esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Simulation and Analysis
### Simulation Result Analysis
#### 4.2.2 Simulation Results and Analysis
As shown in Figure 7, the system throughput of all five algorithms shows an upward trend with the increase of the number of allocatable time slots because the more bandwidth resources can be allocated, the more services each user can transmit and the higher the system throughput. When the number of time slots is 300-450, the throughput of the five algorithms does not differ significantly as the relatively limited available resources result in a less significant difference in allocation schemes. When the number of time slots is 450-900, the throughput of the IBES-ABA algorithm is much higher than other algorithms because it fully utilizes transmission rate, encoding efficiency, and modulation order to characterize the channel state. Poor channel state corresponds to lower modulation order and encoding efficiency, thereby improving system throughput. When the number of time slots is between 900 and 1200, the gap between several algorithms begins to narrow and gradually stabilizes. At this time, the number of time slots is sufficient, and most of the user's requests have been met, so the gap in allocation schemes gradually narrows. Due to the neglect of channel status and business QoS information during the allocation process by the BABEE algorithm, the throughput is significantly lower than other algorithms. By calculating the average system throughput of each algorithm under different time slots, it can be concluded that the algorithm proposed in this paper has improved the system throughput by at least 6.55% compared to the IHQB algorithm, BABEE algorithm, and BO-CL-DBA algorithm. As shown in Figure 8, as the number of time slots increases, the user satisfaction of all five algorithms increases. Among them, the user satisfaction of the IBES-ABA algorithm and the BES-ABA algorithm is relatively close and always higher than other algorithms. This is because the algorithm proposed in this article comprehensively considers factors, such as business QoS, channel state information, and the number of user time slots, that were not satisfied in the previous cycle, which can better meet the needs of various users and improve user satisfaction. The BABEE algorithm focuses on allocating the same bandwidth resources to different users. Although this algorithm improves the absolute fairness of bandwidth allocation, it ignores the bandwidth needs of users with different priorities, resulting in the lowest user satisfaction under this algorithm. By calculating the average user satisfaction of each algorithm under different time slots, it can be concluded that the algorithm proposed in this paper has improved user satisfaction by at least 4.28% compared to the IHQB algorithm, BABEE algorithm, and BO-CL-DBA algorithm.
## 3 Results and Discussion
### Validation of MW-FLR Method
#### 3.3.1 Linearity and Sensitivity  
Under the optimum conditions of the method (Table 1), the calibration graph for the determination of DUV was generated by plotting the fluorescence intensity as a function of the corresponding DUV concentrations (Figure 6). The regression analysis for the results  
Figure 5: The emission spectra of blank plasma sample (contains no DUV) after excitation at 275 nm (1), blank plasma sample (contains no DUV) after excitation at 340 nm (2), plasma sample spiked with DUV at concentrations of 100, 450 and 600 ng/mL (3, 4 and 5, respectively) after excitation at 340 nm.  
was conducted. The linear equation was Y = 0.5956X + 2.9481, where Y is the fluorescence intensity at 405 nm (after excitation at 340 nm) and X is the concentration of DUV in ng/mL in the range of 25-800 ng/mL. The correlation coefficient (r) was 0.9995. A summary of the statistical parameters is given in Table 2.  
The limit of detection (LOD) and limit of quantification (LOQ) of the MW-FLR method were calculated. LOD, defined as the minimum detectable DUV concentration, was calculated using the formula: 3.3 \(\times\) Standard deviation (SD) of intercept/Slope. LOQ, defined as the DUV concentration that can be quantified with satisfactory accuracy and precision under the stated procedures of the MW-FLR method, was calculated using the formula: 10 \(\times\) Standard deviation (SD) of intercept/Slope. LOD and LOQ values were 20.5 and 62 ng/mL, respectively. It is worth mentioning that the achieved sensitivity of the MW-FLR  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable** & **Studied Range** & **Optimum Condition** \\ \hline Excitation wavelength (nm) & 220–400 & 275 and 340 \\ Emission wavelength (nm) & 360–500 & 405 \\ Solvent & Different a & Water \\ Surfactant & Different b & No \\ Buffer pH & 2–12 & No \\ \hline \hline \end{tabular}  
* Solutions tested: water, methanol, ethanol, isopropanol, dichloroethane, dichloromethane, chloroform, diethylI ether, dioxane and acetonitrile. b Surfactants tested: sodium dodecyl sulphate, cremophor RH 40, cremophor EL and tween 20.  
\end{table}
Table 1: Optimization of MW-FLR method for determination of DUV.  
Figure 6: Calibration curve for determination of DUV by the proposed MW-FLR method. Values of fluorescence intensity are mean of 3 determinations. Linear fitting equation and its correlation coefficient (r) is given on the calibration line.  
method is adequate for the determination of DUV in dosage form as well as in plasma samples. This assessment based on a study involving 207 patients receiving DUV at different doses ranging from 8 to 100 mg twice a day revealed that DUV is reaching its peak plasma concentration (C\({}_{\rm max}\)) of 471 to 3294 ng/mL after 1 to 2 h from the administration time [33]. The C\({}_{\rm max}\) values after a therapeutic DUV dose (25 mg) were found to be 1511 ng/mL, and systemic exposure ranged from 2001 to 19,059 ng/h/mL.
## 2 Materials and Methods
### Theory
#### 2.1.2 Crash Analysis under Pre-Crash Conditions
Assuming that the other types of dissipated energy can be neglected, the deformation energy (Equation (5)) can be initially deduced according to conservation of energy (Equation (4)):  
\[E_{d}=\frac{1}{2}m_{1}\Big{(}v_{1n}^{2}+v_{1t}^{2}\Big{)}+\frac{1}{2}m_{2} \Big{(}v_{2n}^{2}+v_{2t}^{2}\Big{)}-\frac{1}{2}m_{1}\Big{(}V_{1n}^{2}+V_{1t}^ {2}\Big{)}-\frac{1}{2}m_{2}\Big{(}V_{2n}^{2}+V_{2t}^{2}\Big{)} \tag{4}\]  
\[E_{d}=\frac{1}{2}m_{c}(v_{2n}-v_{1n})^{2}(1+\varepsilon)\Big{[}(1-\varepsilon )+2\mu r-(1+\varepsilon)\mu^{2}\Big{]} \tag{5}\]  
where \(m_{c}\) is the system mass of the two-vehicle crash, which can be calculated by \(m_{1}\) and \(m_{2}\):  
\[m_{c}=\frac{m_{1}m_{2}}{m_{1}+m_{2}}\]  
Variable \(r\) is the ratio of velocity component in the \(n\) and \(t\) directions, denoted as follows:  
\[r=\frac{v_{2t}-v_{1t}}{v_{2n}-v_{1n}}\]  
The impulse ratio \(\mu\) in Equation (5) depends on the sliding state of the vehicle and ranges among \(0\leq\mu\leq\mu_{0}\). The impulse ratio, \(\mu_{0}\), called the critical impulse ratio, characterizes the maximum kinetic energy loss within the system and is defined as \(\mu_{0}=r/(1+\varepsilon)\)[25]. In the case of side impact with unknown post-impact parameters, the potential deformation energy can be obtained by substituting \(\mu_{0}\) into Equation (5) as follows:  
\[E_{d}=\frac{1}{2}m_{c}(v_{2n}-v_{1n})^{2}(1-\varepsilon^{2}+r^{2}\Big{)} \tag{6}\]  
In the case of a frontal or rear-end crash, no tangential force exists in the vehicle contact plane, and the tangential impulse \(P_{t}=0\). The deformation energy can be simplified as follows:  
\[E_{d}=\frac{1}{2}m_{c}(v_{2n}-v_{1n})^{2}(1-\varepsilon)^{2} \tag{7}\]  
In addition, the energy distribution within the system depends on the mass ratio of the vehicle. Vehicles with smaller mass will absorb more energy and suffer more potential severe damage. The kinetic energy absorbed by each vehicle during the compression phase can be expressed as follows:  
\[\frac{E_{d1}}{E_{d2}}=\left(\frac{m_{1}}{m_{2}}\right)^{\alpha} \tag{8}\]The value of the negative constant \(\alpha\) mainly depends on the impact position. A previous study has concluded that \(\alpha\) is \(-5.0\) for the side impact and \(-0.8\) for the frontal or rear impacts, indicating that lighter vehicles subjected to side impacts can result in more severe outcomes [24]. Based on Equations (2)-(8), the simplified deformation energy under pre-crash conditions can be achieved using the pre-impact indicators, including approaching speed, moving direction, and vehicle configuration.
# A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images
## 1 Introduction
Conventional data gathering and classification cannot be automated because of the human labor necessary for segment size adjusting, feature selection, and rule-based classifier development. The primary focus of agricultural mechanization research is on improving crop yields while reducing weed infestations. Data scientists have found weed detection to be a fruitful field of study due to the prevalence of algorithms based on machine learning [9]. To classify agricultural and weed plants, many researchers have turned to computer vision techniques [10]. Significant advances have also been made through the publication of several deep-learning and hand-crafted models [11] As a subfield of both ML and AI, deep learning (DL) is making significant progress toward automating precision agriculture [12; 13]. DL has made important contributions to many fields of agriculture [14; 15], including: disease detection [16], crop plant detection and counting [17], crop row detection [18], crop stress detection [19], fruit detection and freshness grading [20], fruit harvesting [18; 21], and site-specific weed control [21]. Hand-crafted methodologies require substantial testing to extract essential features and may also be insufficiently robust in complicated environments and biological variability [20]. CNNs have improved object detection [22]. CNNs can be taught end to end without feature extraction, making them superior at classifying and detecting weeds [21]. This paper's contribution can be summarized as follows:A deep-learning-based framework, \"DenseHHO\", that is designed to improve the detection of weeds using pre-trained CNNs such as DenseNet-121 and DenseNet-201 is presented. The devised model's CNN architecture is adopted based on the analysis of weed images captured by sprayer drones structured as an optimization problem addressed by an HHO natural-inspired algorithm. The performance of the devised model is improved using HHO for binary class classification to automatically select as well as optimize the CNN's hyperparameters. The performance of DenseHHO has been evaluated through a comparative analysis and performance evaluation based on various performance metrics. This paper is organized into five sections. The problem addressed in this paper is introduced in Section 1, along with a summary of the proposed solution. Section 2 discusses the major milestones in the weed-detection literature. Section 3 describes the materials and procedures used in the proposed solution. Section 4 explains the proposed algorithms and solutions, while Section 5 discusses the experimental results. Section 6 concludes with a discussion of the findings.
## 2 Model Building
### The Fitness Function
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sets:** & \\ \hline \(C\) & Set of courses; \\ \(T\) & Set of teachers; \\ \(R\) & Set of classrooms; \\ \(A\) & Set of course types; \\ \(D\) & Set of days in a week, with a weekly schedule consisting of 5 days; \\ \(W\) & Set of weeks; \\ \(E\) & Set of administrative classes; \\ \(P\) & Set of teaching classes; \\ \(M\) & Set of teaching events; \\ \(K\) & Set of time slots in a week, with 5 days in a week and 5 time slots in a day; \\ \(B_{c}\) & Weekly contact hours of course \(c\), \(c\in C\); \\ \(C_{t}\) & Set of courses that the teacher \(t\) needs to teach; \(t\in T\). \\ \(C_{r}\) & Set of courses scheduled in classroom \(r\), \(r\in R\); \\ \(C_{a}\) & Set of courses of type \(a\), \(a\in A\); \\ \(C_{e}\) & Set of courses for administrative class \(e\), \(e\in E\); \\ \(C_{m}\) & Set of courses specified by teaching event \(m\), \(m\in M\); \\ \(R_{c}\) & Set of classrooms of course \(c\), \(c\in C\); \\ \(R_{a}\) & Set of classrooms of type \(a\), \(a\in A\); \\ \(W_{m}\) & Set of weeks specified by teaching event \(m\), \(m\in M\); \\ \(E_{p}\) & Set of administrative classes belonging to teaching class \(p\), \(p\in P\); \\ \(M_{1}\) & Set of teaching events with fixed time slots; \\ \(K_{m}\) & Set of time slots specified by teaching event \(m\), \(m\in M\). \\
**Parameters:** & \\ \(N_{r}\) & The maximum capacity of classroom \(r\), \(r\in R\); \\ \(S_{p}\) & The number of administrative classes belonging to teaching class \(p\), \(p\in P\); \\ \(\delta_{r,w,k}\) & The number of students in classroom \(r\) during time slot \(k\) in week \(w\), \(r\in R\), \(w\in W\), \(k\in K\); \\ \(Y_{e,d}\) & The number of class hours scheduled for administrative class \(e\) on day \(d\), \(e\in E\), \(d\in D\); \\ \(Z_{t,d}\) & The number of teaching hours scheduled for teacher \(t\) on day \(d\), \(t\in T\), \(d\in D\); \\
**Decision Variables:** & \\ \(x_{w,k,c}\) & 1 if course \(c\) is scheduled in week \(w\) during time slot \(k\); 0 otherwise. \\ \(L_{w,k,r,e}\) & 1 if administrative class \(e\) uses classroom \(r\) during time slot \(k\) in week \(w\); 0 otherwise. \\ \(\theta_{r,k}\) & 1 if a course is scheduled in time slot \(k\) of classroom \(r\) during the set of weeks \(w\); 0 otherwise. \\ \(\sigma_{w,d,c}\) & 1 if course \(c\) is scheduled on day \(d\) of week \(w\); 0 otherwise. \\ \(a_{c}\) & The type of course \(c\), \(c\in C\). \\ \(a_{r}\) & The type of classroom \(r\), \(r\in R\). \\ \(Q_{t}\) & The quality of the teaching schedule for teacher \(t\), \(t\in T\). \\ \(\tau\) & The number of consecutive teaching sessions for teacher \(t\), \(t\in T\). \\ \(\theta_{\tau}\) & 1 if \(\tau>1\); 0 otherwise. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variable descriptions.
# RRM Prediction of Erythrocyte Band3 Protein as Alternative Receptor for SARS-CoV-2 Virus
###### Abstract  
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new coronavirus causing a worldwide pandemic. It is infecting respiratory organs and, in more severe cases, the lungs, where it is infecting the human cells through the angiotensin-converting enzyme 2 (ACE2) receptor. In severe cases, it is characterized not only by difficulties in breathing through infected lungs, but also with disproportionally and, thus far, unexplained low levels of oxygen in the blood. Here, we propose that, besides the infection of respiratory organs through ACE2 receptors, there is an additional infection in the red blood cells (erythrocytes). There could be a possible for SARS-CoV-2 to pass through the alveoli membrane in the lungs and infect the red blood cells through another receptor. Using our own biophysical model, the Resonant Recognition Model, we propose that the red blood cell (RBC) Band3 protein on the surface of red blood cells is a possible entry point for the SARS-CoV-2 virus into red blood cells.  
COVID-19; SARS-CoV-2; coronavirus; ACE2; blood cells; protein-receptor interaction; resonant recognition model +
Footnote †: journal: Medical  
As SARS-CoV-2 is a completely new virus, causing a worldwide pandemic, there is an urgent need to investigate all aspects of SARS-CoV-2 virus infection and its activity. Bearing in mind that 80-90% of infected individuals have only mild or no symptoms, while the rest of infected individuals may present severe symptoms, which can even cause death, there is particular need to find out the difference between virus activity in mild versus severe cases. In severe cases, SARS-CoV-2 infects the lungs, but, apart from affecting lung function, it also extremely and dangerously lowers the oxygen level in the blood, disproportional to lung damage and the ability to breathe. In such cases, patients' oxygen levels are falling dangerously low, while they are not experiencing the shortness of breath that usually signals the life-threatening condition. This \"silent hypoxia\" is making some SARS-CoV-2 virus infected patients critically ill and, in some cases, is causing death [2].  
Our research is specifically focused on investigating an anomaly of severe cases of COVID-19 infection, where the function of infected lungs is not severely impaired, but oxygen levels in the blood become dangerously low. Here, we have utilized our own biophysical Resonant Recognition Model (RRM) [3; 4; 5; 6] to investigate the possibility that SARS-CoV-2 can not only infect lung cells, but can also pass through the alveoli membrane to infect red blood cells and consequently cause an unexpectedly disproportional drop in the oxygen levels in blood.
## 4 Results
### Inventory Related Costs
#### 4.1.5 Total Inventory-Related Costs
With the increase in R + LT group values, average C\({}_{\text{TOT}}\) decreases in most scenarios; however, in approximately 1/3 of groups, average C\({}_{\text{TOT}}\) increased compared to the lower value of the R + LT group. On average, with the increase in R + LT value for a single time unit, average C\({}_{\text{TOT}}\) decreases 3.7% in a 5-day and 4.6% in a 7-day WS, regardless of demand variability. Regardless of the WS and demand variability, the most significant relative decrease in average C\({}_{\text{TOT}}\) occurs for R + LT = 2/1. This research shows that reducing R + LT from 1 to 2 days will reduce C\({}_{\text{TOT}}\) by 41.8% on average. As mentioned, approximately 1/3 of R + LT groups show higher average C\({}_{\text{TOT}}\) than those of lower R + LT value. The average increase on a 5-day is 1.3%; on a 7-day WS, it is slightly lower, 0.9%. The average total costs are about 3.8% higher for a 7-day compared to a 5-day WS, regardless of demand variability. However, with the increase in R + LT values, at around 15 days, C\({}_{\text{TOT}}\) becomes lower for a 7-day WS, as visible in Figure 12a. The maximum increase in C\({}_{\text{TOT}}\) comparing a 7-day to 5-day WS is noticed for the R + LT = 2 group, with a 33.4% increase for SD 10 and a 33.2% increase for SD 30 in R + LT = 1. On the other hand, the maximal decrease in C\({}_{\text{TOT}}\) related to the WS happens in R + LT = 25, with an 11.8% reduction for SD 10 and 8.4% for SD 30. Significant differences between maximal and minimal absolute C\({}_{\text{TOT}}\) within R + LT groups are observed. On average, comparing within the same WS and SD, the differences between maximal and minimal values of C\({}_{\text{TOT}}\) are between 21% and 33%. Unlike partial costs, no R + LT groups with differences higher than 100% in C\({}_{\text{TOT}}\) exist. The maximal relative difference between the maximal and minimal absolute values of C\({}_{\text{TOT}}\) in a 5-day WS is noticed in the R + LT = 9 group, with values of 56.1% for SD 30 and 47.9% for SD 10. Figure 12: (a) Change in C\({}_{\text{TOT}}\) for FR 99.9% per R + LT group for a 7-day compared to 5-day WS; (b) Change in C\({}_{\text{TOT}}\) per R + LT group and WS for an FR 99.9% and SD 30 compared to FR 89.9%. In a 7-day WS, slightly lower differences occur in the R + LT = 3 group, 53.9% for SD 30 and 42.5% for SD 10. In every combination of R + LT groups in a 7-day WS, the average C\({}_{\text{TOT}}\) is higher for an FR of 99.9% compared to an FR of 89.9%. On average, in a 5-day, C\({}_{\text{TOT}}\) is 8.1%, while in 7-day WSs, C\({}_{\text{TOT}}\) is higher by 6.4% compared to a 10% lower FR of market demand. However, in a 5-day WS, there are three R + LT groups with higher costs for a lower FR group, with an average increase of 2.5% over the higher FR, as visible in Figure 12b.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 6 Discussion of Simulation Results
### Impact of Traffic Load and Placement of Nodes
#### 6.1.4 Average Radio Duty Cycle  
The average radio duty cycle represents the amount of time that a node has its radio on relative to the time of the simulation runs. The time that a node has its radio on is an indicator of power consumption, since the radio unit is one of the most energy-consuming components. Figure 14 shows the radio duty cycle in function of the traffic load. The radio duty cycle is comparable for both schedulers when the traffic is below 10 packets per minute. The increase observed for our scheduler is due to the additional cells allocated to accommodate the increase in traffic. For RBS Orchestra, the significant increase is related to contention leading to unstable links, multiple retransmissions, and additional control traffic. Analyzing the performance of the new scheduler, one can see that the radio duty cycle is higher for placement 2. This is explained by the placement of nodes and the resulting  
Figure 13: Average delay for each sender node in low and high traffic situations. (**a**) We observe that in normal traffic situation, the average delay is comparable for the two schedulers. (**b**) We observe that in high traffic situation for RBS Orchestra the average delay is highest for the root’s children, being nodes 2–5.  
topology. On the one hand, the sink node has more children than in placement 1, and on the other hand, the nodes are disposed around the root and the root's children, leading to higher chances of allocating more additional cells for all the children.
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
###### Abstract
This is important because the traditional averaging of objective measures may mask the artifacts that this system could confront end users with. In our opinion, the results from this work provide video distribution system architects with the necessary insight to consider and evaluate the proposed keyframe insertion technique. This paper proceeds with a thorough investigation of state-of-the-art techniques used for random access and packet loss repair in Section 2. Details on the materials and methods of this work are provided in Section 3 followed by a comprehensive description of the results in Section 4. Finally, this work is completed with an example application in Section 5 and a discussion in Section 6.
## 3 Materials and Methods
### Determination of Antimicrobial Activity  
#### 3.4.1 MIC and MBC Assay  
The broth dilution method was used for determining both the MIC and MBC of the EOEP against all strains of tested bacteria. This method was used as modified from [PERSON] et al. [46]. Test tubes containing 5 mL of each bacterium (4-5 Log CFU/mL) and double strength Mueller-Hinton broth (MHB; Merck, Darmstadt, Germany), MHBYE for _Listeria_ spp. or MRS broth for lactic acid bacteria were mixed to a single concentration in either EOEP or sterilized deionized (DI) water to give a final volume of 10 mL. The EOEP was mixed into a test tube to give a final concentration in the range of 0.01 to 5% (_v/v_). The methodology also included positive and negative controls. The positive control included tubes containing inoculum and MHB medium without EOEP, and the negative control comprised tubes containing EOEP and MHB medium, without inoculum. The test tubes with only MHB medium served as a blank control. After mixing, the test tubes were incubated at 37 \({}^{\circ}\)C, except _B. cereus_ was incubated at 30 \({}^{\circ}\)C for 24 h. Survival numbers of tested bacteria were examined using the spread plate method. _B. cereus_, _S. aureus_, _E. coli_, _P. aeruginosa_ and _S. Typhimurium_ were grown on TSA. _Listeria_ spp. was grown on TSAYE and lactic acid bacteria was grown in MRS agar. Then, the agar was incubated at 37 \({}^{\circ}\)C, while _B. cereus_ was incubated at 30 \({}^{\circ}\)C. All experiments were repeated in triplicate and duplicated in each experiment.  
The MIC was defined as the lowest concentration of antimicrobial concentration required to inhibit the visible growth in the test tube. The MBC was defined as the lowest concentration of antimicrobial concentration required to diminish more than 99.9% (3 Log reduction) of the initial bacterial amount at 24 h [46; 47].