esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Heavy Metal Removal Approaches
### Microbial Mechanisms Involved in Heavy Metal bioremediation  
Bioremediation is a biological technique that is employed to extract heavy metal ions from sites that have been contaminated with heavy metals [63]. In bioremediation, absorption/adsorption, bioaccumulation, biotransformation, and bioleaching are included in the remediation of toxic metal ions. There are several natural components, such as bark, coconut husk, rice husk, wheat husk, seaweeds, seeds, aquatic plants, agro-waste, and microorganisms, which are utilized to reduce toxic heavy metal concentrations in polluted sites. In these materials, microorganisms play a significant role in bioremediation [64]. The phenomenon of microorganisms altering the ionic state of heavy metals is a significant process that has far-reaching implications for their solubility, bioavailability, and movement in both soil and aquatic environments [65]. The mobilization and immobilization of heavy metals is a crucial aspect of microbial remediation, a complex process that involves a multitude of intricate mechanisms including, but not limited to, oxidation-reduction, chelation, modification of the metallic complex, and biomethylation [63]. It should be emphasized that microbial enzymatic catalysis plays a pivotal role in the solubilization of metals with higher oxidation states to lower oxidation states. For example, the enzymatic oxidation of uranium by _Thiboacillus ferrooxidans_ and _T. thiooxidants_ serves as a prime illustration of this phenomenon [66]. Although the isolation of microorganisms responsible for the degradation of heavy metals from both aerobic and anaerobic environments is feasible, aerobic microbes are considered to be more suitable for bioremediation when compared to anaerobic microorganisms [67]. Furthermore, microorganisms utilize membrane-linked transport mechanisms to transport heavy metals and convert them into non-hazardous forms, which is of utmost importance for their survival in a metal-polluted environment [68]. In this regard, microorganisms employ a range of processes, such as biosorption, bioaccumulation, biotransformation, and bioleaching, to counteract the detrimental effects of heavy metals and ensure their sustainability [68; 69].  
It is of great importance to note that the utilization of the aforementioned techniques has proven to be efficacious in facilitating the process of environmental clean-up, thereby emphasizing the substantial capacity possessed by microorganisms in ameliorating the deleterious impact of heavy metals on the ecosystem [70].
## 4 Experimental Results
### Ensemble Averaging  
According to the Ensemble Averaging concept described in Section 3.3, the average values of each prediction model are shown in Table 2. It can be seen from Table 2, the predicted values for each expression of nutrient deficiency are summed up and compared with one another. Then, the highest predicted values for each category are selected as the final forecast result. In fact, the validation value increases to 91% after calculating the average value of both predictive models described above.  
Compared with the results in Figure 7a using Inception-ResNet v2, the Autoencoder model incorrectly predicted Calcium deficiency expression on the fruit of tomato, in Figure 9a. Meanwhile, the Inception-ResNet v2 had a higher result than Autoencoder for predicting Nitrogen deficiency, as shown in Figures 7g and 9g. In addition, with respect to the results of the expression of Nitrogen deficiency in Figures 7h and 9h, both models have a low predictive rate for Nitrogen shortage. The results in Table 2 reveal that the ensemble model achieves a summed value of 10.83, which is higher than other nutrients, leading to an accuracy of 1.0. Simultaneously, the prediction of Nitrogen deficiency increased to 0.989, which is higher than the predicted results for either of the two models, as shown in Table 3.  
Figure 9: Autoencoder prediction results. (**a–c**) Prediction of Calcium shortage; (**d–f**) Potassium deficiency forecast; (**g.h**) Nitrogen deficiency prediction.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.9 Perfect Forward and Backward Secrecy  
It is clearly mentioned in Section 4.1.8 that even the leakage of one session key does not impact upon the privacy of upcoming/previous session keys. Therefore, the devised protocol offers perfect backward as well as forward secrecy.
## 4 Concluding Remarks  
In this study distributed optic fiber has been used to measure both Rayleigh and Brillouin frequency shift of two different sandstone core samples under increasing confining/pore pressure. Rayleigh frequency has been found to have a superior performance over Brillouin frequency in terms of better consistency (less scattering) in the tests carried out.  
The strain gauge readings reveal considerable anisotropy in the stiffness of the Berea core between perpendicular (vertical) and parallel to the bedding (hoop) directions. The strains converted from Rayleigh frequency shift measurements agree reasonably well with readings by one of the four hoop strain gauge channels (Ch7) (the other three are damaged/malfunctioned during the test) under increasing confining/pore pressure. This has provided confidence that Rayleigh frequency shift is capable of measuring relatively homogeneous Berea sandstone strain with conventional strain gauge accuracy. Brillouin frequency measurement at stage 1, although being somewhat less accurate than Rayleigh frequency (using simple conversion coefficient), has further confirmed accuracy of the strain measurement by fiber optic (Rayleigh frequency) and the strain gauge.  
In contrast to the Berea core, the Tako sandstone core is visibly heterogeneous with a coarse-grain and a fine-grain region. This contrast in the grain-size, and thus rock elastic properties, is clearly reflected in the hoop strain measurement by both strain gauges and distributed optic fiber. Interestingly, there appears to be much less variations in the vertical strain between the two regions, especially at stage 1. The outcomes of the test have demonstrated successfully the use of a single optic fiber for measuring rock strain response at different regions of a heterogeneous core sample along a continuous trajectory.  
We successfully demonstrated the usefulness of distributed fiber optic sensing from our laboratory measurements. Strains measured by optic fiber with the heterogeneous sandstone suggest that optic fiber responded well to different lithology under pressure conditions. By deploying optic fiber cables behind well casing we can observe that which depth and how much deformation of formation and well casing formed in subsurface. The contiguous deformation data in depth provide important insight on tracking the uplifting ground movements at the CO\({}_{2}\) injection operations.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/11/2103/s1](http://www.mdpi.com/2076-3417/8/11/2103/s1)]([http://www.mdpi.com/2076-3417/8/11/2103/s1](http://www.mdpi.com/2076-3417/8/11/2103/s1)).  
[PERSON] and [PERSON] designed the experiments and collected the data; [PERSON] and [PERSON] prepared the manuscript; [PERSON] participated in the discussion with other authors on interpretation the experimental results.  
This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO) and the Ministry of Economy, Trade and Industry (METI) of Japan.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Volatile Aroma Compounds Identification and Quantification  
A slight modification to the method described by [PERSON] et al. [25] was employed for volatile compounds determination using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The wine sample (8 mL) was added to a sample vial containing 2.5 g sodium chloride (NaCl) and 10 \(\mu\)L internal standard (2-octanol, with a concentration of 820.7 mg/L) and then equilibrated in a water bath at 40 \({}^{\circ}\)C for 30 min with stirring (20 r/min) [26]. A DVB/CAR/PDMS fiber (50/30 \(\mu\)m film thickness, Supelco, Bellefonte, PA, USA) coupled with a manual holder was immersed in the vial for 30 min with continuous stirring (20 rpm), followed by desorption into the GC injector for 5 min at 240 \({}^{\circ}\)C. Gas chromatography and mass spectrometry system (TRACE 1310-ISQ, Thermo Fisher Scientific, San Jose, CA, USA) equipped with a TG-WAX capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.5 \(\mu\)m film thickness, Thermo) was used for the analysis of volatile compounds, and the carrier gas was helium (purity 99.999%) at a flow rate of 1 mL/min. The temperature of the GC oven started at 40 \({}^{\circ}\)C for 5 min, then was increased to 180 \({}^{\circ}\)C at 3.5 \({}^{\circ}\)C/min and maintained for 15 min. The MS transfer line and ion source temperatures were set at 180 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. The ionization voltage (EI) was 70 eV, and the mass range was \(m/z\) 50-350.  
Model wine solution (11% _v/v_ ethanol, 6 g/L tartaric acid, and pH adjusted to 3.3-3.4 with 1 mol NaOH) used for obtaining standard calibration curves was made using standard chemical solutions as described by [PERSON] et al. [27]. Supplementary Table S1 contains the qualitative and quantitative data for eleven chromatography pure standards as well as standard calibration curves, R\({}^{2}\) value, and linear range used for the quantification of the identified volatile compounds. Volatile compounds were identified according to the retention time and mass spectra of the pure standards compared with the NIST2.0 and Wiley databases (Thermo Scientific Software, San Jose, CA, USA). Standard calibration curves provided quantitative data for 11 volatiles, while the remaining 7 compounds were semi-quantified based on an internal standard (2-octanol).
# Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation
## 1 Introduction
In this study, a grand canonical Monte Carlo (GCMC) molecular simulation was used to calculate the CO\({}_{2}\) adsorption equilibrium on a MOF material MIL-101 (Cr) [22; 23; 24] to explore characteristics of changes in adsorption energy. There are two reasons for the selection of MIL-101 (Cr) as an adsorbent in this study: (1) MIL-101 (Cr) has three different types of pores, which has microporous and mesoporous characteristics, and the structure is relatively complex, so it is more meaningful to calculate its adsorption energy changes, and the results have universal applicability for the adsorption energy changes of other simple systems such as activated carbon and molecular sieves; (2) MIL-101 (Cr) has a high adsorption capacity for CO\({}_{2}\), and the adsorption capacity varies greatly under different pressures, so it is helpful to research the characteristic changes of adsorption energy with pressure during the adsorption process. The results that were obtained at the molecular simulation level were used to modify the DSL model and the TDSL model. The modified models were used to correlate the adsorption equilibrium data on CO\({}_{2}\)-MIL-101 (Cr), C\({}_{2}\)H\({}_{4}\)-HHPAC, CH\({}_{4}\)-BPL, and CO\({}_{2}\)-H-Mordenite to investigate their accuracy and generality and provide a basis for improving other adsorption equilibrium models.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 3 Construction of Guaranteed Cost Strategy with Perfect Information
### The Guaranteed Cost Strategy
 \tag{14}\]  
Integrate Equation (14) to obtain  
\[\|\mathbf{Z}\Big{(}t_{f}\Big{)}\|\leq\|\mathbf{Z}(t_{0})\|+\int_{t_{0}}^{t_{f}}-\rho_{ P}\lambda_{\min}^{1/2}+\rho_{E}\lambda_{\max}^{1/2}dt=H_{1}\Big{(}t_{0},t_{f} \Big{)},\ \ \text{provided}\ H_{1}\Big{(}t_{0},t_{f}\Big{)}\geq 0 \tag{15}\]  
From Equation (15), it is seen that when the pursuuer adopts \(\mathbf{U}_{P}^{*}\), the cost (i.e., miss distance) will not exceed the value \(H_{1}\Big{(}t_{0},t_{f}\Big{)}\). If the safe distance between the players is \(m\), we can calculate the time-to-go \(t_{go}^{P}=t_{f}-t_{0}\) by solving the equation \(H_{1}\Big{(}t_{0},t_{f}\Big{)}=m\). Thus, when the pursuuer adopts \(\mathbf{U}_{P}^{*}\), the capture of the evader can be guaranteed within \(t_{go}^{P}\). Similarly, we get  
\[\|\mathbf{Z}\Big{(}t_{f}\Big{)}\|\geq\|\mathbf{Z}(t_{0})\|+\int_{t_{0}}^{t_{f}}-\rho p \lambda_{\max}^{1/2}+\rho_{E}\lambda_{\min}^{1/2}dt=H_{2}\Big{(}t_{0},t_{f}\Big{)} \tag{16}\]  
From Equation (16), it is seen that when the evader adopts \(\mathbf{U}_{E}^{*}\), the cost (i.e., miss distance) is not less than the value \(H_{2}\Big{(}t_{0},t_{f}\Big{)}\). Then, we can calculate the time-to-go \(t_{go}^{E}=t_{f}-t_{0}\) by solving the equation \(H_{2}\Big{(}t_{0},t_{f}\Big{)}=m\). Due to the control constraint \(\rho_{P}>\rho_{E}>0\), the evader cannot avoid being captured when the pursuer chooses the appropriate strategy. However, when the evader adopts \(\mathbf{U}_{E}^{*}\), it can escape from the pursuer before \(t_{go}^{E}\).
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 2 Simulation on the Sensor Response
Furthermore, the influence of FeNi thickness and aspect ratio (\(a_{0}/b_{0}\)) on sensor response can be analyzed using the physical parameters listed in Table 1. The sensor response was defined as the difference between the perturbed and unperturbed SAW velocity. The given electromagnetic field intensity and working frequency of sensor in the calculation are set to 200 Oe and 150 MHz. Nonlinear relationship was observed between the sensor response and FeNi thickness, as shown in Figure 4a. The sensor response increases rapidly with increasing the FeNi thickness up to \(\sim\)0.5 \(\upmu\)m and then reaches the max response value. Hence, the optimal value in FeNi thickness is advised to \(\sim\)500 nm. Additionally, the effect of the aspect ratio of the FeNi on sensor response was also analyzed as shown in Figure 4b. With increasing the aspect ratio (1:1 to 2:1), larger sensor response was observed. However, layer aspect ratio will lengthen SAW propagation path, resulting in increase of the acoustic propagation attenuation. So, in our opinion, proper increase in aspect ratio of FeNi film is an effective way to improve the sensor sensitivity. Then, referring to the Biot-Savart law mentioned Equation (16) and velocity-frequency relationship of \(\Delta\)v/v\({}_{\text{s}}\) = \(\Delta\)f/f\({}_{0}\) (\(\Delta\)v and \(\Delta\)f are the shift in SAW velocity v\({}_{\text{s}}\) and sensor frequency f\({}_{0}\)), the current sensitivity of the 150 MHz SAW sensor coated with 500 nm FeNi (aspect ratio is set to 2:1) was calculated as shown in Figure 5, and sensitivity of \(\sim\)11 KHz/A was expected. Figure 4: The calculated relationship among the sensor response and the FeNi film thickness (**a**), and the aspect ratio (**b**), where the electromagnetic field intensity is set to 200 Oe. Figure 5: The calculated sensor response of FeNi coated SAW current sensor. Figure 3: Measured relationship between the young modulus of FeNi and magnetic field intensity.
## 2 Experimental Methods and Materials
### Sample Characterization  
Powder X-ray diffraction (XRD) patterns were recorded at room temperature on a Rigaku High Resolution Smartlab X-ray diffractometer using Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5406 A) with a scan speed of 5 \({}^{\circ}\)/min and a step width of 0.01 \({}^{\circ}\) and in-situ high temperature using Pt sample holder with the high temperature set-up. The sintered NiO/ScSZ pellet and the reduced Ni/ScSZ pellet were characterized for their microstructure using a Hitachi S-3400N scanning electron microscope (SEM). High magnification morphological images were obtained using a Hitachi SU-8230 field-emission scanning electron microscope (FE-SEM). Using the same FE-SEM machine, energy-dispersive spectroscopy (EDS) data were obtained using a Horiba X-maxN 50 sq. mm window X-Ray detector at an acceleration voltage of 10 kV.  
Conductivity measurements for the bulk pellets with Ag paste as the current collector were performed at intermediate temperatures from 500 \({}^{\circ}\)C to 700 \({}^{\circ}\)C in 50 \({}^{\circ}\)C increments using electrochemical impedance spectroscopy (EIS) in a two-electrode configuration using Biologic VMP-300 Potentiostat/Galvanostat system with a signal amplitude of 100 mV in the frequency range of 7 MHzto 1 Hz, and using either Ar, O\({}_{2}\), or Ar/5\(\%\)H\({}_{2}\) gas flow environment at 100 cc/min flow rate. Resistance values were determined from impedance data using the Z-fit tool of EC-Lab(version 11.16) software.
# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 2 Mot Techniques
The RGB frame is passed into ENet [91], which will output a semantic map. Both maps are matched and passed into the Complexer-YOLO network to output tracks. The approach in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracker** & **RGB Camera** & **Point Cloud LIDAR** & **Data Fusion** & **Target Tracking** \\ \hline [PERSON] et al. [85] & Enet & 3D Voxel Quantization & Complex-YOLO & LMB RFS \\ \hline [PERSON] et al. [86] & VGG-16 & PointNet &
\begin{tabular}{c} Point-wise convolution \\ + Start and end estimator \\ \end{tabular} & Linear Programming \\ \hline [PERSON] et al. [87] & MV3D & MV3D & MV3D & Linear Programming \\ \hline [PERSON] et al. [71] & Faster R-CNN & 34-layer DLA-up & Monocular 3D estimation & LSTM Network \\ \hline [PERSON] et al. [88] & ResNet & PointNet & Addition & Graph Neural Network \\ \hline [PERSON] et al. [89] & YOLOv3 & IMM-UKF-JPDAF & 3D projection & Munkres Association \\ \hline [PERSON] et al. [90] & Mask RCNN & F-PointNet & 3-layered fully connected network & JPDA \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the sensors fusion approaches used in 3D MOT techniques. Figure 7: Motion segmentation. The trajectory points are projected across multiple frames. Ref. [86] extracts features from the RGB frame and the point cloud data. An end-to-end approach was introduced in Ref. [87] for dealing with features extraction and fusing from RGB and Point Cloud Data, as illustrated in Figure 9. Point-wise convolution in addition to a start and end estimator are utilized for fusing both types of data to be used for tracking. Figure 8: Complexer-YOLO [85]. Data from RGB frame and point cloud data are mapped and passed into Complexer-YOLO for tracking and matching. Figure 9: An end-to-end approach for 3D detection and tracking [87]. The RGB and point cloud data are passed into a detection network. Matching and scoring nets are then trained to generate trajectories across multiple frames.
## 3 Method Development
### Boundary Conditions
\]  
where \(r\) is the vertical distance from the neutral beam axis to the mass center of \(m\), and \(k^{i}=G_{i}I_{i}/I_{i}\) denotes the equivalent torsional elastic coefficient of \(e_{i}\). The angular mass accelerations can be expressed in terms of slopes by using (1) and (4) and assuming that torsional angles are small, i.e.,  
\[ \begin{split}\frac{d^{2}\phi_{j}^{e}}{dt^{2}}& \simeq\frac{\partial^{2}}{\partial t^{2}}\frac{\partial w_{h}(x_{h},t)}{ \partial x_{h}}\bigg{|}_{x_{h}^{e}}=-\frac{E_{h}I_{h}}{\rho_{h}A_{h}}\phi_{i}^ {4}\frac{\partial w_{h}(x_{h},t)}{\partial x_{h}}\bigg{|}_{h}\bigg{|}_{x_{h}^{ e}}\\ \frac{d^{2}\phi_{h}^{e}}{dt^{2}}&\simeq-\frac{E_{j} I_{j}}{\rho_{j}A_{j}}\phi_{i}^{4}\frac{\partial w_{j}(x_{j},t)}{\partial x_{j}} \bigg{|}_{x_{j}^{e}}\end{split} \]  
The corresponding modal formulation is  
\[ \begin{split} E_{h}I_{h}g_{xx}(\beta_{h},x_{h}^{e})\mathbf{c}_{h} -E_{i}I_{i}g_{xx}(\beta_{i},x_{i}^{e})\mathbf{c}_{i}-k^{j}\left(\hat{\phi}_{j} ^{e}-\hat{\phi}_{j}^{f}\right)\\ -k^{l}\left(\hat{\phi}_{l}^{e}-\hat{\phi}_{l}^{f}\right)=-mr^{2} \frac{E_{h}I_{h}}{\rho_{h}A_{h}}\hat{\rho}_{h}^{4}g_{X}(\beta_{h},x_{h}^{e}) \mathbf{c}_{h}\\ E_{j}I_{j}g_{xx}(\beta_{j},x_{j}^{e})\mathbf{c}_{j}-E_{l}I_{l}g_{ xx}(\beta_{l},x_{l}^{e})\mathbf{c}_{l}-k^{h}\left(\hat{\phi}_{h}^{e}-\hat{\phi}_{h}^{ f}\right)\\ -k^{i}\left(\hat{\phi}_{l}^{e}-\hat{\phi}_{i}^{f}\right)=-mr^{2} \frac{E_{j}I_{j}}{\rho_{j}A_{j}}\hat{\rho}_{j}^{4}g_{X}(\beta_{j},x_{j}^{e}) \mathbf{c}_{j}\end{split}  \tag{18}\]  
where the variables \(\hat{\phi}\) are the modal components of the corresponding angles \(\phi\), e.g., considering \(\phi_{j}^{e}\) given by (11), we have  
\[\left.\hat{\phi}_{j}^{e}\simeq-\left.\frac{\partial W_{h}(x_{h})}{\partial x_{ h}}\right|_{x_{h}^{e}}=g_{x}(\beta_{h},x_{h}^{e})\mathbf{c}_{h}\]  
In case that torsion is not allowed, the difference between torsional angles at the end-points of any beam must null, e.g., \((\phi_{j}^{e}-\phi_{j}^{f})=0\), thus \((\hat{\phi}_{j}^{e}-\hat{\phi}_{j}^{f})=0\), which would imply that slopes at the coupling endpoints were null. Conditions (15)-(18) can be straightforwardly applied to the other configurations of Figure 2, just by removing the terms of the missing beams. Be aware that if the sense of the local coordinate system of a beam is changed, the signs of the corresponding terms in the boundary conditions must also be changed.
## 3 Methods
### Non-Maximum Suppression Improvement  
In the prediction stage, the object detection algorithm usually uses non-maximum suppression to eliminate the redundant detection frames. The idea is to traverse all detection frames and retain the highest-scoring prediction frame, calculate the intersection ratio IoU between the remaining frames and the highest-scoring prediction frame, and reject the detection frame if the IoU is greater than the set threshold. In most cases, the NMS method is effective, but in the case of dense target scene, due to the large number of detected objects and serious occlusion, the objects to be detected are too close to each other, and the overlapping area of detection frames of adjacent objects is too large, which easily leads to the NMS incorrectly rejecting the detection frames of a certain object. In the experiments of this paper, the classrooms are densely packed with students and heavily occluded, and the traditional NMS is not applicable to the dataset of this paper, so DIoU is introduced in this paper to improve the post-processing NMS process.  
The blue box in Figure 11 is the real frame, and its center point is marked as \(b^{gt}\), the green box is the predicted frame, and its center point is marked as \(b\), and the outer frame is the smallest box that wraps both the real frame and the predicted frame. Denoted as \(C\), \(c\) is the length of the diagonal of the outer frame, \(d\) is the length of the center of the real frame and the center of the predicted frame, and the gray shaded part is the intersection of the real frame and the predicted frame.  
Compared with IoU, DIoU takes into account the distance, overlap rate, and scale between the object and the anchor, making the object box regression more stable, as shown in formula:  
\[\text{DIoU}=\text{IoU}-\frac{\rho^{2}(b,b^{gt})}{c^{2}} \tag{4}\]  
In the formula, \(\rho^{2}(\cdot)\) represents the Euclidean distance, and the definitions of \(b\), \(b^{gt}\), and \(c\) are the same as in Figure 9.  
Figure 11: Example of detection frame.  
Figure 10: Feature visualization after adding attention mechanism.  
The definition of DIoU is as formula:  
\[s_{i}=\left\{\begin{array}{l}s_{i},\text{DIoU}(M,B_{i})<\epsilon\\ 0,\text{DIoU}(M,B_{i})\geq\epsilon\end{array}\right\} \tag{5}\]  
In the formula, \(s_{i}\) represents the classification score, \(M\) represents the prediction frame with the highest score, \(B_{i}\) represents the prediction frame to be eliminated, and \(\epsilon\) represents the artificially set IoU threshold. Compared with IoU, the calculation of DIoU takes into account the information of the center points of the two frames, which helps to solve the occlusion problem caused by the object distance being too close. Therefore, the NMS effect judged by DIoU is more realistic and the effect is better.