esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 6 Bioremediation Applications Using Adapted Microbes
### Field Trials
#### 6.2.1 Potential of Applied Phytoremediation  
[PERSON] et al. [182] conducted a two-year phytoremediation project distributed along the Huanjiang River, in China, using the Cd hyperaccumulator _Sedum alfredii_ and the Pb and As hyperaccumulator _Pteris vittata_ to restore the functionality of these farmlands. They applied an intercropping technique in which hyperaccumulators were planted on middle contaminated zones, while cash crops were planted in the lowest contaminated zones. The results showed a significant decrease of available As, Cd, and Pb by 55.3%, 85.8%, and 30.4%, respectively, so that the produced food was safe again for the populations.  
[PERSON] et al. [31] conducted an open-air experiment in large pot bags with the halophyte _Sesuvium portulacstrum_, a candidate for phytoremediation which has received much attention partly because of its metal-extraction capacities [71; 183; 184; 185; 186]. Seeds used for the experiment were harvested from native populations. Without introducing any microbial inoculant, encouraging results were obtained after 6 months, with high metal accumulation rates.  
In their field experiment, [PERSON] and [PERSON] [80] obtained high capacities of metals' extraction in the leaves and roots of _Suaeda fructicosa_ from a tannery effluent polluted site. Moreover, growth was augmented in the field compared with the pot experiment.
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 5 Conclusions  
This study provides a method for faster and more efficient WebAR services. Large amounts of 3D data require latency from the users. A method was proposed that reduces  
Figure 12: Results of the content request order for the proposed method: (**a**) hit ratio; (**b**) latency.  
Figure 13: Result of waste ratio: (**a**) conventional methods; (**b**) proposed method.  
latency in WebAR services by 81.5%. In the experiments, edge servers could physically reduce delays and were 14.49% faster than cloud servers. Further, the stability increased by 4.54%. The waste ratio related to unnecessary content prefetching was 16% lower than that of the conventional method. The Markov model was used to prefetch the predicted content rather than random prefetching to increase the hit ratio. The proposed method is more advantageous as it provides a large amount of AR content. Further, it is suitable for the realization of high-quality data in Web AR services. The results of this study have potential applications in areas such as AR games and AR decent services. A limitation of this study is that latency increased when prediction failed. Future research should increase the hit ratio by considering multiple parameters and advanced prediction algorithms. To this end, it is necessary to secure user data and apply machine learning.  
Conceptualization, S.C.; methodology, S.C.; software, S.C., S.H. and S.L.; investigation, S.H. and H.K.; writing--original draft preparation, S.C.; writing--review and editing, S.L. and S.K.; supervision, S.K.; project administration, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020 RIF1A1069079) and by Ministry of Culture, Sports and Tourism and Korea Creative Content Agency (Project Number: R2021040083).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available because of privacy concerns.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Sample Preparation  
Sixteen samples (Figure 1) were taken from two different places, the Deksare district, 56.63311, 26.88392 and the Sigulda district, 57.173335, 24.898171, consisting of 4 leaf samples of _Vaccinium myrtillus_ (VM), 4 leaf samples of VM aerial parts, 4 leaf samples of _Vaccinium vitis-idaea_ (VV) leaf samples, and 4 leaf samples of VV aerial parts. To extract the active compounds, 5 grammes of crushed plant material were macerated in 50 mL of 70% ethanol (ratio 1:10, according to the European Pharmacopoeia, 8 th edition [39]) at room temperature for 80 min. Subsequently, the solvent was removed from 8 samples (2 VM leaves, 2 VM aerial parts, 2 VV leaves, and 2 VV aerial parts) by rotary vacuum evaporation (Model: RV 3 ECO S099 IKA(tm), Staufen, Germany), followed by lyophilisation (Zirbus Technologies VaCo series 2.0 Standart 2018, Bad Grund, Germany), and then frozen at \(-\)70 \({}^{\circ}\)C for 2 days. The remaining 8 samples (2 VM leaves, 2 VM aerial parts, 2 VV leaves, and 2 VV aerial parts) were filtered and retained in the form of ethanol extracts.  
Figure 1: Sample preparation.  
In summary, 16 varieties of samples were used in the study: 8 ethanol extracts and 8 freeze-dried samples.
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 5 Game Theory Analysis of the CSMA/CA Problem When \(N_{p}=2\)
### Nash Equilibrium Solution to the CSMA/CA Game  
The CSMA/CA game can be solved using the mixed NE concept. Using (16), the game presents the following NE, where the payoff matrices used are (13):  
\[y^{*}=\frac{\beta_{c}}{\beta_{c}+\beta_{s}}\qquad z^{*}=\frac{\alpha_{f}}{ \alpha_{f}+\alpha_{m}+\alpha_{c}} \tag{17}\]  
This means that the server plays \(d\) with probability \(1-y^{*}\) and \(nd\) with probability \(y^{*}\). The client plays \(s\) with probability \(z^{*}\) and \(ns\) with probability \(1-z^{*}\). We define the expected payoff that each player obtains if they play mixed strategies with probability \((y,1-y)\) for the server and \((z,1-z)\) for the client as:  
\[u_{1}(y,z)= (y,1-y)R_{1}(z,1-z)^{T}=-zy(\alpha_{m}+\alpha_{c}+\alpha_{f})+z( \alpha_{c}+\alpha_{f})+\alpha_{f}(y-1) \tag{18}\] \[u_{2}(y,z)= (y,1-y)R_{2}(z,1-z)^{T}=zy(\beta_{s}+\beta_{c})-z\beta_{c}\]  
Thus, the payoff that each player receives by playing their mixed NE strategy, from (17) and (18) is:  
\[u_{1}=-\frac{\alpha_{f}\alpha_{m}}{\alpha_{m}+\alpha_{c}+\alpha_{f}}\qquad u_{ 2}=0 \tag{19}\]  
The values in (19) show that the equilibrium payoff for the client is 0, regardless of the game parameters. The equilibrium payoff for the server will depend on the values that the \(\alpha\) parameters take. This means that the client can always guarantee a throughput as good as if he behaved normally. The server will have always a loss, derived from the cost of detecting (\(k_{d}\), collected by the parameter \(\alpha_{f}\)).
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 4 Evaluation Metrics  
To select the detection model, we evaluated the performance using average precision (_AP_) [23], as defined in Equation (1).  
\[AP=\sum_{n=0}\left(r_{n+1}-r_{n}\right)\,p_{interp}(r_{n+1}) \tag{1}\]  
where \(r\) is the recall, and \(p_{interp}\) is the interpolated precision given by \(p_{interp}(r_{n+1})=\underset{\tilde{r}\geq r_{n+1}}{\max}p(\tilde{r})\), wherein \(p\) is the precision at \(\tilde{r}\), and \(n\) includes all the recall points. _AP_ is also defined as the area under the precision-recall curve. The m_AP_ is the average of the _APs_ calculated for each class of threat objects. This is one of the most used metrics for evaluating detection and classification performance.  
Moreover, the segmentation model is chosen on the basis of its performance on various evaluation metrics, namely, intersection-over-union (_IoU_), dice coefficient (_DC_), precision, and recall, as defined in Equations (2)-(5).  
\[IoU=\frac{\left|A\,\cap B\right|}{\left|A\,\cup B\right|} \tag{2}\]  
\[DC=\frac{2\left|A\,\cap B\right|}{\left|A\right|+\left|B\right|} \tag{3}\]  
where \(A\) and \(B\) represent the target and predicted segmentation masks, respectively. _IoU_, also known as the Jaccard index, is calculated for each class of threat objects, and the average is reported as the mean _IoU_ (m_IoU_). _DC_ is another metric that has become increasingly popular in reporting the performance of modern segmentation algorithms, which bears some resemblance to the _IoU_ metric.  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
where _TP_, _FP_, and _FN_ are the proportions of the pixels in the masks that are considered true positives, false positives, and false negatives, respectively. Precision and recall can be calculated for each class and as the cumulative of all classes.  
As per the benchmark standard set in [3], we again used the m_AP_, defined in Equation (1), to evaluate our proposed method against previous works. We took the _AP_ on each of the classes by ranking the classification predictions by their confidence  
Figure 6: The proportion of positive samples, mislabeled negative samples, and the rest of the negative samples for the (a) SIXray10, (b) SIXray100, and (c) SIXray1000 subsets.  
scores, then took the average (m_AP_) across all the classes to report the overall performance of the models.
## 3 Results and Discussion
### Detection of E. coli in Licorice Extractum  
The applicability of the proposed strategy is tested by the detection the _E. coli_ in real licorice extractum samples at different concentrations, and validated by the plate counting method. In the electrochemical detections, the cultured _E. coli_ at three unknown gradient concentrations are incubated with the electrochemical biosensor in the diluted licorice extracts with the solid content of 4%. At the same time, the parallel _E. coli_ samples at the same unknown gradient concentrations are measured by the plate counting method. The results of DPV test and plate counting method are compared in Table 2. The amounts of strains obtained by these two methods are consistent in magnitude, but the specific values are slightly different. The electrochemical measurement results are higher than those measured by the plate counting method, which may be due to the sensitivity of the electrochemical test method. The _p_-values in the 10\({}^{5}\) and 10\({}^{6}\) concentration gradient groups are much larger than 0. 1, indicating that our method and the plate counting method test results are not significantly different in these two groups, but in the 10\({}^{4}\) concentration gradient group, the difference between our method and the plate counting method is slightly significant. This may be ascribed to the large systematic error caused by the fewer repeated measurements. In order to clearly illustrate the consistency of these two methods, a large number of repetitive experiments are required. The detection time for plate counting method is 72 h, while the proposed method is only 2.6 h. The significantly shortened detection time will greatly improve the efficiency and expand its application areas in future.
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 3 DG and Load Timing Characteristics
### Load Timing Characteristics
#### DG installation capacity constraint  
Light intensity, temperature, wind speed are all uncertain factors. If too much of this type of DG is added to the distribution network, the power quality of the system will be degraded. It is therefore necessary to limit the installation capacity of PV and WG.  
\[\left\{\begin{array}{c}S_{\text{pv},i}\leq S_{\text{pv,max}},i\in N_{\text {pv}}\\ S_{\text{wg},i}\leq S_{\text{wg,max}},i\in N_{\text{wg}}\end{array}\right. \tag{18}\]  
where \(S_{\text{pv},i}\) and \(S_{\text{wg},i}\) are respectively the PV installation capacity at the \(i\)-th PV candidate node and the WG installation capacity at the \(i\)-th WG candidate node; \(S_{\text{pv},\text{max}}\) and \(S_{\text{wg},\text{max}}\) are respectively the maximum installation capacity of PV and WG.  
(2) Power flow equation  
\[\left\{\begin{array}{l}P_{i}=U_{i}\sum\limits_{j\in i}U_{j}(G_{ij}\cos\theta_{ ij}+B_{ij}\sin\theta_{ij})\\ Q_{i}=U_{i}\sum\limits_{j\in i}U_{j}(G_{ij}\cos\theta_{ij}-B_{ij}\sin\theta_{ij}) \end{array}\right. \tag{19}\]  
where \(P_{i}\) and \(Q_{i}\) are the active power and reactive power injected into node \(i\), respectively; \(U_{i}\) is the voltage amplitude of node \(i\), \(j\in i\) represents all nodes connected to node \(i\); \(G_{ij}\) and \(B_{ij}\) are respectively the admittance matrix real part and virtual part of the system; and \(\theta_{ij}\) is the phase angle difference between node \(i\) and node \(j\).  
(3) Voltage constraint  
\[U_{i,\min}\leq U_{i}\leq U_{i,\max} \tag{20}\]  
\[U_{\rm dc,i,\min}\leq U_{i}\leq U_{\rm dc,i,\max} \tag{21}\]  
where \(U_{i,\max}\) and \(U_{i,\min}\) are the upper and lower limits of the AC node voltage amplitude; \(U_{\rm dc,i,\max}\) and \(U_{\rm dc,i,\min}\) are the upper and lower limits of the DC node voltage amplitude.  
(4) Branch flow constraint  
\[S_{j}\leq S_{j,\max} \tag{22}\]  
\[S_{\rm dc,j}\leq S_{\rm dc,j,\max} \tag{23}\]  
\[S_{\rm VSC,j}\leq S_{\rm VSC,j,\max} \tag{24}\]  
where \(S_{j}\) is the power on branch \(j\); \(S_{j,\max}\) is the maximum allowable capacity on AC branch \(j\); \(S_{\rm dc,j,\max}\) is the maximum allowable capacity on DC branch \(j\); \(S_{\rm VSC,j,\max}\) is the maximum allowable capacity on VSC.  
(5) Pollution compensation cost  
\[\sum\limits_{i\in N_{\rm DG}}E_{i}(t)\geq L_{\rm tol}(t)\;{\rm t}=1,\;2,\ldots 8 760 \tag{25}\]  
where \(\sum\limits_{i\in N_{\rm DG}}E_{i}(t)\) is the total capacities of the DGs at the time \(t\), and \(L_{\rm tol}(t)\) is the sum of the loads of all nodes at time \(t\).
## 4 Numerical Results and Analysis
### Evaluation of Trajectory and Resource Joint Optimization  
With UAV location, bandwidth allocation, and transmission power all optimized, total throughput in the communication system has an obvious increase, as illustrated in Figure 11.  
The optimization of the three components, U, B, and P, can be seen to have all contributed to the improvement in throughput, with the combined optimization demonstrating better performance compared to the contrast algorithms. Among them, the optimization of B presents a step-like pattern, attributed to the consideration of the issue where ground users leaving the valid area are not involved in allocation in this algorithm. It can be observed that the number of steps is consistent with the total number of users.  
Moreover, we select the average total throughput observed over the time sequence of simulation as the evaluation metric and compare our method with several alternative strategies, experimenting with various sets of environmental information. At different heights, the UAV adjusts the routing plan and resource allocation strategy according to the optimization functions, maintaining a stable optimization effect, as shown in Figure 12.  
In Figure 13, the throughput optimization achieved by various methods is depicted, highlighting the superior performance of our ISATR method compared to the others. The compared methods include A*, GA, and fixed algorithm, in which \"fixed\" refers to the non-optimized case.  
Figure 11: Throughput optimized with different decision variables. (a) Throughput with U (UAV location) optimized. (b) Throughput with B (bandwidth allocation) optimized. (c) Throughput with P (power of UAV) optimized. (d) Throughput with U, B, and P optimized.  
Figure 12: **Average total throughput illustration in typical environments.** (**a**) Throughput optimized at different heights. (**b**) Throughput optimized in different environments. Compared to the non-optimized value, the ISATR method has an increase of 42%.  
Figure 13: **Throughput optimization with different methods.** Throughput with location, bandwidth allocation, and power control optimized via different algorithms, including the proposed opt method (ISATR), GA method, A\({}^{\star}\) method, and fixed case. ISATR outperforms the others.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 6 Research Challenges and Future Directions
### Designing more Lightweight Modulation Recognition Networks  
Due to the limited resources of current hardware circuits, it is difficult to deal with huge neural networks, so most of the modulation recognition algorithms based on deep learning are still focused on software simulation, and there are not many studies on applying these networks to hardware circuits. However, in order to apply the excellent deep learning-based modulation recognition algorithm to the actual communication system, it is necessary to solve the difficulty that the neural network is difficult to realize on the hardware circuit. Designing a lightweight neural network is a better way to solve this problem [115]. Therefore, in future research, designing a neural network with lighter weight and higher recognition efficiency has a better development prospect.
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 2 Methodology
### Modified Feynman-X  
In semi-inclusive deep inelastic scattering (SIDIS) events, the final-state hadrons can originate from two main sources: the struck quark or the remaining target remnants. To distinguish these sources and analyze the hadronization process better, we introduce the Feynman-\(X\) (\(x_{F}\)) variable. The purpose of using \(x_{F}\) is to isolate hadrons produced primarily by the struck quark, minimizing the influence of the nuclear environment. To do so, we use a cut of \(x_{F}>0.1\). This cut reduces the contribution of the target fragments, emphasizing particles originating from the struck quark. However, this cut also leads to reduced statistics and potential mismatches at lower energy ranges due to the exclusion of low-energy hadrons.  
In the laboratory frame, \(x_{F}\) is defined as  
\[x_{F}=\frac{2\left[(\
u+m_{p})\sqrt{p^{2}-p_{T}^{2}}-\sqrt{Q^{2}+\
u^{2}}\cdot Z _{h}\
u\right]}{(\
u+m_{n})\sqrt{(W^{2}-m_{n}^{2}+m_{\pi^{+}}^{2})^{2}-4W^{2} \cdot m_{\pi^{+}}^{2}}} \tag{3}\]  
where \(m_{p}\), \(m_{n}\), and \(m_{\pi^{+}}\) are the masses of the proton, neutron, and pion, respectively. Notice that \(x_{F}\) depends on the energy of the detected hadron.  
Since our methodology involves manually introducing an energy shift \(\Delta E\) (representing the quark energy loss) into the energy distribution, the \(x_{F}\) cut must be recalculated each time a shift is applied. The modified Feynman-\(X\) incorporating \(\Delta E\) is given by  
\[x_{F}=\frac{2\left[(\
u+m_{p})\sqrt{(P+\Delta E)^{2}-\left(1+\frac{\Delta E}{ P}\right)^{2}p_{T}^{2}}-\sqrt{Q^{2}+\
u^{2}}\cdot(Z_{h}\
u+\Delta E)\right]}{( \
u+m_{n})\sqrt{(W^{2}-m_{n}^{2}+m_{\pi^{+}}^{2})^{2}-4W^{2}\cdot m_{\pi^{+}} ^{2}}} \tag{4}\]
# Network Measurement and Performance Analysis at Server Side
## 4 Datasets  
We used packet traces from three sources in our study. The first packet trace was collected on a CDN server of UCloud [32] that delivered files to end users. Most contents the server delivered were small images and videos. Besides, the server ran Nginx [33] and Linux OS with an optimized congestion control algorithm and we did not know its details. The second packet trace was collected on a user-facing reverse proxy (Nginx) of a campus website. The site provided static page access and file download. Besides, the server ran Linux OS with Cubic congestion control. The last packet trace was collected on a P2P server (Transmission [34]) of campus network, which shared video contents among students. Note that the server shared only a few videos and the video size varied from 5 GB to 10 GB. The server ran Linux OS and BBR congestion control. All of the packet traces were collected directly on server side NIC using _tcpdump_ (i.e., the CDN trace was directly collected on the CDN server NIC, etc.). We disabled TSO [35] support of NIC before collecting. Note that our tool supports online analysis, but in order to facilitate the reproducibility and the verifiability of the analysis results, we choose to first collect the data package online, and then do offline analysis.  
The statistics of the traces are summarized in Table 2. We also show the distributions of flow duration, size, and rate of each trace. Flow duration is the time between the first packet and the last one. Flow size is the number of bytes transmitted in the payload, including retransmissions. Flow rate is just size divided by duration. In addition, we report the packet size (header excluded) statistics to further understand the characteristics of the traces. Note that we only count packets with payload. The flow size distribution plotted by Figure 2 shows that more than 60% of the flows transmit less than 10 KB data and more than 95% of the flows transmit less than 1 MB data. In other words, most flows are small. Figure 3 reports similar statistics: more than 80% of the flows live shorter than 30 s and more than 94% of the flows live shorter than 100 s. Flow rate distribution shown in Figure 4 is also similar: more than 60% of the flows are slower than 30 KB/s and more than 95% of the flows are slower than 1 MB/s. Although most flows are slow, a few have rather high transmission rates. As for packet size distribution shown in Figure 5, it is not surprising that most data packets are MSS-sized.  
Figure 4: Flow rate distribution of each dataset.  
Figure 5: Packet size (payload) distribution of each dataset.  
Figure 3: Flow duration distribution of each dataset.
## 2 Wireless Body Sensor Networks and QoS
### QoS in Wireless Body Sensor Networks
#### 2.2.1 QoS Requirements from the Network Perspective  
In identifying QoS requirements, we rely on the type of applications, especially their data delivery model from sensors to the base station, which can be: continuous, query driven, event driven and hybrid [18, 36, 37]. Each of these data delivery models has its own QoS requirements. Table 1 summarizes the key QoS requirements for each data delivery model and its associated QoS metrics. As shown in the table, all of the data delivery models, apart from the continuous one, need all four key requirements, including real-time delivery, reliable delivery, energy efficiency and adaptability to the network's channel conditions. Typically, the continuous data delivery model can tolerate some delay and packet losses. If the communication channel suffers from a high rate of packet loss, QoS frameworks or mechanisms are required to take special care in transmitting critical data (e.g., events like a heart attack). This is to make sure that critical data are received at the destination correctly and on time [18, 36, 37].
## 4 Evaluation Results and Discussion  
As has already been established in the section where the proposed methodology is described, in any given scenario the QUAKE framework begins with the calculation of all possible paths in order to proceed with their proper classification. By adopting such an analytical methodology, the developed strategy inherits the NP-hardness of VNE [6]. Therefore, this solution is designed to scale more efficiently with small or medium-sized SNs, like sparse rural IoV environments [79], drone-assisted recovery networks for limited-scale suburban disasters [80], and minimal digital twin support in manageable industrial XR-enabled spaces [81]. The main focus of the conducted research is to confirm the validity and optimality of its contribution. Under this prism, the deployed experimental schemes adhere to these parameters and are modeled accordingly to provide results in deterministic execution time. In an effort to scale for larger SNs and subsequently become applicable for a broader set of use cases, the proposed QUAKE-driven framework is aimed to be reformed in an upcoming study.