esa-sceva/satcom-chunk-collection huggingface dataset:
# Disturbance Observer-Based Anti-Shock Controller for Laser Beam Steering Systems
## 2 FSM Control System  
### Dynamic Characteristics of PZT Actuator  
In the conventional approach to designing a High-precision FSM control system for tip-tilt control, a PZT actuator is used. The frequency response function (FRF) of an actuating plant is typically obtained using the sine-sweep method. Meanwhile, the real plant considered herein consists of a PZT actuator, a DC-DC converter-type amplifier, and a sensor amplifier. The dynamic specifications of the real plant indicate that its resonance frequency is 450.2 Hz. The DC sensitivity, voltage amplifier gain, and sensor amplifier gain are 168.3 \(\upmu\)rad/V, 10 V/V, and 0.0059 V/\(\upmu\)rad, respectively. Therefore, the real plant can be modeled as the following fifth-order transfer function [33]:  
\[P(s)=\frac{6.08\times 10^{3}S^{3}+7.09\times 10^{6}S^{2}+5.85\times 10^{10}S+6.27 \times 10^{13}}{S^{5}+8.95\times 10^{3}S^{4}+7.49\times 10^{7}S^{3}+1.50\times 1 0^{11}S^{2}+5.21\times 10^{14}S+3.73\times 10^{17}} \tag{1}\]  
The FRF of the real actuator and its modeling plant is illustrated in Figure 1. In addition, the dynamic characteristics of the real plant are summarized in Table 1.  
The open-loop transfer function of a nominal plant is expressed as follows [34]:  
\[\begin{array}{ll}P_{n}(s)&=\frac{K_{FSM}}{S^{2}+2\times\zeta_{m}\omega_{n}+( \omega_{n})^{2}},\\ &=\frac{K_{FZM}\times K_{Sensor}\times K_{Driver}}{S^{2}+2\times\zeta_{m}\omega_ {n}+(\omega_{n})^{2}},\\ &=\frac{1.08\times 10^{3}\times 0.0059\times 10}{S^{2}+2(0.0057)(450.2)s+(450.2 )^{2}}\end{array} \tag{2}\]  
where \(\zeta\) and \(\omega_{n}\) are the damping ratio and resonance frequency of the actuator, respectively. \(K_{FSM}\) denotes the DC gain of the nominal plant (i.e., FSM actuation system), which is  
\begin{table}
\begin{tabular}{c c} \hline
**Specification** & **Value** \\ \hline Resonance frequency & 450.2 Hz \\
5 Hz sensitivity & 168.3 \(\upmu\)rad/V \\ Gain of voltage amplifier & 10 V/V \\ Gain of sensor amplifier & 0.0059 V/\(\upmu\)rad \\ \hline \end{tabular}
\end{table}
Table 1: Dynamic characteristics of real plant.  
Figure 1: Frequency response and linear transfer function of real plant.  
composed of the DC gain of the PZT actuator, as well as the gains of the position sensor and PZT actuator driver.  
The real plant and digital signal processor (DSP) platform used to evaluate the control performance of the FSM control system are depicted in Figure 2. A Scalexio AutoBox (dSPACE) is used to control the FSM control system. In addition, an S-340 piezo tip-tilt platform and an amplifier module (PI Ceramic GmbH, Lederhose, Germany) are used in the tip-tilt actuating platform.
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
###### Abstract  
Medication safety administration is a complicated process involving the information of patients, drugs, and data storage. The sensitive data transmitted through wireless sensor networks (WSNs) from Internet of things (IoT) over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. Taking medication safety into consideration, this paper presents a secure authentication protocol for wireless medical sensor networks. The XOR scheme-based algorithm is applied to achieve the purposes of data confidentiality. The proposed architecture is realized as hardware in a field-programmable gate array (FPGA) device which acts as a secure edge computing device. The performance of the proposed protocol is evaluated and simulated via Verilog hardware description language. The functionality of the proposed protocol is verified using the Altera Quartus II software tool and implemented in the Altera Cyclone II DE2-70 FPGA development module. Furthermore, the output signals from the FPGA are measured in the 16702A logic analyzer system to demonstrate real-time functional verification.  
field-programmable gate array; wireless medical sensor networks; mutual authentication protocol +
Footnote †: journal: Low Power Electronics and Applications  
1 Medical Research Department, E-DA Hospital, Kaohsiung 82445, Taiwan; [EMAIL_ADDRESS]  
2
Footnote 2: email: {[[https://doi.org/10.3390/fjpea11040048](https://doi.org/10.3390/fjpea11040048)]([https://doi.org/10.3390/fjpea11040048](https://doi.org/10.3390/fjpea11040048))}
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods
### Generalized Sidelobe Cancel  
A useful implementation of the LCMV beamformer, known as generalized sidelobe canceler (GSC), can be obtained by dividing the data into two subspaces: the _constraint_ subspace, which corresponds to the signal components of interest, and an _orthogonal_ subspace, in which we find the noise and interference components [20]. A representation of the GSC's structure is shown in Figure 1. The weights of the GSC are given by \(W_{\text{GSC}}=W_{h}-W_{0}\). There, \(W_{h}\) is the quiescent component of the beamformer and it is given by the solution of (2) for the noiseless condition:  
\[W_{h}=A\left[A^{T}A\right]^{-1}, \tag{10}\]  
hence \(\widehat{Q}_{h}=W_{h}^{T}Y_{k}\). Note that \(A=A(\mathbf{\theta})\) is used from now on for convenience.  
The lower path of the GSC in Figure 1 is comprised by a _blocking matrix_, \(C_{\perp}\), and \(W_{0}\) that corresponds to the projection of \(W_{\text{GSC}}\) onto \(C_{\perp}\). Note that the output of the blocking matrix cannot contain any components in the space of \(A\), and the design on \(C_{\perp}\) is not unique. One method for constructing it is to find \(P_{A}^{\perp}=I-A\left[A^{T}A\right]^{-1}A^{T}\), then orthonormalize \(P_{A}^{\perp}\) and choose the first \(M-3L\) columns of the orthonormalized matrix. Such way of designing \(C_{\perp}\) was used in [15] in our first proposal for a specialized neural index based on the GSC.  
After applying \(C_{\perp}\) to the measurements, we are left with an estimate of the undesired signals (denoted by \(X_{0}\)). Since \(W_{0}\) is the projection of \(W_{\text{GSC}}\) onto \(C_{\perp}\), it can be expressed as \(W_{0}=R_{X_{0}}^{-}C_{\perp}^{T}\widehat{R}W_{h}\) where \(R_{X_{0}}=E[X_{0}X_{0}^{T}]\) and \((\cdot)^{-}\) indicates the generalized inverse (see [16] for more details).
## 1 Introduction
### Scope of the Present Study  
The present work employs the RFM model, in conjunction with the tabulation approach, in order to reproduce the experimental observations of CO\({}_{2}\) condensation recorded by [PERSON] et al. [10]. The primary objective of the work is to show that this equilibrium real fluid approach is capable of accurately capturing the transient appearance of the condensation of the supercritical CO\({}_{2}\) observed experimentally in the divergent part of the de Laval nozzle. The second objective is to demonstrate the significant effects of impurities in CO\({}_{2}\), which has been neglected in previous numerical simulations [8, 11, 12]. Indeed, as mentioned in the introduction, this can have a considerable impact on the phase diagram of mixtures (CO\({}_{2}\)-impurities), which, as we will show in this work, leads, in particular, to different quantities of condensed CO\({}_{2}\). Among the conclusions, this work shows that the results of numerical simulations taking impurities in CO\({}_{2}\) into account are not only closer to real industrial CCS conditions, but also numerically more robust and therefore easier to perform, which was not expected. It is customary to use numerical methods on a simplified model prior to tackling more intricate systems, such as a complete 3D pump. In order to streamline the examination of effects seen in pumps, we performed the study using a 2D De Laval nozzle. This enables us to reduce computational time and complexity while demonstrating the model's ability to replicate physical phenomena, including phase change.  
The key contribution of this article is to propose a real fluid model for accurate and robust two-phase and multicomponent CFD simulations. This study reveals the following features: (1) the structure and dynamics of the de Laval in-nozzle flow including shock waves and condensation, (2) the amount of condensed CO\({}_{2}\) in supersonic nozzle flows and (3) the effects on condensation of the inlet conditions and impurities.  
The rest of this article is organized as follows. Section 2 will present the numerical and theoretical methods, including the governing equations and tabulation methods. The description of the experimental data base and the numerical setup are then described in Section 3. Section 4 will present the results discussion, including a comparison of the numerical simulation with the available experimental data. Finally, the conclusion of this study, along with suggestions for future work, is presented in Section 5.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Algorithm Flow  
Based on the above theory, the pseudo-code of the AACO algorithm proposed in this paper is described in Algorithm 2.  
```
Input:\(m,\{d_{ij}^{k}\},p_{1,...,m},PCK,SRC,G(V,E)\) Output:\(\omega_{1},\omega_{2},\ldots,\omega_{m}\)
1 Initialization parameters \(Z,Q,C_{0},\alpha,\beta,\gamma\);
2 Establish \(m\) two-dimensional pheromone matrices and combine them into a three-dimensional pheromone array \([V\times V,m]=C_{0}\);
3 Initialize \(k\) colonies with \(Z\) ants;
4 foriterator from 1 to \(I_{max}\)do
5 forall an ant populationsdo
6 foreach ant in colonydo
7 ifThe node is not the destination node dst then
8 Add the current node to the tabu list;
9 Get the set of adjacent nodes, exclude nodes in the tabu list;
10 Calculate state transition probabilities according to Equation (14);
11 Visit next node by roulette, record the path;
12 else
13 Return to the source node \(src\) and collect path set;
14 Calculate the total delay \(c_{src,dst}\) of the path ;
15
16 end if
17 foreach edge \(e\in E\)do
18 Calculate the pheromone increments according to Equation (17,18);
19 Update pheromone according to Equation (16);
20
21 end if
22
23 end for
24 for\(i\) from 1 to \(m\)do
25 Add the nodes in the path set to \(\omega_{i}\);
26 end for
```  
**Algorithm 2**AACO algorithm for non-TT flow route planning.
## 2 Materials and Methods
### Assessment of (p)ppGpp Accumulation in Bacteria  
Cellular levels of the stringent response alarmones, i.e., ppGpp and ppPGPpp, were measured basically as described previously [24], with modifications. The overnight bacterial culture was grown in the MOPS (4-morpholinepropanesulfonic acid) minimal medium and then was diluted in the same medium, but with a low phosphate concentration (0.4 mM) and then cultivated to A\({}_{600}\) of 0.2. Then, the cultures were diluted in a 1:10 ratio in the same medium, but with addition of [32P]orthophosphoric acid (150 \(\upmu\)Ci/mL) and grown for two generations. Next, at time zero, ITCs at 1 \(\times\) MIC, or serine hydroxamate (SHX), at 1 mM was added. At specific times, samples were collected and lysed with formic acid (13 M) in three cycles of the freeze-thaw procedure. Following centrifugation, nucleotides were separated by thin-layer chromatography on PEI cellulose plates (Sigma-Aldrich, Darmstadt, Germany) in 1.5 M potassium phosphate buffer (pH 3.4). The chromatograms were analyzed using the Typhoon 9200 Phosphorimager (GE Healthcare, Uppsala, Sweden). QuantityOne (BioRad, Hercules, CA, USA) software was used for densitometry analysis.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 3 Agriculture 4.0 Security Threats in Multi-Layered Paradigm
### Security Risks at Edge Layer  
The edge contains important aspects that monitor, and control modules communicate with all layers and access resources. The physical layer's large volumes of data can be processed locally rather than centralized in the cloud. The following are major edge security issues.  
Actuator control Forge: It introduces false measures of data into the system in order to manipulate it. Typically, the data is received by the gateway in plain text. Corrupted data will lead to poor decision-making. An opponent, for example, could inject incorrect soil moisture measurements into a smart irrigation system to manipulate it.  
Booting Attacks: Devices are vulnerable to attacks due to a lack of security processes during boot [37]. Memory cards and flash drive sticks, for example, may detect dangerous codes which run at startup [38]. Suspicious booting practices could launch numerous attacks on the edge with insufficient protection.  
Signature wrapping: The attacker modulates the cipher text by inserting a bogus element that allows you to perform a random Web Application requisition while identifying as an authenticated user [39].  
Measure infusion Forgery: It is the injection of wrong specifications into a system in order to modify it. An attacker sends known data patterns through actuators to the cloud storage, which is then injected into the system through computational units. Corrupted data could lead to poor decision-making.  
Unauthorized access: The process of validating is a critical security component. The authentication process is a technology that ensures security and privacy, honesty, and accessibility [40]. Several agricultural practices, however, use gateways with insufficient or no access controls.  
Gateway-cloud fabrication: The cloud and the gateway are linked by ISPs or wireless connections. A network attacker could fabricate cloud requests by interrogating a gateway. The attacker could use these requests to change parameters in precision agriculture, control queries for sensitive services, or interpret system equipment. The cloud can implement security services as it has computational power. In order to ensure maximum system reliability, these strategies must be incorporated into the system.
# Quad-Band Uniformly Spaced Array Antenna Using Diverse Patch and Fractal Antennas
## 2 Materials and Methods
### Feed Network Integrated with the Array Antenna  
Figure 5 depicts the topology of a four-port feed network. The dimensions of the four microstrip feed lines are provided in Table 2. The microstrip feed line of each port is designed to carry signals to four antenna elements. The microstrip line is matched to 50 \(\Omega\). The lengths of the feed lines from an antenna port to each element antenna are identical. The feed network is implemented using a T-shape power divider, and the 50 \(\Omega\) microstrip line is matched to the 100 \(\Omega\) line with a quarter-wave impedance transformer. The widths of the microstrip line for each port are set to be identical.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(l_{\mathcal{H}}\) & \(l_{\mathcal{Z}}\) & \(l_{\mathcal{B}}\) & \(l_{\mathcal{H}}\) & \(l_{\mathcal{S}}\) & \(l_{\mathcal{H}}\) & \(l_{\mathcal{T}}\) & \(l_{\mathcal{B}}\) & \(l_{\mathcal{B}}\) & \(l_{\mathcal{H}0}\) \\ \hline Port1 & 16.7 & 5.5 & 22.2 & 7.3 & 5.5 & 16.7 & 22.2 & 5.6 & 62.1 & 28.1 \\ Port2 & 13.9 & 13.9 & 12.7 & 12 & 7.55 & 20.2 & 12.7 & 19.4 & 62.1 & 21.7 \\ Port3 & 19.2 & 8.5 & 14.7 & 7.9 & 7.6 & 20.5 & 14.7 & 2.8 & 51.3 & 35.3 \\ Port4 & 12.1 & 12.1 & 10.9 & 8.4 & 6.7 & 17.5 & 10.9 & 15.5 & 51.7 & 32.8 \\  & \(w_{\mathcal{H}}\) & \(w_{\mathcal{Z}}\) & \(w_{\mathcal{B}}\) & \(w_{\mathcal{H}}\) & \(w_{\mathcal{S}}\) & \(w_{\mathcal{H}}\) & \(w_{\mathcal{T}}\) & \(w_{\mathcal{B}}\) & & \\  & 3 & 2.4 & 1.4 & 0.7 & 2.4 & 1.4 & 0.7 & 2.4 & & \\ \end{tabular}
\end{table}
Table 2: Dimensions of the four-port feed network (mm).  
Figure 4: Reflection coefficients of bow tie, fat bow tie and square patch antennas with center frequencies of 2.8 GHz, 4.4 GHz, and 5.1 GHz, respectively.  
Figure 5: Topology of the feed network on the bottom layer.
# Removal of the Previous Root Canal Filling Material for Retreatment: Implications and Techniques
## 1 Introduction
### Effectiveness of Different Protocols  
Numerous nickel-titanium (NiTi) instrument systems for root canal preparation have been introduced into the market over the years. Unfortunately, most of them are released without sufficient information regarding their mechanical and geometric properties and, even worse, without any demonstration of their effectiveness. As for retreatment, rotary NiTi instruments have been used for both removal of the previous filling material and re-instrumentation [41]. Depending on the system, it is even possible to perform both steps simultaneously [42].  
Chemical solvents (chloroform, xylol or eucalypt) have traditionally been used in association with manual or rotary instruments to facilitate instrument penetration into the filling mass. In addition, the solvent may contribute some antimicrobial effects [43], though the magnitude and relevance of this property remain questionable, given the small amount of substance used and the fact that more potent antimicrobial agents, such as sodium hypochlorite, will be used next in the procedure and in much higher volumes. A study evaluated the influence of a solvent (eucalypt) in enhancing the removal of filling material for retreatment of [PERSON]'s type II mandibular molars [21]. The Mtwo instrument system (VDW) was used for filling removal, with or without a solvent, and then the residual filling volume was assessed by micro-computed tomography. Findings revealed that the amount of material removed from the canal, including the isthmus area, was 83.2% without solvent and 83.8% with solvent, with no significant difference [21]. Other studies have confirmed these findings, not showing superior removal of filling material when using solvents [44; 45]. Besides, the formation of a thin layer of chemically softened gutta-percha on the root canal wall may impair further cleaning and disinfection. This, along with solvent toxicity and unproved effectiveness, may discourage the use of these substances during retreatment [46; 47].  
Regardless of the retreatment technique, studies have shown that the complete removal of filling material is not commonly achieved [28; 48; 49], especially in the apical portion of root canals (Figures 1 and 2) [19; 42; 50]. The mean percentage of residual filling material has been demonstrated to range from 0.02% [51] to 43.9% [52] of the initial filling volume, with the great majority of studies reporting values below 10%. For this reason, supplementary approaches have been recommended to enhance filling material removal [42; 50] and improve root canal disinfection [53; 54; 55; 56; 57] (see below).  
Figure 1: Microcomputed tomographic images of the apical segment of a mesial inferior molar root (class II of Vertucci, with isthmus) subjected to retreatment. **Left**, initial microtomographic scan performed after root canal obturation. **Middle**, post-preparation image obtained after retreatment with the Mtwo system (final instrument 35/0.04) in both canals. **Right**, final image after using the XP-Endo Finisher R.
## 4 Experimental Results
### Dataset  
LiDAR data were collected using the robot operating system (ROS) in a real mining scenario in Inner Mongolia, China. The collected dataset contained 3000 frames of point clouds and image data, including the whole process of the mining operation for the autonomous truck on straight, turning, uphill, and downhill roads. There were 1828 frames of data containing moving obstacles such as trucks, vehicles, and pedestrians. The image data could be used for comparison with point clouds.  
Datasets were annotated in KITTI format, including a point-cloud semantic segmentation dataset and an obstacle detection dataset. The semantic segmentation dataset com  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Definition** & **Value** \\ \hline H & Height & 4.1 m \\ \(\theta_{y}\) & Yaw & 0.12 deg \\ \(\theta_{p}\) & Pitch & \(-\)16.1 deg \\ \(\theta_{r}\) & Roll & \(-\)0.031 deg \\ \(\alpha\) & Horizontal angular resolution & 1024/0.703 deg \\ \(\beta\) & Vertical angular resolution & 0.518 deg \\ \(f\) & Out frequency & 10 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration parameters for the LiDAR.  
Figure 8: Truck equipped with an ouster OS1-64 LiDAR.  
prised 3000 frames for RangeNet++ algorithm training and testing. The obstacle detection dataset, called TG-Mine-3D, comprised 1828 frames for performance evaluation. According to the objects in the mine, the TG-Mine-3D dataset had three categories of objects: 'truck','vehicle', and 'pedestrian'. We used custom dataset TG-Mine-3D to evaluate the performance of 3 DSG since, to our knowledge, there were no open-source datasets in the mining area.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 4 In-Tube Solid-Phase Microextraction (In-Tube SPME)
### In-Tube SPME Applications  
In-tube SPME has been successfully applied to the determination of various analytes in food and beverage samples. Regarding beverages analysis, the liquid samples are usually filtered, and in some cases when necessary diluted before passing through the in-tube device resulting in a simplified sample preparation procedure. Table 3 summarizes some representative applications of in-tube SPME in beverages, which includes juices, coffee, sodas, teas, milk, and bottled water. The main goal in these applications has been the determination of residual contaminants as pesticides and other pollutants or compounds related to food security as food additives and mycotoxins.  
In a pioneer work [PERSON] et al. [64] employed on-line in-tube SPME-DART-MS to the analysis of triazines in orange juice. Briefly, they employed as extraction device a syringe barrel with a pinhead coupled to a capillary extraction column filled with a carbon nanotube incorporated monolith. The-sample preparation step, which involves activation, sampling, washing, and on-line desorption, was made using a syringe infusion pump, being the analytes desorbed from the column directly ionized by DART and analyzed by mass spectrometry. This method allowed the rapid analysis of these herbicides without using a chromatographic separation. Thus, when compared to an LC-MS method, it showed a reduction of the total time analysis from 16 to 3 min by employing the on-line in-tube SPME DART-MS set up. Additionally, the in-tube SPME-DART-MS method was evaluated using both off-line and on-line modes obtaining enrichment factors between 21-49 and 411-832 respectively, indicating a significant sensitivity gain when the on-line mode is used.  
Although the analysis of trans-fatty acids (TFAs) is usually made by GC, recently, [PERSON] et al. [75] employed in-tube SPME using a hydrophobic monolithic microcolumn coupled to HPLC-UV for the determination of TFAs as their fatty acid methyl esters (FAMEs), in instant coffee samples. This method allowed the selective separation between cis- and trans-isomers in a short period (18 min), under a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
simple operation and reaching very low LODs (3.0-7.0 \(\upmu\)g/kg), below the official method of analysis by GC- flame ionization detection (FID). Therefore, this high sensitivity method is a new and potential alternative for the analysis of TFAs in complex samples.