esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Phenolic Profile by LC-QToF-MS Analysis
#### 2.3.2 Mass Spectra Analysis  
The mass spectra of samples were acquired using an Agilent 6530 Quadrupole Time of Flight (QToF) system equipped with an electrospray ionization (ESI) interface (Agilent Technologies, Santa Clara, CA, USA). The QToF system was coupled with a UHPLC system (Agilent 1290 Infinity) and an autosampler obtained from Agilent Technologies (Santa Clara, CA, USA). The mass spectrometer was operated at negative ion mode (-ESI). The following parameters were applied: capillary voltage 4000 V; nebulizer gas pressure 45 psig; drying gas flow rate 10 L/min; gas temperature 300 \({}^{\circ}\)C; fragmentor; and skimmer voltages 150 V and 65 V, respectively. For the MS/MS experiments, an auto MS/MS method was applied with the following parameters: collision energy slope, 5 V; offset, 2.5 V; acquisition rate of MS/MS, 1 spectra/s; preferred charge state, 2, 1, unknown. The QToF-MS system was calibrated before the analysis using a reference solution acquired from Agilent Technologies (Santa Clara, CA, USA). A constant infusion of a reference mass solution with ions 112.9856 and 1033.9881 was applied during this analysis. The Agilent MassHunter Data Acquisition software (version B.06.00) was used for data acquisition. A Nucleoshell EC 18 (100 mm \(\times\) 4.6 mm, 2.7 \(\upmu\)m) column (Macherey-Nagel GmbH & Co., Duren, Germany) was used for the chromatographic study. The mobile phase consisted of (A) ultrapure water-acetic acid 0.1% and (B) acetonitrile-acetic acid 0.1%, with a flow rate of 1.0 mL min\({}^{-1}\). The gradient program was as follows: 0 min: 10% B; 8 min: 30% B; 12 min: 40% B; 16 min: 50% B; 18 min: 10% B; and 33 min: 10% B, with a total run time of 33 min. The injection volume was 10 \(\upmu\)L, while the column temperature was set at 30 \({}^{\circ}\)C. Data processing was performed using the Agilent MassHunter Qualitative Analysis software (version B.07.00). Compound identification was based on the retention time relative to that of the standards, the accurate mass of ions, and their MS/MS spectra. The identification of compounds for which there were no standards available was based on the Riken Tandem Mass Spectral Data Base library database ([[http://spectra.psc.riken.jp/](http://spectra.psc.riken.jp/)]([http://spectra.psc.riken.jp/](http://spectra.psc.riken.jp/)), accessed on 1 May 2023).
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 2 Background
### Software-Defined Radios
#### 2.2.2 GNURadio  
GNURadio is a free and open-source software radio ecosystem. The project is written and developed for both C++ and Python [52, 53]. GNURadio has a built-in block library that contains a number of useful signal processing components and provides a Graphical User Interface (GUI) to connect these components into a processing chain--called a flowgraph--for real-time execution [54]. GNURadio greatly simplifies the implementation of simple radio applications, but is primarily limited by how many SDRs it can manage with a single host computer. Developing custom processing blocks in GNURadio can often be tedious, so it is typically limited to single-radio applications. GNURadio performs excellently in this use-case but does not provide the capabilities required for larger network applications.
## 2 The Proposed VA-RAIM
### Va-Raim  
This section presents the VA-RAIM method with the calibrated vision model. We begin with showing the integration of vision/GPS measurement equation. Then the procedure of PL calculation and fault detection of the VA-RAIM is proposed.  
By combining Equations (11) and (18), the linearized measurement model at time \(k\) for the vision/GPS integrated system is obtained as:  
\[\mathbf{z}_{{}_{I(k)}}=\mathbf{H}_{{}_{(k)}}\mathbf{x}_{{}_{(k)}}+\mathbf{\varepsilon}_{{}_{I(k )}}+\mathbf{b}_{{}_{I(k)}} \tag{19}\]  
where \(\mathbf{z}_{{}_{I(k)}}=\begin{bmatrix}\mathbf{z}_{{}_{G(k)}}\\ \hat{\mathbf{z}}_{{}_{V(k)}}^{{}^{\prime}}\end{bmatrix}\), \(\mathbf{H}_{{}_{I(k)}}=\begin{bmatrix}\mathbf{H}_{{}_{G(k)}}\\ \mathbf{H}_{{}_{V(k)}}\end{bmatrix}\), \(\mathbf{\varepsilon}_{{}_{I(k)}}=\begin{bmatrix}\mathbf{\varepsilon}_{{}_{G(k)}}\\ \hat{\mathbf{e}}_{{}_{V(k)}}^{{}^{\prime}}\end{bmatrix}\), \(\mathbf{b}_{{}_{I(k)}}=\begin{bmatrix}\mathbf{b}_{{}_{G(k)}}\\ \hat{\mathbf{b}}_{{}_{V(k)}}^{{}^{\prime}}\end{bmatrix}\). The random error \(\mathbf{\varepsilon}_{{}_{I(k)}}\) follows a normal distribution with zero mean and covariance matrix \(\mathbf{\Sigma}_{{}_{I(k)}}=\begin{bmatrix}\mathbf{\Sigma}_{{}_{G(k)}}&\mathbf{0}\\ \mathbf{0}&\hat{\mathbf{\Sigma}}_{{}_{V(k)}}^{{}^{\prime}}\end{bmatrix}\).  
With the aid of vision measurements, the over-determined integrated vision/GPS system can improve the integrity performance in the approach and landing phase.
## 3 Constructing the Dataset  
A large amount of unstructured data is recorded in the electric power marketing system of the Guangxi district. In order to study these data, this paper selects the marketing system data from the system in the past two years, which contain textual data such as accident investigation information, user feedback, device operation, etc., and constructs an electric power marketing corpus. The current smart grid system has a low utilization rate of these data, and only supports simple text queries, without conducting deep mining. As a result, the behavioral knowledge contained in them remains untapped. In this paper, we use the power marketing corpus to build a named entity recognition dataset in the power marketing domain, employing deep learning methods for analysis and modeling.  
The power marketing corpus is derived from the actual operation system of the power grid, and through screening, sentences with unknown meaning, structural mutilation and semantic repetition are eliminated, and finally, 6909 high-quality data are extracted from the corpus for training and testing. By analyzing the characteristics of the power marketing corpus and considering the actual needs of the power company, the entity types are classified into nine categories, such as time, transmission equipment and voltage level (see Table 1 for details). During the subsequent recognition process, the entities will be classified into their respective categories. The labeled dataset comprises 53,378 entities. It is divided into three sets: the training set, the validation set and the test set. The distribution ratio among these sets is 8:1:1.  
The distribution of entity categories is shown in Figure 1. The largest number of entities belongs to the \"equ\" type and the \"line\" type categories, while the \"other\" type and the \"name\" type categories have the smallest number of entities. The remaining entity types are distributed relatively evenly, and there is the problem of uneven sample distribution.  
In this paper, we utilize the label-studio annotation platform, which offers a visual interface, to annotate entities within sentences. The BIO annotation method is employed for this purpose. In this method, \"B\" represents the begin of an entity, \"I\" represents the inside part of an entity, and \"O\" represents the outside part of an entity. If a word in the dataset is labeled as B/I-XXX, it indicates that the word represents either the beginning or inside part of a named entity, with XXX denoting the type of the named entity. Examples of the labeling are shown in Table 2 and Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity Type** & **Entity Example** & **Number of Entities** \\ \hline time & 2022\(\#\)03\(\#\)30\(\#\) [30 March 2022] & 6219 \\ level & 60 kV & 5969 \\ line & 60 Kv \(\#\)0\(\#\)0\(\#\) [60 kV Zhenlong Line] & 9743 \\ equ & 901\(\#\)0\(\#\) [901 switch] & 12,234 \\ add & \(\#\)0\(\#\)0\(\#\)0\(\#\)0\(\#\)0\(\#\)0\(\#\)0\(\#\
## 6 Numerical Experiments
### Solving the TOP Using Exact Methods  
The proposed mathematical model has been implemented using the IBM ILOG CPLEX Optimization Studio v12.6.2, and the computations were conducted on a PC with an Intel i5-6500 quad-core processor running at 3.6 GHz and with 20 GB of RAM. The CPLEX default options have been used in the experimentation, with the specification of the following settings: (_i_) a time limit of 3600 s, (_ii_) a relative gap of 0.01%, and (_iii_) a tolerance integrality of 0.001%. The computational results are reported in Table 2. Columns 1 and 5 identify the instance for sets \(p1\) and \(p2\), respectively. Columns 2 and 6 show the solution value obtained with CPLEX. The computational time (in seconds) requested to obtain these values is given in columns 3 and 7. Furthermore, the CPLEX final status indicates whether the solution is guaranteed to be optimal or not. In the latter case, the best-known solution (BKS) from the literature is provided.  
It is observed that in set \(p2\) (instances with 21 nodes), optimal solutions can usually be achieved after a few seconds or minutes. However, in set \(p1\) (instances with 33 nodes), only a few optimal solutions can be achieved, whereas others cannot reach the optimal solution even when employing up to 60 min of computation. It is noticed that some solutions differ significantly from the BKS, such as instances \(p1.2.r\) and \(p1.3.p\). In conclusion, exact methods are not a feasible option to provide real-time solutions for large-sized instances with hundreds of nodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Set (\(x\))** & **Nodes** & **Reward** & **Vehicles (\(y\))** & **Driving Range (\(z\))** & **Number of Instances** \\ \hline \multirow{3}{*}{p1} & \multirow{3}{*}{32} & \multirow{3}{*}{285} & 2 & 2.5–42.5 & \multirow{3}{*}{56} \\  & & & 3 & 1.7–28.3 & & \\  & & & 4 & 1.2–21.2 & & \\ \hline \multirow{3}{*}{p2} & \multirow{3}{*}{21} & \multirow{3}{*}{450} & 2 & 7.5–22.5 & \multirow{3}{*}{33} \\  & & & 4 & 3.8–11.2 & & \\ \hline \multirow{3}{*}{p3} & \multirow{3}{*}{33} & \multirow{3}{*}{800} & 2 & 7.5–55 & \multirow{3}{*}{60} \\  & & & 3 & 5.0–36.7 & & \\  & & & 4 & 3.8–27.5 & & \\ \hline \multirow{3}{*}{p4} & \multirow{3}{*}{100} & \multirow{3}{*}{1306} & 2 & 25.0–120.0 & \multirow{3}{*}{60} \\  & & & 3 & 16.7–80.0 & & \\  & & & 4 & 12.5–60.0 & & \\ \hline \multirow{3}{*}{p5} & \multirow{3}{*}{66} & \multirow{3}{*}{1680} & 2 & 2.5–65.0 & \multirow{3}{*}{78} \\  & & & 3 & 1.7–43.3 & & \\  & & & 4 & 1.2–32.5 & & \\ \hline \multirow{3}{*}{p6} & \multirow{3}{*}{64} & \multirow{3}{*}{1344} & 2 & 7.5–40.0 & \multirow{3}{*}{42} \\  & & & 3 & 5.0–26.7 & & \\  & & & 4 & 3.8–20.0 & & \\ \hline \multirow{3}{*}{p7} & \multirow{3}{*}{102} & \multirow{3}{*}{1458} & 2 & 10.0–200.0 & \multirow{3}{*}{60} \\  & & & 3 & 6.7–133.3 & & \\ \cline{1-1}  & & & 4 & 5.0–100.0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the benchmark sets used in the experiments.
# Distributed Combinatorial Maps for Parallel Mesh Processing
## 5 Experiments
### Comparison in Computation Time  
In a second experiment, we studied the computation time depending on the number of processes. To do so, we fixed a depth limit for each mesh, and ran our software by increasing the number of processes from 1 to 32. Using the same depth limit for each run implies doing exactly the same subdivision whatever the number of processes, and thus all the final 3-dmaps, and the corresponding 3-maps, are equal. For 1 process, we use the non distributed version of combinatorial maps provided in Cgal([PERSON], 2017).  
The depth limits were respectively equal to 5, 4, 5, 5, 5 and 4 for the different meshes Armadillo, Blade, Bunny, Cow, Elephant and Iphigenia. We chose these limits in order to respect the memory limit of 3 gigabytes per process allowed by the IN2P3 computing center.  
We can see in Figure 11 the overall computation time of our subdivision method, depending on the number of processes, for each mesh. On average, the method takes 82 s for one process, and 13 s for 32 processes. The speedup is thus about 6.3. This time saving is interesting, even if we are far from the maximal possible speedup of 32. This difference is explained by the cost of the message passing.  
Figure 11: Comparison of global computation time (in seconds) to compute the same final hexahedral subdivision, for increasing number of processes.  
Figure 10: Comparison of two possible communication methods, for increasing number of processes (from 1 to 32). “Direct”: messages are sent directly to concerned blocks; “Broadcast”: messages are sent to all processes. (**a**) Total computation time, in seconds, average for the six meshes. (**b**) Number of messages exchanged, average for the six meshes.  
We can probably improve our results by using classical optimization used in parallel algorithms, for example by using message grouping, or by optimizing the clustering of initial hexahedra in order to decrease the number of critical darts and thus the number of messages sent.  
We recall that improving the computation time is not the main goal of this work: our main goal is to be able to deal with big 3-maps that do not fit in the memory of a single computer. For this reason, the speedup of 6.3 is good enough for our method which does not use any specific optimization, and we did a last experiment in order to study how the memory space of the 3-dmap evolves depending on the number of processes.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 3 Linguistic Analysis of Uyghur, Kazakh, and Kyrgyz
### Individualities of Each Language
#### 3.2.4 Vowel Reduction  
Vowel reduction refers to the \"weakening\" effect of vowels when suffixes are added. With the weakening effect, an open vowel will be changed to a closed vowel (position from bottom to top). For example, in Uyghur, \(a\) weakens to \(e\): \(ati\gets eti\) (\"his horse\") and \(\varepsilon\) weakens to \(e\): \(beli\gets beli\) (\"her waist\"). The vowel reduction of \(a\) and \(\varepsilon\) is a unique characteristic of Uyghur.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design
### Path Discovery and Maintenance  
The path discovery of Q-LBR is performed by route request (RREQ) flooding, and the basic routing search method is similar to the source-based multipath routing protocol adopted in the existing VANET. The destination node responds to the RREQ, including the optimal and suboptimal paths, using a route reply (RREP) message. This increases survivability of the VANET routing through the use of suboptimal paths when the optimal path is disconnected. In the path-discovery process, the URN can receive multiple RREQs for the same destination from many GVNs, and thus the number of URN responses is limited. Through the Q-LBR path discovery process, the source node can acquire route information to the destination node, including a URN. Q-LBR periodically transmits a probe packet for routing updates to maintain the optimal and suboptimal paths. If all paths are disconnected, the intermediate node sends a route error (RERR) message to the source node.
## 3 Result and Discussion
### Calibration  
The sensor-node calibration was conducted by comparing the sensor-node measurement results with the measurement result of a reference measurement device (calibrated device). The Testo 440-Climate Measuring Instrument with CO\({}_{2}\) Probe was used as the reference device. It has a measurement range from 0 to 10,000 ppm with the accuracy of \(\pm\) (50 ppm + 3% of m.v) (0 to 5000 ppm), \(\pm\) (100 ppm + 5% of m.v) (0 to 10,000 ppm).  
The calibration configuration is shown in Figure 10a. It is composed of a test chamber that includes CO\({}_{2}\) gas source in the gas storage, a CO\({}_{2}\) sensor node that is connected wirelessly to the terminal monitor using a Wi-Fi connection, and a CO\({}_{2}\) probe connected wirelessly to the Testo 440 using a Bluetooth connection. During the calibration process, CO\({}_{2}\) gas was supplied with a given concentration controlled by an electronic valve from CO\({}_{2}\) gas storage to the test chamber.  
Figure 9: The CO\({}_{2}\) sensor node functionality testing: (**a**) configuration; (**b**) testing process and result.  
The calibration results were carried out by CO\({}_{2}\) gas concentration measurement, calibration factor calculation, and validation.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 2 Drugs Acting on the Cardiovascular System
### Antihypertensive Drugs
#### 2.2.3 Neprilysin Inhibitors  
Ecadotril (**13**) is a chiral potent and selective inhibitor of neprilysin (or neutral endopeptidase, NEP), which is a membrane-bound zinc metallopeptidase responsible for the inactivation of numerous vasoactive peptides, such as natriuretic peptide, vasoactive intestinal peptide and bradykinin. Ecadotril acts as a prodrug of the thiorphan enantiomers. The enantiomers exhibit different pharmaceutical profiles: deexecadotril, the (_R_)-enantiomer of ecatodril, has an intestinal antisecretory action, whereas (_S_)-ecadotril shows cardiovascular activity [70; 71].
# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 2 Development of the Fumigation Robot  
The fumigation robot shown in Figure 2 is designed to identify and fumigate mosquito hotspots. The robot's motion is based on differential drive, allowing it to maneuver easily during fumigation. The robot uses 2D and 3D LiDARs and an inertial measurement unit for autonomous navigation. The robot is also equipped with a spray gun and a chemical tank. The spray gun's top and bottom ends are connected to one end of the linear actuator. The opposite end of the linear actuator is attached to the bottom end of the metal shaft. The system is on top of a stepper motor to allow for the panning motion of the gun. The linear actuator facilitates the tilt motion of the spray gun. The adjustable gun can rotate 360\({}^{\circ}\) and fumigate up to 4.5 m from the ground and 2.5 m to 6 m away from the robot. The specifications of the major components are listed in Table 2.  
In this paper, mosquito breeding grounds are referred to as hotspots as these are considered the \"ground zero\" for the growth of the mosquito population. The main aim of the fumigation robot is to identify possible mosquito hotspots and fumigate them. For the robot to work efficiently, daily remapping is necessary so that the robot can locate new hotspots and fumigate them while being able to identify when a hotspot is no longer active and halt fumigation for the area.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Product** & **Specifications** \\ \hline Oriental motors & BLHM450 KC-30 \\ IMU & Vectornav VN-100 \\ Voltage regulator & DDR-480C-24, DDR-240C-24 \\
2D LiDAR & SICK TiM581-2050101 \\
3D LiDAR & Hesai QT128 \\ Depth camera & Intel RealSense D435i \\ Industrial PC (IPC) & Nuvo-10108 GC-RTX3080 \\ Battery & 48 V, 25 Ah, Lithium Iron Phosphate \\ Fogging unit & 10 L tank, 50-micron droplet size, and flow rate 330 mL/min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Major components of the proposed precision fumigation robot.  
Figure 2: Fumigation robot and its major components.
## 6 Current Challenges and Future Prospects
### Present Obstacles Faced by AM Technologies
#### 6.2.2 Micro-Nano-Scale Fabrication  
Micro-nano-scale fabrication involves creating and shaping materials and structures from a few micrometers to nanometers in size using additive manufacturing techniques. This interdisciplinary discipline combines biology, chemistry, physics, and engineering to create devices with exact control over their composition and size [142]. Key methods include lithography, etching, deposition, and self-assembly, enabling the creation of tiny systems with unique features and applications.  
Micro-nano-scale fabrication is advancing science and technology by developing innovative devices at micro and nano levels, manipulating matter at its most fundamental scales [143].  
This technology faces challenges in achieving precision, consistency, material integration, affordability, and scalability. Advances in lithography, nanomaterials, and precise instruments are needed, along with solutions for material compatibility and large-scale manufacturing consistency [144; 145; 146]. Overcoming these challenges will unlock the potential of micro-nano-scale fabrication in various fields [147; 148].  
Micro-nano-scale manufacturing has a bright future, with potential breakthroughs in electronics, photonics, medicine, and energy. This technology could lead to smaller, faster, and more efficient devices, transformative optical communication and quantum computing, personalized healthcare solutions, and advanced sustainable energy systems. Continuous advancements and interdisciplinary collaborations are essential to fully realize these potentials [149; 150; 151; 152].
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 4 Dead Zone Compensation Method of PV Inverter  
A voltage adaptive compensation method for low-order harmonics is introduced in this section. The segmented error modeling method is used to quantify compensation values for different current operating conditions. By sampling the IGBT switching status and adding the error voltage on the modulation signal evenly, the nonideal waveform caused by the dead zone can be greatly decreased. Therefore, the harmonic current of the PV inverter can be effectively suppressed. There is no need to change any hardware and switching state.