esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Plants Metals Content
Our findings suggest that direct cutting propagation does not compromise the plant's ability to accumulate trace metals in aerial tissues, supporting its potential to boost clonal screening for SRM recovery in phytomanagement schemes. With the perspective of long-term cultivation, Cu, Mg, and Mn contents were evaluated in CP + CI substrate. Stems contained 18.5 mg kg\({}^{-1}\) of Cu, 2081 mg kg\({}^{-1}\) of Mg, and 37.7 mg kg\({}^{-1}\) of Mn, while sprouts had 67.1 mg kg\({}^{-1}\) of Cu, 1411 mg kg\({}^{-1}\) of Mg, and 87.8 mg kg\({}^{-1}\) of Mn. Mg data align with [24], who reported up to 0.6% Mg in shoots and leaves, noting Zn and Pb exposure could decrease Mg concentration. In 30-month-old _Atriplex halimus_ clones, Ref. [66] found Cu, Mg, and Mn mainly in aerial biomass, with the highest amounts in leaves, averaging 1.4% Mg and about 450 mg kg\({}^{-1}\) Mn. Mg concentrations in leaves seem positively related to soil content [67]. Cu and Mn values can be compared with [52]. Cu values align with [29] and [26], who reported increasing metal content in tissues over time. An increase of Mn over time with compost, boosting shoot translocation, was reported by [17]. The literature is divided regarding the phytoremediation role of _Atriplex halimus_. While some authors emphasize its phytostabilization potential [18], others report significant metal translocation to shoots, supporting a phytoextraction role for Cd and Pb [10, 29]. Therefore, while the early accumulation patterns observed here are promising, longer-term studies are needed to assess the maximum uptake capacity of _Atriplex halimus_ and to validate its phytoextraction potential.
# Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation
## 4 Experiments
### Results of the Indoor Experiment  
In the indoor experiment, we put an electric fan to simulate a constant wind disturbance (Figure 4). We used a self-made quadrotor with a flight control board and GPS mounted in the plane. An OptiTrack motion capture system provides reliable state information, and a multisensor fusion framework in the flight computer fuses the measurement from the onboard IMU and the motion capture data to compensate for the time delay and low update frequency of the OptiTrack system. We compared the position errors between RL with and without compensation under wind disturbance. The measured wind speed was 3.6 m/s at the center of the \(x\)-axis of the quadrotor. Figure 5 displays the position error histogram. The mean errors of the original RL and DCRL controllers were 8.4 and 2 cm, respectively, which reduced the hovering error by 75%. Figure 6 presents the estimated position error and external disturbance force for a 30-s flight. Video clips of the indoor experiment can be found at [[https://youtu.be/RtaoiljZTSI](https://youtu.be/RtaoiljZTSI)]([https://youtu.be/RtaoiljZTSI](https://youtu.be/RtaoiljZTSI)) (accessed on 5 April 2021).  
used as a reference for trajectory tracking without a velocity command. Thus, the quadrotor had to maintain a certain position error to obtain the moving velocity for following the waypoint. The tracking errors may have also been caused by the 2.5-m horizontal position accuracy and 10-Hz update rate of the adopted GPS sensor. However, the experimental results still indicate that the DCRL structure can reduce the quadrotor positioning error.  
Figure 4: Experimental setup for indoor fixed-position hovering. An electric fan was used for simulating a constant wind disturbance with a speed of 3.6 m/s which was applied along the \(x\)-axis of the quadrotor.  
Figure 5: The position error histogram distribution of indoor fix-position hover experiment. The mean error of the original RL controller and the disturbance compensation RL (DCRL) is 8.4 cm and is 2 cm respectively.  
Figure 6: (**a**) The indoor fix-position hover experiment setup. An electric fan was used for simulating a constant wind disturbance with wind speed 3.6 m/s applied onto quadrotor \(x\)-axis. (**b**) The position error along \(x\)-axis. (**c**) The estimation of external force on \(x\)-axis.  
Figure 7: The position of tracking a 10 m square trajectory in an outdoor experiment. The GPS sensor was used for position information measurement.
# Intelligent Neural Network Schemes for Multi-Class Classification
## 4 Experimental Results
### Effects of Dimensionality Reduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **WO** & **MI** & **Pearson** & **LDA** & **IG** \\ \hline birds & 0.0466 & 0.0687 & 0.0546 & 0.1065 & **0.0460** \\ emotion & 0.3966 & 0.3751 & 0.2874 & **0.1497** & 0.3793 \\ yeast-ml & 0.2131 & 0.2144 & **0.1429** & 0.2071 & 0.2079 \\ genbase & **0.0016** & 0.0345 & 0.0421 & 0.0452 & 0.0018 \\ mediamill & 0.0299 & 0.0284 & **0.0204** & 0.0322 & 0.0300 \\ \hline average & 0.1376 & 0.1442 & 0.1095 & **0.1081** & 0.1330 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Testing HL obtained by ML-Scheme with different reduction techniques. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **WO** & **MI** & **Pearson** & **LDA** & **IG** \\ \hline iris & 0.9689 & 0.9689 & 0.9689 & **0.9778** & 0.9689 \\ soybean & 0.9778 & 0.9778 & 0.8727 & 0.6808 & **0.9889** \\ yeast-sl & **0.9362** & 0.8966 & 0.8924 & 0.8976 & 0.8880 \\ ecoli & 0.7806 & **0.9661** & 0.8765 & 0.9329 & 0.9433 \\ wine & 0.9555 & 0.9367 & 0.7461 & **0.9924** & 0.9364 \\ libras & 0.9548 & **0.9641** & 0.9300 & 0.9300 & 0.9585 \\ breast & 0.9318 & 0.9298 & 0.6241 & **0.9789** & 0.9213 \\ \hline average & 0.9294 & **0.9486** & 0.8444 & 0.9129 & 0.9436 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Testing ACC obtained by SL-Scheme with different reduction techniques. Figure 6: Performance of SL-Scheme with mutual information (MI), linear discriminant analysis (LDA), and MI+LDA.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 5 The Protocol Details
### Clusters Formation  
After all CHs have been determined, normal nodes will select the appropriate CHs to join clusters. For a normal sensor node which has received one or more final CH election messages, it first estimates the distances to these CHs by utilizing received signal strengths. Then, a joining message including node ID, CH ID and residual energy will be sent to the nearest CH to join a cluster. If a normal node has not received any final CH election messages, then it is not covered by any CH and becomes an isolated node. Each isolated sensor node has to change its power level to enable its communication with the nearest CH, and then joins the corresponding cluster. Once a CH node has received all joining messages, it first calculates the probabilities to be CH for these normal nodes which have sent the joining messages. Then it replies acceptance messages to these normal nodes including node ID, and the probability to be CH in the next round.
## 2 Brief Overview of the 5G, 6G, and IoT Requirements
_Low cost_: 5G systems are targeted to be 100 times more efficient compared to the 4G systems by delivering 100 times more data traffic using the same energy over the network. As a consequence, they will require low-cost network equipment, lower deployment costs, and enhanced power saving functionality on the network and user equipment sides [35]. _Offloading of heavy traffic to indoors_: Nearly 80% of the mobile traffic volume is generated indoors [36]. Offloading this volume of data to indoor dense small cells can release expensive and valuable resources of macrocells. Hence, offloading of data to indoor small cells will be another important characteristic of 5G and 6G networks. The IoT networks also have important characteristics. Some key requirements of IoT systems are low device cost, low deployment cost, high energy efficiency, high security and privacy, and support for a massive number of devices [11].
# Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes
## 5 Conclusions  
This paper introduces a method for the early identification and stability evaluation of dangerous rock masses of high slopes, which is based on the data processed by UAV. This method is mainly aimed at high side slopes, and uses a series of calculation models and methods to identify dangerous rock masses and evaluate their stability. The results obtained can directly serve the disaster prevention of dangerous rock masses. The key findings are as follows:  
1. The UAV photogrammetry technology was utilized to acquire high-resolution remote sensing images, a three-dimensional point-cloud model, and other data results. The topographic conditions of the slope were extracted and superimposed through the geographic information system in order to identify potential dangerous rock areas and generate a map of hazardous rock zones.
2. Based on the three-dimensional model, a detailed investigation was conducted into the development characteristics and instability mode of the hazardous rocks. The distribution and development characteristics of the hazardous rock mass were categorized into three dangerous rock belts, with consensus identifying 17 hazardous rock masses. These masses were primarily distributed within an elevation range of 150-200 m, mostly small- to medium-sized in scale.
3. An evaluation index system for assessing the stability of hazardous rock masses has been established, with nine influencing factors selected as stability evaluation indices. The weights of these indices have been determined using analytic hierarchy process. Evaluation results indicate that W4, W6, and W10 rocks in the study area are unstable, accounting for 17.6% based on verification by applying the limit equilibrium principle.  
Conceptualization, M.B.; data curation, K.M.; formal analysis, Z.C.; funding acquisition, M.B.; investigation, K.M.; methodology, M.B.; project administration, K.M.; resources, M.B.; software, Z.C.; supervision, K.M.; validation, M.B.; visualization, K.M.; writing--original draft, M.B.; writing--review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) (grant no. 42172311).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Jiangsu Communications Planning and Design Institute Limited by Share Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Related Work
### System Model  
We consider a centralized CSS scenario, where there are K local detectors and an FC. The local detector makes a binary decision on the local SS result, based on ED, and the local sensing results are sent to the FC for the final decision on whether the observed spectrum is idle (Figure 1).  
For the \(i\)th local detector, the false alarm probability and detection probability are respectively denoted as \(P_{f-i}\) and \(P_{d-i}\). We assume the channel between SU and FC is  
Figure 1: System scenario.  
independent and identically distributed, and so stationary random process with error probability \(P_{e}\), the false alarm probability \(P_{f-i}^{FC}\) and detection probability \(P_{d-i}^{FC}\) of the \(i\)th local detector [30] at the FC can be formulated as it is shown in Equations (1) and (2):  
\[P_{f-i}^{FC}=P_{f-i}(1-P_{e})+(1-P_{f-i})P_{e}, \tag{1}\]  
\[P_{d-i}^{FC}=P_{d-i}(1-P_{e})+(1-P_{d-i})P_{e}. \tag{2}\]  
We assume the judgment result of the \(i\)th local detector \(D_{i}\) can be written as it is shown in Equation (3),  
\[D_{i}=\begin{cases}1&H_{1}\\ 0&H_{0}\end{cases}, \tag{3}\]  
where \(H_{0}\) denotes the observed spectrum band is idle while \(H_{1}\) denotes the observed spectrum band is busy.  
Then, the final false alarm probability and detection probability at the FC with N-out-of-K rule [31] can be denoted as it is shown in Equations (4) and (5):  
\[P_{f}=P(\sum_{i=1}^{K}D_{i}\geq N|H_{0}), \tag{4}\]  
\[P_{d}=P(\sum_{i=1}^{K}D_{i}\geq N|H_{1}). \tag{5}\]  
This paper mainly considers the case where the local sensing results in Equations (4) and (5) are independent and non-identically distributed Bernoulli random variables, then the mass function probability at the FC on \(D_{i}\)[30] can be described as it is shown in Equations (6) and (7):  
\[P(D_{i}=N|H_{0},\ P_{f-i}^{FC})=\frac{1}{K+1}\sum_{m=0}^{K}e^{-\frac{2 nNm}{K+1 }}\times\prod_{i=1}^{K}\biggl{[}P_{f-i}^{FC}e^{\frac{2m}{K+1}}+(1-P_{f-i}^{FC })\biggr{]}, \tag{6}\]  
\[P(D_{i}=N|H_{1},\ P_{d-i}^{FC})=\frac{1}{K+1}\sum_{m=0}^{K}e^{-\frac{2 nNm}{K+1 }}\times\prod_{i=1}^{K}\biggl{[}P_{d-i}^{FC}e^{\frac{2m}{K+1}}+(1-P_{d-i}^{FC })\biggr{]}, \tag{7}\]  
Finally, the false alarm probability and its corresponding detection probability [32] can be reformulated as it is shown in Equations (8)-(10):  
\[P_{F}(N,\ P_{f})=\sum_{l=N}^{K}P(D=l|H_{0},\ P_{f}), \tag{8}\]  
\[P_{D}(N,\ P_{D})=\sum_{l=N}^{K}P(D=l|H_{1},\ P_{D}), \tag{9}\]  
\[D=\sum_{l=1}^{K}D_{i}. \tag{10}\]
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 6 Conclusions and Future Work  
Various empirical and theoretical research works on SBFL risk evaluation formulas have been proposed to compare the performance between different formulas. Five maximal formula groups--\(ER_{1}^{{}^{\prime}}\), \(ER_{5}\), GP02, GP03 and GP19--have been proven and it was also proven that there does not exist a greatest risk evaluation formula in terms of fault localization effectiveness. From the experimental observation, we notice that some maximal formulas can perform better than others more frequently. Hence, we propose a notion of \"more-frequently-better\" to compare between two maximal formulas \(R_{1}\) and \(R_{2}\) in this paper.  
To verify our proposition that there exists one maximal formula performs more-frequently-better than another one, an empirical study on 11 C programs with real and seeded faults has been conducted. According to the experimental results, we conclude that \(ER_{1}^{{}^{\prime}}\dashrightarrow\) GP03 \(\dashrightarrow\) GP02 \(\dashrightarrow\)\(ER_{5}\dashrightarrow\) GP19. This means, \(ER_{1}^{{}^{\prime}}\) performs more-frequently-better than any of the other four. Therefore, we haveprovided a way to compare the performance between any two maximal formulas using the notion of more-frequently-better, and illustrated the feasibility of our approach with an empirical study.  
Since we make the assumption that there is a single fault in the faulty program, more experimental studies using programs with multiple faults should be conducted to investigate the effectiveness of our approach.  
Conceptualization, T.W. and M.F.L.; data curation, T.W.; formal analysis, T.W., M.F.L., and T.Y.C.; funding acquisition, Y.D.; investigation, T.W. and M.F.L.; methodology, T.W. and M.F.L.; project administration, Y.D.; supervision, Y.D. and T.Y.C.; validation, T.W., Y.D., M.F.L., and T.Y.C.; writing--original draft, T.W. and M.F.L.; writing--review and editing, T.W., Y.D., M.F.L., S.N., T.Y.C., and M.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Power Grid Company science and technical plan project (Research on System Protection Test and Verification Technology Based on Multi-mode Real-time Co-simulation and Flexible Reconfiguration of Control Strategy) and the National Natural Science Foundation of China under grant number 61772423.  
The authors declare no conflict of interest.
# Assessment of Tire Features for Modeling Vehicle Stability in Case of Vertical Road Excitation
## 3 Vehicle Excitation by Road Unevenness
### Comparison of Excitation from Road Unevenness and Experimental Study  
During the modeling, it was assumed that the compact car was moving on a road of a known profile in the city and on the highway at the speed of 50, 60, 70, and 80 km/h. To compare the results of modeling and experiment, a 100 m long road section was used. Road unevenness of the examined section was measured using profilograph DYNATEST 5051 RSP. The vehicle and suspension data used for the modeling are presented in Table 7. Matlab program (3D model with nonlinear suspension elements) was used for the modeling. The modeling results are presented in Figures 28-30.  
Comparison of the curves of experimental and numerical modeling results suggested that in both cases very similar acceleration values were obtained (acceleration peak values differed by 5-7% for sprung masses and 20-25% for unnsprung masses). Comparison of the dependences of accelerations by means of Fourier analysis (Figure 29) with the experimental results suggested that a share of the vibrations caused by the resonance and stability of the suspension and body parts may be omitted from the assessment in the research findings.  
Figure 28: Accelerations of the unsprung (**a,b**) and sprung (**c,d**) masses of compact city measured by modeling on a city-type road at 50 km/h speed: (**a,c**)—front and (**b,d**)—rear.  
Figure 29: Frequency dependence of amplitudes of compact city, obtained by Fourier analysis: (**a**)—front damped unsprung mass and (**b**)—front damped sprung mass (city-type road, \(v=70\) km/h).  
The obtained results show that the recording and analysis of signals required for active suspensions is an area of separate studies, as the additional noise and signal sources were recorded during the experiment, which also interfered with the analysis of directional stability. Therefore, the differences between the displacements of the sprung and unsprung masses were examined for further analysis (Figure 30). The maximum amplitude of the difference between the suspension and body displacements was 47 mm for the experiment and 40 mm for the modeling.
## 2 Materials and Methods
### Scanning Electron Microscope (SEM) Analysis  
Dentin samples were evaluated using scanning electron microscope VEGA II LSH (TESCAN, Brno, Czech Republic). Ten images at 2000\(\times\) magnification were obtained of each dentin sample. The images were then assessed independently by two well-trained examiners, blinded to the tested toothpastes, to evaluate the degree of dentinal tubule occlusion on a five-grade scale, according to the tubule occlusion classification scoring system used in previous studies: 1 = occluded (100% of tubules occluded); 2 = mostly occluded (50-\(\sim\) 100% of tubules occluded); 3 = partially occluded (25-\(<\) 50% of tubules occluded); 4 = mostly unoccluded (\(<\)25% of tubules occluded); 5 = unoccluded (0%, no tubule occlusion) [26]. Each examiner counted the tubules in each of the 2000X images and established individually the dentinal tubule occlusion score on every image. If there was a disagreement in scoring between the two examiners, they re-examined the image until they arrived to a common opinion. For each sample, the average score of tubule occlusion from ten image evaluations was used for analysis.
## 3 Experiment Setup  
This section discusses the hardware and software used for the data gathering and the methodology used to perform the nozzle thermal estimation. Also, for testing the performance of the proposed TCN-TS-SW algorithm, two experiment set-up is conducted.  
The actual set-up is presented in Figure 3. The experiment set-up includes a cartesian FDM printer single extruder system (EDISON multi) with a single extruder system and the nozzle attached has a size of 0.4 mm diameter. The printing bed has an initial pre-heated temperature of 90 \({}^{\circ}\)C. The summary of the printer specifications are listed in Table 1. The sensor is selected based on the consideration the enclosed FDM printer structure of EDISON Multikit, thermocouple is a suitable sensor to provide precise temperature reading without depending on any external factors like light sources. It is capable of measuring up to 350 \({}^{\circ}\)C and can withstand the high working temperature of the nozzle. The sensor is connected to a cold junction-compensated K-Thermocouple to-Digital converter (MAX6675) (Kuongshun Electronic Limited, Manufactured in Guangdong, China) which is capable of data output in a 12-bit resolution, 0.25 \({}^{\circ}\)C resolution. The MAX6675 module is then connected to the Arduino Uno device as the data acquisition module.  
Then, the Arduino uno is connected on the desktop computer by a serial connection. The maximum sampling rate for Arduino uno is 400 samples per second. The aggregated sensor readings are then saved on an edge device in comma-separated value (.csv) format that is utilized for the machine learning processing. The software used in this project are  
\begin{table}
\begin{tabular}{c c c} \hline
**3D Printer (Type: FDM)** & **Edison Multi (Single Extruder)** & \\ \hline Filament & Material & PLA \\  & Diameter & 1.75 mm \\ Nozzle diameter & 0.4 mm & \\ Slicing software & CreatorK & \\ X/Y/Z accuracy & 11/11/2.5 μm & \\ Speed & Feed rate & 5 mm/s \\  & Extruder travel & 40 mm/s \\ Temperature & Extruder & 210 \({}^{\circ}\)C \\  & Bed & 90 \({}^{\circ}\)C \\ Layer resolution & 0.1 mm & \\ \hline \end{tabular}
\end{table}
Table 1: 3D Printer Experimental conditions.  
Figure 3: The experimental set-up for the data acquisition and process monitoring.  
the following: LabVIEW 2020 for data logging and en-thought python integration toolkit is used to process the data for machine learning prediction integrated on the LabVIEW program. The TCN model that was utilized in this paper is based on the [38] and was compared to model [39].  
The proposed system, a uniform specimen is printed during the experiment. The ASTM D638 type I, is a standard specimen created to perform the tensile test method for polymers. These are printed to perform uniform comparison throughout the experiment. The specimen has a dimension of (165 mm \(\times\) 19 mm \(\times\) 13 mm). The file designed in stereolithography file (.stl) and is later converted on the employed FDM printer's compatible file format (.x3g).
# Dry Sintered Metal Coating of Halloysite Nanotubes
###### Abstract  
Halloysite nanotubes (HNTs) are a naturally-occurring aluminosilicate whose dimensions measure microns in length and tens of nanometers in diameter. Bonding defects between the alumina and silica lead to net negative and positive charges on the exterior and interior lumen, respectively. HNTs have been shown to enhance the material properties of polymer matrices and enable the sustained release of loaded chemicals, drugs, and growth factors. Due to the net charges, these nanotubes can also be readily coated in layered-depositions using the HNT exterior lumen's net negative charge as the basis for assembly. These coatings are primarily done through wet chemical processes, the majority of which are limited in their use of desired chemicals, due to the polarity of the halloysite. Furthermore, this restriction in the type of chemicals used often requires the use of more toxic chemicals in place of greener options, and typically necessitates the use of a significantly longer chemical process to achieve the desired coating. In this study, we show that HNTs can be coated with metal acetylacetonates--compounds primarily employed in the synthesis of nanoparticles, as metal catalysts, and as NMR shift reagents--through a dry sintering process. This method was capable of thermally decaying the metal acetylacetonate, resulting in a free positively-charged metal ion that readily bonded to the negatively-charged HNT exterior, resulting in metallic coatings forming on the HNT surface. Our coating method may enable greater deposition of coated material onto these nanotubes as required for a desired application. Furthermore, the use of chemical processes using toxic chemicals is not required, thus eliminating exposure to toxic chemicals and costs associated with the disposal of the resultant chemical waste.  
3D printing; filaments; additive manufacturing; halloysite +
Footnote †: journal: Applied Physics Letters