esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
When pharmaceutical treatments for Parkinson's Disease (PD) are no longer effective, Deep Brain Stimulation (DBS) surgery, a procedure that entails the stimulation of the Subthalamic Nucleus (STN), is another treatment option. However, the success rate of this surgery heavily relies on the precise location of the STN, as well as the correct positioning of the stimulation electrode. In order to ensure the correct location, Micro-Electrode Recordings (MERs) are analyzed. During surgery, MERs capture brain signals while inserted in the brain, receiving different brain activity depending on the crossed brain area. The location of the STN is guaranteed when brain signals from MERs meet certain criteria. Nevertheless, MER signals are sensitive to various artifacts coming from machinery or other electrical equipment in the operating theater; patient activity; and electrode motion. These all lower the signal-to-noise ratio of the MER signals. MER signals are stochastic, multicomponent, transient, and non-stationary in nature, and they contain multi-unit neural activity in the form of spikes and artefacts. Thus, accurately defining that MERs are located in the STN is not an easy task. This work analyzes relevant features from MER, based on analyzing spike activity and local field signals. Six different classification algorithms are used, together with the optimal input feature selection. The algorithms are trained using supervised Leave-One-Out Cross-Validation. MER data were collected in a real scenario from 14 PD patients during DBS implantation surgery. The dataset is publicly available. The results derived from the use of this method show an accuracy of up to 100% in detecting where the MER electrode is located in the STN brain area.  
spike-dependent features; spike-independent features; machine learning; Subthalamic Nucleus (STN) detection; Parkinson's disease; Deep Brain Stimulation (DBS) Article Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson's Disease.  
1
## 2 Selenium from Soil to Food Bowl
### Metabolism and Se Speciation in Plants  
Selenate reduction to selenite then to selenide is coupled to an oxidation of glutathione [35; 37]. The Selenide is converted to Se-Cys in a manner analogous to sulfur metabolism. Se-Cys is converted by reverse trans-sulfuration to Se-Met and the two amino acids can be incorporated nonspecifically into proteins in the place of methionine and cysteine [33].  
Selenomethonine can be further metabolized to Adenosyl-Se-Met (SeAM), DMeSe and then converted to MeSeCys and \(\gamma\)-glutamine-Se-MethylselenoCysteine (\(\gamma\)-glu-SeMeSeCys) [34; 42]. At high levels of Se, MeSeCys becomes the main Se compound, although other compounds are present but at low content [43].  
Figure 1 also shows the process of biogenic volatilization of Se from soil and plants, which is in fact a detoxification process [33]. It is used to decontaminate soils rich in Se, such as oil refinery effluent fields [33]. Since some plants can absorb large amounts of Se from the soil, it is therefore very important that plants exhale various volatile Se compounds. The main product of plant volatilization is DMeSe [33]. Heat-treated food products can lose up to 10% of total Se due to the formation of volatile Se compounds (DMeSe) [23].  
Different factors can influence the ability of plants to volatilize Se. First, the concentration of Se in the roots, the Se species as well as the concentration of sulfate compared to selenate (they can compete for particular enzymes for the process of volatilization) [33].
# Analysis of the Development Status of eLoran Time Service System in China
## 5 Construction Content
### Time Frequency System  
The deviation between the time signal generated by the eLoran Time Service station's time frequency system and UTC (NTSC) is maintained within 5 ns. Multiple methods, such as fiber optic, GNSS CV/PPP, and satellite bidirectional comparison links, are integrated and complementary to achieve the traceability of time frequency signals to UTC (NTSC). The time and frequency system is equipped with three hydrogen atomic clocks to provide the main clock frequency signal of the system. Seamless switching between the main and backup is adopted to ensure the consistency and stability of the output time and frequency signals.
# On Finding Optimal (Dynamic) Arborescences
## 4 Implementation Details and Analysis
### Complexity  
Let us discuss the complexity of our implementation for finding a (static) optimal arborescence in a graph \(G\) with \(n\) vertices and \(m\) edges.  
In the initialization phase, we mainly have the \(n\) INIT operations for the priority queues, the \(n\) SMAKE-SET operations for the augmented disjoint sets, the \(n\) WMAKE-SET operations for the disjoint sets, and the \(O(n)\) operations for the other data structures. All these operations take constant time each; thus, the initialization takes \(O(n)\) time.  
In the contraction phase, only the operations on priority queues and disjoint sets may not take constant time. The operations on priority queues are at most the \(m\) EXTRACT-MIN operations and the \(n\) MELD operations. Since EXTRACT-MIN takes \(O(\log n)\) time, and (for pairing heaps) the MELD operation takes constant time, then it takes \(O(m\log n)\) total time for maintaining priority queues. The operations for disjoint sets are \(m\) WFIND and SFIND operations, \(n\) WUNIONION and SUNION operations, and \(n\) SADD-WEIGHT operations. The disjoint set operations take \(O(m\alpha(n))\) total time, where \(\alpha\) is the inverse of the Ackermann function [24]. The other operations run in \(O(m+n)\) time. Therefore, the contraction phase takes \(O(m\log n)\) time.  
In the expansion phase, \(F\) contains no more than \(2n-2\) nodes, and each node of \(F\) is visited exactly once, so the procedure takes \(O(n)\) time. The total time required to find an optimal arborescence is therefore dominated by the priority queue operations yielding a final time complexity of \(O(m\log n)\).  
Let us analyze now the cost of dynamically maintaining the optimal arborescence. Let \(\rho\) be the set of affected vertices and edges, \(|\rho|\) be the number of affected vertices, and \(||\rho||\) be the number of affected edges. A vertex is affected if it is included in a different contraction in the new output after an edge insertion or removal. Note that \(|\rho|<n\), thus meaning that all operations in an addition or deletion of an edge occur in \(O(n)\) time and that a re-execution of the Edmonds' algorithm only processes the affected vertices. The update of an optimal arborescence, using the implementation presented in Section 2.2, can then be achieved in \(O(n+||\rho||\log|\rho|)\) time per edge insertion or removal.
# Edible Films Based on Ovine Second Cheese Whey with Oregon Essential Oil
## 3 Results and Discussion
### Water Vapor Permeability, Thickness, Dry Matter Content, Solubility and Density  
Water vapor permeability (WVP), thickness, dry matter content, solubility and density values of films are shown in Table 1. Two-way ANOVA of these characteristics in films of WPI-SCW mixture are shown in Table 2. No significant effects of the substitution of WPI by SCW and the addition of EO were found in the water vapor permeability. However, the films produced with the addition of 2% of EO showed lower values of WVP than the corresponding films with the incorporation of 1% of EO and these samples showed lower values than films without EO. It has been reported that the addition of essential oils reduces the vapor permeability of the films due to that the incorporation of a hydrophobic agent, such as an essential oil, decreases the hydrophilic portion of the films and their affinity to the water and enhances their ability to act as a barrier to water vapor permeation [18; 30; 31]. A different result was observed by [PERSON] et al. [17], one which indicated that the addition of oregano essential oil increased the WVP due to the oil not binding to the proteins,  
Figure 1: Visual appearance of the films described in the materials and methods section. (**1**) WPI; (**2**) WS 2:1; (**3**) WS 1:1; (**4**) WS 1:2; (**5**) SCW; (**6**) WS 2:1O1; (**7**) WS 1:1O1; (**8**) WS 1:2O1; (**9**) WS 2:1O2; (**10**) WS 1:1O2; (**11**) WS 1:2O2. leaving empty spaces through which water can permeate. Thus, the effects of the addition of oregano EO in the water permeability of the films are not clear. No significant effects of the substitution of WPI by SCW and the addition of EO were found in the thickness of the films. Other researchers have also indicated that the addition of essential oils did not modify the thickness of the films [32]. However, [PERSON] et al. [16] have observed that the incorporation of oregano essential oil causes an increase in film thickness due to the way in which that addition causes disruption to the structure. This different result could be due to the different homogenizers used. No remarkable effects of the substitution of WPI by SCW or the addition of EO were found in density of the films. Despite the compositional differences between SCW and WPI (higher lipid and ash contents in SCW), the effective homogenization of the film-forming solutions likely maintained structural uniformity, preventing significant changes in the mass-to-volume ratio. In relation to the dry matter content, it is remarkable that the films made only with WPI showed lower values of dry matter than the films with a mixture WPI-SCW or with only SCW. The dry matter content of the films was not influenced by the incorporation of
# Handheld Optical System for Pectus Excavatum Assessment
## 3 Results  
### Clinical Experience  
The new scanner was tested directly in an outpatient environment. The specialized clinic for thoracic deformities at the Meyer Children's Hospital was provided with the new device to carry out a period of testing. Throughout the research, patient data were anonymized before being included in the study in accordance with common health-information practices. To perform a comparative study and test the efficacy and value of the scanner, all the collected data were investigated to select the patients scanned at least twice to compare data collected at different times during VB treatment.  
The choice to monitor the effectiveness of VB resulted from the fact that, compared to surgery, VB therapy can be effectively applied to children from 5 to 6 years old, as the application is easy and the side effects are typically minor and self-limited. In fact, the vacuum bell allows, through the creation of an internal negative pressure, the lifting of the chest as long as the device remains applied. The response of the chest to this treatment varies significantly from patient to patient, and therefore it is currently not possible to define a standardized treatment.  
The collected data consisted of 46 patients (40 males and 6 females) with an average age equal to 11.7 [\(\pm\)5.5] years old. After acquisition, each patient was analyzed with the new optical scanner to measure the above-mentioned Correction Index 3D.  
Overall, the evaluations showed that VB treatment led to a clinically visible improvement of the chest wall defect.  
For a visual evaluation, the CI3D trends of variation with the respective wearing times are shown below (Figure 7). Blue and red lines indicate a CI3D value reduction or increase, respectively, during treatment.  
In the present study, the acquired images led us to observe that the greatest improvement in chest wall deformity occurred during the first 12 months of treatment in patients who started VB treatment at the time of the first 3D body scan, whereas it remained stable or slightly improved in the following months. In a few patients, the 3D body scans showed only a slight deterioration or worsening of the defect, although globally there was a substantial thoracic deformity improvement.
## 2 Materials and Methods
### Data
#### 2.3.2 Forcing Datasets for the FGM  
Model inputs related to vegetation and environmental forcing data are listed in Table 3. PFTs on a large scale were determined using MODIS land use and land cover data according to the MODIS IGBP classification protocol. The FGM uses meteorological data (downward shortwave radiation (DSR), mean air temperature, and vapor pressure deficit (VPD)) from the Climatic Research Unit-NCEP (CRUNCEP), LAI data from the Global Land Surface Satellite (GLASS) product, and ambient CO\({}_{2}\) concentration from the Mauna Loa Observatory (MLO) as inputs. We further included the spatially resolved model inputs for the clumping index (CI) and V\({}_{\rm cmax}\). The CI data were from the global CI map derived from the MODIS bidirectional reflectance distribution function (BRDF) product [102]. Temporally and spatially continuous global V\({}_{\rm cmax}\) maps were estimated based on the remotely sensed chlorophyll content [103; 104] using Rubisco-chlorophyll relationships between vegetation types via meta-analyses [105; 106]. All data were processed to a target spatial resolution of 500 \(\times\) 500 m and temporal resolution of 8 days.
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 3 Proposed Method
### Evaluation Metrics  
One of the main objectives of this study was to systematically evaluate and validate the performance of the model. For this purpose, we used several performance metrics and validation methods.  
* Performance metrics: Basically, we used the confusion matrix to calculate the number of true positives, true negatives, false positives, and false negatives. From this, we derived statistical metrics such as accuracy, precision, and recall to evaluate the performance of the model.
* Validation methodology: To increase the reliability of our performance metrics, we modeled the case of a hacking threat by assuming abnormal situations and adding intentional error data. We can evaluate how our anomaly detection model handles these situations.  
Performance evaluation and validation are important steps that determine the practicality and reliability of a model. In this study, we performed these steps thoroughly, and we expect the model to perform well in real industrial settings.  
Accuracy shows how many samples are correctly classified as a percentage of the total samples.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
Precision shows how many of the predicted positive results are positive as a percentage of the total.  
\[Precision=\frac{TP}{TP+FP}\]  
Recall shows how many of the true positives are predicted to be positive, as a percentage.  
\[Recall=\frac{TP}{TP+FN}\]  
The \(F1\)_score_ is the harmonic mean of _precision_ and _recall_. It is a good indicator of the balance between the two.  
\[F1 Score=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}\]
## 4 Combined Optimization of UAV Location, User Pairing, and Power Allocation  
Once the rental prices of content providers and the caching strategy of UAV are determined, the combined optimization problem for UAV location deployment, NOMA user pairing, and user power allocation can be expressed as:  
\[\begin{array}{l}\text{P4}:\max\limits_{p_{n,m},d_{n,m},\delta}U_{uav}\\ s.t.\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N_{m}}p_{n,m}=P_{\max}\end{array} \tag{19}\]  
Due to the coupling among variables in optimization problem P4, to facilitate its solution, problem P4 is partitioned into three subproblems--transmission power optimization, user pairing optimization, and UAV location optimization--based on BCD [22]. These subproblems are solved through alternating iterative processes utilizing the Lagrange multiplier method, the simulated annealing algorithm, and the particle swarm optimization algorithm, respectively.
# Solid Phase-Based Microextraction Techniques in Therapeutic Drug Monitoring
## 3 Sample Pretreatment--Solid Phase-Based Microextraction Techniques
### Molecularly Imprinted Polymers and Thin Film Microextraction
\begin{table}
\begin{tabular}{c c c c} \hline
**Extraction** & & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{MIPs} & - & Simple, stable, robust, flexible, & \multirow{8}{*}{-} & \multirow{8}{*}{Difficulties with optimization;} \\  & & selective and resistant to a wide & & \\  & & range of pH, solvents and & - & Length of time required for \\  & & temperatures; & & analysis; \\ \cline{1-1}  & - & Extraction efficiency; & - & Potential impossibility of \\  & - & Emulates the interactions & & long-term use due to analyte \\ \cline{1-1}  & & established by natural receptors to & build-up. \\ \cline{1-1}  & - & retain a target molecule; & \\ \cline{1-1}  & - & Low-cost synthesis approach. & & \\ \hline \multirow{8}{*}{TFME} & & - & Requires a larger volume of \\  & & & eluent during the desorption \\ \cline{1-1}  & & & process; & - \\ \cline{1-1}  & & & Lack of conventional interfaces \\ \cline{1-1}  & & & for on-line coupling of the \\ \cline{1-1}  & & & device to the workflow of the \\ \cline{1-1}  & - & Enhanced sensitivity; & \multirow{8}{*}{-} & analytical instrument; \\ \cline{1-1}  & - & Fast mass transfer kinetics for both & & - \\ \cline{1-1}  & & extraction and desorption & & slower in liquid phase, resulting \\ \cline{1-1}  & & processes; & & in relatively longer desorption \\ \cline{1-1}  & - & Wide variety of available or & & times; \\ \cline{1-1}  & & potentially viable extractive phases; & - & Water can penetrate cellulose \\ \cline{1-1}  & - & Easy automation of the sampling & & structure and destroy the \\ \cline{1-1}  & & step. & & network which may decrease \\ \cline{1-1}  & & & the lifetime of the material and \\ \cline{1-1}  & & & its reusability; & \\ \cline{1-1}  & & & Modification of its surface \\ \cline{1-1}  & & & brings an extra cost, and single \\ \cline{1-1}  & & & use of such materials could \\ \cline{1-1}  & & & make the analyses relatively \\ \cline{1-1}  & & & costly. & \\ \hline \end{tabular}
\end{table} 
Table 9: Advantages and disadvantages of the MIPs and TFME procedures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytes** & **Sample** & **Sample Pretreatment and Extraction Procedure** & **Analytical Technique** & **LOD; LOQ** & **Linear Range** & **Ref. ** \\ \hline \multirow{9}{*}{Queeting, Cloaque} & \multirow{9}{*}{200 ul./ Claquire} & The sample is diluted in 2 mL of water, and the pH value of the samples is adjusted to 9.5 and again diluted to volume with water for the next procedure.
###### Abstract  
Radiochromic films change color upon exposures to radiation doses as a result of solid-state polymerization (SSP). Commercially available radiochromic films are primarily designed for, and have become widely used in, clinical X-ray dosimetry. However, many intriguing properties of radiochromic films are not yet fully understood. The present work aimed at developing a theoretical model at both atomic and macroscopic scales to provide a platform for future works to understand these intriguing properties. Despite the fact that radiochromic films were primarily designed for clinical X-ray dosimetry, dose-response curves for the Gafchromic EBT3 film obtained for ultraviolet (UV) radiation were employed to develop our model in order to avoid complications of ionization, non-uniform energy deposition, as well as dispersed doses caused by secondary electrons set in motion by the indirectly ionizing X-ray photons, which might introduce added uncertainties to the model and overshadow the basic SSP processes. The active layer in the EBT3 film consisted of diacetylene (DA) pentacosa-10,12-diynoate monomers, which were modelled using molecular dynamics (MD). The degrees of SSP in the atomic scale upon different UV exposures were obtained to determine the absorption coefficients of the active layer, which were then input into the finite element method (FEM). The classical steady-state Helmholtz equation was engaged to model the reflection from the active layer using the FEM technique. The multifrontal massively parallel sparse direct solver (MUMPS) was employed to solve the present numerical problem. Very good agreement between experimentally and theoretically obtained coloration in terms of net reflective optical density was achieved for different UV exposures. In particular, for UV exposures larger than \(\sim\)40 J/cm\({}^{2}\), the reflected light intensity decreased at a lower rate when compared to other UV exposure values, which was explained by the densely cross-linked structure under near-complete polymerization, and thus the lower efficiency for further bond formation between DA monomer strands.  
Keywords:radiochromic film; EBT3 film; finite element method; molecular dynamics; parallel computation +
Footnote †: journal: Applied Physics Letters
# Optimal Energy Management within a Microgrid: A Comparative Study
## 5 Results  
Before discussing the numeric results related to the optimal energy management in microgrids through the optimal power flow (OPF) implemented in this work, it is necessary to describe the microgrid and the hardware used for the simulation of the OPF.  
Firstly, the microgrid is called microgrid test (MT), and is composed of an integrated system of a photovoltaic panel (PV), a wind turbine (WT), a storage system (battery, BT), a main grid (MG), electric transformers (ET), a Park of EV (PEV), and a point of load within the system. The MT is selected as a case study to show the results of the optimal energy management obtained from the tools selected. Moreover, the generators in the microgrid use a real irradiance profile, wind speed profile of Almeria city, and the electricity demand curve. All previous data were taken from Laboratory 6 of the CIESOL with different climatic variabilities.  
Subsequently, an analysis of the computational performance of the interior point method (fmincon), hybrid genetic algorithm with interior point (GA-IP), and direct search hybrid with interior point (patternsearch-IP) is presented, which allows us to identify the most effective methods forsolving the model presented in Equations (26)-(29). Finally, all the methods to solve the OPF are executed on a DELL computer, with 8 GB RAM and an Intel i5-4210U CPU@ 2.40 GHz processor (DELL, Mexico city, Mexico). The numerical results are reported in terms of per unit (PU) quantities.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
###### Abstract  
Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) _service placement_ determining services to be placed in each MEC node under its storage capacity constraint, and (ii) _request scheduling_ determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.  
utility-centric; service provisioning; service placement; request scheduling; multi-access edge computing +
Footnote †: journal: Applied  
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