esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 3 Use Cases for the Internet-Connected Vehicle Ecosystem
### IoV Use Cases in the Transport Sector  
The transportation-related IoV applications offer several advantages that enhance various aspects of transportation. Some of the key applications are V2V, V2I, vehicle-to-pedestrian (V2P), ITS, connected navigation systems, fleet management, autonomous vehicle communication, parking management, EV integration, and ride-sharing and car-pooling. Transportation systems involve a variety of technologies and applications aimed at enhancing travel safety, mobility, productivity, and the mitigation of traffic-related issues. The origins of the ITS concept can be traced back to US-based researchers in the twentieth century [50]. In the present day, both academia and industry are closely focused on ITS. The reason for this is that such systems not only improve vehicle traffic conditions, but also improve the safety and sustainability of the transportation sector. ITS integrates information and communication technologies (ICTs) into the transport sector to address traffic congestion and mitigate the impact of climate change on transportation [51]. ITS reduces the time spent in traffic jams, therefore reducing traffic jams and fuel costs, monetary losses in cities, and CO emissions. For transportation, numerous applications in vehicular ad hoc network (VANET) including traffic management, safety alerts, and efficient intra- and inter-vehicle communication are possible, resulting in better transportation efficiency and safety [52]. The study [53] developed a protocol called VANET-to-Internet to enable Internet access through vehicular networks while preserving the quality of service. In [54], a proposal for video streaming and uploading in vehicular is introduced, focusing on cooperative forwarding of video streams from moving vehicles to a fixed network using  
Figure 4: Internet of Vehicles applications.  
both V2I and V2V communications. Additionally, [55] discusses how network coding can be employed to support vehicular streaming over the network. It is a technique of recording received packets whereby nodes along the transmission path recover the lost packets. Furthermore, an anonymous authenticated key exchange scheme was adapted for the integration of blockchain technology into the IoV among the smart transportation systems [56]. This scheme provides, the benefits of improved security, privacy and trust, and blockchain-enabled IoV applications, which lead to safer and more reliable smart transportation systems. Advancement of these could change the transportation industry outcomes by making it more efficient, sustainable, and convenient.
## 3 Proposed ASVMR Protocol
### The Determination of Next Hop  
In our selection process for the candidate forwarding set from the neighboring nodes, we take into account both the node depth and the pipe radius of the routing vector, ensuring a comprehensive consideration of these factors. The set of sensor nodes in the UASN is defined as \(S\mathcal{N}=\{n_{1},n_{2},\cdots,n_{m}\}\), where \(m\) is the number of sensor nodes.  
First, the sensor nodes in the routing vector and the above node \(n_{i}\) are grouped into the candidate forwarding set of node \(n_{i}\). The illustration of the candidate forwarding set  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(drRatio\) & \(ddRatio\) & \(f_{\rm e}\) & \(f_{\rm n}\) & **Label** \\ \(x_{1}\in(0,1]\) & \(x_{2}\in(0,1]\) & \(x_{3}\in(0,1]\) & \(x_{4}\in[0,1]\) & \(y\in\{-1,1\}\) \\ \hline
0.6 & 0.6 & 0.9 & 0 & \(-1\) \\
0.2 & 0.1 & 0.7 & 0.6 & \(-1\) \\
0.7 & 0.4 & 0.5 & 0.2 & \(1\) \\
0.4 & 0.8 selection is depicted in Figure 3, where \(\mathcal{N}_{\text{nei,}i}(t)\) represents the set of neighboring nodes of node \(n_{i}\) at time \(t\) and \(\mathcal{S}_{\text{above},i}(t)\) represents the candidate forwarding set selected for node \(n_{i}\) at time \(t\). The shaded rectangle is the routing pipe.  
Second, node \(n_{i}\) acquires the state values of the sensor nodes in the candidate forwarding set and incorporates these values into the SVM model to obtain the one-hop decision value \(v_{t}\) at time \(t\) in (13). In order to reflect the influence of future states on the current state, the decision value at time \(t\) is defined as  
\[V_{t}=v_{t}+\gamma v_{t+1}+\gamma^{2}v_{t+2}+\cdots=\sum_{i=0}^{\infty}\gamma^ {j}v_{t+i}. \tag{18}\]  
where \(\gamma\) denotes the discount factor.  
To reduce the computational complexity, we only compute the value \(V_{t}\) for \(\gamma\) raised to the first power, and the node with the maximum value of \(V_{t}\) is chosen as the next hop. It is worth noticing that the four factors of a node are constantly changing due to the continuous movement underwater and the energy depletion. Therefore, \(V_{t}\) varies with time \(t\), and the latest feature space information is utilized for each calculation of \(V_{t}\).
## 4 Opportunities and Challenges
### Explainability and Interpretability  
Deep learning algorithms typically built on NNs are difficult to be trusted due to their hard-to-explain \"black-box\" nature. Despite their attractive merits, as mentioned, their practical use in power systems is sometimes limited, considering any unreliable decision-making and control demands might result in devastating damages and even blackouts [83]. Therefore, it is crucial to sufficiently understand how the learning model reaches its prediction. Thus, the explainability of AI techniques has been receiving the attention of many researchers, and this concept is introduced by explainable AI (XAI) in the literature.  
One XAI technique, SHapley Additive exPlanation (SHAP) value, already introduced in power systems, explains the results of the ML model based on game theory by providing measures of feature importance [84]. Deep-SHAP-based methods are exploited for power system emergency control [85] and fault identification in grid-connected PV systems [86]. Even though various works have explored the application of XAI to power systems, most of them focus on transmission systems [87], while branching out to one grid-edge application, i.e., NILM [88; 89; 90]. It can be speculated that the complexity of the distribution system hinders the extension of such application, which demands deliberation.  
The performance of XAI techniques is vulnerable to the quality of the training population. Thus, the training conducted on limited data could introduce \"biases\" to the XAI model, leading to an inaccurate explanation of the result. A potential approach to mitigating this effect could be inspired by the combination of GL and PGNN with XAI. It is demonstrated, as discussed before, that these techniques have been utilized to address partial observability in distribution systems. Therefore, XAI techniques should be developed with realistic assumptions about the data quality, enhancing its robustness. Yet another issue arises due to the problem of partial observability of distribution systems: the distinction between model interpretation and certification [91]. However, there is no consensus regarding the effectiveness of explanation and evaluation on such distinction.
## 3 The Proposed Coverage Flight Path Planning Algorithm  
This section explains the conceptual overview of the proposed CPP algorithm and outlines its procedural steps. Figure 2 shows the conceptual overview of our proposed global CPP algorithm.  
To find a path that covers an entire free space of a certain region of interest \(Q\) of a 3D urban environment of known geometry, we used the seven principle steps. Brief discussion about each principle step with the equations, procedures and examples are explained below.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation
### Decoding Complexity  
#### 4.6.1 Number of Arithmetic Operations  
Compared with FSCD, the difference in the LSNNAD scheme and the KSNNAD scheme lies in node recognition and the training of the NN model. The complexity of NNN recognition and NN training can be ignored because they can be performed offline before the decoding. As to the complexity of node recognition online, LSNNAD needs at most \((2+N)\cdot\log_{2}N-18\) comparisons in the worst situation but only \(2N/3-16\) comparisons in the best situation, while KSNNAD needs at most \((4+N)\cdot\log_{2}N-18\) comparisons but only \(2N/3-16\) comparisons in the best situation.  
Figure 16: Decoding results of different decoding schemes.  
One symbolic operation, taking absolute values and subtracting, is recorded as one addition; and one logarithmic and exponential operation is recorded as two multiplications. The operations of each decoding process are shown as Table 3.  
An exclusive or operation is recorded as an addition, so FSCFD's CRC check needs \(N\) additions at most. Calling the model once will introduce \(164K_{S}^{2}\) multiplications and \(164K_{S}^{2}+13K_{S}\) additions in LANNAD and KSNNAD. Now we have the number of arithmetic operations for different special nodes and the subcode recognition, as well as the number of special nodes, so the total number of arithmetic operations for different decoding schemes can be calculated, and the results are shown in Table 4.  
As shown in Table 4, when \(T_{max}\) = 2, FSCFD and KSNNAD are the closest in complexity, and the performance of KSNNAD is better than FSCFD, according to simulation results in Figure 16. RTD with \(\tau\)= 6 is much more computationally intensive than KSNNAD but does not perform as well as KSNNAD in Figure 16.
## 3 Experimental Analysis of Mountain Sand Paste Filling Material  
### Selection and Physicochemical Property Testing of Mountain Sand Paste Filling Material  
The area surrounding Cuncaota II Mine is abundant in four types of filling materials: slag, mountain sand, loess, and fly ash. However, the high viscosity of loess makes it unsuitable as a filling aggregate. Additionally, there is no coarse slag warehouse in the mining area. The concrete mixing station in the mining area mainly contains mountain sand and fly ash, creating a unique situation. Mountain sand and fly ash are chosen as the primary raw materials for developing a new inorganic paste filling material, and selective experiments are carried out to determine the optimum ratio for the mountain sand-based paste filling material.  
The dry screening method was used to determine the gradation of mountain sand and fly ash in the concrete mixing station of the mining area. An X-ray diffraction analysis was then conducted using a diffractometer. The results show that the particle distribution of the mountain sand is relatively uniform, although its continuity is poor, making it suitable for use as a filling aggregate. In contrast, the particle distribution of fly ash is discontinuous, making it unsuitable as a filling aggregate, but it can serve as a substitute for cement in filling material. Adding fly ash to slurry can reduce the amount of cement used and improve the workability and durability of the paste [27]. Table 1 provides detailed information on the particle size characteristics and primary chemical compositions of the filling materials. In contrast, the X-ray diffraction analysis results for the filling materials are depicted in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Filling** \\ **Material** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Control** \\ **Particle Size** \\ **\(d_{60}\) (\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Median Particle Size** \\ **\(d_{50}\) (\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Effective** \\ **Particle Size** \\ **\(d_{30}\) (\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Particle Size** \\ **\(d_{10}\) (\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Uniformity** \\ **Particle Size** \\ **\(d_{cp}\) (\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Curvature** \\ **Coefficient** \\ **C\({}_{\textit{c}}\)** \\ \end{tabular} } \\ \hline Mountain & \multirow{2}{*}{134.68} & \multirow{2}{*}{114.00} & \multirow{2}{*}{77.17} & \multirow{2}{*}{42.40} & \multirow{2}{*}{146.41} & \multirow{2}{*}{3.18} & \multirow{2}{*}{1.04} \\ sand & & & & & & & \\ Fly ash & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size characteristics of filling materials.  
Figure 3: X-ray diffraction analysis results of filling materials: (**a**) mountain sand; (**b**) fly ash.
## 4 The New Hybrid Optimization Algorithm
### The LM-D Algorithm
#### 4.3.2 The Improvement Strategy of the DIRECT Algorithm  
As previously explained, if the L-M algorithm identifies only a local minimum for the function, the DIRECT algorithm holds the potential to find a smaller function value. To realize this objective, we integrate the DIRECT algorithm with essential modifications.  
The first improvement of the algorithm is the addition of a control parameter \(\mu\), which governs the search range. By configuring the control parameter \(\mu\) governing the search range, we can regulate the extent of the search space. Commencing with an initial value of \(\mu\), the search is initially confined to a narrow range. In the event that a smaller local minimum eludes discovery, an attempt is made to adjust the value of the control parameter \(\mu\), progressively broadening the search scope.  
The second improvement of the algorithm is the establishment of a new termination criterion for the loop:  
\[\left\{\begin{array}{l}F(x_{k})\geq F(x^{*})\qquad\qquad(\text{loop})\\ F(x_{k})<F(x^{*})\qquad(\text{stop},x^{*}=x_{k})\end{array}\right. \tag{10}\]  
Following the completion of each iteration loop, the parameters \(x_{k}\) and \(F(x_{k})\) are updated and \(F(x_{k})\) is compared with \(F(x^{*})\). If \(F(x_{k})\geq F(x^{*})\), this suggests that during this iteration, a superior solution compared to the L-M algorithm has not been identified. In this case, the next iteration continues until the termination condition is satisfied. The iterative loop commences with a narrow search range. If a smaller local minimum is not identified, we progressively expand the search space by \(\mu\) until the maximum search range is reached. When \(F(x_{k})<F(x^{*})\), it signifies that the current iteration has discovered a superior solution compared to the L-M algorithm, suggesting that the algorithm has transitioned beyond the local minimum basin within the L-M algorithm. At this point, the iterative loop can be concluded, the DIRECT algorithm exited and \(x_{k}\) can be employed as the initial parameter value. Subsequently, it can be input into the L-M algorithm for further iterations to continue the search for a local optimum.  
The improvement strategy in the DIRECT algorithm eliminates the need for a global optimum. It solely focuses on identifying the new parameter \(x_{k}\) that satisfies \(F(x_{k})<F(x^{*})\) to exit the loop. Leveraging the attributes of the DIRECT algorithm, it can promptly pinpoint regions within the constraint range likely to harbor optimal solutions, particularly in the initial phases of the algorithm. Consequently, it adeptly circumvents the issue of asymptotic ineffectiveness, swiftly uncovering a solution surpassing the local optimum attained by the L-M algorithm.
# Strategies to Combat Multidrug-Resistant and Persistent Infectious Diseases
## 2 New Anti-Infectious Treatments against MDR and Persistent Bacteria
### New Molecules  
In 2018, [PERSON] and colleagues described the antibacterial activity of two synthetic retinoids (vitamin A analogues), named CD437 and CD1530 [55]. These compounds exhibited high in vivo killing rates of both growing and persister _S. aureus_ cells, by disrupting lipid bilayers. The mechanism of action is as follows: the carboxylic acid and the phenolic groups anchor the retinoids to the surface of the bacterial membrane bilayer, by strongly binding to hydrophilic lipid heads. As a result, the retinoids penetrate the bilayers and become embedded orthogonally in the lipid molecules in the outer membrane leaflet, inducing substantial perturbations and permeabilizing the membranes of MRSA and persisters. In addition, both compounds displayed synergistic interactions with gentamicin, a low probability of resistance selection, and potent activity against a panel of clinical _S. aureus_ and _E. faccium_ strains. The major obstacle for developing retinoids as therapeutic agents is their potential cytotoxicity, which is a matter of considerable debate. However, none of the tested molecules exhibited cytotoxicity in vitro [55].  
This discovery suggests the possibility of chemically changing these molecules to minimize their cytotoxicity and synthesizing more antimicrobial retinoid-based compounds. Indeed, approximately 4000 retinoid analogues were synthesized so far [56]. Deciphering the chemical properties and interactions between bacterial compounds and the candidate molecules will be essential in order to produce good antibacterial agents.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 4 Poly(3,4-ethylenedioxythiophene)-Based Electrodes
### 2D Nanostructures  
Even though electrochemical polymerization is most commonly employed to manufacture conducting polymer films, the use of PEDOT tends to be more widespread since it results in uniformity in the large surface area and high stability. [PERSON] et al. fabricated PEDOT:PSS-based \"layer-by-layer\" films consisting of PEDOT:PSS/chitosan/glucose oxidase + N-graphene molecules for glucose detection [127]. Briefly, PEDOT:PSS was obtained via electrodeposition method before the positively charged chitosan layer was adsorbed onto the negative PSS chain. A third layer consisting of glucose oxidase + N-graphene was introduced via physical adsorption onto the positively charged chitosan layer. [PERSON] et al. generated Au nanoparticles coated with PEDOT films using a one-pot modified electrodeposition method [128]. Next, anti-vascular endothelial growth factor (VEGF) antibodies were added for detecting VEGF, which is associated with severe vision loss due to exudative age-related macular degeneration, advanced diabetic retinopathy, and retinal vascular occlusive disease. Moreover, [PERSON] et al. fabricated layer-by-layer PEDOT/graphite nanosheet-based film using simple spin-coating method [129]. In detail, EDOT and graphite composite solution was deposited by spin-coating process and then exposed oxidant (Fe (III) toylate) with high temperature. Then, glucose oxidase (GOx) and osmium-modified polyallylamine (OsPA) were coated layer-by-layer on the films surface to detect glucose. In the sensing performance, incorporated graphite nanosheet in the PEDOT structure not only improves the voltammetric response of the resulting all-polymer electrodes but also produces a better integration of the electrochemically active supramolecular assembly allowing the effective glucose sensing. In addition, [PERSON] et al. suggested low fouling electrochemical biosensor  
Figure 8: (**a**) Pt nanoparticle-decorated–PEDOT microspheres using CaCO\({}_{3}\) sphere template-assisted chemical polymerization reactions for glucose detection [118]. Copyright 2018, Elsevier. (**b**) PEDOT-MnO\({}_{2}\) nanoellipsoids via reverse microemulsion reactions following reduction processes to detect catechol amine in PC-12 cells [119]. Copyright 2013, Wiley. (**c**) Polyethylenimine binding with ferrocene (BPEI-FC)/PEDOT:PSS nanobeads for electrochemical glucose biosensors [120]. Copyright 2013, American Chemical Society. (**d**) PEDOT:PSS/chitosan micelle-based H\({}_{2}\)O\({}_{2}\) electrochemical sensors [121]. Copyright 2018, Springer.  
using zwitterionic polypeptide doted PEDOT film by simple electrodeposition process to detect breast cancer biomarker (BRCA1) detection [130]. Especially, the polypeptide in the film structure acted as a bifunctional material that not only inhibits the nonspecific adsorption of proteins but also allows for the usage of its carboxylic to fix DNA probes (terminated with amino group).
## 4 Results  
### Performance Comparison  
Figure 3A,B shows the statistical comparison of RMSE and adjusted \(R^{2}\) between the proposed method and comparative design matrices that have no elements to explain SBF with a 350-sample-wide SWA. Those indicators expressed in the performance of our proposed method was significantly higher than the conventional design matrices: lower estimation error (for vs. simple, paired t = 3.41, \(p<0.003\); for vs. standard, paired t = 3.64, \(p<0.002\)) and higher GLM fitting (for vs. simple, paired t = 17.96, \(p<0.0001\); for vs. standard, paired t = 12.16, \(p<0.0001\)) in task-related SBF situations. In contrast, Figure 3C, comparing with off-line ShortPCA GLM, shows that the RMSE of rt-ShortPCA GLM (on-line) was significantly higher (paired t = 5.72, \(p<0.0001\)) even though there was no significant difference in adjusted \(R^{2}\) between on-line and off-line (paired t = 1.71, \(p<0.1\)).
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 6 Experiments and Simulation Results
### Experimental Methodology  
To evaluate the performance of SMOC and M-SMOC, we have done simulations in the MATLAB v9.2 environment. This is similar to other environments such as OMNET ++ and NS-2 with the difference that MATLAB provides a feature architecture and allows rapid simulations by combining multiple components. To provide a better presentation of the experimental results, we use OriginLab for deep analysis. Multiple scenarios and various parameters are considered to evaluate the performance.
## 4 Experimental Results
### Performance Evaluation  
The significant difference between the measurements is the use of enhancements such as motion detection and the use of the ROI. It is important to note that the ROI occupies  
Figure 9: The user interface. (**a**) Region of interest section; (**b**) an analysis section; (**c**) an analysis section.  
15.28% of the total image. Thus, the first measurement uses the entire image without motion detection. The input selection from the video does not contain excessive parts where no motion is detected. This is because object detection is the process that burdens the hardware the most. All the measurements utilize the video with a length of 18 s depicted in Figure 9(a).  
Two charts that have values acquired from the detection using GPU are shown in Figure 11. From the results, it is clear that the use of enhancements increases performance by approximately 2 FPS. This is due to motion detection, which does not have high hardware requirements, and the input image was in this case only 15.28% compared to the whole image. Evaluation on the CPU showed comparable results, but the performance reached 4 FPS.