esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antimicrobial Activity: Minimum Bactericidal Concentration and Sublethal Concentrations  
The Minimum Bactericidal Concentration (MBC) was determined using the micrololution method from CISI [45] with slight modifications. A 96-well microplate (Corning, Costar; Cambridge, MA, USA) was used, with each well filled with 190 \(\mu\)L of BHI broth previously inoculated with _L. monocytogenes_ (1% \(\upsilon/\upsilon\); 1 \(\times\) 10\({}^{6}\) CFU/mL). Subsequently, 10 \(\mu\)L of PEO and FIV working solution was added to the first column of wells, followed by 10 \(\mu\)L dilutions across the rows. This resulted in final concentrations ranging from 1.0% to 0.0078% \(\upsilon/\upsilon\) in the microplate wells. Each assay included a positive control (inoculated BHI without essential oil) to confirm bacterial growth and a negative control (uninoculated BHI) to verify sterility. Each experiment was performed in duplicate, with two replicates per treatment (PEO, FIV, and a control without OEO). After incubation at 37 \({}^{\circ}\)C for 24 h, 10 \(\mu\)L from each well was transferred to Trypticase Soy Agar (TSA; Difco Laboratories, Sparks, MD, USA) plates using the drip method. The plates were incubated under the same conditions, and the MBC was identified as the lowest concentration of PEO or FIV, at which no bacterial growth was observed. To estimate the SLC, the same microdilution method used for MBC was used but, in this case, the OEO was diluted to concentrations lower than MBC to achieve a reduction in _L. monocytogenes_ without reaching a lethal effect, ensuring that the concentration remains compatible with minimal processing conditions, such as the use of mild temperatures [46]. SLC was considered the maximum concentration where the OEO did not cause a decrease in the bacterial population.
## 3 Results and Discussion
### Aging Test Results and Electron Microscopy Microscopic Analysis
The images reveal that, regarding (a) untreated geotextile (pre-aging), the fibers exhibit an intact structure with no cracks, fractures, or signs of aging, discoloration, or chemical degradation. (b) The untreated geotextile (post-aging): after prolonged exposure to UV radiation and high temperatures, the fiber structure shows varying degrees of damage, including multiple fractures. (c) Regarding slurry-coated geotextile (post-aging), a significant amount of sediment particles adhere to the fibers, enhancing their resistance to abrasion and erosion. By comparing the fiber conditions in (a), (b), and (c), it is evident that the slurry coating effectively mitigates damage caused by UV exposure and high temperatures, thereby extending the geotextile's service life. In summary, the SEM observations not only confirm the damaging effects of ultraviolet (UV) radiation and high temperatures on the fiber structure of geotextiles but also  
Figure 16: SEM images of untreated and slurry-coated geotextiles: (**a**) pristine geotextile before aging test, (**b**) untreated geotextile after aging test, and (**c**) slurry-coated geotextile after aging test. Figure 15: SEM images of spray-coated geotextile (sample J) post-aging tests: (**a**) 300 g/m\({}^{2}\) + 1-layer sprayed geotextile; (**b**) 300 g/m\({}^{2}\) + 2-layer sprayed geotextile; (**c**) 400 g/m\({}^{2}\) + 2-layer sprayed geotextile; (**d**) 500 g/m\({}^{2}\) + 3-layer sprayed geotextile. demonstrate the significant effectiveness of slurry coating in enhancing the durability of geotextiles.
## 2 Materials and Methods
### Data Analysis  
Statistical analyses were conducted using IBM SPSS, version 27 (Chicago, IL, USA). A mixed-model analysis of variance (ANOVA) of phase (ascending/arm elevation and descending/arm depression), HT angle (minimum, 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), 90\({}^{\circ}\), 105\({}^{\circ}\) and 120\({}^{\circ}\)) and task (PNF-D2, and shoulder abduction, flexion and scaption), as within-subject variation factors, and group (athletes \"normal\", athletes with scapula dyskines, and non-athletes \"normal\"), as between-subject variation factor, with interaction (phase \(\times\) HT angle \(\times\) task \(\times\) group), was performed for each scapular rotation. A separate ANOVA was also performed for each phase and task condition. The Greenhouse-Geisser correction was used to adjust the degrees of freedom when the sphericity assumption (Mauchly's test) was violated. When a significant interaction was present, pairwise comparisons were conducted using Bonferroni correction for multiple comparisons. The significance level was set at 0.05.  
To complement inferential statistics, eta square partial (\(\eta_{p}^{2}\)) was calculated. For \(\eta_{p}^{2}\) > 0.5 the effect size is deemed very large; 0.25 < \(\eta_{p}^{2}\) < 0.50 is large; 0.05 < \(\eta_{p}^{2}\) < 0.25 is moderate; and for \(\eta_{p}^{2}\) < 0.05 the effect is small.
## 3 Results
### Example Application of the Methodology
#### 3.1.3 Evaluation Assessment
Quality of service:  
As mentioned in Lessons learned, the main issue was the late retrieval of information on crossing caused by insufficient event coverage. This issue negatively affects other important technical parameters such as latency and accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean** & **Mean Max** & **Mean Min** & **Acceleration** & **Standard** \\  & **Acceleration** & **Acceleration** & **Range** & **Deviation** \\  & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** \\ \hline Without C-ITS & 0.000406 & 1.15 & \(-\)1.73 & 4.65 & 0.69 \\ With C-ITS & 0.059209 & 1.17 & \(-\)1.05 & 3.30 & 0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Box plot of acceleration for all tested vehicles. Figure 9: Box plot of acceleration for all tested vehicles. HMI:  
For illustration, the warning about the public transport safety crossing is depicted in Figure 10 where the drivers are generally satisfied with the information from HMI while some drivers would welcome a larger pictogram of this use-case. Figure 10 also shows the distance to the event, which is 22 m. At the time the vehicle is waiting for the tram to pass, there is the word \"Now\" instead of distance. Some drivers would simply omit this word. The general comments regarding HMI also show that one of the problems that needs to be addressed in the future is the size and position of the HMI to inform the driver.
## 2 Outline of the Rocket and the Launcher
### Engine
Combustion pressure was not measured because of the difficulty of setting a pressure port in the engine without leakage of flame, except for in the May 16 test, shown in Table 3. Thus, the thrust was measured in the load cells, except for in the May 16 test. The thrust of the May 16 test was estimated simply as follows: (combustion pressure) \(\times\) (Nozzle throat cross-section area) \(\times\) (Calculated nozzle coefficient for mean combustion pressure). Figure 4 shows the thrust and time curves of the five successful combustion tests. The 5 s differences in the start times of the thrust curves were intended because using the same start time would have prevented us from showing the shapes of the five thrust curves. Table 3 confirms the reproducibility of the total impulse (integration of thrust and time curve), which was within \(-5.0\%\) to \(+5.1\%\) of the average. Note that significant vibrations of thrust were observed. The cause of the vibration was not the mechanical resonance of the measuring system; this was confirmed by a hammering test. Thus, the cause of the vibration may have been the combustion vibration; however, the combustion pressure was not measured by the above-described reason, and the investigation of the cause is intended for future work. \begin{table} \begin{tabular}{c c c c c} \hline
**Item** & **Transducer or Load Cells** & \begin{tabular}{c} **Dynamic Strain** \\ **Amplifier** \\ \end{tabular} & **Data Recorder** & \begin{tabular}{c} **Measurement** \\ **Uncertainty (RSS)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Pressure of oxidizer} & Transducer, & & & \\  & \begin{tabular}{c} SSV-010 MP-02, \\ 10 MPa FS \\ \(\pm\)0.15\% RO *1 \\ \end{tabular} & Not used & & & \(\pm\)0.21\% \\ \hline \multirow{3}{*}{Weight of oxidizer} & Load cell, 9 EO1-L3-50K, & \begin{tabular}{c} DPM-712B \\ \(\pm\)5 V FS \\ Nonlinearity: 0.15\% RO *1 \\ Hysteresis: 0.15\% RO *1 \\ \end{tabular} & \begin{tabular}{c} DPM-712B \\ \(\pm\)5 V FS \\ Nonlinearity: \(\pm\)0.5\% FS *1 \\ Accuracy: \(\pm\)0.5\% FS *3 \\ \end{tabular} & \begin{tabular}{c} LR8400, \\ \(\pm\)10 V FS \\ Accuracy: 0.15\% FS \\ \end{tabular} & \begin{tabular}{c} \(\pm\)0.57\% \\ \(\pm\)10 V FS \\ \end{tabular} \\ \hline \multirow{3}{*}{Thrust 1} & Load cell, 9 EO1-L3-5T, & \begin{tabular}{c} DPM-712B \\ 50 kN FS \\ Nonlinearity: 0.15\% RO *1 \\ \end{tabular} & Accuracy: \(\pm\)0.1\% FS *1 & \begin{tabular}{c} \(\pm\)0.57\% \\ \(\pm\)5 V FS \\ \end{tabular} \\ \hline \multirow{3}{*}{Thrust 2} & Load cell, TCLA-20 kNB, & \begin{tabular}{c} DPM-712B \\ \(\pm\)5 V FS \\ Nonlinearity: 0.08\% RO *2 \\ Hysteresis: 0.05\% RO *2 \\ \end{tabular} & \
\begin{tabular}{c} \(\pm\)0.57\% \\ \(\pm\)5 V FS \\ Nonlinearity: \(\pm\)0.1\% FS *1 \\ Accuracy: \(\pm\)0.5\% FS *3 \\ \end{tabular} &
\begin{tabular}{c} \(\pm\)0.54\% \\ \(\pm\)0.54\% \\ \end{tabular} \\ \hline \end{tabular}   
* *  
Figure 3: Combustion test facility.
## 4 Discussion  
### Method Characteristic Analysis  
In this section, we analyze the characteristics of this method in three aspects. Firstly, the advantages of the combination of the polarization feature and the statistical texture feature are discussed. Furthermore, we compare the effects of using a single feature and multiple features in the same way. Secondly, the ability of CNN to utilize the spatial information of polarimetric SAR building is analyzed. In the case of the same features, we use CNN and multi-layer perceptrons (MLP), respectively, to extract PolSAR buildings and then compare the accuracy of their results. Finally, the effect of applying superpixels is discussed on the accuracy of the results. By keeping the characteristics and methods unchanged, the effects of using a superpixel constraint on the accuracy of the results are compared.
## 5 Discussion
### Limitations and Future Directions  
Since the calculation of EVs depends on LULC data, the error in the EVs presented in this paper depends on the quality of the ESA and RES LULC data. The EVs calculated by different LULC are different, so high-precision LULC data calculation is needed to obtain high-precision EVs. In addition, we note that the EVs and LULC scales conform to a logarithmic relationship. However, due to the limited sample data, this logarithmic relationship may not be correct, and further analysis is needed to confirm it in future work.  
Figure 6: Change trends of the EVs with RES (**a**) and ESA (**b**) LULC scale changes.  
Instead, this paper aimed mainly to verify that different LULC product data and different scales affect the calculation of ESVs. In the future research, more LULC product data should be included to analyze the change rules of ESVs of different data, so as to provide scientific basis for calculating high-precision ESVs.
## 1 Introduction
A thirty-three component fluxgate sensor array was used for magnetic field measurements, and data were collected by passive measurements. The data collected were recorded in 32 \(\times\) 25 dimensional matrices, and a k-nearest neighborhood (kNN) classification study was performed. The sizes of the 32 \(\times\) 25 dimensional data matrices were reduced to 32 \(\times\) 2, and these matrices were used for classification. Classification results obtained with 32 \(\times\) 25 and 32 \(\times\) 2 dimensional matrices were compared with each other. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. Fluxgate or anisotropic magneto resistive (AMR) sensor arrays have been integrated to three-axis motion systems in explosive and buried object detection studies with the MA method in the literature, and data have been collected. Although the sensor arrays were moved along the x, y, and z axes during the data acquisition phase, the positions of the sensors were not changed [33; 34; 35; 36; 37]. However, even if there is no MA, changing the position of the sensor creates voltage variations at the output of the sensors. In this case, it becomes difficult to determine whether the change in the sensor output is due to a buried object or a change in the sensor position (present systems get different results based on the positioning of the sensor). In this paper, a solution is presented with circuit designed for this important problem.
# Development and Assist-As-Needed Control of an End-Effector Upper Limb Rehabilitation Robot
## 2 Development of the End-Effector Upper Limb Rehabilitation Robot (EULRR)
### Kinematics Modeling of EULRR  
As shown in Figure 1, only the two manipulators are moving during the rehabilitation training; therefore, the forward kinematics analysis is mainly to study the transformation relationship between the handles coordinate frame and the world coordinate frame. The origin of the world coordinate frame for EULRR is set as the intersection point of the joint axis 1 of the two manipulators (as shown in Figure 1); the base coordinate frame of the manipulators and the end-effector coordinate frame are set at the center of the base of the manipulators and the center of the end-effector, respectively. The link coordinate frames and DH (Denavit & Hartenbrtg) parameters of the manipulator, according to the DH method, are as shown in Figure 3 and Table 1, respectively. The transformation matrix of the handle coordinate frame in the world coordinate frame is shown in Equations (1)-(3).  
\[\begin{array}{c}T_{HL}^{World}=T_{OL}^{World}\cdot T_{0}^{OL}\cdot T_{7}^{0} \cdot T_{HL}^{7}\\ =\left[\begin{array}{ccc}n_{L}&o_{L}&a_{L}&p_{L}\\ 0&0&0&1\end{array}\right]\end{array} \tag{1}\]  
\[\begin{array}{c}T_{HR}^{World}=T_{OR}^{World}\cdot T_{0}^{OR}\cdot T_{7}^{0} \cdot T_{HR}^{7}\\ =\left[\begin{array}{ccc}n_{R}&o_{R}&a_{R}&p_{R}\\ 0&0&0&1\end{array}\right]\end{array} \tag{2}\]  
\[\begin{array}{c}T_{7}^{0}(q)=\prod_{i=1}^{7}\left(T_{i}^{i-1}(q_{i})\right) \end{array} \tag{3}\]  
where \(q\) is the joint position, \([n,o,a]\) and \(p\) are the orientation matrix and position vector of the handle presented in the world coordinate frame, respectively.  
Figure 3: The link coordinate frames of the manipulator.
## 2 System and Channel Models
### Signal Models
Considering the HAP-to-users RF link, the received RF signal at ground users is shown by  
\[y_{u}=\left(\frac{\eta(1-\rho)P_{H}G_{r}^{Ht}G_{r}^{Ur}}{FL_{r}h_{l}}\right)^{ \frac{1}{2}}h_{RF}\hat{y}_{Hf}+n_{r} \tag{7}\]where \(G_{r}^{Ht}\) is the transmit antenna gain, \(G_{r}^{UIr}\) is the receive antenna gain, \(FL_{r}\) is the free space loss defined as \(FL_{r}=\frac{4\pi L_{HUI}}{\lambda_{r}}\), \(L_{HUI}\) is the length of RF link, \(\lambda_{r}\) is the wavelength of RF signal, \(h_{l}\) is the link attenuation due to weather conditions defined as \(h_{l}=\omega_{oxy}L_{HUI}+\omega_{rain}L_{HUI}\)[27], \(\omega_{oxy}\) and \(\omega_{rain}\) are the attenuation coefficients cause by the oxygen and rain scattering, respectively, \(h_{RF}\) is the channel coefficient, \(n_{r}\) is the AWGN with variance \(\sigma_{r}^{2}\). The instantaneous received SNR at the end users is given as  
\[\gamma_{RF}=\frac{\eta(1-\rho)P_{H}G_{r}^{Ht}G_{r}^{UIr}}{FL_{r}h_{l}\sigma_{r }^{2}}h_{RF}^{2}=\overline{\gamma}_{RF}h_{RF}^{2} \tag{8}\]  
where \(\overline{\gamma}_{RF}=\frac{\eta(1-\rho)P_{H}G_{r}^{Ht}G_{r}^{UIr}}{FL_{rh} \sigma_{r}^{2}}\) is the average received SNR of RF signal at end users.
## 2 Methodology  
This study was ethically approved by the ethical committee of the University of Leeds, Leeds, UK. It includes two case studies from evolving (Head-Marala, Pakistan) and evolved (York, UK) economies to understand the differences of barriers in implementing ICTs. A wide range of ICTs (Table 1) were investigated to understand the barriers to implementing technological resources in the life cycle of a flood. Table 2 shows the institutions and designations of experts from both case studies who deal with the barriers. Figure 3: Advanced ICTs used by experts can bridge the gap left by ICTs used by communities. Figure 2: Information and communication gaps exist between communities and authorities. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ICTs** & **ICF Technologies Used by Communities and Institutions** \\ \hline \hline \multirow{8}{*}{**ICTs-1: used by people and experts**} & Newspaper, newsletters/brochures \\  & Landline/mobile phones/smartphones \\  & Newsletter (mobile phone- and email subscription-based) \\  & Smartphone earthquake and flood site-specific information application \\  & Smartphone communication applications (Skype) and messengers (Viber, WhatsApp, Line) \\  & Social media websites: Facebook and Twitter with geo-tags (earthquake and flood \\  & information pages) \\  & Information websites/blogs (specific disaster websites and information pages to offer help \\  & and flood and earthquake-related information) \\  & News channels, both national and international \\  & Pictures or videos on YouTube, \\  & Web technologies/contributory websites (Google Maps, Google Media) \\  & Radio \\  & Variable message signs \\ \hline \multirow{8}{*}{**ICTs-2: used by experts**} & Remote sensing technologies (satellite images/Radar, Lidar) \\  & Airborne technologies (airplane and helicopter) \\  & Small drones \\ \cline{1-1}  & Bluetooth technology (wireless and non-wireless) \\ \cline{1-1}  & Navigation system (GPS). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information and communication technologies (ICTs) used for information and communication flow (ICF) in floods.
# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
###### Abstract  
In order to efficiently and exactly in tracking the desired path points, autonomous cleaning vehicles have to adapt their own behavior according to the perceived environmental information. This paper proposes a target points tracking control algorithm based on the Long Short-Term Memory network, which can generate the speed and yaw rate to arrive at the target point in real time. The target point is obtained by a parameter named foresight distance that is deduced based on the fuzzy control, whose inputs are the speed and yaw rate of the vehicle at the current point. The effectiveness of the proposed algorithm is illustrated by the simulation and field experiments. Compared with other classical algorithms, this algorithm can track the point sequence on straight path and multiple curvature path more accurately. The field experiment indicates the proposed controller is efficient in following the pre-defined path points, furthermore, it can make the autonomous cleaning vehicle run smoothly in the path which is disturbed by bounded disturbances. The distance errors can meet the actual requirement of the cleaning vehicle during the tracking process.  
target points tracking; LSTM network; fuzzy control; automatic vehicle +
Footnote †: journal: Applied  
Article
# Comparison of PCR Primers for Analyzing Dentirifying Microorganisms in the Hyporheic Zone
## 3 Results
### PCR Amplification Testing of Denitrification-Associated Genes  
Efficient PCR primer sets for each denitrification-associated gene were selected based on the formation of specific bands and the number of type strains detected. The PCR amplification experiments for six denitrification-associated genes from sixteen aqueous environmental samples showed the amplification of denitrification-associated genes in fifteen samples. Of the six denitrification-associated genes, a specific band for \(narG\) was most frequently observed (fourteen samples), followed by \(nosZ\) (13 samples), \(nirS\) (13 samples), and \(napA\) (11 samples). Specific bands for \(nirK\) and \(cnorB\) were observed in fewer samples (Table 3).  
_NosZ_ has been reported to be more related to \(nirS\) than to \(nirK\)[26]. In this study, fifteen samples were observed to have a denitrification-associated gene that might be negatively correlated with nitrous oxide emission, which might allow for a reduction in the levels of nitrous oxide and nitrogen dioxide.
## 4 Simulation
### Numerical Conditions  
We evaluated the proposed system considering the torque oscillation at the engine start pattern. The waveforms were created using the engine simulator that was provided as a sample model [17]. In real-life operation, the conditions exhibit many variations in terms of engine temperature, humidity, etc. To consider such variations, we set two parameters, i.e., friction torque of the engine and starter speed, to have a normal distribution with a variation of 5 %. The training and test data sets for the ESN were established based on the engine simulation with random noise.  
Figure 4a illustrates a sample of typical torque oscillation during the engine start condition. The engine does not burn fuel until approximately 0.8 s. In this no combustion condition, torque oscillation is mainly caused by pumping and gas exchange loss in the engine cylinder. After approximately 0.8 s, the engine torque increases sharply with the initiation of fuel combustion and later decreases to the torque demand for the idle speed. Figure 4b shows the overlap of 50 variations of the waveform. It can be noted that the frequency of the waveform exhibits considerable variations. To evaluate the control performance in cases involving irregular disturbances, we applied the aforementioned waveforms to the active vibration reduction controller. The difference in the control performance between the conventional MPC and the proposed method is discussed in the subsequent section. The specifications of the active vibration controller for the HEV powertrain are presented in Table 1.  
Figure 4: Torque oscillation at the engine start condition: (**a**) a sample of a typical torque ripple; (**b**) variations of a trained waveform.