esa-sceva/satcom-chunk-collection huggingface dataset:
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 3 System Model and Problem Formulation
### Mobility Model  
Flying ad hoc networks (FANETs) consisting of HATBBSs exhibit some mobility in the troposphere due to the wind field and turbulence. This mobility leads to changes in the speed and position of base stations, which may degrade the network performance. The movement of balloons is both somewhat predictable and subject to unexpected factors. In order to accurately capture the complex dynamics of airborne mobile nodes and to facilitate efficient communication relaying, the Gauss-Markov Mobility Model (GMM) has been proposed and widely used [21].  
In this model, a node is initially assigned a specific speed, direction, and pitch angle. Subsequently, the node's velocity, direction, and pitch angle at the next moment depend on the state at the current moment and are updated after a fixed time interval [22]. This approach balances complete randomness and complete determinism, where the parameter \(\alpha\) determines the degree of randomness of the node's movement. When \(\alpha=0\), the model exhibits no memory properties; when \(\alpha=1\), the model exhibits full memory properties. Specifically, the model uses the following three equations to calculate the velocity \(v(t)\), direction \(o(t)\), and pitch angle \(p(t)\) at moment \(t\), respectively, which are based on the corresponding values at moment \((t-1)\),  
\[v(t) =\alpha v(t-1)+(1-\alpha)\vartheta+\sqrt{1-\alpha^{2}}v_{x_{t-1}}, \tag{2}\] \[o(t) =\alpha o(t-1)+(1-\alpha)\vartheta+\sqrt{1-\alpha^{2}}v_{x_{t-1}},\] (3) \[p(t) =\alpha p(t-1)+(1-\alpha)\bar{p}+\sqrt{1-\alpha^{2}}p_{x_{t-1}}, \tag{4}\]  
where \(\vartheta,\vartheta\), and \(\bar{p}\) are mean constants for velocity, direction, and pitch angle, respectively, and \(v_{x_{t-1}}\), \(o_{x_{t-1}}\), and \(p_{x_{t-1}}\) are random variables obeying Gaussian distribution. The node's position at the moment \(t\) is jointly determined by the position, velocity, direction, and pitch angle at the moment \((t-1)\)[21; 22]. \(\forall n\in\mathcal{N}\), the 3D coordinates of node \(n\) are updated as follows:  
Figure 1: Wireless emergency communication system supported by HATBBSs.  
\[x(t) =x(t-1)+v(t-1)cos(o(t-1))cos(p(t-1)), \tag{5}\] \[y(t) =y(t-1)+v(t-1)sin(o(t-1))cos(p(t-1)),\] (6) \[h(t)=h(t-1)+v(t-1)sin(p(t-1)). \tag{7}\]
# Object Detection Based on Multiple Information Fusion Net
## 5 Conclusions  
This paper proposed a network that fuses both the scene-level and relationship information for object detection in images. Compared with other methods, the most important advantage of our approach is that we leverage the attention mechanism to model the relationship between objects adaptively. Besides, the relationship weights are obtained using not only the geometric structure, but also the appearance feature of the objects. At last, the scene-level information is also considered in our model. Two widely used databases are employed in our experiment. From the experimental results, we can see that through fusing the scene-level and object relationship information, our proposed MIFNet outperforms some classical and state-of-the-art approaches. Furthermore, some ablation experiments are also carried out to test the effectiveness of our MIFNet.  
**Author Contributions:** Conceptualization, Y.Z. and J.W.; methodology, Y.Z. and J.W.; validation, Y.Z., J.W., and J.K.; formal analysis, M.Q.; data curation, Y.Z. and J.W.; software, Y.Z. and Y.L. ([PERSON]); writing--original draft preparation, Y.Z.; writing--review and editing, J.W. and M.Q.; supervision, Y.L. ([PERSON]); project administration, J.K. and Y.L. ([PERSON]); funding acquisition, J.K. and Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Nos. 61672150, 61907007, 61702092), by the Fund of the Jilin Provincial Science and Technology Department Project (Nos. 20180201089 GX, 20190201305 JC) and the Fundamental Research Funds for the Central Universities (No. 2412019 FZ049).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**GRU Settings** & **mAP** & **Aero** & **Bike** & **Bird** & **Boot** & **Bottle Bus** & **Car** & **Cat** & **Chair** & **Cow** & **Table** & **Dog** & **Horse** & **Mhike** & **Person Plant** & **Sheep** & **Sofa** & **Train** & **Tv** \\ \hline
1 \(\times\) & **77.6** & **79.2** & **79.8** & **77.4** & **71.4** & 63.3 & 86.3 & 87.1 & **89.4** & **63.1** & **85.1** & **72.4** & 86.7 & **86.8** & 78.2 & 79.0 & **80.7** & **77.9** & 75.4 & **81.8** & **77.6** \\ \hline
2 \(\times\) & \multirow{2}{*}{76.9} & \multirow{2}{*}{77.4} & \multirow{2}{*}{79.8} & \multirow{2}{*}{76.8} & \multirow{2}{*}{68.5} & \multirow{2}{*}{63.7} & \multirow{2}{*}{85.6} & \multirow{2}{*}{87.2} & \multirow{2}{*}{87.1} & \multirow{2}{*}{60.4} & \multirow{2}{*}{85.0} &
# Bioremediation of Persistent Organic Pollutant--Oxybenzene with _Pleurotus diamor_
## 1 Introduction
Examples include 4-hydroxybenzophenone and benzophenone-1 (2,4-dihydroxybenzophenone), which have higher estrogenic activity than BP-3 itself. Urine is the primary route of excretion of BP-3 and its metabolites [15,17,19]. Exposure to BP-3 is positively correlated with diseases such as endometriosis, reproductive disorders, cancer, and osteoarthritis [14]. Organic UV filters do not degrade quickly in the natural environment and thus constitute persistent organic pollutants. Moreover, conventional purification processes used in wastewater treatment plants are ineffective at removing these substances [20]. Mycoremediation is a form of bioremediation that involves the use of extracellular ligninolytic enzymes produced by fungi, such as lacase, lignin peroxidase, and manganese peroxidase, to remove or neutralize environmental pollutants and toxins [20,21, 22]. Lacases belong to a large family of enzymes referred to as \"multicopper oxidases\". These enzymes are charac terized by low substrate specificity and high oxidation capacity for various phenolic and non-phenolic compounds [23]. The evaluation of the removal efficiency of three organic UV filters (2-ethylhexyl salicylate (EHS), homosalate (HMS), and ethylhexyl methoxycinnamate (EHMC)) using Spent Mushroom Composts (SMCs) from four different mushrooms (_P. etyngii_, _P. djamor_, _P. ostreatus_, and _Auricularia polytricha_) revealed that _P. djamor_ showed the highest efficiency in removing EHS and HMS [20]. Therefore, _P. djamor_ was selected for further study. The species _P. djamor_, commonly known as pink oyster mushrooms, is renowned for its diverse applications in medicine and cosmetics due to its immunostimulatory, anti-cancer, and anti-aging properties. _P. djamor_ is an excellent candidate for the mycoremediation of oxybenzene due to its adaptability to various substrates and environmental friendliness. This species has demonstrated significant ligninolytic enzyme production crucial for breaking down organic pollutants. Given the ability of _P. djamor_ to degrade compounds with similar chemical structures, there is potential for its use in the decomposition of BP-3. Currently, to the best of our knowledge, the presence of biologically active compounds in the mycelia derived from in vitro cultures of _P. djamor_ following the addition of oxybenzene has not been previously explored. Consequently, we seek to evaluate the efficiency of oxybenzene elimination from the culture medium and to analyze the structure and toxicity of the resulting bioremediation products. Additionally, our study aims to investigate the profile of biologically active substances in the mushroom material produced.
## 3 Results
### Calibration of the RF Model  
The calibration curves of the RF model are plotted in Figure 3. After isotonic calibration, the performance of the RF classifier showed a better performance in each predicted probability bin. The BS indicated no calibration (0.225) and isotonic calibration (0.211).  
Figure 3: Isotonic calibration curves of the random forest algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **LR** & **DT** & **RF** & **SVM** \\ \hline BA & 0.636 & 0.599 & 0.664 & 0.650 \\ FI score & 0.682 & 0.620 & 0.699 & 0.616 \\ Sensitivity & 0.748 & 0.632 & 0.748 & 0.548 \\ Specificity & 0.524 & 0.566 & 0.579 & 0.752 \\ PPV & 0.627 & 0.609 & 0.655 & 0.702 \\ NPV & 0.661 & 0.590 & 0.683 & 0.609 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model evaluation parameters according to the machine-learning algorithm.  
Figure 2: Receiver operating characteristic curve (**left**) and precision-recall curve (**right**). LR, logistic regression; SVM, support vector machine; DT, decision tree; RF, random forest; AUROC, area-under-the-receiver-operating-characteristic curve; AUPRC, area-under-the-precision-recall curve.  
Calibration curves compare how the probabilistic predictions of the RF binary classifier are calibrated. The true frequency of the positive label was plotted against its predicted probability for binned predictions. The x-axis represents the average predicted probability in each bin. The y-axis is the fraction of positives.
## 3 Methodology
### Ideally Continuous Corner Model  
It is intuitive to imagine that a corner is located at a junction of two edges, and has the smallest radius of curvature; pixels around it appear as a high change of brightness in all directions. As represented in Figure 1, a square region \(C\) with a center \(\mathbf{o}=[0,0]\) and an area of \((2r+1)^{2}\) pixels is observed to analyze a chessboard image for the following description. Geometrically, a straight line \(L\) passing through \(\mathbf{o}\) can be given as  
\[L:\chi(\omega,u,v)=u\sin\omega-v\cos\omega=0,\text{with}\{\omega,u,v\}\in \mathbb{R}, \tag{1}\]  
where \(\omega\) is the angle of inclination. Using the sign function \(\text{sgn}(x)\) yields an ideal edge \(E\) related to \(L\) via  
\[E(\omega,u,v)=\text{sgn}[\chi(\omega,u,v)]. \tag{2}\]  
An ideally continuous chessboard image, with a gray value \(+1\) in the white and \(-1\) in the black regions, is then defined:  
\[C_{\text{i}}(u,v)=E(\alpha,u,v)E(\beta,u,v),\ 0\leq\alpha<\pi,\ \alpha<\beta<\pi+\alpha, \tag{3}\]  
where \(\alpha\) and \(\beta\) are the angles similar to \(\omega\), and determine two edges \(E_{1}\) and \(E_{2}\). It is worth mentioning that Equation (3) is subject to a reasonably small \(r\). Otherwise, \(E_{1}\) and \(E_{2}\) may be re-defined by two curve functions for a suitable approximation of lens distortions.  
In actual imaging, however, \(C_{\text{i}}\) is inevitably blurred by the lens of a vision system. A point input, represented as a single pixel in \(C_{\text{i}}\), will be reproduced as a spread region in a blurred image \(C_{\text{f}}\). For practical purposes, the blurring response described by the point spread function (PSF) is always  
Figure 1: Definition of a chessboard image.  
approximated by a radio-symmetrical Gaussian kernel [26]. Similarly, \(C_{\mathrm{f}}\) is modeled by convolving \(C_{\mathrm{i}}\) with a 2-D Gaussian filter:  
\[C_{\mathrm{f}}(u,v)=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{u^{2}+v^{2}}{2 \sigma^{2}}\right)\otimes C_{\mathrm{i}}(u,v), \tag{4}\]  
with \(\sigma\) effectively denoting the blurring strength.  
Distinctly, the gray level of \(C_{\mathrm{f}}\) is not in the same range as that of a digital image in the common use. Under the assumption that the vision system has a linear response to the light intensity within a reasonable range, \(C_{\mathrm{f}}\) can be transformed by  
\[C_{\mathrm{s}}(u,v)=\lambda C_{\mathrm{f}}(u,v)+\kappa, \tag{5}\]  
with \(\kappa\) and \(\lambda\) related to the maximum and minimum gray values \(g_{\mathrm{max}}\) and \(g_{\mathrm{min}}\) of \(C_{\mathrm{s}}\) via  
\[\kappa=\frac{g_{\mathrm{max}}+g_{\mathrm{min}}}{2},\;\lambda=\frac{g_{\mathrm{ max}}-g_{\mathrm{min}}}{2}. \tag{6}\]
## 3 Perturbation Analysis and Modulation Equations
Then, we employ the reconstitution method to express the modulation equations in the third-order  
\[2\mathrm{i}\omega_{v}(\dot{A}_{v}+\mu_{v}A_{v}) =K_{3}A_{w}^{2}\bar{A}_{v}\mathrm{e}^{2\mathrm{i}\omega_{2}T_{2}}+ K_{11}A_{v}^{2}\bar{A}_{v}+K_{12}A_{v}A_{w}\bar{A}_{w}+\frac{1}{2}f_{v} \delta_{kv}\mathrm{e}^{\mathrm{i}\omega_{1}T_{2}}, \tag{14}\] \[2\mathrm{i}\omega_{w}(\dot{A}_{w}+\mu_{w}A_{w}) =K_{3}A_{v}^{2}\bar{A}_{w}\mathrm{e}^{-2\mathrm{i}\omega_{2}T_{2} }+K_{22}A_{w}^{2}\bar{A}_{v}+K_{21}A_{v}A_{w}\bar{A}_{v}+\frac{1}{2}f_{w} \delta_{kw}\mathrm{e}^{\mathrm{i}\omega_{1}T_{2}},\]  
where the non-linear coefficients \(K_{11}\), \(K_{12}\), \(K_{21}\), \(K_{22}\), and \(K_{3}\) are expressed in Appendix C.  
Furthermore, \(A_{v}\) and \(A_{w}\) in Equation (14) can be written as in the polar forms \(A_{k}=1/2a_{k}(t)\mathrm{e}^{\mathrm{i}\beta_{k}(t)},(k=v,w)\), where \(a_{k}\) and \(\beta_{k}\) are the response amplitude and phase, respectively. Substituting the polar equations into Equation (14) and separating the real and imaginary parts, the polar form of modulation equations are obtained  
\[\dot{a}_{v} =-\mu_{v}a_{v}+\frac{K_{3}}{8\omega_{v}}a_{v}a_{w}^{2}\sin 2 \Delta+\frac{f_{v}\delta_{kv}}{2\omega_{v}}\sin\gamma_{1}, \tag{15}\] \[a_{v}\dot{\beta}_{v} =-\frac{K_{11}a_{v}^{3}}{8\omega_{v}}-\frac{K_{12}a_{v}a_{w}^{2}} {8\omega_{v}}-\frac{K_{3}a_{v}a_{w}^{2}}{8\omega_{v}}\cos 2\Delta-\frac{f_{v}}{2 \omega_{v}}\delta_{kv}\cos\gamma_{1},\] \[\dot{a}_{w} =-\mu_{w}a_{w}-\frac{K_{3}a_{v}^{2}a_{w}}{8\omega_{w}}\sin 2\Delta+ \frac{f_{w}\delta_{kw}}{2\omega_{w}}\sin\gamma_{2},\] \[a_{w}\dot{\beta}_{w} =-\frac{K_{21}a_{v}^{2}a_{w}}{8\omega_{w}}-\frac{K_{22}a_{w}^{3}} {8\omega_{w}}-\frac{K_{3}a_{v}^{2}a_{w}}{8\omega_{w}}\cos 2\Delta-\frac{f_{w}}{2 \omega_{w}}\delta_{kw}\cos\gamma_{2},\]  
where \(\Delta=\sigma_{2}t-\beta_{v}+\beta_{w}\). \(\Omega\approx\omega_{v},\gamma_{1}=\sigma_{1}t-\beta_{v},\gamma_{2}=(\sigma_{1 }-\sigma_{2})t-\beta_{w}\), and \(\Omega\approx\omega_{w}\), \(\gamma_{1}=(\sigma_{1}+\sigma_{2})t-\beta_{v},\gamma_{2}=\sigma_{1}t-\beta_{w}\).
## 4 Conclusions  
The current study explored the potential of integrating a variable geometry radial turbine instead of a fixed geometry turbine for an engine-ORC system. The study was carried out using different engine operating conditions, where different exhaust temperatures and  
Figure 9: Comparison between fixed and variable geometry turbines for (**a**) powertrain power and (**b**) engine BSFC.  
mass flow rates were investigated. Moreover, different stator openings were explored: 30, 60, and 100%. The results confirmed the potential of utilizing variable geometry turbines in improving the overall system performance. This is because variable geometry turbines can adapt to the different operating conditions of the exhaust gases by changing the stator opening accordingly. The conclusions can be summarized as follows:  
* The total to static efficiency of a variable geometry turbine is 25.45% higher than that of a fixed geometry turbine.
* Due to the increased mass capacity with a variable geometry radial turbine, the powertrain power increased substantially when integrated with an ORC as a bottom cycle. This increase could be as high as 5.5%.
* The ORC performance benefited significantly from the utilization of the variable geometry turbine. The ORC efficiency could be improved by at least 20%.
* The results also showed that even at low engine torque and power, P1, the overall system performance improved substantially by integrating an ORC system with a variable geometry turbine. However, this improvement is trivial (1%) and cannot justify the added complexity and cost.
* It is worth noting that the ORC system has a large weight, which will significantly affect its benefits. In the future, this phenomenon will be investigated in addition to cost analysis.  
Conceptualization, F.A. and I.A.; Data curation, F.A., A.A. ([PERSON]), I.A., A.A. ([PERSON]) and A.A. ([PERSON]); Formal analysis, F.A. and A.A. ([PERSON]); Funding acquisition, F.A.; Investigation, F.A., A.S.A. and A.A. ([PERSON]); Methodology, F.A., I.A., A.S.A. and A.A. ([PERSON]); Project administration, F.A.; Resources, A.S.A.; Software, F.A. and A.A. ([PERSON]); Supervision, F.A.; Validation, F.A., I.A., A.S.A. and A.A. ([PERSON]); Visualization, F.A.; Writing--original draft, F.A.; Writing--review and editing, A.A. ([PERSON]), I.A., A.S.A., A.A. ([PERSON]) and A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Deanship at the University of Ha'il--Saudi Arabia through project number \"RD-21 003\".  
Data availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 5 Simulation Results and Discussions
### Performance Analysis of Users
#### 5.3.2 Performance Analysis with Cost  
In addition, in Figures 11 and 12, we make comparisons of User 1's cumulative normalized utilities with different channel switching cost and cooperation cost, and in Figure 13, we analyze the relationship between channel switching cost and channel switching number. As is shown in Figure 11, with the increase of channel switching cost, User 1's cumulative normalized utility decreased in the ICADCSA algorithm. Moreover, as is shown in Figure 12, with the increase of cooperation cost, user 1's cumulative normalized utility decreased greatly in the multi-agent Q-learning stages, and the utility kept invariant in the single Q-learning stages. The reason is that in multi-agent Q-learning, users cooperated with each other and shared their joint Q tables, as well as actions, whereas in single Q-learning, users only needed to take actions and update their Q tables independently. Moreover, if the cooperation cost was too high, the cost of cooperation was greater than the negative influence of co-channel interference, which made it unwise for users to cooperate to avoid co-channel interference. In addition, in Figure 13, we make a record of User 1's channel switching number under different channel switching cost units during 0 to 30 s. As an example, during 0 to 6 s, User 1 switched its transmission channel by about 2400 times with no channel switching cost and switched its transmission channel by about 1500 times with \(w_{s}=0.1\), which depicts that under the influence of channel switching cost, users were willing to decrease the number of channel switching as much as possible. However, if channel switching cost was higher, the channel switching number decreased with a smaller quantity.  
Figure 10: Cumulative normalized utility of User 3.  
Figure 11: The cumulative normalized utility of User 1 with different channel switching costs.  
Figure 12: The cumulative normalized utility of User 1 with different cooperation costs.  
Figure 13: The channel switching number of User 1 with different channel switching costs.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 4 Methodology and the Proposed Algorithms
### Greedy Algorithm  
This algorithm uses a simple greedy logic. It defines a limit for the maximum allowed height \(\tau_{max}\). The algorithm chooses the charging intervals for each bus, so that the \(H_{max}\leq\tau_{max}\).The limit \(\tau_{max}\) is reduced iteratively as long as it is possible to schedule all charging jobs. The smallest limit for which the algorithm manages to schedule all jobs is considered the minimum peak demand. Before executing the algorithm, the following steps are required.  
1. Schedule all preconditioning jobs (these jobs are fixed and cannot be rescheduled).
2. Calculate all possible charging intervals for all buses and write them into tuples \(P_{b}\).
3. Organize all buses ascending by the arrival time.
4. Initialize the limit \(\tau_{max}=c\times number\) of buses, where \(c\) is a curtailment factor.  
The curtailment factor \(c\) depends on the number of buses at the depot. [PERSON] et al. did a sensitivity analysis and calculated the curtailment factor for different number of buses per module for the analyzed depot Alsterdorf [27]. They allowed the buses to charge on demand, without any charging schedule. The curtailment factor \(c\) is a limit for the number of buses that can charge simultaneously without affecting the charging process itself. The study showed that for 127 buses at the depot, the curtailment factor is 54%. \(\tau_{max}\) is therefore initialized with \(c=0.54\).  
Figure 6 shows the flowchart of the proposed algorithm for a scenario of \(n\) buses. The variable _MinPeak_ represents the minimum peak demand.  
Figure 6: Greedy algorithm presented with a flowchart.  
Organizing the buses ascending by the arrival time is an important step for this algorithm. It allows the buses to charge as soon as possible, usually at the far left side of the available interval \([a_{b},d_{b}]\).
## 4 Results
### Humidity/Temperature Compensations via Interface Electronics  
The changes in ambient humidity and temperature affect the center frequencies of both the SAW sensor and the reference SAW device equally. By subtracting the frequency shifts from both devices at the mixer, the system can provide built-in compensation that eliminates output frequency shifts caused by variations in ambient temperature and humidity. Two separate experimental tests were conducted to confirm the effectiveness of this built-in compensation. First, the signal generator is connected to the port of the reference SAW device at the mixer. A change in relative humidity (RH) from 10% to 90% causes a significant frequency shift of up to 25 kHz when a constant signal from the signal generator is used as the reference (Figure 12a). This large shift makes it difficult to accurately observe NO\({}_{2}\) gas responses due to the influence of humidity. In contrast, when the reference SAW device, with performance identical to the SAW sensor, is used, no noticeable frequency change is observed due to humidity variations, and no frequency shifts are detected at the output. A similar trend was observed for temperature changes. Although the LiNbO\({}_{3}\) substrate is sensitive to temperature, the temperature variations are identical in both SAW devices. By subtracting these variations in the mixer, it was confirmed that no change occurs in the output due to ambient temperature fluctuations. Additionally, when the surface temperature of the two SAW devices was raised to 90 \({}^{\circ}\)C, and the ambient temperature was  
Figure 11: (**a**) Response and recovery of 5 ppm NO\({}_{2}\) on graphene/MoS\({}_{2}\) flakes SAW sensor at 90 \({}^{\circ}\)C, (**b**) repeatability of 5 ppm NO\({}_{2}\) on SAW sensor, (**c**) response to 2-10 ppm NO\({}_{2}\) on SAW sensor, (**d**) calibration plot for different NO\({}_{2}\) concentrations on SAW sensor, and (**e**) selectivity of SAW sensor.  
lower than the surface temperature of the devices, the output signals from both devices showed no change, owing to the built-in compensation (Figure 12b).