esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Pseudomonas Fluorescens on Isofetamid Dissipation and Soil Microbial Activity
## 3 Results
### Soil Enzyme Activity  
Figure 3 shows the effect of IS and IS+P on the activity of \(\beta\)-glucosidase (\(\beta\)-Glu), N-acetylglucosamidase (N-Ac), phosphomonoesterase (PP) and arylsulfatase (AS). The activity of \(\beta\)-Glu after the application of IS+P on D1 decreased by 33% compared to the control. In turn, on D21, a statistically significant increase in the activity of this enzyme was observed of about 83% compared to D1. The application of IS+P influenced the increase in the activity of PP on D21 and amounted to about 11 \(\mu\)g MUF/g dm/h, while, after the application of IS, the activity of PP on D21 decreased to the level of about 8.5 \(\mu\)g MUF/g dm/h and was lower than that of the samples taken from the control pots by about 26%. N-Ac activity in the collected soil samples ranged from 1.44 \(\mu\)g MUF/g dm/h (C, D1) to 2.77 \(\mu\)g MUF/g dm/h (IS, D21). Both in IS and IS+P, significantly increasing N-Ac activity on D21 was observed. IS and IS+P applications significantly increased AS activity in the soil. AS activity averaged to about 1.15 \(\mu\)g MUF/g dm/h in control samples, 1.90 \(\mu\)g MUF/g dm/h in IS samples, and 1.99 \(\mu\)g MUF/g dm/h in IS+P samples.  
Figure 3: \(\beta\)-Glucosidase (\(\beta\)-Glu), N-acetylglucosamidase (N-Ac), phosphomonoesterase (PP) and arylsulfatase (AS) activities in control (C), soil with isofetamid (IS) and soil with isofetamid and _P. fluorescens_ (IS+P). Data are presented as means \(\pm\) SE; different letters and error bars indicate statistically significant differences assessed by [PERSON]’s test at \(p<0.05\).  
Figure 2: Dissipation isofetamid (mg/kg) in soil with isofetamid (IS) and with isofetamid and _P. fluorescens_ (IS+P).
# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 5 Results and Analysis
### Robustness Experiments  
To thoroughly evaluate the model's performance under different environmental conditions, this study conducts simulation experiments on the self-constructed dataset. These experiments include low-light, low-resolution, and rainy and foggy weather scenarios. The aim is to validate the robustness of MNTReID in complex and suboptimal environments. The simulated experimental scenarios are as follows: In low-light conditions, pedestrian images are simulated by adjusting image brightness and introducing noise. In low-resolution conditions, the original images are downsampled to mimic pedestrian re-identification performance in low resolution. For rainy and foggy weather, pedestrian re-identification is simulated by adding rain effects to the images. Table 5 presents the performance comparison of MNTReID in these environments. The experimental results demonstrate that MNTReID exhibits strong robustness in these challenging conditions, particularly in rainy and foggy weather and low-resolution environments. Its performance remains at a high level despite these challenges. Under low-resolution conditions, the mAP of MNTReID is 84.7%, which is only slightly lower than the 88.9% achieved under normal weather. This indicates a strong anti-interference capability on low-resolution images. In rainy and foggy weather conditions, MNTReID combined with the non-local attention mechanism effectively integrates global contextual information. This integration reduces environmental interference and maintains high identification accuracy with a Rank-1 of 92.4%. Under low-light conditions, the mAP of MNTReID is 81.3%, which is 7.6% lower than under normal weather. Nonetheless, it still maintains excellent identification accuracy. Overall, the experimental results confirm that MNTReID performs robustly across various challenging environments, demonstrating its effectiveness in pedestrian re-identification tasks under diverse and suboptimal conditions.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Effect of Control Variables
#### 3.5.2 Effect of the High/Low Limit Set Points for Energy Arbitrage  
The effect of varying these set points is shown in Table 10. Evaluating the cited system by changing the set points \(Pr_{E\text{purch}\_sell\_Low\_stp}\) in \(-\)0.02 EUR/kWh and \(Pr_{E\text{purch}\_sell\_High\_stp}\) in \(+\) 0.02 EUR/kWh (increasing the dead band for the arbitrage from 0 to 0.04 EUR/kWh) reveals that the energies of pump, turbine, buy, and sell are reduced by different percentages, whereas the NPC increases by 7.5%.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Results Obtained** & **Results after Changing:** \\  & **(\(Pr_{E\text{purch}\_sell\_Low\_stp}\) =** & \(Pr_{E\text{purch}\_sell\_Low\_stp}\) =** & \(0.02\) EUR/kWh \(Pr_{E\text{purch}\_sell\_High\_stp}\) +** \\  & **0.0484 EUR/kWh** & & **0.02** EUR/kWh \\ \hline E. pump (GWh/yr) & 2.556 & 1.944 (\(-\)24\%) \\ E. turb (GWh/yr) & 2.061 & 1.552 (\(-\)24.7\%) \\ E. buy (GWh/yr) & 1.813 & 1.56 (\(-\)14\%) \\ E. sell (GWh/yr) & 7.140 & 7.048 (\(-\)1.3\%) \\ NPC (M EUR) & 3.210 & 3.353 (\(+\)7.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Effect of the high/low limit set point for arbitrage. Optimal LSS–A–547 EUR/kW + 2.7 EUR/m3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Results Obtained** & **Results after Changing to Type B** \\ \hline E. pump (GWh/yr) & 2.556 & 2.048 (\(-\)19.9\%) \\ E. turb (GWh/yr) & 2.061 & 1.628 (\(-\)21\%) \\ E. buy (GWh/yr) & 1.813 & 1.182 (\(-\)34.8\%) \\ E. sell (GWh/yr) & 7.140 & 6.621 (\(-\)7.3\%) \\ NPC (M EUR) & 3.210 & 3.527 (\(+\)13\%) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effect of the type of system. Optimal LSS–A–547 EUR/kW + 2.7 EUR/m3.
## 4 Data Processing and Analysis
### Plugging Effect Analysis of Large-Diameter Tubular Piles  
An obvious plugging effect was observed in the indoor tests on large-diameter tubular piles when they penetrated the strata. Displacement variations in three stages of the plugging effect are shown in Figure 6. Figure 14a-c show that, in the early piling stage, foundation soils suffered shear failure due to the influence of the pile wall. Meanwhile, the soil mass below the pile wall close to the pile end developed shear displacement. However, the soil mass in the piles did not move downward significantly as the pile penetrated further.  
Figure 14d-f show that the soil plug height and the frictional force between the soil plug and pile wall increased with increases in the penetration depth of tubular piles. Under these circumstances, the soil plug moved downward continuously due to frictional resistance. Accordingly, the altitude difference between the top surface of the soil plug and the original sand surface increased. Since the shear deformation near the pile wall was larger than that of sand particles in the middle of the soil plug, the dyeing sand layer was 'convex'. Further, and the soil plugs in piles produced a 'convex' arch due to frictional resistance.  
Figure 14g,h show that, as the piles pressed in continuously, the soil plug was compacted gradually due to the combined action of downward frictional resistance with the pile wall and the counterforce of the soil mass at the pile end. The dyeing sand layer developed a ball cap-like 'concave' arch. The soil plug in the pile developed an extremely large frictional force, stopping the further inrush of the soil mass into the pile like a 'bottle plug'. The length of the soil plug did not increase any more after this, reflecting a significant plugging effect. Consequently, the bearing characteristics of open-ended piles were the same as those of closed-ended ones.  
Additionally, a displacement contour map of the foundation soil was also drawn when the model piles penetrated the strata. The sphere of influence and size of the plugging effect were analysed quantitatively.  
Figure 15 shows that the horizontal and longitudinal spheres of influence expanded gradually as the piles settled. However, the vertical influences were significantly more obvious than the horizontal influences. The contour lines in the vertical and horizontal spheres of influence were rectangular. Model piles may have developed a horizontal squeezing effect when the plugging effect began to form. The horizontal sphere of influence of the squeezing effect also expanded severalfold.  
Figure 14: Soil plug phenomenon of pipe pile (**a**) before pressing piles, (**b**) at the pressed depth of 50 mm, (**c**) at the pressed depth of 100 mm, (**d**) at the pressed depth of 150 mm, (**e**) at the pressed depth of 200 mm, (**f**) at the pressed depth of 250 mm, (**g**) at the pressed depth of 300 mm, and (**h**) at the pressed depth of 350 mm.  
Figure 15: Soil body displacement contour diagram for different penetration of piles at the (**a**) pile penetration of 100 mm, and (**b**) pile penetration of 150 mm.
## 3 Principles of Infrared Spectroscopic Analysis Techniques
### Near-Infrared (NIR) SPECTROSCOpy vs. Mid Infrared Spectroscopy  
\"Near\" is related on the position of the electromagnetic energy lying next to or near the visible (Vis) energy range. Molecular vibrations in the MIR cover absorptions in a range between 400-4000 cm\({}^{-1}\) and are known to be the most intense and fundamental bands [51]. Light can interact with the sample in different ways. It can be reflected, scattered and absorbed or it can partly penetrate through the sample. The ratio of the portion reflected and scattered back upon interactions, _i.e._, the remitted radiation power IR, to the light intensity incident on the sample is defined as the reflectance R:  
\[R=\frac{I_{R}}{I_{0}} \tag{1}\]Reflectance \(R\) is in most applications then converted into the absorbance \(A_{R}\):  
\[A_{R}=-log_{10}\left(\frac{I_{R}}{I_{0}}\right)=-log_{10}R \tag{2}\]  
Transmittance \(T\), for measurements of light intensities transmitted through the sample is defined as:  
\[\mathrm{T}=\frac{I_{T}}{I_{0}}=10^{-\varepsilon_{A}cd} \tag{3}\]  
This enables one to establish the following equation:  
\[A=\varepsilon_{A}cd=-log_{10}\left(\frac{I_{T}}{I_{0}}\right)=-log_{10}T \tag{4}\]  
So finally the respective absorbance can be expressed as:  
\[A_{T}=-log_{10}\left(\frac{1}{T}\right)\text{ \ and }A_{R}=-log_{10}\left(\frac{1}{R}\right) \tag{5}\]  
[PERSON]'s law can be applied to determine the thickness of a membrane layer and the pathlength-concentration in terms of mass per unit area.  
Transflectance \(T\) represents a special case of transmission and reflection measurement. This measurement mode is often used for in- and on-line measurements of liquids or solutions. The incoming light beam passes the sample, then gets reflected on a non-absorbing mirror substance (in many cases Teflon) and is directed to the detector after penetrating the sample a second time. Due to this doubling of the path length, the sensitivity of the measurement setup can be enhanced (Figure 1).  
The absorbance \(\log(1/T)\) according to Beer law is rigorously only valid for non-scattering materials. In case of solid samples (powders, bulk samples) showing scattering effects, the absorbance is a so called pseudo-absorbance not exactly following the Beer law.
## Materials and Methods
### Statistical Analysis and Accuracy Assessment  
The algorithm's performance was assessed using four indices based on the absolute error (\(\alpha=\mathrm{Y_{i}}-\mathrm{X_{i}}\)) and the relative error (\(\beta=(\mathrm{Y_{i}}-\mathrm{X_{i}})/\mathrm{X_{i}}\)), namely, the root mean square error (RMSE = root mean square of \(\alpha\)), relative root mean square error (RMSE\({}_{\mathrm{rel}}\) = root mean square of \(\beta\)), mean normalized bias (MNB = arithmetic mean of \(\beta\)), and normalized root mean square error (NRMS = standard deviation of \(\beta\)) [42]. \(\mathrm{X_{i}}\) and \(\mathrm{Y_{i}}\) refer to the measured and predicted values for the \(i\)th sample, respectively. Note that MNB is a measure of the systematic errors, whereas NRMS is a measure of the random errors.
# Implementation of Explosion Safety Regulations in Design of a Mobile Robot for Coal Mines
## 4 Implementation of IEC 60079 for TeleRescuer
### Other Protections  
The two main independent protections mentioned above (mechanical protection--flameproof enclosure; electrical protection--automatic methane detector) are supplemented by many different partial protections.  
All electronic components of the main control system are sealed with a compound according to IEC 60079-18 (Explosive atmospheres--Part 18: Equipment protection by encapsulation \"m\") [35]. All cables leading outside of the flameproof enclosure are going through certified flameproof enclosure bushings.  
In critical parts of the robotic system are installed thermometers that continuously monitor temperatures and allow for the control system to turn the robot off in case of unexpected overheating of some components.  
The regulations also preclude the use of some types of materials for construction of the robot--for example, all light metal alloys (aluminium, titanium... ). Plastic components pose a threat because of static charge and are thus allowed only with special precautions--the maximal continuous surface area of plastic without grounding is limited. All mechanical parts of the mobile robot TeleRescuer are made from steel, rubber (tracks), and glass (camera lenses covers).
###### Abstract
A high resolution does not necessarily promote an improvement in interpretation accuracy. 3. In terms of the sophistication of geographic scenes, the structure and composition of the geographic scenes in the HSR remote sensing images are complex, highly variable and even messy. The types of geographic scenes with the same ground objects may be different. However, different types of ground objects also appear in similar geographic scenes [9]. Consequently, understanding the semantic information of the geographic scene and constructing the corresponding semantic feature description is crucial. As an extension of remote sensing image interpretation, the complexity and comprehensiveness of geographic scene understanding based on HSR images is beyond the general processing task of remote sensing. Although significant progress has been made in the research of feature extraction, target detection, scene classification and other sub-tasks, these sub-tasks lack a unified framework to cross the \"semantic gap\" to understand the high-level semantics of the geographic scenes. Thus, it is necessary to integrate these sub-tasks according to the human cognitive model in understanding the geographic scenes of HSR remote sensing images. In recent years, many researchers who are engaged in computer vision have realized the importance of a \"holistic understanding\" of geographic scenes and put forward the research approaches of task integration and feature integration. However, there is no systematic research on the geographic scene of HSR remote sensing images as a comprehensive and complete field of intelligent information processing. This paper is focused on answering the following research questions:What are the objectives of geographic scene understanding? 2. How are remote sensing approaches being used for geographic scene understanding? 3. What are the current gaps in HSR remote-sensing-based geographic scene understanding? The rest of the paper is organized as follows: Section 2 describes the basic ideas of geographic scene understanding based on HSR remote sensing images; Section 3 presents the semantic understanding approaches of the visual layer; Section 4 presents the semantic understanding approaches of the object layer; Section 5 presents the semantic understanding approaches of the concept layer; and Section 6 discusses the open problems and challenges in the future. The paper closes with a conclusion in Section 7.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 4 Results  
In Sections 2 and 3, we have discussed the methods which convert complex signal samples into images without noticeable information loss and introduced the structures of our DL framework. Table 1 shows the time complexity of our DL framework on the different process, in which N is the number of signals in the wideband range. It can be seen that our framework has low time complexity due to the evolution of GPUs, which is acceptable for many practical communication systems.  
We also have conducted several experiments to demonstrate the performances of the DL framework for joint signal detection and modulation recognition in wireless communication systems. Our experiments can be divided into two parts: (1) performances on multi-signals detection and (2) performances on modulation recognition. The rest of this section is organized as follows  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simple for Signal** & \multirow{2}{*}{**Signal Detection**} & \multirow{2}{*}{**Down Conversion**} & **Simple for Modulation** & **Modulation** \\
**Detection** & & & **Recognition** & **Recognition** \\ \hline
28.5 & 50.5 & \(10.3\times N\) & 20.8 & \(10.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The time complexity of the DL framework (ms).  
Figure 9: System model.  
**Multi-signals detection:** First, we show some results of our detection network, and explain the reasons for these results. Then, we evaluate the model performances from three aspects: the modulation format, carrier frequency, and start-stop time. We also compare our network with other detection networks.  
**Signal modulation recognition:** We evaluate our model recognition performances on each modulated signal. We also discuss the network performances when the frequency offset exists. Meanwhile, we compare our method with traditional methods and other DL based methods. Finally, we discuss the influence of symbol and eye numbers and compare the performance between signal-input networks and multi-inputs networks.
###### Abstract  
The high reliability of modern engineering systems is achieved by performing predictive maintenance. Mathematical models based on stochastic timed colored Petri nets are an effective tool for developing complex production processes for Industry 4.0. This article discusses the maintainability evaluation used in hierarchical Petri net models. The hierarchical simulation model was built using timed colored Petri nets, and was constructed with four levels of repair and maintenance modules. New module structures are proposed for simulating the schedule of production tasks and interaction with technological units. The emphasis is on the processes of predicting maintenance and repair, moving units to service, replacing units, and forming a reserve. The design of the simulation modules allows the setting of probabilistic parameters for the distributions of equipment failures, requests for unit maintenance, repair time, and recovery time after repair. The article proposes to use the hierarchical Petri model in conjunction with solving the problem of minimizing the cost of service. The iterative procedure consists of obtaining an approximate unit distribution by tasks, subsequent simulation of the technological process, and adjusting the optimization problem constraints. For example, the hierarchical Petri net is considered to assess the maintainability of autonomous agricultural vehicles. The results of the simulation experiments are presented. A simulation of the agotechnical production process was performed, during which vehicles were maneuvered, taken out for repair or maintenance, and returned to the reserve fund. The interdependencies of preventive maintenance periods, service operations, failure rates, and predictive maintenance requests were obtained in order to comply with the task scheduling. The proposed model is a generalization, but it is especially effective in studying mobile equipment servicing.  
model-based development; timed colored Petri nets; predictive maintenance; maintainability evaluation; discrete-event simulation Article
## 4 Example Verification
### Influence of the Change in Gravity Potential Energy on Permanent Displacement  
In order to explore the influence of the reduction in gravity potential energy on the permanent displacement of the slope, the inclination angle of the slip surface is changed without considering the vertical acceleration, the critical acceleration, permanent displacement, gravity potential energy drop and seismic input energy of sliding soil are calculated by using the derived theoretical formulas, as shown in Table 5. The ratio of the decrease in seismic input energy to gravitational potential energy and the variation of permanent displacement with the inclination angle of slip surface are calculated, as shown in Figure 14.  
It can be seen from Table 5 and Figure 14 that with the increase in the inclination angle of slip surface, the permanent displacement changes greatly at 30\({}^{\circ}\), and it suddenly increases sharply from the steady increase before 30\({}^{\circ}\). Meanwhile, the ratio of seismic input energy to gravitational potential energy drop decreases sharply after 10\({}^{\circ}\), and the absolute values of positive critical acceleration and negative critical acceleration decrease and increase, respectively. When the inclination angle of the potential sliding surface is greater than 15\({}^{\circ}\), the contribution of the seismic input energy to the permanent displacement of the slope under the action of the earthquake is relatively limited. It shows that for the permanent displacement of the slope excited by earthquake, earthquake is only a triggering condition and factor, and the real factor causing permanent displacement is gravitational potential energy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Calculation Method** & **Permanent Displacement/m** \\ \hline The method of this paper & 0.186 \\ The method of PLAXIS 3D & 0.118 \\ The method of GEO-Studio & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of permanent displacement of sliding soil.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Inclination** & \multicolumn{3}{c}{**Critical Acceleration**} & \multicolumn{2}{c}{**Permanent Displacement/m**} & \multicolumn{1}{c}{**Geismic Input**} \\ \cline{2-5}
**Angle of the Slip** & **Positive Critical** & **Negative Critical** & **Displacement x/m** & **Potential** & **Seismic Input** \\
**Surface \(\theta\)/(\({}^{\circ}\))** & **Acceleration \(a_{\text{cr}}\)/g** & **Acceleration \(a_{\text{co}}\)/g** & **Displacement x/m** & **Energy/kJ** & **Energy/kJ** \\ \hline
0 & 1.25 & \(-\)0.81 & 0.003 & 0 & 4.8 \\
5 & 1.16 & \(-\)0.97 & 0.006 & 15.8 & 12.9 \\
10 & 0.93 & \(-\)1.16 & 0.029 & 132 & 35.7 \\
15 & 0.75 & \(-\)1.39 & 0.058 & 458 & 86.4 \\
20 & 0.60 & \(-\)1.68 & 0.147 & 1247 & 143.2 \\
25 & 0.48 & \(-\)2.07 & 0.238 & 2956 & 175.8 \\
30 & 0.37 & \(-\)2.63 & 0.445 & 7234 & 239.6 \\
35 & 0.27 & \(-\)3.53 & 1.175 & 26472 & 257.7 \\
40 & 0.18 & \(-\)5.21 & 1.864 & 125479 & 277.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Critical acceleration, permanent displacement and gravitational potential energy drop of sliding soil.