esa-sceva/satcom-chunk-collection huggingface dataset:
# Manipulator Control of the Robotized TMS System with Incurved TMS Coil Case
## 4 Force and Torque Control for Adhering Between the TMS Coil and the Head
### Torque Control  
The floor shape of the coil used in this study is curved and symmetrical, as shown in Figure 1. Therefore, the contact area between the head of the subject and the TMS coil can be increased by locating the contact area closer to the center of the coil. By increasing the contact area, the coil can contact stably and better resist any slight movement of the head by increasing the surface friction. To make the contacting surface near the center of the coil, proportional torque control is applied as described below.  
Suppose that the force between the subject's head and the floor of the coil is consistent and that the force's distribution on the contact area is uniform. Then, the closer the torque acting on the center of the coil's floor is to zero, the closer the centroid of the contact area is to the center of the coil's floor. Therefore, the torque is commanded to the controller as follows:  
\[\mathbf{\tau}(\mathbf{\tau}_{c})=k_{p}\big{[}\tau_{c\mathbf{x}},\tau_{c\mathbf{y}},0\big{]}^{T} \tag{10}\]  
where \(\mathbf{\tau}_{c}=\big{[}\tau_{c\mathbf{x}},\tau_{c\mathbf{y}},\tau_{c\mathbf{z}}\big{]}^{T}\) is the measured torque acting on the center of the coil's floor, and \(k_{p}\) is the proportional gain of the torque control. Since it is similar to P (proportional) control, where the desired torques along the \(x\)- and \(y\)-axes are zero, it is referred to as \"proportional torque control\" in this study. The coordinate system of \(\mathbf{\tau}\) and \(\mathbf{\tau}_{c}\) are the same as the tool's. The \(z\) value of \(\mathbf{\tau}\) is zero because it cannot tilt the coil, hence it rarely affects the adhesion. This overall control is described visually in Figure 9.
###### Abstract  
Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution Estimation with Remote Sensing (DPeRS) model, a distributed NPSP model proposed by Chinese researchers, seeks to predict agricultural NPSP and includes modules estimating nitrogen and phosphorus balance, vegetation coverage, dissolved pollution, and absorbed pollution. By applying the DPeRS model, the present work aims to predict the distribution of all nitrogen and phosphorus pollutants in Henan Province, China in 2021. We used statistical yearbook, remotely sensed, and hydrological data as input. To facilitate uncertainty characterization in pollution predictions, we performed sensitivity analysis, which identified the model input variables that contributed most to uncertainty in model output. Specifically, we used ArcGIS for processing data for nitrogen and phosphorus balance equations, an ENVI 5.3 software system for deriving vegetation cover, and the RUSLE soil erosion model for predicting absorption pollution. Dissolved pollution was estimated using a unified approach to estimating agricultural runoff, urban runoff, rural resident, and livestock pollutants. Absorbed pollution was estimated by considering the soil erosion model and precipitation. Moreover, [PERSON]'s method was applied for sensitivity analysis. We found that regardless of the accumulation of nitrogen or phosphorus, indicators of the dissolved pollution of Zhoukou were relatively high. Sensitivity analysis of the models for estimating dissolved pollution and absorbed pollution revealed that the top four influential variables for dissolved pollution were standard runoff coefficient \(\varepsilon_{0}\), natural factor correction coefficient \(\mathrm{N_{i}}\), the newly produced TN pollutants per area \(\mathrm{Q_{N}}\), and runoff coefficient \(\varepsilon\). For absorbed pollution, influential variables were rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. The total discharges of Henan Province were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, \(\mathrm{NH_{4}^{+}N}\), and COD, respectively, in 2021. This paper provides a valuable reference for understanding the status of NPSP in Henan province. The DPeRS approach presented in this paper provides strong support for policymakers in the field of environmental management in China. This study confirmed that the DPeRS model can be feasibly applied to larger areas for NPSP prediction enhanced with sensitivity analysis due to its fast computation and reliance on accessible and simple data sources.  
agricultural non-point source pollution (NPSP); Diffuse Pollution Estimation with Remote Sensing (DPeRS); parameter sensitivity analysis +
Footnote †: journal: Applied Sciences  
Article
## 4 Experimental Results
### Segmental SNR Improvement  
The average segmental SNR improvement (\(Avg\_Seg\)SNR_Imp) can evaluate the quantities of speech distortion, residual noise and noise reduction for denoised speech. The \(Avg\_Seg\)SNR_Imp can be computed by  
\[Avg\_Seg\text{SNR\_Imp}=Avg\_Seg\text{SNR}(\hat{s})-Avg\_Seg\text{SNR}(y) \tag{37}\]  
where \(Avg\_Seg\)SNR\((\hat{s})\) and \(Avg\_Seg\)SNR\((y)\) represent the \(Avg\_Seg\)SNR of denoised speech and observed signals, respectively. The \(Avg\_Seg\)SNR\((\hat{s})\) and \(Avg\_Seg\)SNR\((y)\) can be computed by  
\[Avg\_Seg\text{SNR}(\hat{s})=\frac{1}{M^{\prime}}\sum\limits_{\eta\ \in\ \{I\}}10\cdot\log_{10}(\frac{\sum\limits_{v=0}^{N-1}|s(\eta,v)|^{2}}{\sum \limits_{v=0}^{N-1}|s(\eta,v)-\hat{s}(\eta,v)|^{2}}) \tag{38}\]  
\[Avg\_Seg\text{SNR}(y)=\frac{1}{M^{\prime}}\sum\limits_{\eta\ \in\ \{I\}}10\cdot\log_{10}(\frac{\sum\limits_{v=0}^{N-1}|s(\eta,v)|^{2}}{\sum \limits_{v=0}^{N-1}|s(\eta,v)-y(\eta,v)|^{2}}) \tag{39}\]  
where \(\{I\}\) and \(M^{\prime}\) denote the set of speech-presence frames in an utterance and the number of speech-presence frames, respectively.  
From (37), the quality of denoised speech becomes better if this denoised speech obtains a larger \(Avg\_Seg\)SNR_Imp value. Table 1 presents the performance comparisons for various noise estimation methods by the \(Avg\_Seg\)SNR_Imp. The proposed method is superior to the MCRAand FB_MCRA algorithms in most conditions. This is due to a quantity of consonants and weak vowels that are preserved by the underestimation of interference noise. These results are achieved by increasing the segment length to track the minimum spectral magnitude of noisy speech. In addition, the segment length reduces during speech-pause regions. This enables the spectral magnitude of noise to update quickly, yielding noise spectra being effectively removed by speech denoising. Accordingly, the proposed method can obtain higher scores of the average segmental-SNR improvement than the other approaches. In the cases of the babble (speech-like) noise interference, the proposed noise estimator also outperforms the other two methods for slight noise corruption (input SNR equaling 5 dB and 10 dB). The performances of the three methods are very comparable in heavy corruption of babble noise.
###### Abstract  
A dental implant surgical guide fabricated by 3-dimensional (3D) printing technology is widely used in clinical practice due to its convenience and fast fabrication. However, the 3D printing technology produces an incorrect guide hole due to the shrinkage of the resin materials, and in order to solve this, the guide hole is adjusted using a trimmer or a metal sleeve is attached to the guide hole. These methods can lead to another inaccuracy. The present method reports a technique to compensate for a decreased guide hole caused by shrinkage that can occur when a computer-guided implant surgical guide is fabricated with a 3D printer. The present report describes a technique to adjust the size of the guide hole using a free software program to identify the optimized guide hole size that is fabricated with the 3D printer.  
surgical guide; 3D printing; guided hole; dental CAD/CAM; dental implant +
Footnote †: journal:  
and the layer thickness can affect the result [10; 11; 12]. Since no factors are defined in the dental clinical environment, an optimization process is required [13]. The deviation of the dental implant's position is caused by cumulative errors in the design and manufacture of the surgical guide [14; 15]. The present technical note describes how to make guided holes with minimum tolerances using a 3D printer in dental clinical environments.  
Inaccuracies in the placement of the placed dental implant can lead to surgical failures, such as nerve damage and complications that may occur in the maxillary sinus [16; 17]. Therefore, the accuracy of the surgical guide is an important factor for the success of implant surgery [18]. In order to improve the accuracy of the surgical guide, there have been many studies to improve the accuracy of cone-beam computed tomography and intraoral impressions, and the accuracy of registration in software [19; 20; 21; 22]. However, there are still insufficient studies to compensate for the shrinkage of the guide hole due to the shrinkage of the light-polymerized resin for 3D printing.  
Although many software programs and 3D printers for CGI surgery are available, there is still a need for a method that can be optimized for more accurate implant positioning. This report describes a technique that uses free software to adjust the size of the guide hole in the 3D printing surgical guide. This technical note describes a method for creating a 3D printing surgical guide with minimal tolerance of guide holes in clinical practice.
## 2 Theory and Method
### Reflection Coefficient Equation Analysis  
To assess the rationality and validity of the derived equation, we counted some logging data in the reservoir section of a basin in eastern China, and then we constructed a two-layer model with the specific parameters listed in Table 1. Using this model, we compared the reflection coefficient obtained in this study with the reflection coefficient derived by [PERSON] (2021), [PERSON], and [PERSON] (2004) [15; 17]. The results of this comparison are presented in Figure 2. To further illustrate the accuracy of the reflection coefficient derived in this paper, we calculated the difference between the reflection coefficient obtained by [PERSON] and [PERSON] and the reflection coefficient derived by us and normalized this difference by dividing it by the value of the reflection coefficient. The resulting relative error is shown in Figure 3, providing a more intuitive measure of the accuracy of our derivation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & \(M\) **[Gpa]** & \(\mu\) **[Gpa]** & \(\delta_{N}\) & \(\delta_{T}\) \\ \hline
**Upeer** & 1.85 & 1.15 & 0 & 0 \\
**Lower** & 2.48 & 1.32 & 0.12 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two-layer model parameter values.  
Figure 2: Comparison of reflection coefficients: **(a)** the azimuth angle is \(60^{\circ}\) and **(b)** the incidence angle is \(30^{\circ}\).  
As illustrated in Figure 3, the largest error values are observed primarily around the \(0^{\circ}\) and \(180^{\circ}\) azimuths. This is due to the increased sensitivity of the reflection coefficient's azimuthal response as the fracture orientation becomes more perpendicular (i.e., as the value of \(\xi\) decreases). While significant errors are evident at certain azimuth angles, the overall error in the reflection coefficient derived remains well within 5%. This indicates that the derived reflection coefficient maintains a high degree of accuracy and consistency. Consequently, it can be concluded that the reflection coefficient derived in this study has considerable reference value and practical applicability for fracture analysis.
# Protein Dynamics in F-like Bacterial Conjugation
## 3 Structures Involved in Pilin Processing, Pilus Extension, and Retraction  
### Proteins Responsible for Propilin Maturation  
The conjugative pili, an external appendage ubiquitous in the bacterial kingdom, mediates bacterial conjugation. On average, an _Escherichia coli_ cell will have anywhere from 1-5 pili, with some variants having up to 20 [55]. Pili can range in length and flexibility; cells expressing rigid pili use these appendages to manifest an aggregate network of rigid polymers, thus, encouraging non-specific clumping of donor and recipient cells to form mating pairs [56]. Some pili, such as the F pilus, are capable of extension and retraction allowing for close contact between a donor and recipient cell for bacterial conjugation. Other pili will use retraction and extension as a manner of maneuverability termed \"twitching motility\" [57]. During phage infection, prior to DNA or RNA being injected into a bacterial cell, a major site of absorption is the pilus [16, 58]. As the pilus is an important aspect in virulence, as well as providing bacteria adaptivity to their environment it, is deemed an attractive target for novel therapeutics.  
The first step in pilus production is the post-translational processing of propilin subunits. Within the F _tra_ system, proton motive force embeds TraA within the IM in a sec-independent mechanism [59, 60]. The inner membrane (IM) protein TraQ then interacts with proTraA to ensure stabilization within the membrane. ProTraA is synthesized with a leader sequence; this sequence is acetylated by TraX at A52 and the preceding N-terminus is cleaved to produce the mature pilin [61]. Mature TraA is then extracted from the IM for pilus assembly.
## 3 Results and Discussion
### Correlation Between the Total Polyphenol Content (TPC) and Antioxidant Activity  
The correlation between total phenol content (TPC) and Trolox equivalent antioxidant capacity (TEAC) of the freeze-dried vegetables is presented in Figure 3. There is a strong correlation for beetroot (r = 0.7555), which means that the increase in total polyphenols increases their antioxidant potential, a moderate correlation for red pepper (r = 0.6881) and green pepper (r = 0.68830). On the other hand, there is no correlation for red cabbage and kale, which suggests that other non-polyphenol bioactive factors and compounds affect their antioxidant potential [31]. The compound mainly responsible for antioxidant properties in red cabbage is 4-(methylsulfonyl)butyl ITC (4 MSOB-ITC, sulforaphone), which was not determined in this study [36]. In the case of kale, the lack of correlation may result from the fact that the main compounds responsible for antioxidant capacity are vitamin C and A (lutein and \(\beta\)-carotene) [37; 38; 39].  
Figure 2: Trolox equivalents antioxidant capacity of vegetable extracts tested by DPPH method.
# FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot
## 3 System Implementation
### IP Core Construction of the Line Segment Detection Algorithm  
Based on previous studies, this paper builds on the overall structure of the IP core of the line segment detection algorithm, in which region growth is the key and difficult point of the IP. As shown in Figure 2, the IP core of the line segment detection algorithm first uses the image preprocessing algorithm for feature enhancement and smoothing and then detects the edge information of a single pixel in the image through the improved Canny operator. Finally, it achieves line segment detection by merging the edges with similar edge directions through region growth.
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 4 Proposed Approach
### Scheme Formulation
Given above formulation, maximum transmission time \(maxTx_{i}\) for total data \(d_{i}\) generated by source node \(S_{i}\) can be computed as  
\[maxTx_{i}=\phi_{i,A_{i}}(\beta) \tag{4}\]  
whereas throughput \(\theta_{G_{i}}\) of gateway \(G_{i}\) node becomes  
\[\theta_{G_{i}}=\frac{d_{i}}{\phi_{i,A_{i}}(\beta)-\phi_{i,1}(\alpha)} \tag{5}\]  
Overall network throughput \(\theta_{G}\) can be formulated as below  
\[\theta_{G}=\frac{\sum_{i=1}^{n}d_{i}}{max(\phi_{i,A_{i}}(\beta))-min(\phi_{i,1 }(\alpha))} \tag{6}\]  
The goal is to maximise overall throughput while minimising the transmission time at individual gateway nodes. Thus, objective function can be formulated as below  
\[\frac{\theta_{G}}{max(\phi_{i,A_{i}}(\beta)-\phi_{i,1}(\alpha))} \tag{7}\]  
Constraints:  
\[0<minTx_{i}\leq maxTx_{i}\quad\text{for all }i=1...n \tag{8}\] \[t_{bi}=RTS+CTS+T_{x}(b_{i})+Ack \tag{1}\]0 (9) \[t_{bi}=RTS+CTS+T_{x}(b_{i})+Ack \tag{1}\]1 \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(S_{i}\) & ith source node \\ \hline \(d_{i}\) & Total data generated by source \(S_{i}\) \\ \hline \(r_{i}\) & Data generation rate by source \(S_{i}\) \\ \hline \(p_{i}\) & Packet size at each source node \\ \hline \(G_{i}\) & ith gateway node \\ \hline \(b_{i}\) & Aggregated data size at \(G_{i}\) \\ \hline \(t_{bi}\) & Data transmission time by gateway node \(G_{i}\) \\ \hline \(minTx_{i}\) & Minimum transmission time for data generated by source node \(S_{i}\) \\ \hline \(maxTx_{i}\) & Maximum transmission time for data generated by source node \(S_{i}\) \\ \hline \(A_{i}\) & Total number of aggregated packets at gateway \(G_{i}\) \\ \hline \(\phi_{i,k}\) & Scheduling interval for kth packet at gateway \(G_{i}\) \\ \hline \(\alpha\) & Starting time of an interval \\ \hline \(\beta\) & Ending time of an interval \\ \hline \(\theta_{G_{i}}\) & Throughput of gateway \(G_{i}\) node \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the symbols used in the formulation.
## 3 Materials and Fabrication Techniques
### Fabrication Techniques
#### 3.3.3 Inkjet-Printing  
Inkjet-printing has recently attracted considerable attention for the fabrication of OECTs-based sensors, mostly because it is a digital technique not requiring a physical printing master. It allows the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Top-Down Approach** & & \\ \hline
**Spin Speed** & **Spin Duration** & **Post-Deposition Treatment** & **Final Thickness** & **Reference** \\
**[rpm]** & **[s]** & **Post-Deposition Treatment** & **[nm]** & **Reference** \\ \hline
500 & 3 & [-] & [-] & [105] \\ \hline
3000 & [-] & Pre-baking at 100 \({}^{\circ}\)C for 90 s, followed by immersion in deionized water and thermal annealing 140 \({}^{\circ}\)C for 30 min & [-] & [106] \\ \hline
[-] & [-] & Thermal annealing at 130 \({}^{\circ}\)C for 10 min & & \\  & & followed by another baking at 150 \({}^{\circ}\)C & [-] & [107] \\  & & for 1 h under high purity N\({}_{2}\) & & \\ \hline
[-] & [-] & Thermal annealing at 140 \({}^{\circ}\)C for 60 min & [-] & [108] \\ \hline
600 + 3000 & 9 + 30 & Thermal annealing at 185 \({}^{\circ}\)C for 60 min & [-] & [109] \\ \hline
[-] & [-] & Thermal annealing at 185 \({}^{\circ}\)C for 60 min & -60 & [110] \\  & & under N\({}_{2}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 10: Screen-Printed Organic Electrochemical Transistors for metabolite sensing: (**a**) schematic representation of the OECT enzymatic sensor and the enzymatic reaction mechanism; and (**b**) photograph of the corresponding wafer with an OECT biosensor in inset. Reproduced with permission from [111]. © Materials Research Society 2015.  
utilization of relatively small quantities of inks and be easily adapted to different sizes of production scale, varying from rapid circuit prototyping on small-scale substrates to large-scale, large-area production [113].  
Thus far, inkjet-printing has been successfully used for the deposition of a suspension of polypyrrole nanoparticles and glucose oxidase [114] or a suspension of PEDOT:PSS, glucose oxidase and horseradish peroxidase [115], in both cases for the detection of glucose, or monoclonal murine antibodies for the fabrication of immunosensors [116]. Despite these encouraging results, to our best knowledge, no examples of inkjet-printed OECTs for the detection of metabolic analytes have been reported until now.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 3 AUV Control System
### Kalman Filter  
A Kalman Filter (KF) has been considered to estimate the vehicle state vector. Because of the hard nonlinearities in the AUV dynamics, a Linear Parameter-Varying (LPV) version of the Kalman filter was developed to iteratively compute the angular velocities of the vehicle. The LPV-KF improves the standard linear KF by updating the state-space model of the system given the last measured value of the time-varying parameters (measured or estimated) that defines the system's behavior [13; 14; 15]. The LPV-KF algorithm consists of the following steps:  
\[S(k)=C_{d}(k)P_{KF}(k|k-1)C_{d}^{T}(k)+R_{KF} \tag{48}\]  
\[L(k)=P_{KF}(k|k-1)C_{d}^{T}(k)S^{-1}(k) \tag{49}\]  
\[\widetilde{y}_{AUV}(k)=y_{AUV}(k)-C_{d}(k)\hat{x}_{AUV}(k|k-1) \tag{50}\]  
\[\hat{x}_{AUV}(k|k)=\hat{x}_{AUV}(k|k-1)+L(k)\widetilde{y}_{AUV}(k) \tag{51}\]  
\[P_{KF}(k|k)=P_{KF}(k|k-1)-L(k)S(k)L^{T}(k) \tag{52}\]  
\[\hat{x}_{AUV}(k+1|k)=A_{d}(k)\hat{x}_{AUV}(k|k)+B_{d}(k)\tau_{V}(k)+d_{d}(k) \tag{53}\]  
\[P_{KF}(k+1|k)=A_{d}(k)P_{KF}(k|k)A_{d}^{T}(k)+Q_{KF} \tag{54}\]  
where matrices \(R_{KF}\) and \(Q_{KF}\) describe the variances of the measurement and the process noise terms. In the current problem they are assumed constant and defined during the algorithm set-up. The covariance matrix \(P_{KF}\) and the system states are evaluated on-line iteratively.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 4 Diagnostic Approaches of Foodborne Pathogens  
The best way to prevent and control human foodborne illness is to continuously monitor and accurately identify the pathogens responsible for it [76; 97]. By accurately identifying the pathogens that cause foodborne illness, food producers and distributors can take the necessary steps to ensure that their products are safe for consumption. This includes the implementation of safety protocols, such as proper storage and handling of food, to prevent contamination. It is increasingly accepted that traditional microbiological diagnostic approaches are too labor-intensive and time-consuming, particularly when it comes to identifying foodborne pathogens in order to meet the needs of rapid analysis of food samples [98; 99]. As a consequence, new methods are needed in order to rapidly detect minute concentrations of live microorganisms within a given volume of food, which makes it very challenging to detect [99]. Recent advances in fast diagnosis and characterization of foodborne pathogens have been enabled by the use of nucleic acid-based techniques, immunologic methodologies and biosensor-based techniques [2; 100; 26]. As a result of false-negative or false-positive results, further testing may be necessary to determine the origin of these results [2]. Screening for food microbes has long been regarded as an essential step in the preparation of food, but it is typically used for reliable confirmation of the presence or absence of a given pathogen in a food sample.  
As far as microbiological analysis goes, there are two major purposes. One is to ensure that there are no pathogens or toxins in the food to ensure that it is safe to eat, while the other is to establish the total microbial load in order to determine product quality and shelf-life. The purpose of this section is to discuss a number of existing techniques, including culture-based techniques, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), polymerase chain reaction (PCR, and antibody-based immunosassays, which may be applied in the field (Figure 1). Culturing procedures, meanwhile, are based on the microbial growth that is used to identify particular pathogens [101; 102; 103]. The immunoassays are intended to measure the quantitative interaction of an antigen with its antibody [18].