitats, and life support systems [128]. Engineers and designers working on Martian outposts must develop seismic-resistant technologies and construction methods to ensure the safety and longevity of habitats and infrastructure.
# Bio-Inspired Design of an Underwater Robot Exploiting Fin Undulation Propulsion
## 3 Results  
The final assembly of the robot, as described in the previous section, is shown in Figure 20.  
Figure 20: Bioinspired robot without (**a**) and with (**b**) the external surface.  
The gains in the PI controllers were obtained experimentally with a trial-and-error procedure and their values are as follows:  
* \(k_{p}^{(m)}=0.98\) \(s^{-1}\)
* \(k_{i}^{(m)}=2.23\) \(s^{-2}\)
* \(k_{p}^{(f)}=1.47\) \(s^{-1}\)
* \(k_{i}^{(f)}=2.88\) \(s^{-2}\)  
The angular positions were acquired at regular intervals of 1 ms, synchronized by the internal clock of the board. Then, a low-pass filter, with a cutoff frequency of 10 Hz is applied to the acquired signal to delete some high-frequency noise.  
The control algorithm was tested experimentally, as displayed in the supplementary video and in Figure 21, where the fin mechanisms are shown in different instants of their motion.  
The results of the control algorithm can be better appreciated by looking at Figure 22, where the signals coming from the encoders are plotted. In Figure 22a, the encoder measurements are taken when the fish swims forward and it is possible to note that, for both fins, the front mechanisms (Motor 0 and Motor 3) are in advance with respect to the central mechanisms (Motor 1 and Motor 4), and the back mechanisms (Motor 2 and Motor 5) are in delay. On the other side, in Figure 22b, the measurements come from a left turn maneuvre; for the right fin, the mechanisms have the same phase shift as for forward swimming, whereas for the left fin, the back mechanism (Motor 5) is in advance with respect to the central mechanism (Motor 4) and the front mechanism (Motor 3) is in delay.  
Some preliminary tests underwater were carried out, as represented in Figure 23.  
A fin of the robot was immersed in a basin filled with water, and the motion of the fin was imposed. This test showed that the motors are powerful enough to exert a force on the surrounding water and to move the fin in the desired way. During the test, the span of the fin was oriented in a vertical direction, instead of being horizontal. This means that, in this test, weight and buoyancy forces were not present. Nevertheless, this is still representative of the working condition because the robot is self buoyant.  
Figure 21: Instances of fin motion during forward swimming in sequence (**a–d**).
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 2 Improved RRT Path Planning Algorithm
### Variable Steering Angle Strategy  
The principle behind the variable steering angle strategy is to prevent the search tree from growing in a reverse direction. This strategy is based on the concept of RRT node extension. When the sampling area narrows down to a specific range, the growth of new branches, without constraints on the steering angle, may deviate from the target point, creating unnecessary branches and generating a large number of redundant search nodes. As a result, search efficiency and accuracy are reduced. However, blindly pursuing high directionality can also lead to local oscillations, which in turn reduces search efficiency. This article introduces a variable steering angle strategy, the principles of which are illustrated in Figure 2.  
Figure 1: Schematic diagram of RRT algorithm.  
In the Range region, after the random tree child node \(p_{rand}\) finds the closest point \(p_{near}\) in the tree, a new child node \(p_{new}\) is generated according to the preset step size \(\mu\). If the distance between two points is less than \(\mu\), then \(p_{rand}\) is taken as the new search node; if the distance between two points is greater than \(\mu\), then \(p_{new}\) passes the collision detection, and at this point, the angle \(\theta\) is greater than \(90^{\circ}\) between the connecting line of \(p_{new}\), \(p_{near}\) and the connecting line between \(p_{near}\), \(p_{goal}\), then it means that the tree branching sub-node There is a reverse growth situation where, in order to solve this problem, the steering angle \(\theta\) is changed to one-third of the original to generate a new child node, \(p_{newbest}\), as a child node of this search tree. The formula for updating the steering angle is shown in Equation (1).  
\[\theta=\left\{\begin{array}{ll}\theta,&\theta e\left[0,\frac{\pi}{2}\right] \\ \theta,&\theta e\left(\frac{\pi}{2},&\pi\right]\end{array}\right. \tag{1}\]  
In the above equation, when \(\theta e\left[0,\ \frac{\pi}{2}\right]\), no steering angle processing is required; otherwise, the steering angle is updated to one-third of the original. Defining \(\tau\) as the updated step size, q as the sum of the normalized vectors of the new child node's outward extension, and \(p_{newbest}\) as the child node after the variable steering angle strategy, the new node generation equations are as follows:  
\[\tau=\min\left(\left|\mathrm{P}_{\mathrm{rand}}-\mathrm{P}_{\mathrm{near}} \right|,\ \mu\right) \tag{2}\]  
\[q=\frac{\mathrm{P}_{\mathrm{goal}}-\mathrm{P}_{\mathrm{near}}}{\parallel \mathrm{P}_{\mathrm{goal}}-\mathrm{P}_{\mathrm{near}}\parallel_{2}}+\frac{ \mathrm{P}_{\mathrm{rand}}-\mathrm{P}_{\mathrm{near}}}{\parallel\mathrm{P}_{ \mathrm{rand}}-\mathrm{P}_{\mathrm{near}}\parallel_{2}} \tag{3}\]  
\[p_{newbest}=\left\{\begin{array}{ll}p_{near}+\tau\times\frac{x_{rand}-x_{near }}{\parallel x_{rand}-x_{near}}\,&0\leq\theta\leq\frac{\pi}{2}\\ p_{near}+\tau\times\frac{\theta}{\parallel x_{2}}&,&\frac{\pi}{2}\leq\theta \leq\pi\end{array}\right. \tag{4}\]
## 1 Introduction
### The Objectives and Contributions of This Study  
Motivated by the concepts of material chemistry, the Material Generation Optimization (MGO) algorithm is a noveloptimization methodology [58]. The MGO algorithm simulates the formation and stabilization of chemical compounds to investigate and optimize the searching space of variables. To provide newer potential solutions, the MGO method simulates the creation of ionic and covalent connections [59]. It then evaluates the stability of these solutions to guarantee convergence to the desired values based on the fitness score.  
In this paper, a Quadratic Interpolated Learner Process (QILP) is incorporated into an Improved MGO (IMGO). By mixing data from three distinct chemical compounds, the suggested integration of the QILP with the given IMGO method adds diversification to the solutions. Strong search space exploration is aided by the randomization and permutations provided. The proposed IMGO technique is designed against several mathematical benchmarks for solving the CEC 2017 test suite and other engineering problems including Welded Beam Design (WBD), rolling Element Bearing Design (REBD), and Pressure Vessel Design (PVD). It was also tested on a real Egyptian power distribution system to optimize the placement of PV and capacitor units with the aim of minimizing energy losses. Therefore, the key contributions of this paper are as follows:  
* An IMGO variant is proposed, integrating QILP to enhance search efficiency and improve results.
* Both the original MGO and the enhanced IMGO algorithms are rigorously tested against multiple benchmarks from the CEC 2017 suite to validate their optimization capabilities.
* The IMGO algorithm's evaluation across 23 benchmark models demonstrates its robustness, with significant improvements in accuracy and consistency metrics, outperforming the standard MGO in over 65% of the cases.
* The IMGO algorithm is applied to three well-known engineering design problems, demonstrating superior performance compared to various bio-inspired algorithms.
* The IMGO algorithm is also evaluated on a real Egyptian power distribution system to optimize the placement of PV and capacitor units.
* The use of IMGO for PV parameter estimation illustrates its capability to minimize computational inaccuracies effectively, offering superior performance in a real-world engineering context.
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 4 Discussion  
### Effectiveness of eDNA Monitoring  
Our results showed that eDNA monitoring based on NGS holds great potential as a complementary monitoring tool to identify spatial taxonomic distribution patterns in coastal areas. We characterized detailed zonation patterns of the outer bay of Gwangyang Bay previously categorized by [PERSON] et al. [25]. Specifically, Zones III-2 and III-3 were clearly resolved by CCA using genus-level identification of aquatic organisms based on meta-barcoding data (Figure 2). When divided into categories (habitat types, trophic level, and ISR), averages of abundant sequences showed different patterns among the zones (I, II, III-1-2-3). The NIS detection of Zone III-3 was significantly higher than the other zones. Therefore, eDNA based on meta-barcoding is a promising ecological tool for monitoring, such as biodiversity assessment or NIS management [24; 37; 38; 39; 40]. This approach by meta-barcoding in complex coastal ecosystems confers three distinct advantages over other methods. First, the range of aquatic organisms in coastal ecosystems open to study is widened. Second, identification of aquatic organism to the species or genus level (Appendix A), which can be converted into ecological values such as FFC and NIS, is possible. Finally, sensitivity of the meta-barcoding approach using small volumes of water (1 L) is a promising alternative to traditional methods (i.e., dredges and surber samplers) for biodiversity detection in coastal areas. Using these advantages, eDNA monitoring can provide a useful tool for use in environmental management. However, some taxa were unable to be identified to the species or genus level due to the incompleteness of reference databases (i.e., NCBI GenBank). More accurate target regions such as the cytochrome oxidase I region, which is called the standard region of barcoding, are needed to identify target species at the species level and are required for identification of aquatic organisms [41]. Another challenge associated with eDNA monitoring is the risk of false-positive and false-negative detections [42]. The reliability of the eDNA sampling method should be demonstrated using in silico, in vitro and in situ validation tests in the coastal area. Even though this approach has limitations, taxonomic expertise is not required and it can supplement observational records and field surveys to obtain marine ecosystem samples and information, as NGS reveals biodiversity and the number of NIS in one specific area along the their temporal and spatial distribution.
## 3 Results
### Question 6: What Is the Combined Effect of Individual IEQ Aspects on Overall IEQ Satisfaction?
#### 3.6.3 Analytical Hierarchy Process (AHP)  
The analytical hierarchy process (AHP) is a basic approach for decision-making; it applies simple pairwise comparisons to rank the aspects of IEQ regarding their importance to the overall IEQ performance. Usually, to conduct an AHP, experts in the related fields are generally invited to participate in a questionnaire survey consisting of a set of comparisons between every two of the investigated indicators, with a 9-symmetric point scale. Based on these experts' perceptions, a numerical weighting system can be derived for each investigated indicator. However, previous studies show that experts from different fields or countries might hold different opinions. For example, an online survey showed that Chinese engineers believed that all the IEQ aspects were equally important, while Chinese building designers and planners believed that IAQ was more important than other IEQ aspects.  
In contrast, English experts, no matter their fields, believed that thermal quality was the most critical IEQ aspect. Therefore, a careful selection of expert panels is required when applying this method. Besides, by using AHP alone, researchers can only understand the rankings of IEQ aspects. To achieve a final model for quantitative evaluation, AHP must be combined with other statistical analysis methods, such as fuzzy comprehensive evaluation (FCE) [50].