esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design
### Model Generalizability Analysis  
We performed tests using the PASCAL VOC dataset to assess YOLO-TC's generalizability further. We also evaluated the YOLO-TC network model using the mAP metrics to demonstrate its good detection performance for ordinary small objects. The PASCAL-VOC dataset consists of batches of 80 images. The optimizer used is SGD, with a total of 300 training epochs. The learning rate gradually increases from 0 to 0.015, while the momentum and weight decay are set to 0.9 and 5 \(\times\) 10\({}^{-4}\), respectively. The experimental results of these data are described in detail below. The average accuracy plots for YOLOv7, YOLOv8 and YOLO-TC on PASCAL VOC for each category are shown in Figure 11:  
Figure 10: YOLO-TC accuracy–recall curve and visualized confusion matrix. (**a**) Accuracy–recall curve. (**b**) Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **AP\({}_{\text{C}}\)/\% \(\uparrow\)** & **AP\({}_{\text{P}}\)/\% \(\uparrow\)** & **[EMAIL_ADDRESS]/\% \(\uparrow\)** & **Params/10\({}^{6}\)** & **FPS** \\ \hline Retina-Net & 52.1 & 87.1 & 67.1 & 37.30 & 34 \\ Center-Net & 60.6 & 88.3 & 74.1 & 32.00 & 80 \\ YOLOv5 & 66.5 & 90.9 & 77.7 & 6.60 & 65 \\ YOLOX & 67.9 & 91.4 & 78.3 & 8.27 & 62 \\ YOLOv7 & 73.5 & 96.2 & 85.1 & 36.54 & 87 \\ YOLOv8 & 76.1 & 98.7 & 87.1 & 29.14 & 101 \\ Ours & 78.6 & 99.0 & 88.8 & 38.67 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of comparison with other algorithms.  
In the PASCAL VOC dataset, many small objects with relatively small sizes, insufficiently distinctive features, and small sample areas can be used to test the detection capability of the object detection model for small objects. From the average precision comparison chart, it can be observed that the YOLO-TC model improves the detection accuracy of several small objects on the PASCAL VOC dataset. Specifically, the average precision is increased by 2.2 percentage points compared to YOLOv7 and by 0.7 percentage points compared to the more advanced YOLOv8. This indicates that YOLO-TC is more effective at accurately identifying and localizing small objects, thereby enhancing the detection capability for these challenging targets. Being able to detect more small-scale objects means that YOLO-TC is highly generalizable for small object detection tasks and can be applied to small object detection tasks in more practical application scenarios.
## 3 Results and Discussion
### Adsorption from Water Solution
\begin{table}
\begin{tabular}{c c c c} \hline
**Pollutant** & **Q\({}_{0}\) (mg g\({}^{-1}\))** & **b (L mg\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline m-aminophenol & 165 & 0.0828 & 0.9988 \\ \hline pentachlorophenol & 22 & 4.7400 & 0.9653 \\ \hline p-nitrophenol & 152 & 0.4842 & 0.9881 \\ \hline \end{tabular}
\end{table} 
Table 4: Results obtained from the Langmuir equation applied to the adsorption isotherms of phenolic compounds on the studied activated carbons. Figure 4: Equilibrium adsorption isotherms of pentachlorophenol, m-aminophenol and p-nitrophenol on carbon. On the other hand, the trend observed for the affinity parameter seem to be related to the electron-withdrawing or electron-donating properties of the moieties present in the phenolic compound (i.e., nitro-, amino- and Cl\({}^{-}\) groups). The NH\({}_{2}\)\({}^{-}\) moiety is a powerful electron-donating group, which increases the \(\pi\)-electron density of the aromatic ring; in contrast, the NO\({}_{2}\)\({}^{-}\) moiety is a strong deactivator of the aromatic ring. It is well known that oxygen functionalities withdraw the \(\pi\)-electron density of the basal planes of the carbons [18]; therefore, it seems reasonable to expect that the affinity of both aromatics would be higher in carbons with a rich electron density, and stronger for m-aminophenol, which aromatic ring is not deactivated by the amino group. Whereas previous studies reported in the literature [2, 25, 26] deal exclusively with hydrophobic carbon adsorbents, disregarding the complexity of the adsorption process, our studies reveal the essential importance of the carbon surface chemistry and porous structure. In this regard, carbon exhibits a strong interaction with p-nitrophenol, m-aminophenol and pentachlorophenol, despite its relatively high oxygen content (11.52 wt.%), which is often associated with a large deactivation effect of the \(\pi\)-electron density of graphene layers. Our results suggest that oxygen content should not be considered as the key factor in the removal of aromatics; rather the basic/acidic nature of the surface functionalities and porous structure should be taken into account.
## 5 Performance Evaluation Results and Discussion
### Evaluation Viewpoints
#### 5.1.2 Effects of Misbehaving REPs and Mitigation of the Effects with S-REPs  
We confirmed the effects of misbehaving REPs that fail to fulfill the role of REP and the mitigation of these effects with S-REPs by evaluating the differences in the evaluation scores of successful bids between one case without a misbehaving mobile host and four cases with some misbehaving mobile hosts, that is, (a), (b), (c), and (d) in Table 2. We measured the number of different successful bids between the cases with and without misbehaving mobile hosts among 1970 data requests. Simulations were conducted 10 times with different random number seeds for 1000 s with 250 and 500 mobile hosts in an environment where mobile hosts were selected by a uniform random number at the beginning of a simulation to drop packets intentionally.
## 4 XAI Methods in Medicine
### Post Hoc Explainability Methods
#### 4.3.2 Explanation Using Feature Relevance Methods  
In the category of feature relevance methods, we can find many popular examples of explainability techniques. These approaches try to find the most relevant variables or features to the model's predictions, those that most influence the outcome in each case or in general.  
The ancestry to these techniques can be found in both statistical and heuristic approaches dating back to the 1930s with Principal Component Analysis (PCA), which explains the weightings of features, or contributions to relevance in terms of their contribution to inter- and intra-population patterns of multinomial variance and covariance [98]. These techniques were also shown to be central to both dimensionality reduction and its explanation in terms of information content for pattern recognition [99] and clinical diagnostic classification and prediction using subspace methods from atomic logic [100]. Later, related techniques for feature extraction by projection pursuit were developed and applied to clinical decision-making.  
More recently, with LIME (that could also be included in this group), SHAP (SHapley Additive exPlanations) is one of the most widely used XAI model agnostic techniques, and it is the main example of the category of feature relevance methods. It is based on concepts from game theory that allow the computing, which are the features that contribute the most to the outcomes of the black-box model, by trying different feature set permutations [101]. SHAP explanations increase trust by helping to test prior knowledge and can also help to get insights into new ones [102].  
Other well-known similar examples that measure the importance of different parts of the input by trying different changes is SA (Sensitivity Analysis) [103], and LRP (Layer-Wise Relevance Propagation) [104]. Deep Taylor Decomposition (an evolution of LRP) [105] and DeepLIFT [106] are other model-specific alternatives for neural networks, that propagate the activation of neurons with respect to the inputs to compute feature importance.
## 5 Challenges, Open Research Problems, and Future of WBANs
### The Future of WBAN Systems
* Eventually, the WBANs systems should have their own standards for data collection and storing methods, as well as for wireless communication to eliminate coexistence issues.
## 3 Design and Analysis
### Electrostatic Field Optimization of the FGIL  
Electric field optimization is obligatory in electrical systems in order to eradicate the prospect of dielectric failure due to partial discharge or gap discharge [58]. Considering the stranded conductor as a potential candidate for pliable GILs and concerning its surface irregularity, detailed electrostatic appraisal is essentially required for the proposed scheme. Protrusions and surface irregularities in stranded conductors may lead to escalated electric fields on the conductor's surface contour, which may result in detrimental partial discharge activity followed by dielectric strength degradation due to streamers [43; 44; 45; 58]. Thus, considering the importance of field dispersion in pliable GIL, COMSOL Multiphysics(r)-based electrostatic analysis was performed for the FGIL regarding the stranded conductor specimens given in Table 2 in comparison to existing GILs in order to achieve minimal electrostatic stresses as per the required standards for gas-insulated equipment. The stranded conductors used in the electrostatic examination were developed using Autodesk Inventor(r). Dimensional and technical specifications like electrode gap, thickness, and diameter for the conventional and proposed schemes were based upon ASTM B 232, ASTM B 857, and 132 kV GIL standards along with the evaluations of Section 3.1 [59; 60]. Table 2 presents the detailed specifications of different conductor specimens used in the electrostatic stress investigation [59; 61; 62].
## 3 Results and Analysis
### Effect of Proportion of ISWs on UCS and the Optimal Proportion
SS serves as the fixed factor in Figure 5d. It can be observed that, regardless of the level of SS, the influence pattern of the proportion of the other three ISWs on the strength of stabilized aeolian sand remains consistent. Specifically, the _UCS_ increases with the increase in GGBFS dosage but decreases with the increase in PS and CS dosages. This is because the primary role of SS in the system is to provide Fe\({}_{2}\)O\({}_{3}\), which is one of the precursors of C\({}_{4}\)AF and mainly affects the working properties of the cementitious material rather than its strength. The analysis above elucidates a prevailing pattern in experiment results: the incremental dosage of GGBFS substantially enhances the strength of stabilized aeolian sand. This phenomenon primarily stems from GGBFS's superior pozzolanic reactivity compared to other investigated ISWs, attributable to its vitreous microstructure containing reactive compounds that readily participate in hydration processes. PS demonstrates comparatively lower reactivity, while SS exhibits the least reactive characteristics. These observations suggest that the exclusive utilization of GGBFS could theoretically yield cementitious materials with maximum compressive strength. However, practical engineering considerations regarding the workability of cementitious composites and long-term stability of stabilized aeolian sand necessitate maintaining oxide proportions within reasonable ranges. Therefore, through dual optimization employing TCM theory and SLM, the optimal proportion of ISWs used to stabilize aeolian sand was determined as 5:35:20:40, which corresponds to the specimen exhibiting the highest _UCS_ in SLM experiments. Furthermore, comparative analysis with the cement-stabilized aeolian sand control group (CE) revealed that the _UCS_ of aeolian sand that stabilized ISW-CMs with the basic proportion was 45% lower than that of CE, whereas the ISW-CMs with optimal proportion stabilized specimen exhibited a 3% strength enhancement over CE. This demonstrates the following: (a) the proportion determined by the dual optimization strategy incorporating TCM and the SLM was superior than that determined solely by TCM; (b) synergistically prepared ISW-CMs achieve comparable effectiveness to cement in aeolian sand stabilization.
## 4 Numerical Simulation and Discussion
Therefore, it can be concluded that assigning more weights to factors of traffic density and acceleration of front vehicles has a positive effect on centralizing the velocity distribution and reducing the formation of kink-antikink waves. Figure 3: Evolution of space-time evolution in headway for different parameters. : (**a**,**b**) \(\beta=0\), \(p=0\), \(m=1\); (**c**,**d**) \(\beta=0.1\), \(p=0.1\), \(m=1\); (**e**,**f**) \(\beta=0.2\), \(p=0.2\), \(m=5\). Stability characteristic of the DAVD model can also be certificated by the hysteresis loops of three patterns, shown in Figure 6. As shown, pattern (b) exhibits smaller size and more concentration of hysteresis loops than pattern (a). Moreover, under the control of pattern (c), the velocity and headway space of vehicles are stable and hysteresis loops shrink to one point. It means that by increasing attention to the traffic situation ahead, density waves can be effectively suppressed. Figure 4: Standard deviation values of velocity distributions simulated by various parameter values: (a) \(\beta=0\), \(p=0\), \(m=1\); (b) \(\beta=0.1\), \(p=0.1\), \(m=1\); (c) \(\beta=0.2\), \(p=0.2\), \(m=5\). Figure 5: Standard deviation of the velocity of each car simulated with various parameter values. Figure 6: Hysteresis loops of traffic flow. Situation 2:  
Different from simulation 1, the initial environment is that 250 vehicles of simulation 2 are randomly distributed on a 4-kilometer single-lane ring road. At the same time, update rules for position and velocity still follow Equation (18). Under the control of the DAVD model, the simulation results are shown in Figures 7 and 8, which is the whole evolution process of traffic flow from disturbance to stabilization. Obviously, even if the vehicles are not evenly distributed and the scale of simulation continues to increase, the amplitude of the headway gradually weakens under the intervention of the DAVD model. Figure 8: Evolution of the velocity standard deviation with large experimental scales. Figure 7: Evolution of headway with non-uniform vehicle distribution: the parameters of the DAVD model is \(\beta=0.2\), \(p=0.2\), \(m=5\). All in all, the simulation results illustrate that taking into account the acceleration and traffic density of preceding vehicles with the FVD model can effectively suppress the effect of external disturbance and enhance traffic flow stability, which complies with theoretical analytical results.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 2 Materials and Methods
### General Procedure for the Preparation of Hydrazones 8-23
#### 2.3.18 N-(2,5-Dimethyl-1H-pyrrol-1-yl)-3-(2-oxobenzo[d]oxazol-3(2_H_)-yl)  
propanamide (**25**)  
To a solution of hydrazide **3** (0.3 g, 1.4 mmol) in hot in propan-2-ol (20 mL), hexane-2,5-dione (0.42 g, 2.8 mmol) and acetic acid (six drops) were added dropwise, and the mixture was heated under reflux for 10 h. After the completion of the reaction, the reaction mixture was cooled, and the formed precipitate was filtered off, washed with cold propan-2-ol, and recrystallized from propan-2-ol.  
Yield 0.33 g (79%), mp 248-250 \({}^{\circ}\)C;  
\({}^{1}\)H-NMR (400 MHz): 5 1.77, 1.96 (2s, 6H, 2 CH\({}_{3}\)), 2.82 (t, 2H, \(J\) = 6.4 Hz, CH\({}_{2}\)CO), 4.13 (t, 2H, \(J\) = 6.4 Hz, CH\({}_{2}\)N), 5.55, 5.70 (2s, 2H, CH-CH), 7.13, 7.22 (2t, 2H, \(J\) = 7.7 Hz, Har), 7.30, 7.33 (2d, 2H, \(J\) = 7.8 Hz, Har), 10.72 (s, 1H, NH);  
\({}^{13}\)C-NMR (101 MHz): 8 10.52, 10.88 (2 CH\({}_{3}\)), 31.05, 38.25 (CH\({}_{2}\)CO, CH\({}_{2}\)N), 102.91, 104.06, 109.52, 109.76, 122.23, 123.81, 126.57, 127.03, 130.86, 141.98, 153.58, 169.13 (C\({}_{\rm ar}\), C\({}_{\rm pyrr}\), CO);  
IR (KBr): \(\gamma_{\rm max}\) = 3242, 3191 (NH), 1751 (C=O), 1658 (C=N) cm\({}^{-1}\).  
Anal. Calcd for C\({}_{16}\)H\({}_{17}\)N\({}_{3}\)O\({}_{3}\) (299.33), %: C, 64.20; H, 5.72; N, 14.04. Found: C, 63.97; H, 5.55; N, 13.83.  
5-Oxo-1-(3-(2-oxobenzo[d]oxazol-3(2_H_)-yl)propanamido)pyrrolidine-3-carboxylic acid (**26**) prepared via the procedure described in [46].  
Yield 0.37 g (48%), mp 204-206 \({}^{\circ}\)C (Ref. [46], 213-214 \({}^{\circ}\)C).
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Model** & **Advantages** & **Limitations** \\ \hline
[34] & \
\begin{tabular}{c} Entanglement Distribution in \\ spatial multipath channels \\ \end{tabular} & Enables secure data transfer &
\begin{tabular}{c} Only limit for the security and \\ high entanglement complexity \\ \end{tabular} \\ \hline
[35] & \
\begin{tabular}{c} Quantum teleportation MIMO \\ \end{tabular} &
\begin{tabular}{c} Improve the throughput and \\ reliability of the \\ quantum channel \\ \end{tabular} & Limited data rates \\ \hline
[36] & QKD with MIMO architecture & Enhanced security & Complexity overhead \\ \hline
[37] & \
\begin{tabular}{c} MIMO architecture for digital \\ communications \\ \end{tabular} & Spatial diversity gain &
\begin{tabular}{c} Measurement \\ alignment sensitivity \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} Diversity and multiplexing \\ \end{tabular} & \
\begin{tabular}{c} Quantum diversity multiplexing \\ tradeoff, robustness via \\ imperfect cloning \\ \end{tabular} &
\begin{tabular}{c} Cloning-induced fidelity loss, \\ complexity in channel modeling \\ \end{tabular} \\ \hline
[39] & \begin{tabular}{c} QKD with MIMO architecture \\ \end{tabular} & \
\begin{tabular}{c} Robust against eavesdropping, \\ higher secret key rate \\ \end{tabular} &
\begin{tabular}{c} High path loss at THz \\ frequencies, hardware \\ complexity with \\ multiple antennas \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} QKD with MIMO architecture \\ \end{tabular} & \
\begin{tabular}{c} Ultrawide bandwidth for \\ high-capacity communication, \\ Enhanced secret key rate \\ \end{tabular} &
\begin{tabular}{c} Complex and costly hardware, \\ challenging practical \\ deployment in \\ non-laboratory environments \\ \end{tabular} \\ \hline
[41] & \
\begin{tabular}{c} MIMO quantum communication \\ in pure-loss channels \\ \end{tabular} & Improved transmission fidelity &
\begin{tabular}{c} Cloning fidelity constraints, \\ limited scalability \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of MIMO Applications in Quantum Communication Systems.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.2 Deluge  
Deluge [29] is a data dissemination protocol and algorithm for propagating large amounts of data throughout a WSN using incremental upgrades for enhanced performance, building on Trickle. It is particularly aimed at disseminating software image updates, identified by incremental version numbers. The same image is disseminated to all nodes in the network. The program image is split into fixed size pages, and each page is split into fixed size packets so suit the PDU size of the TinyOS network stack. A bit vector of pages received can also fit in a single packet. Nodes broadcast advertisements containing a version number and a bit vector for any new pages received, using a variable period based on updating activity. To upgrade part of its image to match a newer version, a node listens to further advertisements for a time, and then requests the page number/packets required from a selected neighbour. A sender collects several requests before selecting a page and broadcasting the requested packets. When a node receives the last packet required to complete a page, it broadcasts an advertisement before requesting further pages--this enhances pipelining of the update within the network. State data takes a fixed size, independent of the number of neighbours. There are no ACKs or NACKs--the requester pulls the date by requesting either packets for a new page, or missing packets from a previous page. Radio network contention is reduced through heuristics used to select more distant senders. Whereas Trickle provides support for single-packet updates, Deluge supports essentially unlimited sizes.  
Deluge has support for reducing energy consumption, but is not an energy-aware protocol. It has no planning features, and provides no model for the anticipated energy use. It supports dissemination and activation, but has no specific support for fault detection and recovery (apart from the watchdog timer under TinyOS); it also has no feedback mechanisms.
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
## 3 Electrospinning of High-Performance Polymers
### Pbi
To obtain the molecular imprint, a small amount of acrylonitrile solution containing sulfone and a dispersant was added to the polymer solution of PBI and lithium bromide salt (LiBr) in DMAc before electrospinning. The adsorption of sulfone was found to be significantly improved after performing the molecular imprinting on the nanofibres. Another study used phosphoric acid (PA)-doped PBI nanofibrous membranes to improve proton conductivity for application as a high-temperature proton exchange membrane ([PERSON] et al., 2016). Electrospun PBI membranes were doped in PA for durations of 24, 48, 72, and 96 h, then their conductivity was measured. It was found that a 72 h PA immersion period led to the highest conductivity; at 96 h, free PA in the membrane led to acid leaking. PBI nanofibres have also been used as a substrate for the growth of carbon nanotubes via chemical vapour deposition ([PERSON] et al., 2017). The high surface area, porosity, and temperature resistance of the PBI nanofibrous membrane provided an excellent substrate for the carbon nanotubes, producing a nanocomposite suitable for lithium-sulphur batteries and laminated composites. In summary, PBI nanofibres have been successfully electrospun by a variety of researchers. Currently, there is a major focus on improving the mechanical properties of the membrane using post-treatments such as crosslinking, thermal calendering, and laminating with support materials ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017). Moreover, treatments to improve the thermal properties of the membrane have also been explored to overcome the high thermal shrinkage that is experienced by PBI nanofibrous membranes ([PERSON] et al., 2017). Specialized treatments of the PBI nanofibrous membrane have also been used to make the membrane more suitable for specific applications ([PERSON] et al., 2016; [PERSON] et al., 2017). Despite the focus on applications of the studies on PBI nanofibres membranes, it remains to be determined whether the electrospinning process for PBI can be effectively scaled up. In addition, it will be especially important to consider which post-treatments are most scalable to improve the mechanical and thermal performance of the PBI nanofibrous membranes.