esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Genetically Engineered Bacteria for Anti-Cancer Therapy  
Bacterial ability to colonize in tumors has been exploited to design genetically engineered bacteria for confining its activity to tumor sites. A plethora of genetically engineered bacteria have been generated depending on their specificity of accumulation. A multitude of reporter genes, cytotoxic proteins, and tumor specific antigens can be expressed using genetically engineered bacteria as a vector. The engineered bacterium enters the target cells and expresses the transgene, resulting in the expression of therapeutic proteins. This process is referred to as backofection. The invasive bacteria can deliver genes intracellularly into the tumor cells thus, fostering backofection into tumor cells whereas the non-invasive strains are engineered to release therapeutic proteins external to thetumor microenvironment (TME) [47]. Figure 2 depicts the process of backofection of engineered bacteria in tumor cells. Bioengineering bacteria for carrying the therapeutic cargo involves critical steps like virulence attenuation, enhancement of tumor targeting, and strategies for delivery of the anti-cancer cargo.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 2 Analysis of High Traffic Load Cells
### Relationship between PRB Utilization Ratio and Downlink Data Rate  
We used 3 Mbps servers as the baseline of the downlink data rate to investigate the performance of a cellular network. According to the field data reported in Figure 3, the number of users with downlink speed lower than 3 Mbps significantly increases when the average number of users and PRB utilization ratio increases. As shown in Table 5, we realize that the percentages of users with low data rates are around 73% and 88% when downlink PRB utilization ratio ranges from 70% to 100%, respectively. Intuitively, we can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Meximum CPU Utilization Ratio** & **RRC Setup Success Rate** & **Number of Dropped Messages** & **Maximum CPU Utilization Ratio** & **RRC Setup Success Rate** & **Number of Dropped Messages** \\ \hline  & LBBP & & & UMPT \\ \hline
72\% & 99.4\% & 0 & 67\% & 100.0\% & 0 \\
84\% & 83.7\% & 4429 & 71\% & 86.5\% & 192 \\
85\% & 82.2\% & 4730 & 71\% & 75.4\% & 1161 \\
86\% & 81.5\% & 4819 & 72\% & 70.2\% & 1230 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RRC setup success rate vs. CPU utilization ratio of UMPT and LBBP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **All cells** & **High Traffic Load Cells** \\ \hline Access success rate & 98\% & 90\% \\ Single user data rate (Mbps) & 8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Access success rate and downlink speed comparison between all cells and high traffic load cells.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RRC Setup Success Rate** & **MNOU** & **Unsuccess Times of PUCCH Allocation** \\ \hline
99.90\% & 356 & 0 \\
45.30\% & 402 & 10,161 \\
30.90\% & 399 & 10,850 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of online users is larger than the threshold in different times.  
set up the threshold of the utilization ratio of PRB according to field data as well as the threshold of ANOU, which are discussed in Sections 3-5.
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 3 AO-AOMDV Routing Protocol
### Fitness Function
As packets enter or leave the network, the number of packets cached in the MAC layer interface queue of a node changes continuously, so the ratio of the number of packets cached in the MAC layer interface queue to the length of the interface queue varies at different times, indicating different congestion degrees of the node. The fitness function in the AO-AOMDV considers the link holding time, residual energy, and congestion degree, and the fitness function of path \(p_{i}\) can be represented as:  
\[F(p_{i})=\omega_{2}HT(p_{i})+\omega_{1}E(p_{i})+\frac{\omega_{3}}{C(p_{i})}; \tag{6}\]  
where \(HT(p_{i})\) represents the minimum link holding time of Equation (1) on path \(p_{i}\), \(E(p_{i})\) represents the minimum residual energy of Equation (4) on path \(p_{i}\), and \(C(p_{i})\) represents the maximum congestion degree of Equation (5) on path \(p_{i}\). Here, \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are the weights, and each weight has a range of values between [0, 1].
## 2 Related Works
### Pose Refinement Research  
Pose refinement research aimed at improving coordinate accuracy in human pose estimation models can be categorized into end-to-end methods and pose-processing methods [14]. This research [14] has focused on enhancing the accuracy of the coordinates estimated by human pose estimation models, which is one of the main topics of this paper. Models in [15; 16; 17; 18; 19; 20; 21] are studied using an end-to-end learning approach. Although the implementation methods differ among models, they share the characteristic that pose estimation and refinement occur together within the model. In the case of [19], a method was used that employs iterative error feedback, transferring errors step-by-step within the model and incrementally improving the pose by correcting the model's estimation results. Similar to other models, the step-by-step pose refinement processes are implemented together with the pose estimation process within the model. In [20], a PRM (Pose Refine Machine) is used to improve the estimated pose within the model at the last step of the pose estimation process. In this study [20], PRM makes more precise pose estimation possible by using high-level discriminative semantic information and low-level spatial information. In [21], a structure was used to estimate human pose through a two-step process. A GPR (Graph Pose Refinement) module in the second step was used to obtain an improved pose. The GPR module was designed as a refinement module with a graph structure that considers the relationship between joints. These kinds of end-to-end refinement modules rely on the estimation results of the pose estimation model for their output values, which does not guarantee that the refinement module will work successfully within the model.  
The models that use a post-processing method in performance refinement research are in [14; 22; 23]. These models have a network structure that corrects the coordinates by receiving the coordinates output by the human pose estimation model. Similar implementation methods are shown in [14; 22], but the method proposed in [14], which showed higher performance, used a method that obtains corrected coordinates by passing the input image and coordinates through a CNN (Convolutional Neural Network) backbone + upsampler structure. In [23], research was conducted to recognize actions using extracted joints, correcting coordinates through a PRM (pose refinement module), and then utilizing them. The PRM used in [23] was designed as a structure that obtains corrected poses by passing through a GCL (Graph Convolutional Layer) and TCL (Temporal Convolutional Layer). Unlike the end-to-end methods that operate within the model, these post-processing methods are applied after the model estimates the coordinates, which reduces model dependency. However, they also present the problem of increased computational requirements due to the additional coordinate correction process.
## 3 Experimental Section
### Antioxidant Activity
#### 3.6.1 HO\(\bullet\) Scavenging Activity  
In this system, hydroxyl radicals are generated by the Fenton reaction. Hydroxyl radicals can oxidize Fe\({}^{2+}\) into Fe\({}^{3+}\), and only Fe\({}^{2+}\) can combine with 1,10-phenanthroline to form a red compound (1,10-phenanthroline-Fe\({}^{2+}\)) with the maximum absorbance at 536 nm. The concentration of hydroxyl radical is reflected by the degree of decolorization of the reaction solution. Briefly, 1,10-phenanthroline solution (1.0 mL, 1.865 mM) and the sample (2.0 mL) were added into a screw-capped tube and mixed. The FeSO\({}_{4}\cdot\)7H\({}_{2}\)O solution (1.0 mL, 1.865 mM) was then pipetted into the mixture. The reaction was initiated by adding 1.0 mL H\({}_{2}\)O\({}_{2}\) (0.03% \(v/v\)). After being incubated at 37 \({}^{\circ}\)C for 60 min in a water bath, the absorbance of the reaction mixture was measured at 536 nm against a reagent blank. The reaction mixture without any antioxidant was used as the negative control, and mixture without H\({}_{2}\)O\({}_{2}\) was used as the blank. The HO\(\bullet\) scavenging activity was calculated by the following formula:  
\[\text{HO}\bullet\text{scavenging activity (\%)}=[(\text{A}_{\text{s}}-\text{A}_{ \text{n}})/(\text{A}_{\text{b}}-\text{A}_{\text{n}})]\times 100\%\]  
where A\({}_{\text{s}}\), A\({}_{\text{n}}\), and A\({}_{\text{b}}\) were the absorbance values determined at 536 nm of the sample, the negative control, and the blank after reaction, respectively.
## 5 Conclusions  
A three-dimensional CFD model was developed to evaluate the algae cell mixing features and hydrodynamic performance of a TLC reactor. Numerical simulations were performed considering the different geometrical variations (water depth, slope angle, and channel width) of the reactor. A comparison in terms of the average velocity was made with the reference experimental data of [PERSON] et al. [11]. The reactor dimensions significantly affected the algae cell mixing and hydrodynamic performance of the TLC reactor. The subsequent conclusion can be derived from the numerical calculation of present study.  
* The mixing process is more effective when the reactor has narrow geometry and operates at high mass flow rates.
* The residence time of algae cells increases with the increase in water depth and aspect ratio.
* The increases in liquid volume with the increase in channel width and water depth causes a substantial reduction in the velocity magnitude.
* The hydraulic power consumption required to move the liquid along the channel increases with the increase in mass flow rate, slope angle, and water depth.  
In order to achieve high mass densities of microalgae in the TLC reactor, an optimal inclination of the surface must be adopted to reduce the power consumption without decreasing the productivity of algae due to a low liquid velocity, causing lower turbulence. Therefore, a detailed design optimization study of the TLC reactor is recommended to overcome this compromised situation.  
All the authors were involved in the conception and design of the study. All the simulations, analysis, and interpretation of data have been done by [PERSON] and supported by [PERSON] and C.W.P. The manuscript was written by [PERSON] and [PERSON]. The work was supervised and supported for improvement with critical questions by [PERSON]. The paper review and edit was done by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a grant from the National Research Foundation of Korea grant funded by the Korea government (No. 2017R1A2B2005515).  
The authors declare no conflict of interest.
## 3 Results  
### Accuracy of Eight-Day GPP and ET  
Figure 2 shows the comparison of the eight-day observed GPP and RS-based GPP from the PMLv2 and MOD17 products for C4 maize and C3 soybean at the five cropland sites. For maize, the PMLv2 product could achieve paramount improvements over the MOD17 product in simulating GPP, as indicated by the RMSE reducing from 49.29 g C m\({}^{-2}\)8d\({}^{-1}\) to 31.68 g C m\({}^{-2}\)8d\({}^{-1}\), the R\({}^{2}\) increasing from 0.72 to 0.86, and the Bias decreasing from \(-\)18.88 g C m\({}^{-2}\)8d\({}^{-1}\) to \(-\)6.05 g C m\({}^{-2}\)8d\({}^{-1}\). It is noteworthy that the PMLv2 product and the MOD17 product both displayed obvious underestimations of medium and high observed GPP and slight overestimations of low observed GPP. These underestimations of medium and high observed GPP found in the MOD17 product could be partially alleviated by the PMLv2 product; however, it is still noticeable. For soybean, the PMLv2 product (with the RMSE = 17.32 g C m\({}^{-2}\)8d\({}^{-1}\), R\({}^{2}\) = 0.81, and Bias = 8.45 g C m\({}^{-2}\)8d\({}^{-1}\)) also outperformed the MOD17 product (with the RMSE = 22.23 g C m\({}^{-2}\)8d\({}^{-1}\), R\({}^{2}\) = 0.62, and Bias = \(-\)1.13 g C m\({}^{-2}\)8d\({}^{-1}\)) in reproducing observed GPP. Similar to the result for maize, the MOD17 product still seriously underestimated the medium and high observed GPP and slightly overestimated the low observed GPP for soybean. By contrast, the PMLv2 product presented an overall overestimation of observed GPP.  
Figure 3 shows the comparison of the eight-day observed ET and RS-based ET from the PMLv2 and MOD16 products for C4 maize and C3 soybean at the five cropland sites. Overall, the PMLv2 product exhibited similar performances to the MOD16 product for maize and soybean. Moreover, the PMLv2 and MOD16 products tended to overestimate and underestimate the observed ET for both maize and soybean, respectively. For maize, the PMLv2 product showed a decreased RMSE of 1.05 mm 8d\({}^{-1}\), an increased R\({}^{2}\) of 0.05, and a decreased absolute Bias of 1.20 mm 8d\({}^{-1}\) in comparison to the MOD16 product. For soybean, these two products showed negligible disparities in ET estimations.
## 3 Pattern Selection with SymCHM
### SymCHMMerge: Improved Pattern Selection
#### 3.2.2 Increasing Diversity  
To address the problem of pattern diversity, we needed to increase the number of patterns found by the model. This was achieved with three simple adjustments. First, we lowered the candidate selection thresholds in the greedy phase of the learning process to add more parts to each layer (evaluation showed that on average 16% more parts were added). Second, more layers were considered when searching for pattern occurrences, and third, hallucination was increased during inference. All these modifications could also be made with the basic pattern selection approach; however, they would result in an even higher number of redundant patterns. With SymCHMMerge, redundant occurrences are merged and thus the diversity of the found patterns increases.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 2 Materials and Methods
### Software  
This study exclusively employed noncommercial and open-source software and programming frameworks. ESA SNAP software (version 10.0.0) [47] was utilized for Sentinel-2 MSI data preprocessing, while Quantum GIS (version 3.34.5) [48] facilitated the manipulation of vector data, including yield data.  
To enhance workflow efficiency and automation, Python (version 3.11.1) [49] was employed within a Jupyter Notebook (version 7.0.6) environment [50] for data analysis, including regression analysis.  
The development of the web application utilized Google Earth Engine (GEE) [51] for data access and analysis, implemented using the Streamlit (version 1.24.0) [52] open-source application framework.
## 3 The Switch Control Strategy of the Hydro-Pneumatic Suspension
### Handling Stability Control  
Once the gravity center of the heavy vehicle is laterally offset owing to severe maneuvers, a centrifugal moment (\(M_{c}\)) is generated, and the vehicle is at risk of rolling over. The actuator of the adjustable hydro-pneumatic suspension can generate a torque (\(M_{t}\)) against the centrifugal moment under the appropriate control of the controller, thereby preventing the vehicle from rolling over. Consequently, the following vehicle roll dynamic model is obtained.  
\[\begin{array}{l}I_{x}\ddot{\varphi}=T(F_{t}-F_{r})+m_{s}a_{y}h+M_{c}-M_{t}+ \kappa\\ M_{c}=m_{s}gh\sin\varphi,M_{t}=f_{ul}T\end{array} \tag{9}\]  
where \(\kappa\) represents the allowable uncertainties, and \(\kappa\leq\kappa_{1}\big{|}\varphi\big{|}+\kappa_{2}\big{|}\dot{\varphi}\big{|}\). Both \(\kappa_{1}\kappa_{2}\) are positive constants.  
As we can see from Equation (9), the system input is \(M_{t}\), and the output is \(\varphi\). From SMC theory, the sliding surface is defined as below.  
\[s=\rho e+\dot{e}=\rho\varphi+\dot{\varphi} \tag{10}\]  
where \(\rho(\rho>0)\) indicates the performance of system converging to the equilibrium point. \(e\) is the difference between the ideal output and actual output, and the ideal output of the roll angle is zero, in other words, \(e=\varphi\).  
Figure 6: Dynamic switch control block diagram of the hydro-pneumatic suspension system.  
For the purpose of effectively improving the asymptotic stability and decreasing chattering aroused from SMC's switch characteristic, the exponential reaching law is selected to converge towards 0 exponentially in this paper, which is rewritten in Equation (11).  
\[\dot{s}=-\mu\mathrm{sgn}s-\eta s \tag{11}\]  
In order to satisfy the [PERSON] criteria [26], Equation (12) is selected as the Lyapunov candidate function, which is strictly negative. Equation (13) should be satisfied to ensure the robustness stability of the controller.  
\[V=\frac{1}{2}\Big{(}e^{2}+s^{2}\Big{)} \tag{12}\]  
\[\eta>\kappa_{2} \tag{13}\]  
\[\mu>(1+\kappa_{1}+\kappa_{2})\varphi_{\mathrm{max}} \tag{14}\]  
where \(\mu\) and \(\eta\) are the coefficients that represent the speed reaching the sliding surface, \(\mu>0\)\(\eta>0\).  
According to Equations (10)-(13) mentioned above, the control input can be rewritten as Equation (14).  
\[f_{ul}=-f_{ur}=I_{x}\Big{(}-\mu\mathrm{sgn}s-\eta s-\rho\dot{\varphi}\Big{)}-(F _{l}-F_{r})-\frac{m_{s}a_{y}h+m_{s}gh\sin\varphi+\kappa}{T} \tag{15}\]
## 2 Mathematical Modelling
### Model Formulation
#### 2.2.4 Constraints  
The constraints of the model are shown below.  
\[\sum_{i\in M}y_{ij}\leq 1\ \ \forall j\in M \tag{4}\]  
\[\sum_{j\in M}y_{ij}\leq 1\ \ \forall i\in M \tag{5}\]  
\[(t_{i}^{d}+t_{ij})y_{ij}\leq t_{j}^{o}\ \ \forall i,j\in M \tag{6}\]  
\[(k_{i}-k_{j})y_{ij}=0\ \ \forall i,j\in M \tag{7}\]  
\[(t_{j}^{o}-t_{i}^{d})y_{ij}\leq\beta\ \ \forall i,j\in M \tag{8}\]  
\[y_{ij}=\left\{\begin{array}{ll}1\ i\ \text{match}\ j\\ 0\ \text{otherwise}\end{array}\right. \tag{9}\]  
In the above constraints, Equation (4) means that every task can only be matched once by one vehicle or not. Equation (5) means that every vehicle can only match one task or not. Equations (6)-(8) are the necessary conditions for matching between two tasks. Equation (6) indicates that the time when the vehicle is transferred to the departure station of the next task after the previous task is finished must be earlier than the departure time of the next vehicle. Equation (7) indicates that the required vehicle sizes of the two tasks that can be matched must be the same. Since there is demand matching between different vehicle sizes, the case of larger-size vehicles replacing smaller-size vehicles is not considered. Equation (8) indicates that unrealistic matching scheduling of excessive time and distance is avoided. Equation (9) indicates that the decision variable \(y_{ij}\) is a 0-1 variable. When \(y_{ij}=1\), it means that vehicle task \(i\) matches vehicle task \(j\), i.e., the vehicle executes vehicle task \(j\) after completing vehicle task \(i\). When \(y_{ij}=0\), vehicle task \(i\) does not match vehicle task \(j\), i.e., the vehicle cannot execute vehicle task \(j\) after completing vehicle task \(i\).  
The station locations determine the distance parameter in the model, and the inter-station distance can be obtained by knowing the two station locations. Equations (10) and (11) show the calculation method of \(s_{i}\) and \(s_{ij}\).  
\[s_{i}=F(l_{i}^{o},l_{i}^{d}) \tag{10}\]  
\[s_{ij}=F(l_{i}^{d},l_{j}^{o}) \tag{11}\]  
In addition, although the departure time of each task is determined, the actual travel time may be affected by many factors, so the vehicle's arrival time needs to be predicted. Since the travel time prediction is not the focus of this paper, the travel time in this model is calculated by using the travel distance and the average running speed of the vehicle, i.e., so:  
\[t_{i}^{d}=t_{i}^{o}+\frac{s_{i}}{v} \tag{12}\]  
\[t_{ij}=\frac{s_{ij}}{v} \tag{13}\]
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 7 Conclusions  
After conducting a comprehensive study on the feasibility of utilising deep learning models to improve static malware detection through hyperparameter optimisation using covering arrays, our findings indicate a highly promising approach. The performance of our optimised deep learning models using covering arrays outperformed the baseline model on both the Ember and Kaggle datasets. Specifically, our optimised model achieved an accuracy of 95.7% on the Ember dataset and 98.6% on the Kaggle dataset, highlighting the potential of this approach to significantly enhance the performance of malware detection systems.  
Moreover, our study provides evidence that incorporating hyperparameter optimisation through covering arrays can be a highly effective technique for enhancing the performance of deep learning models in the field of malware detection. Our results indicate that fine-tuning deep learning models using covering arrays significantly outperformed the traditional grid search method in terms of time and computational resources required. This finding is particularly significant as it demonstrates that the use of covering arrays is not only more efficient, but also leads to improved accuracy.  
It is worth noting that the optimal configuration of hyperparameters, such as the number of neurons, layers, activation functions, dropout relay, number of epochs, and batch size, was carefully determined through a rigorous and comprehensive experimentation process. The use of covering arrays allowed for the exploration of a wide range of hyperparameter configurations, enabling us to fine-tune our models to achieve the highest accuracy possible.  
In conclusion, our study provides further evidence that deep learning models, combined with hyperparameter optimisation using covering arrays, can significantly enhance the performance of static malware-detection systems. The results of our study suggest that this approach has the potential to be widely adopted in the field of cybersecurity, leading to more efficient and accurate malware-detection systems. Future work could investigate the application of this approach to other domains, and it has the potential for further optimisation techniques.  
Conceptualization, F.T.A. and A.S.A. Authors Contributed equally. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The Ember Dataset can be found on ([[https://github.com/elastic/ember](https://github.com/elastic/ember)]([https://github.com/elastic/ember](https://github.com/elastic/ember)), accessed 23 April 2023) and the Kaggle dataset can be found on ([[https://www.kaggle.com/datasets/amauricio/pe-files-malwares](https://www.kaggle.com/datasets/amauricio/pe-files-malwares)]([https://www.kaggle.com/datasets/amauricio/pe-files-malwares](https://www.kaggle.com/datasets/amauricio/pe-files-malwares)), accessed 23 April 2023).  
The authors declare no conflict of interest.