esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Classical Algorithms
#### 3.2.1 Dynamic Voronoi-Based Algorithm  
It is a mathematical model generally used in area division in some regions. These regions are based on the seeds given in the first. Every part has a corresponding region encompassing all points in space closer to itself than others. In this case, the region is called Voronoi cells [58]. The Voronoi algorithm controls swarm robots; each robot acts as a seed and divides the target area into Voronoi cells. Every robot must cover its own Voronoi cell, guaranteeing that each target point is closer to its corresponding robot [59].  
Ref. [60] proposed a dynamic Voronoi-based algorithm, which is used for the area coverage problem; it divides the target area into Voronoi cells dynamically through the moving of robots rather than using a static algorithm and to improve the efficiency of modified bacterial foraging optimisation (MBFO) by using swarm robots in searching. The MBFO is also used to coordinate between robot positions and the swarm robot target area. The MBFO algorithm considers each robot to be a bacterium. Then, it applies the mechanism of bacteria chemotaxis to solve problems of decentralised controlling for swarm robots in experiments, using these two algorithms to the process of area coverage by 26 swarm robots in random locations on a 300 \(\times\) 300 m\({}^{2}\) area by MBFO. The moving of robots depends on following the concentration gradient in the target area and using the sensor control the reaction between them. Also, after using 10, 20, and 30 robots to validate the efficiency of the new dynamic Voronoi bade on MBFO, the results showed the effectiveness of the dynamic Voronoi method.
## 3 Experimental Study
### Theory of Energy Calculation
#### 3.3.3 MIX Number  
[PERSON] et al. [39] introduced the MIX number, which is a widely used metric for characterizing the stratification effect in tanks. This parameter quantifies the thermal stratification within the tank at a particular instant, ranging from zero to one, indicating different degrees of thermal stratification. In an ideal scenario where hot and cold water are perfectly stratified within the tank, the MIX number approaches zero. Conversely, when hot and cold water within the tank was fully mixed, the MIX number attained a value of 1. A reduction in this value indicates an improvement in stratification associated with a reduction in the thermocline thickness within the stratified layers [40; 41]. To calculate the MIX number, the tank was conceptually divided into N horizontally arranged layers, each with an equal volume (V). The energy contained within each tank layer is designated as E, whereas the momentum of this energy is defined as M. Equation (8) provides a mathematical framework for computing the MIX number, drawing upon these fundamental concepts [28].  
\[MIX\ number\ =\ \frac{M_{\text{str}}-M_{\text{exp}}}{M_{\text{str}}-M_{\text{full-mixed }}} \tag{8}\]  
\[M_{exp}\ =\ \sum_{i\ =\ 1}^{n}y_{i}E_{i} \tag{9}\]  
\[E_{i}\ =\ \rho\cdot V_{i}\cdot C_{p}\cdot T_{i} \tag{10}\]  
where \(M_{str}\) is the momentum of energy when the tank is perfectly stratified, kg\(\cdot\)m/s; \(M_{exp}\) is the momentum of energy when the tank is experimented with, kg\(\cdot\)m/s; \(M_{mix}\) is the momentum of energy when the tank is completely mixed, kg\(\cdot\)m/s. \(y_{i}\) is the distance from the center of each water layer to the bottom of the tank (m), and \(E_{i}\) is the energy of each part of the water (J).  
\[E_{str}\ =\ E_{exp} \tag{11}\]  
where  
\[E_{str}\ =\ \rho\cdot C_{p}\cdot V_{hot}\cdot T_{hot}+\rho\cdot C_{p}\cdot V _{cold}\cdot T_{cold} \tag{12}\]  
\[V_{total}\ =\ V_{hot}+V_{cold} \tag{13}\]  
The position of the oblique temperature layer, \(y_{str}\), is determined by Equation (14).  
\[V_{cold}\ =\ y_{str}\cdot\pi\cdot\frac{D^{2}}{4} \tag{14}\]  
Based on \(V_{hot}\) and \(V_{cold}\), the momentum of the ideal stratified water tank was determined using Equation (15).  
\[M_{str}\ =\ \sum_{i\ =\ 1}^{n}y_{i}\cdot E_{str} \tag{15}\]  
where  
\[E_{fully-mixed}\ =\ \rho\cdot V_{i}\cdot C_{p}\cdot T_{fully-mixed} \tag{16}\]  
\[M_{fully-mixed}\ =\ \sum_{i\ =\ 1}^{n}y_{i}E_{fully-mixed} \tag{17}\]This approach offers a scientifically rigorous means of assessing tank stratification, enabling researchers to quantify the performance of thermal storage systems and identify areas for optimization.
# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 4 Methods and Material
### Efficient RANSAC Using Scale Consistency  
With the mesh-based scene representation, we can use the depth from the projection of the mesh to verify whether the solution from MLP combined with homotopy continuation is the real geometry or the input of MLP contains outliers.  
To be more specific, in this step, the input of the MLP is five local feature correspondences in the query and database images. We denote the five local features in the database image as \(\mathbf{f}_{0}\), \(\mathbf{f}_{1}\), \(\mathbf{f}_{2}\), \(\mathbf{f}_{3}\), and \(\mathbf{f}_{4}\). We denote their corresponding depth in the depth map as \(\mathbf{d}_{0}\), \(\mathbf{d}_{1}\), \(\mathbf{d}_{2}\), \(\mathbf{d}_{3}\), and \(\mathbf{d}_{4}\). The output of the MLP is ten depths of these five feature correspondences (five depths for the database image and five for the query image), whose scale is different from the depth map. We denote the output of the five depths in the database image as \(\mathbf{Sol}_{[0]}\), \(\mathbf{Sol}_{[1]}\), \(\mathbf{Sol}_{[2]}\), \(\mathbf{Sol}_{[3]}\), and \(\mathbf{Sol}_{[4]}\). Suppose the MLP has tracked the correct geometry with the correct input; the five depths obtained from five depth maps and MLP, ideally, only differ a scale factor (i.e., for \(i\in\{0,1,2,3,4\}\), the ratio of \(d_{i}\) and \(\mathbf{Sol}_{[i]}\) should be the same). Therefore, we set a deviation threshold for \(\frac{\boldsymbol{Sol}_{[i]}}{\mathbf{d}_{i}}\), \(i\in\{0,1,2,3,4\}\) considering the noise. If there exists a ratio that is obviously different from others, we suppose it is an unreal solution. Thus, we can skip the validation step of this solution in the inner loop of RANSAC, which greatly improves the efficiency of RANSAC. We illustrate our algorithm to filter out 2D outliers in Algorithm 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RoborCar Seasons/Aachen [\%]** & \multicolumn{5}{c}{**Numbe of anchors**} \\ \hline
**RANSAC Sampling Times** & **40** & **80** & **120** & **160** & **200** \\ \hline
5 & 1.5/4.8 & 4.7/7.2 & 8.5/16.4 & 8.9/17.6 & 8.8/17.9 \\
10 & 3.5/6.9 & 6.1/11.3 & 12.5/21.7 & 12.9/22.3 & 13.5/24.3 \\
30  
```
0: Local feature correspondences set \(\Omega\) \(RANSAC\_sampling\_times=N\) \(num\_maximilers=0\) while\(time<N\)do  sample five correspondences \((q_{0},f_{0}),(q_{1},f_{1}),(q_{2},f_{2}),(q_{3},f_{3}),(q_{4},f_{4})\)  get five rendered depth of database features \(d_{0},d_{1},d_{2},d_{3},d_{4}\)  calculate relative pose by MLP if MLP output 0 then  continue else  five output of MLP \(sol[0],sol[1],sol[2],sol[3],sol[4]\)  derive \(s=mean\Big{[}\frac{sol[0]}{d_{0}},\ldots,\frac{sol[4]}{d_{4}}\Big{]}\) if\(\forall i\in 0,1,2,3,4\), \(\Big{[}\frac{sol[i]}{d_{i}}-s\Big{]}>\delta\)then  continue else  calculate number of inliers \(m\) if\(m>num\_maximilers\)then \(num\_maximilers=m\)  end if  end if  end if endwhile Output: bestlinier set \(\Omega_{1}\)
```  
**Algorithm 1** Filtration algorithm using scale consistency
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 2 Basic System Structure of CU-MTDC
### Control Structure of CU-MTDC
#### 2.2.3 Primary Control Strategy  
The master-auxiliary control strategy [22] is adopted in this paper as the primary control, which is shown in Figure 3. Compared with traditional methods, master-auxiliary control is more suitable for CU-MTDC applications due to its better effectiveness of DC voltage control and accurate active power control (APC) of the APC converter under normal conditions. As shown in Figures 1 and 3, the rectifier converters are the master converter and auxiliary converter. The master converter is set to keep constant DC voltage control under normal conditions. The auxiliary converter is set to assist the DC voltage control. The CCUP grid converters are APC stations. The APC converters should turn into droop control under abnormal conditions. In this paper, constant active power control is used in the wind farm converter to simulate wind power variations.  
As shown in Figure 3, \(U_{dc}\) is the DC voltage of the converter, \(P\) is the active power of the converter on the AC side. \(P_{MAX}\) and \(P_{MIN}\) are the maximum and minimum active power of the converter, respectively. \(U_{dch}\) and \(U_{ddcl}\) are the maximum and minimum DC voltage when the converter is under droop control, respectively. \(U_{ddcl}\) and \(U_{ddcl}\) are the DC voltage when the converter turns into droop control, respectively. \(U_{ddcrf}\) is the reference value of the DC system. \(U_{H}\) and \(U_{L}\) are the DC limit values of converters. \(P_{nft}\) and \(P_{n}\) are the active power reference value and the actual active power output value of the APC converter \(n\), respectively. \(k_{max}\) and \(k_{apc}\) are the droop coefficients of the auxiliary converter and APC converter, respectively. For all the converters, the positive direction of active power is defined as an injection to the DC grid.
## 4 Discussion  
The results of the meta-analysis for the three outcomes are in agreement in establishing that the superficial alterations induced by autoclave, from the sodium hypochlorite and from the EDTA, are statistically significant surface alterations that represent points where instrument fractures can be triggered. In addition, the alterations induced on the surface analyzed by SEM and AFM show that the alterations can also be expressed on the cutting surface, altering, in a pejorative sense, the cutting efficacy.  
For the first outcome, the studies, in the literature, that supported a statistically significant alteration are:  
1. In 2007, Inan reported statistically significant data for all the instruments of the ProTaper series (S1, S2, F1, F2), and reported that the superficial deterioration induced by the autoclave is greater for ProTaper finished than for ProTaper shaping;
2. In 2012, [PERSON] confirmed, in agreement with Inan's data, that multiple cycles (autoclave sterilization) modified the surface topography and chemical composition of conventional NiTi (F2 ProTaper) and TiN-coated (alpha kit) instruments, in a statistically significant way (after five autoclave cycles).  
Sodium hypochlorite certainly alters the surface of NiTi instruments and innumerable studies are in agreement such as [PERSON] et al., 2018 [19]; [PERSON] et al., 2010 [12]; [PERSON] et al., 2008 [20]; and [PERSON] et al., 2014 [23]. Furthermore, a study conducted by [PERSON] et al., 2004 [24] stated that the action of sodium hypochlorite causes a worsening of the surface in endodontic instruments that facilitates their rupture following flexor and torsional stress.  
The statistical analysis, in a similar way but with fewer studies, also confirms that the EDTA determines an increase in surface irregularities in a statistically significant way, and studies that confirm it after 10 min of exposure are well highlighted in the forest plot (Figure 5). Studies that are in contrast to the present meta-analysis report conflicting data regarding the action of EDTA on the surface. It seems that for exposures less than 5 min they do not alter the surface, however, according to [PERSON] et al. [25], a passivation phenomenon could lead to the creation of a complex between the metallic ions and the EDTA at a PH lower than four which renders the instrument resistant.
## 2 Materials and Methods
### Membranes  
A previous study [47] conducted by our group allowed us to identify the composition of the membrane that has the best chemical and electrochemical properties to extract lithium from sodium and potassium containing solutions in a very selective way. This membrane is a Lithium Composite Membrane (LCM) composed of 50.5 wt.%, 25.5 wt.%, 18 wt.% and 6 wt.% of LICGC, PECH-DABCO, PES-NH\({}_{2}\) and BRIJ76, respectively, and prepared using the blending technique as described in [47]. The principal purpose behind this composition is to prevent the rigidity of LICGC ceramic membranes and combine the significant Li\({}^{+}\) selectivity of LICGC particles with the flexibility of organic anionic exchange polymer (PECH-DABCO and PES-NH\({}_{2}\)).  
This combination allows simultaneous Li\({}^{+}\) and anion transport across the LCM. Li\({}^{+}\) diffuses through interconnected LICGC particles, and anions are transported through the used polymer. Other cations are generally not allowed to transfer through the membrane polymer because of its anionic exchanger nature. Several characterizations were performed to ensure the LCM morphological homogeneity, thermal stability and mechanical properties. SEM pictures demonstrate the homogeneous dispersion of LICGC particles in the matrix building selective Li\({}^{+}\) percolation pathways.  
The main physicochemical properties of LCM in Table 1 show that this membrane is relatively thin and has a particularly low conductivity compared to conventional ion-exchange membranes. The conductivity of the LCM is mainly provided by ion migration through the PECH-DABCO and PES-NH\({}_{2}\) polymer, with a small proportion generated, when using Li\({}^{+}\), by the interconnected LICGC particles. A water content of 11.3% generally provides a good level of ion mobility with a fairly high selectivity.  
The Anion-Exchange Membranes (AEM) used in this work for constituting the four-compartment ED cell was a commercial AMX membrane. This was composed of a poly(styrene-co-divinylbenzene) (PS-DVB) copolymer containing quaternary ammonium functional sites and supported by a PVC film.
# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 5 Most Significant Challenges Related to Data Quality and Labelled Data  
The efficacy of Machine Learning and Deep Learning in cloud detection and masking is intrinsically linked to the use of labelled data. These data are crucial for training and testing models to differentiate cloud types and cloudy/clear-sky regions. Enhanced generalization abilities of models trained on labelled data ensure accurate predictions on unseen data, bolstering the reliability of cloud masks in various scenarios.  
However, obtaining and labeling extensive datasets is resource-intensive. Techniques like transfer learning [115], data augmentation [128], and active learning [129] have emerged as solutions to mitigate this challenge. They reduce the need for large labelled datasets, enhance the diversity of training data, and optimize the labeling process.  
Despite these advancements, several significant challenges persist:  
* Quality of satellite imagery: Factors such as atmospheric interference and sensor noise affect the accuracy and reliability of satellite imagery. Pre-processing techniques improve input data quality but can introduce uncertainties [130,131].
* Variability of the publicly available annotated datasets: Publicly available annotated datasets are used for the training and testing of models. The development of large, high-quality datasets requires expertise and time-consuming processes; inconsistent labeling, especially in ambiguous situations, complicates model training and evaluation [95]. In addition, the resulting annotated masks are generally specific to a particular sensor. Different formats, resolution, variability of class types, different data volume, non exhaustive geographic scene distributions, are some factors that characterize most of the currently available annotated masks, besides the lack of thin clouds or cloud shadows labels. This leads to an uneasy utilization of the available amount of data. Finally, not all the reference data used by some developers are freely available.
* Diversity of labelled data: The diversity of the labelled data is another important factor that affects the accuracy and generalization performance of the model. The labelled data should be diverse enough to capture the variability and complexity of cloud patterns and atmospheric conditions. However, it can be challenging to obtain diverse labelled data, especially for rare or extreme cloud events.
* Data imbalance: Another challenge is the imbalance between cloud and non-cloud regions in the input data. Cloud regions are often less frequent than non-cloud regions, which can lead to bias and poor model performance. Techniques such as data augmentation and sampling can help to address this issue, but they can also introduce new sources of error and uncertainty.
* Sensor-dependent data: each dataset is linked to the spatial and spectral characteristics of the source sensor.  
Future research should focus on improving and developing new Deep Learning techniques that address these challenges.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 1 Introduction
### The State of the Art
#### 1.2.3 Characteristics of Lamb Waves  
[PERSON] et al. [37] analyzed the influence of material properties, such as \(E_{11}\) (axial elastic modulus), \(E_{22}\) (transversal elastic modulus), \(G_{12}\) (plane shear modulus), and \(\rho\) (density), on Lamb wave propagation/dispersion of three kinds of composite laminates. These composite laminates are unidirectional, cross-layered, and quasi-isotropic, which are all composed of 16 layers of unidirectional carbon fiber epoxy prepreg. The results showed that when Lamb waves are used for damage detection, the \(A_{0}\) mode should be selected when \(fd\) (frequency times thickness) is small, and the \(S_{0}\) mode should be selected when \(fd\) is large. [PERSON] et al. [38] proposed an effective numerical method with which to study the nonlinear scattering and mode conversion of Lamb waves interacting with breathing cracks. The results showed that the scattering phenomenon was related to the incident wave mode. Symmetric and antisymmetric scattering modes exhibit alternate properties, which, in turn, carry most of the energy and dominate the harmonic scattering. [PERSON] et al. [39] used the finite element method to study the propagation characteristics of Lamb waves in thin plates, and when compared with the frequency dispersion curve, the error of the simulation was 1.3%. [PERSON] et al. [40] studied the notch and impact damage of slats, and the results showed that Lamb waves could provide time and cost efficiency in aircraft structure detection compared with other technologies. The finite element method was used by [PERSON] et al. [41] to simulate the propagation of Lamb waves in a thin metal plate. The results showed that when there was damage in the plate, a new packet-wave formed by a mode conversion phenomenon could be observed.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 5 Final Construction: TSKT-ORAM
### Data Query  
To query a data block \(D_{t}\) with ID \(t\), the client increments the counter \(\mathcal{C}\) and searches the index table \(\mathcal{I}\) to find out the leaf k-node that \(D_{t}\) is mapped to, and then, for each k-node \(u\) on the path from the root k-node to this leaf node, the XOR operations similar to those in TSBT-ORAM are performed to retrieve \(D_{t}\). The only difference is that the query bit vector size of TSKT-ORAM is \(3c(k-1)\) bits per vector. As shown in Figure 2(a), to query a data block \(D_{t}\) stored at k-node \(u_{3,21}\), the EIs at \(u_{0,0}\), \(u_{1,1}\), \(u_{2,5}\) and \(u_{3,21}\) are accessed, as these k-nodes are on the path from the root to the leaf node to which \(D_{t}\) is mapped. A dummy data block is retrieved obliviously from \(u_{0,0}\), \(u_{1,1}\), and \(u_{2,5}\), respectively, while \(D_{t}\) is retrieved obliviously from \(u_{3,21}\).
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 3 Related Works
### Data Conversion and Structuring  
Several studies have demonstrated the effectiveness of machine learning and NLP techniques in extracting structured information from scientific text. For example, a recent paper ([PERSON] et al., 2018) demonstrated the effectiveness of finetuned LLMs in extracting structured information from scientific text. Their findings included that finetuning LLMs can extract complex scientific knowledge and format it as JSON objects. The method worked well with only a few hundred training examples. Models showed the ability to automatically correct errors and normalize common entity patterns. In ref. ([PERSON] et al., 2018), the authors tested various LLMs, including GPT-3, InstructGPT, and PaLM, on tasks like question answering, summarization, and code generation. For each task, LLMs were asked to generate responses in three formats: free-form text, structured formats (e.g., tables, lists, code), and a mixture of both. They found that LLMs consistently performed better on free-form text generation compared to structured formats. Moreover, this study revealed that LLMs have inherent biases toward producing natural language text and struggle more with adhering to the constraints of structured data formats. Finally, stricter format constraints resulted in greater performance degradation in the LLMs' reasoning abilities.  
Despite these advancements, there are relatively few tools and methods specifically designed for converting unstructured clinical data into structured formats, particularly in healthcare. Our study addresses this gap by being the first to rigorously test the capabilities of LLMs in converting unstructured clinical data into the highly structured and complex FHIR format. This approach not only highlights the potential of LLMs in healthcare data conversion but also aims to guide other researchers by providing insights into which LLMs to use, how to prompt models effectively, and which parameter values to choose for optimal performance.
## 2 Materials and Methods  
### Chemicals and SPME Device  
All standards (benzaldehyde, benzoic acid, dodecane, ethylbenzene, octanal, tetrade-cane and toluene) were purchased from Supelco (Sigma-Aldrich, St. Louis, MI, Italy) and were certified as reference material, except octanal, which was of analytical degree (\(\geq\)98%, purity). Stock solutions of each compound (10 mg/mL) were prepared in methanol (Sigma-Aldrich, MI, Italy), stored at 8 \({}^{\circ}\)C and daily diluted in a fresh culture medium (Dulbecco's Modified Eagle Medium, Euroclone, MI, Italy) to prepare working standard mixtures.  
The SPME assembly kit (Supelco) included a manual holder and six different fiber coatings (1 cm, length), namely, 60 \(\upmu\)m carbowax-polyethylene glycol (PEG), 85 \(\upmu\)m polyacrylate (PA), 75 \(\upmu\)m carboxen/polydimethylsiloxane (CAR/PDMS), 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (CAR/DVB/PDMS), 65 \(\upmu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB) and 7 \(\upmu\)m polydimetylsiloxane (PDMS). Before use, fibers were conditioned in the GC injector, as suggested by the supplier.