esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Tar Removal
Due to the large amount of nitrogen molecules in the simulated producer gas the main reactions are those with N\({}_{2}\)(A\({}^{3}\)\(\sum\)) excited molecules [40; 42]. Unfortunately, in such a complex mixture as the biomass producer gas, there are many processes that are competitive to useful reactions with excited nitrogen molecules, O and H radicals. Other constituents of the producer gas, in particular CO and CO\({}_{2}\), are responsible for both the production and termination of radicals decomposing tar components. Complete picture of chemical kinetics is very complex and requires numerical modeling for detailed analysis. Unfortunately, rate constants for many reactions of tar derivatives with radicals formed in plasma are not known which makes all theoretical considerations speculative. There is also an energy aspect of the tar removal. Discharge pulse characteristics makes SEI values achieved in this work much higher than in non-thermal plasma systems used by other researchers. Here SEI is in the range 35-7542 J/L whereas [PERSON] et al. [7] obtained 500 J/L in the pulsed corona discharge reactor, [PERSON] et al. [42] reached 768 J/L in the packed-bed DBD reactor, and [PERSON] et al. [43] used 972 J/L in the gliding arc discharge reactor. Such a big difference can give the impression that a lot of energy is lost in the nanosecond pulsed DBD reactor. However, none of the above works was carried out in such a complex mixture which is very close to the real biomass producer gas. As was mentioned in the Introduction, only [PERSON] et al. [9], [PERSON] et al. [21] and [PERSON] et al. [14] have tested plasma application for tar removal from real biomass producer gas. The latter gives SEI value of 5184 J/L at which almost all tar compounds were completely converted. Thus, full tar conversion in the producer gas requires high SEI in the range of several thousand J/L.
## 2 Wireless Multimedia Sensor Networks
### Real-Time Video Data
#### 2.2.1 Delay  
For applications like security surveillance and traffic monitoring, timeliness is key in the delivery of the captured video data. Any miss of deadline means system failure and can cause security breach or traffic incidents. Translate this into network operations, the meanings could be twofold. On one hand, MAC protocols can utilize prioritization and service differentiation schemes to give real-time video data higher priority for earlier access of the wireless medium, or grant it with access to higher quality channels. For example data with higher QoS demands are assigned with the earliest available slots with higher quality channels in [42]. On the other hand, routing protocols can deploy QoS aware mechanisms to choose paths that are able to meet the end-to-end delay requirement. For example choosing the path with the least accumulated delay along the traversed hops [47].
## 4 Framework Development and Validation
### Lane Identification Algorithm Development
Furthermore, the model achieved an inference speed of 21.3 FPS on an RTX 4080 GPU, with GPU memory usage remaining below 2.5 GB and CPU utilization under 40%. These results demonstrate the framework's suitability for real-time deployment with efficient resource usage. To evaluate the reliability of the training results, the performance of YOLOv5 was compared with a CNN (ResNet34) and other YOLO versions. As shown in Figure 9, we present both the pre- and post-training performance of YOLOv5 to highlight the impact of the additional training phase, which used low-visibility and faded lane images that were not included in the original training dataset. YOLOv5 achieved the highest performance after additional training, with a recall of 94.0%, a precision of 98.3%, and a mean average precision (mAP) of 97.4%. It outperformed the CNN (ResNet34), which achieved a precision of 97.0% and mAP of 96.0%, and surpassed YOLOv10, indicating the effectiveness of simpler algorithms in small-scale datasets. Although YOLOv10 is a newer version with architectural enhancements, YOLOv5 showed higher performance in our dataset due to better generalization with smaller training samples and a more stable training process. This observation aligns with prior findings that, despite YOLOv10's improvements in mAP, its significantly increased parameter count, and computational complexity can lead to slower inference and overfitting risks on limited datasets [28].
## 2 Beam Characteristics
### Beam Currents  
In general, FLASH irradiation implies a relatively high beam intensity. The required currents are at least two orders of magnitude higher than currently used in a clinical setting. It translates to beam currents of the order of 100 to 1000 nA in the treatment area. The same beam intensities or higher will be extracted from an accelerator.  
In cyclotron-based facilities, beam monitors upstream of treatment areas are typically suitable for such high currents [14]. Beam coming from a cyclotron has fixed energy; therefore, a degrader is needed. However, this leads to large beam losses; therefore, currents extracted from the cyclotron are high, matching the FLASH range. In the case of synchrotrons, the whole beam diagnostics is usually designed to match beam currents required in the treatment area, as no energy degrader is used and no significant beam losses are present. Therefore, beamline beam monitors are usually optimized for beam current of the order of 1 nA [15]. Detectors based on solutions adequate for low currents, such as ionization chambers or crystal/gas scintillation detectors, will likely experience effects, such as pileup or saturation of signals. These may require either significant modifications or replacement. Some solutions, specifically developed for low currents, can be easily deployed for much higher intensities, as for example, a dielectric-filled reentrant cavity resonator [16]. In addition, beam instrumentation used to monitor proton beams in radioisotope production facilities can potentially find application for FLASH-specific range of beam currents [17; 18]. Radioisotope production facilities use beam currents typically well above 100 \(\upmu\)A, and beam instrumentation used along their beamlines are capable of monitoring beams as intense as 1 \(\upmu\)A or more [19]. However, many of these instruments are slow, so, without specific adaptations, they can be used only for off-line monitoring and beam characterization.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 6 Numerical Results
At perfect SIC, the proposed wireless network ensures the reliability of each node. Further it is noted that only 2 dB additional SNR needed for the far node to achieve the near node reliability. For the target BER of \(10^{-2}\), the uplink OMA need 17 dB SNR, end-to-end near node need 22 dB, far node need 24 dB SNR and overall network need 27 dB SNR. Figure 4: (**a**) Outage probabilityof the proposed network with ABS. (**b**) Outage probability of the proposed network compared with OMA. Figure 5: (**a**) Outage probability of the proposed uplink network. (**b**) Outage probability of the proposed downlink network. In this proposed network, far node pair act as an IoT reader. It is achieved after far node removing its own information. With support of ABS, far node SNR gain in improved. Thus, far node pair used either as a IoT reader or mobile user. Average BER of the proposed network is shown in Figure 6b in the presence of interference. It is noted that both near nodes and far nodes operating with similar performance. Furthermore, it is observed that the effect of interference is dominant at high SNR regime.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 1 Introduction  
### Background  
The rapid economic growth in China nowadays has already brought faster travel growth in its civil aviation market, and the airline industry in China has also been experiencing an annual growth rate of about 6% in passenger as well as cargo load during the past ten years or so [1]. It is of no doubt that air traffic forecasting plays an important role in not only the planning and the management of an airline, but in related service and hardware investment issues as well [2; 3; 4], as enormous pressure to provide better services has been strengthened from the ever-increasing amount of cargo and number of passengers, and most of the air terminals have already been operating at a high proportion: above 80% of their capacity [1]. Accurate short-term air traffic forecasting would help air terminals and airline companies to achieve better service quality by helping to optimize the allocation of limited resources [5]; however, giving such accurate forecasting is not as easy as might be thought, for air traffic data is a set of large-scale, multi-dimensional, nonlinear, and non-normal distribution time series data, and it not only has its own time-dependent changing tendencies, but also has some seasonal periodicity [2]. Therefore, traditional forecasting models based on a single mathematical theory based on statistics or artificial intelligence cannot realize the accuracy and adequacy of air traffic forecasting, so a novel forecasting model is strongly required for such a thorny issue [6].  
Until now, modeling works for the object of forecasting in various industries, including air traffic, have already experienced remarkable progress in development. Single statistical theory-based time series modeling happened to be the earliest approach for forecasting issues [7]; then came the single artificial intelligence (AI) algorithm based on modeling reports [8; 9]; later on, attempts had been made to apply hybrid theory-based models in various areas that require forecasting [10]. As far as the specific models built for air traffic forecasting are concerned, reports can be found either in an impact factors analysis or in the theory research on forecasting itself [11; 12]. Hybrid theory-based models have already been applied in air passenger flow forecasting a little bit, and they have already been proved to have better performance when comparing them with those models supported by only a single mathematical theory [6]; however, reports on finding out the most suitable modeling approach for air traffic forecasting still can hardly be found. It is of no doubt that a proper demonstration of all the possible models as well as the discovery of the optimal one which applies to air traffic forecasting requires further study and discussion.
## 3 Results and Discussion
### Simulation of Multilayer Shield
The particle flux density per unit energy is a measure of the number of radiant energy particles that impinge on a surface over a given period of time, divided by the product of the surface area, characteristic energy of the particles, and the time interval. (**a**) Electron flux distribution in the radiation belt; (**b**) proton flux distribution in the radiation belt; (**c**) solar proton fluence distribution. We conducted a comprehensive evaluation of the photon-counting imaging system, analyzing its sensitivity, maximum counting rates, and dark counts under various shielding conditions. The sensitivity and maximum counting rates are dependent on the quantum efficiency of the detector, the gain, and the system's response time. Our results, presented in Table 4, indicate that a local shield does not affect sensitivity or maximum counting rates since it does not obstruct the inlet light. To determine the effectiveness of the shielding ring proposed in Section 2.1, we simulated the changes in dark count rates under three conditions: multi-layer material as a whole, local shielding, and single-layer Al shielding. The materials used in the shielding layer, from outside to inside, were Al, Ta, and HDPE. Our results showed that the local shielding ring achieved the same radiation protection effect as the global shielding while reducing the weight requirement. However, the single-layer Al shielding scheme had a weaker radiation protection effect than the previous two, due to the simulation's use of an omnidirectional uniform radiation model. This resulted in charged particles being able to penetrate the Al layer more easily, causing more electron multiplication at the detector input.
## 2 Research Methodology  
The two-stage damage identification framework presented in this paper integrates an AE for reconstructing the measured TF and an OC-SVM for interrogating the reconstruction errors. The general structure of this hybrid approach is illustrated in Figure 1. The AE-based framework is trained on the TFs obtained from the measured acceleration data. The reconstruction error from this learned representation is then fed into the OC-SVM to train the algorithm for detecting damage. Once this training is completed with the data from the baseline state of the system, data collected from an unknown state can be tested for monitoring structural health.  
Expanding on this framework, Section 2.1 introduces the local TFs obtained between two sensor locations, Section 2.2 provides a brief overview of the traditional AE implemented for this purpose, and Section 2.3 summarizes the concept of OC-SVM and outlines the specifics of the implemented machine for detecting damaged state. The procedure to fuse the sensor information for localizing the damaged region is described in Section 2.4.
## 4 Results
### Identification and Description of Error Modes
Six error modes have been identified in this category. A8 Operation omitted: Workers can omit to install brackets, bolts, cushioned loop clamps, tie-wraps or cap covers. They can also omit to apply the required force to fasteners. These multiple causes explain why this error mode is the most prevalent with 19 counts. R2 Wrong information obtained: This error mode covers instances in which the work instructions were incorrectly assimilated. The installation of each bracket requires the retrieval of specific spatial information. All cushioned loop clamps are considered part of the same subassembly and information is captured once. In total, four error modes were counted. R3 Information retrieval incomplete: Like wrong information obtained (R2), this kind of error is related to work instructions. In this case, the worker interpreted the work instructions correctly but only read part of them. Four error modes were identified in the same circumstances as for R2. C1 Check omitted: Checking is considered a normal part of the assembly cycle, as opposed to an inspection activity. Three error modes were identified, one for the verification expected at the end of each task. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Error Modes** & **Install Brackets** & **Secure Data Cable** & **Install Cap Covers** & **Total** \\ \hline A3 Operation in & & 3 & 3 & - & 6 \\ wrong direction & & & & & \\ \hline A4 Too little/ & & & & & \\ much operation & & & & & \\ \hline A5 Misalignment & 3 & 3 & - & 6 \\ \hline A8 Operation omitted & 9 & 6 & 4 & 19 \\ \hline R2 Wrong & & & & & \\ information obtained & & & & \\ \hline R3 Information & & & & & \\ retrieval incomplete & & & & & \\ \hline C1 Check omitted & 1 & 1 & 1 & 3 \\ \hline C2 Check incomplete & 1 & 1 & 1 & 3 \\ \hline S2 Wrong & & & & & \\ selection made & & & & & \\ \hline _Total_ & 34 & 19 & 6 & & \\ \hline _Cycle times_ & 155 s & 51 s & 28 s & \\ \hline \hline \end{tabular} 1 Symbol - indicates that no error mode of the associated category (row) was found during the analysis of the task under consideration (column).  
\end{table} 
Table 3: Consolidated results of error modes obtained with SHERPA for the three critical tasks under study 1. C2 Check incomplete: One or more component(s) were not checked during the verification at the end of a task cycle. Incomplete checking is equivalent to a partial omission of the check. There are three error modes in this category, the same as for C1. S2 Wrong selection made: The assembler must obtain the required part from a bin at the workbench. Parts are often similar, which could lead the worker to pick the wrong one: several brackets, cushioned loop clamp and fastener variants can be found in the bins. Nine error modes were counted.
## 4 Discussion  
A literature review for the determination methods of ALP and FLT was conducted and presented in Table 5 for the comparison of this method with old ones. It is evident that the new SB-MSPE method has a low duration of sample pretreatment, by utilizing a reusable sorbent which gives satisfying relative recoveries, rendering it highly precise. The advantages of the newly developed method are the low cost and the ease of synthesis of the nanocomposite GO-Chm and its simple and inexpensive instrumentation. The only drawback is the high LODs and LOQs achieved, due to the use of a PDA detector. The preparation of a graphene oxide with magnetic chitosan (GO-Chm) nanocomposite has been successful and its characterization has taken place. Moreover, its application for the extraction of alprazol (ALP) and flunitrazepam (FLT), two benzodiazepines, from surface water samples has been effective with the development and optimization of a stir bar magnetic solid phase extraction (SB-MSPE), following their chromatographic separation and quantification with a simple, accurate and precise method that was developed and validated. Precision and repeatability were expressed by RSD values, which were lower than 10% in the application of real samples. The relative recoveries that express its accuracy were in the range of 93.6-112.9% in river and sea water samples. The new sample preparation technique that was used complies with the Green Analytical Chemistry (GAC) principles, because of the usage of small amounts of organic solvents that have a low toxicity and its swift duration. The low cost and ease of the preparation of the nanocomposite makes it ideal for an adsorbent of a variety of organic aromatic \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Spiked ng/mL**} & \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Found ALP, FLT**} & \multirow{2}{*}{**Rrel\% (ALP, FLT)**} \\ \cline{3-3}  & & \(\mathbf{X_{average}\pm s}\) & & & \\ \hline \multirow{5}{*}{0} & RW 1 & N/D 1, N/D & - & - \\  & RW 2 & N/D, N/D & - & - \\  & SW 1 & N/D, N/D & - & - \\  & SW 2 & N/D, N/D & - & - \\  & SW 3 & N/D, N/D & - & - \\ \hline \multirow{5}{*}{25} & RW 1 & 24.0 \(\pm\) 1.4, 25.3 \(\pm\) 1.4 & 5.8, 5.4 & 96.1, 101.0 \\  & RW 2 & 24.7 \(\pm\) 1.2, 26.0 \(\pm\) 0.8 & 4.8, 2.9 & 98.9, 104.1 \\  & SW 1 & 24.2 \(\pm\) 1.9, 26.0 \(\pm\) 0.8 & 7.8, 6.2 & 96.6, 95.9 \\  & SW 2 & 25.8 \(\pm\) 1.7, 26.9 \(\pm\) 1.4 & 6.6, 5.4 & 103.1, 107.5 \\  & SW 3 & 23.4 \(\pm\) 0.9, 28.2 \(\pm\) 2.7 & 3.8, 9.5 & 93.6, 112.9 \\ \hline \multirow{5}{*}{50} & RW 1 & 47.2 \(\pm\) 2.0, 48.5 \(\pm\) 0.8 & 4.2, 1.6 & 94.3, 97.0 \\  & RW 2 & 50.9 \(\pm\) 2.0, 49.5 \(\pm\) 2.4 & 4.0, 4.9 & 101.8, 98.9 \\ \cline{1-1}  & SW 1 & 48.8 \(\pm\) 2.7, 48.1 \(\pm\) 2.0 & 5.6, 4.1 & 97.6, 96.3 \\ \cline{1-1}  & SW 2 & 48.8 \(\pm\) 3.4, 45.8 \(\pm\) 0.5 & 7.0, 1.1 & 97.6, 91.7 \\ \cline{1-1}  & SW 3 & 48.5 \(\pm\) 1.5, 48.2 \(\pm\)3.7 & 3.0, 7.7 & 97.0, 96.3 \\ \hline \hline \end{tabular}  
* Not Detected.  
\end{table} 
Table 4: Results from the application of the method in surface water samples for ALP and FLT.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 3 Multi-Attention Video Description Model
### Attention Calculation  
From the previous section, we know that this paper has three attentions: temporal attention, spatial attention, and channel attention. The three attentions are relatively independent, so we can change the order in which they act on the video features. As shown in Figure 2, we apply the spatial attention on video features first and then apply the channel attention. It will be introduced in detail in the following section.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 5 Conclusions  
Waste management issues are the most challenging problems in smart cities. An IoT-based Waste Management System is provided by smart cities. WMS involves several responsibilities, such as collection, disposal, and utilization of waste in relevant facilities. Efficient waste management has a considerable impact on the quality of life of citizens. The interaction between wireless sensor networks and mobile ad hoc networks with IoT provides excellent mobility for users and reduces the deployment costs of networks. This study considers the population and urban growth by using a different number of devices/nodes that ease communication among system entities, such as waste source areas, waste collection trucks, and waste management centers. Different infrastructures of mobile and immovable systems coexist, and Internet applications have seen substantial growth. So, it should maintain data transfer via intermediate devices/nodes to shorten the distance between senders and receivers. An intelligent routing algorithm is proposed to find the efficient paths and reduce the computation time of route selection. An intersection GA-based routing algorithm is proposed to find the efficient paths and reduce the computation time of route selection. Intersection activation function applies the intersection operation step, which is the important step of the proposed method. The experimental results of compliance path selection in MANET show the optimized path selection using the proposed method compared with the DSR. This finding confirms that the proposed method reduces the processing time and avoids the invalid solutions that may be generated during the iterations. The results show that the upper chromosomes representing the compliance paths are close to one another and within the acceptable values. In addition, the use of priority constraints required by the system improves outcomes at considerable constraints by choosing the best solution and using the remaining chromosomes as an alternative pathway. This process allows the proposed method to override the existing methods.  
Conceptualization, N.A. and O.A.A.-w.; methodology, N.A. and O.A.A.-w.; writing--original draft preparation, N.A., O.A.A.-w. and M.B.; writing--review and editing, Z.G.A.-M., M.A., M.S.A. and A.S.A.; supervision, P.S.; project administration, B.A.M. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Scientific Research Deanship at the University of Ha'il, Saudi Arabia, through project number RG-20 044.  
Not applicable.  
We would like to acknowledge the Scientific Research Deanship at the University of Ha'il, Saudi Arabia, for funding this research.  
The authors declare no conflict of interest.