esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
One of the metabolites, (+)-O-desmethyl-tramadol (the (+)-M1 metabolite), has an affinity for \(\mu\) receptors 700 times greater than that of tramadol [17]. Along with its metabolites, it is eliminated mainly by the kidneys [18]. It is also noteworthy that in adults, the volume of distribution per kilogram of body weight (Vd/kg) is 0.71 L/kg [19]. Tramadol enhances the reward system and induces rewarding effects corresponding to psychological dependence [20; 21]. Unlike morphine, which is a potent \(\mu\) receptor stimulant, tramadol stimulates \(\mu\) receptors but also inhibits the reuptake of serotonin and norepinephrine. Although it induces conditioned place preference (CPP) in rats, its abuse potential in humans is considered to be so far reduced [22]. CPP is an experimental prototype widely used to evaluate the reward or aversion effects of exposure to a drug in laboratory animals [23]. The animals associate the reward effect with different sensory features from the external environment that become conditioned stimuli, a process dependent on learning and memory. Several relevant studies have examined the CPP effects of tramadol and are worth mentioning. According to [PERSON] et al., tramadol at 37.5 and 75 mg/kg intraperitoneal (ip) produced CPP at a magnitude comparable to the effect of morphine (5 mg/kg subcutaneous) (sc), concluding that tramadol may possess a higher abuse potential than initially assumed [24]. A later study found that co-administration of naloxone (0.215 mg/kg sc.) during conditioning blocked the effect of tramadol at doses of 3.16-14.7 mg/kg ip [25]. Later, [PERSON] et al. found that when they gave mice a high dose of 80 mg/kg tramadol, it only showed a slight and not very important trend towards conditioned place preference (CPP), which was different from the strong pain relief seen in other tests using the same dose [12]. Electroencephalography (EEG) plays a pivotal role in the diagnosis of epilepsy and is extensively utilized to monitor the efficacy of anticonvulsant therapies, but also for other pharmacological interventions. Quantitative EEG can provide an in-depth look at the frequency on different bands, making it possible to further appreciate brain coherence (the normalized linear correlation that exists between two pairs of EEG channels and informs their common frequency components) [26; 27]. Figure 2: Tramadol structure. Our research aims to explore the complex relationship between tramadol use and the imidazoline system, particularly how idazoxan and efaroxan affect this relationship, by looking at changes in brain activity with EEG.
## 3 Analysis of Test Results  
### Diffusion Pattern of Grouting with Different Water-Cement Ratios  
Slurry diffusion involves the complex interplay of flow and phase change processes. The slurry diffusion morphology diagram provides a precise depiction of how the slurry diffuses at various moments, offering an intuitive analysis of the slurry diffusion patterns during the grouting process.  
The test time was determined to be 25 s through the pre-grouting test and to obtain the diffusion morphology at different moments. The No. 2 Model was opened at the moments of 2.5 s, 5 s, 7.5 s, 10 s, 15 s, 20 s, and 25 s, and the diffusion morphology was recorded, and the diffusion trajectory and morphology were analyzed by using the image processing software (AutoCAD 2020). Analyzing the effect of different water-cement ratios on the radius of grout diffusion, the experimental data on the grouting process of different ultrafine cementitious slurry viscosities were collected to obtain the diffusion patterns of different water-cement ratios (\({\rm{{}^{\prime}W}}/{\rm{C}}=\xi\), or different slurry viscosities) for the second lining of the tunnel, and the experimental results are shown in Figure 4.  
It can be seen from the diffusion pattern in Figure 4, the process of slurry diffusion can be divided into three stages: a round diffusion area, excessive diffusion area, and asymmetric elliptic diffusion area, the diffusion radius increasing with time. The test aims to obtain the diffusion patterns of slurries with different water-cement ratios at various moments. These patterns will be comparatively analyzed at the same moment, from which the variation rules of the diffusion patterns of slurries with different water-cement ratios at a certain moment can be derived.  
In the diffusion pattern in Figure 4, it can be seen that in the beginning of grouting to 2.5 s or less, due to the small amount of injected slurry, the diffusion pattern is a circular diffusion zone; in the time period of 2.5 s to 5 s, the amount of injected slurry gradually becomes bigger, and under the joint action of viscosity and gravity, the slurry changes from a circular to elliptical diffusion pattern, which can be called the over-diffusion zone; in the time period of 5 s to 25 s, the amount of injected slurry is gradually increased by the action of gravity, and the slurry diffusion pattern is an elliptical diffusion zone.  
From Figure 4a-f, it can be concluded that the diffusion radius in the x+, x\(-\), y+, and y\(-\) directions, under the conditions of different parameters of grouting materials with different water-cement ratios, tends to become larger with the increase in the water-cement ratio (increases with the decrease in viscosity). In terms of morphology, the diffusion radius in the x+, x\(-\) direction is approximately symmetric, the vertical direction is affected by gravity, and the diffusion radius in the y+, y\(-\) direction is asymmetric in the up and down directions.
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Proactive Strategy
#### 4.2.3 QoS-Aware Learning-Based Adaptive Spanning Tree Meta-Strategy Routing (MCT)  
In MCT, an adaptive spanning tree is constructed based on a meta-strategic reinforcement learning module. The forwarding phase passes received packets to a node's parent. All nodes that overhear this transfer will update their corresponding NQ-value (the communication cost to the corresponding node) and re-estimate their own Q-value (the minimum cost to the sink from this node) as shown in Equation (1). If the packet is not received by the forwarded node within a certain period, the NQ-value is updated to be the largest among the neighbors and the parent pointer is reset to the neighbor with the minimum cost:  
\[Q_{m}=(1-\alpha)Q_{m}+\alpha\left(\begin{array}{c}o_{m}+\min_{s}NQ_{m}(n) \end{array}\right) \tag{1}\]  
where \(\alpha\) is the learning rate, \(o_{m}\) is the current value of the local objective function, and \(n\) is the corresponding neighbor of the node.
## 5 Experimental Results  
### Rumor Detection Results and Comparisons  
Precision, recall, and F1 measurements are utilized to assess the performance of the proposed features in comparison to the current baseline characteristics [10]. These are conjoint evaluation metrics used for evaluating any classification problem. The experimental setup and hardware requirements are listed in Table 3. To assess the performance of a classification method, experiments are carried out using 5-fold cross validation.in this research use four datasets to train our data and the baseline classifier, and fifth dataset to assess the performance of these classifiers using precision, recall, and the F1 measure. As a consequence of evaluating all five runs of the dataset, the evaluation result indicates undetected topic rumors. Each 5-fold run should be repeated five times for each model. They identify the rumor and non-rumor ranges from 0 to 1. Therefore, the design of the research is a binary classification problem. We use sci-kit-learn library [32] for the classification problem.
## 6 Discussion
### Limitations and Upcoming Work  
The described CAD has numerous limitations such as it does not apply any fine-tuning or optimization in the CNNs. Furthermore, it is presently only used for the classification of pap smear images and has not been tested on other tasks. Furthermore, one of the study's constraints is that no correlation analysis was performed to figure out the link between deep learned/handcrafted representations and clinical findings in pap smear slides. Future directions will tackle these issues by first utilizing an optimization approach to fine-tune DL models' hyper-parameters. Second, upcoming work will conduct a correlation analysis to find out the association among handcrafted descriptors/DL features and clinical interpretations. In addition, the implementation of the presented CAD will be tested on new datasets of different imaging modalities and other classification problems.
# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 4 Research and Analysis  
### Localization  
Based on localization, the system provides both a navigation service and a depth of guide service. For the navigation service, the system guides people along a predefined route using their location. For the depth of guide service, it provides information about artworks based on their location. Therefore, localization accuracy is a crucial performance indicator. Figure 5 presents the accuracy of the machine-learning models used in different areas, showing accuracy rates of 91.28%, 87%, and 95%. In area 2, the accuracy is lower compared to other areas due to specific misclassifications. Figure 6 shows the confusion matrix for the results in area 2, illustrating these errors. In particular, location 5 was frequently misclassified as location 6. This suggests that the data collected at locations 5 and 6 are similar, leading to decreased accuracy. If location 6 is excluded, the accuracy increases to 95%. These results were obtained while the person remained stationary. However, when the person moves, RSSI values can become noisy, potentially affecting localization accuracy. To evaluate this, we conducted an experiment where a person moved along the predefined route. The experiment included two scenarios, with the second scenario involving actual artworks as obstacles within the art gallery.  
Figure 5: Localization model performance: (**a**): accuracy of models per each area. (**b**): Distance between a wall and a user when the system detects a corner.  
The established routes for each scenario are shown in Figures 7a and 8a. The results of the first scenario are presented in Figure 7b, where red text indicates localization errors. These errors are corrected using the calibration method described in Algorithm 1. The blue text represents the adjusted locations after applying the filtering algorithm. In Area 1, the threshold (\(Th\)) value is set to 2, while in other areas, it is set to 1. Similarly, Figure 8b shows the results of the second scenario. The findings demonstrate that navigation along the predefined route can be completed using error calibration, regardless of the presence of obstacles.  
Corner recognition in localization is a crucial aspect of the navigation service. Visually impaired people rely on the system to detect corners and change direction accordingly. If a corner is recognized too late, they may risk colliding with a wall. To evaluate this, we measured the distance between the person and the wall at the moment a corner was  
Figure 8: Scenario 2: (**a**): An established route. (**b**): Localization results when the user moves on the established route.  
Figure 6: Confusion matrix for the results in area 2.  
detected. Figure 5b presents these measurements. The results confirm that corners were detected at a minimum distance of 1 m from the wall, ensuring safe navigation.
# A Review of Failures and Malfunctions in Hydraulic Sandblasting Perforation Guns
## 2 Erosion Wear of the Perforation Gun
### Erosion Prevention Measures on the Gun Surface
In response, they introduced a novel perforation gun design featuring a thin, lightweight body made of hard alloy material and a sacrificial soft material wrapped around the gun's exterior. Laboratory tests showed improved erosion resistance, though the sacrificial layer remains a single-use component with higher installation costs and a limited lifespan. Meanwhile, applying protective layers (e.g., silicon carbide coatings or polyurethane layers) onto nozzle surfaces provides an extra buffer against high-pressure fluid abrasion. Numerical simulations of internal flow fields and fluid trajectories can guide the selection of protective measures and structural refinements, thereby improving the gun's efficiency and lifespan [20]. To mitigate rebound erosion on the perforator's outer wall, [PERSON] et al. [21] introduced a special backflow angle at the nozzle outlet, effectively extending the perforation gun's service life. In China, researchers have proposed a range of design improvements. For instance, [PERSON] et al. [22] at Tianjin Dagang Oilfield developed a perforation gun with a nozzle retention device to prevent nozzle detachment caused by erosion and reduce structural wear around the nozzle, improving tool stability and reliability. Additionally, Baoji Saifu Petroleum Machinery Company [23] designed a high-reliability injector that employs erosion-resistant caps around the nozzle on the perforator's outer wall and liners within the inner cavity to mitigate erosion from perforation fluids. The Jianghan Oilfield Production Technology Research Institute [24] developed a sandblasting perforator integrating caps and protective cover plates, not only alleviating rebound erosion but also enabling multiple perforations in a single trip under complex conditions (Figure 5). In conclusion, selecting suitable wear-resistant materials and optimizing gun structure design can significantly improve erosion resistance, extend service life, and enhance perforation efficiency. Nevertheless, existing designs still require refinement, such as reducing protective material costs and extending their service life, as well as optimizing gun  
Figure 5: Mainstream approaches for perforation gun structure optimization [17; 21; 22; 23; 24]. diameters to maintain backflow efficiency. Future research should focus on integrated applications of novel coating materials and innovative structural designs to meet increasingly stringent perforation conditions.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### RF-Based Detection
#### 2.1.3 Datasets and Metrics for RF-Based Detection  
In this section, public and semi-public datasets in recent years will be reviewed in detail.  
There are several publicly available RF-based UAV datasets that researchers can use to develop and evaluate RF-based UAV detection methods. These include the DroneRF dataset [45], Drone remote controller RF signal dataset [46], Radio-Frequency Control and Video Signal Recordings of Drones [47], Dronesignals Dataset [48], DroneDetect Dataset [49], VTIDroneSETFFT [50], and the CARDINAL RF [51]. These datasets contain recorded segments from 3, 17, 10, 9, 7, 3, and 6 different UAVs, respectively, with VTIDroneSETFFT being the only dataset to contain records of the simultaneous operation of multiple UAVs. Unlike vision-based UAV datasets, there are relatively few publicly available RF-based UAV datasets, and RF datasets are frequently quite sizable due to the considerable amount of data acquired. Despite these challenges, RF-based UAV detection methods have shown promising results in detecting and classifying UAVs based on their RF emissions. Researchers interested in accessing these datasets can find the corresponding links in the references section.  
\begin{table}
\begin{tabular}{c c} \hline
**Authors** & **Accuracy** \\ \hline [PERSON] et al. [33] & 99\% \\ [PERSON] et al. [34] & 100\% \\ [PERSON] et al. [35] & 98\% \\ [PERSON] et al. [36] & 90.2\% \\ [PERSON] et al. [37] & 98.57\% \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of RF-based detection using deep learning.
# Engineering Vehicle Detection Based on Improved YOLOv6
## 5 Conclusions  
This study introduces an improved YOLOv6 model incorporating the Swin Transformer to address the challenge of detecting engineering vehicles. Inspired by the outstanding performance of the Swin Transformer in capturing global image features, we apply it in YOLOv6 as a backbone feature extraction network to improve the extraction of features that are important for identifying engineering vehicles, facilitating more precise detection of incomplete engineering vehicle objects in images. To tackle the challenges arising from the intricate data acquisition process and the high costs of labeling for the engineering vehicle dataset, we employ the SimMIM self-supervised training paradigm in training the backbone. This approach enhances the backbone's capability to represent valuable features, leading to a higher level of accuracy in the model. In comparison to the current mainstream object detection algorithms, our improved YOLOv6 attains superior performance with an 88.5% \(mAP_{0:95}\) and 95.9% \(mAP_{50}\) on the test set, surpassing other algorithms. These results affirm the feasibility and efficacy of the improved YOLOv6 algorithm in the field of engineering vehicle detection. A future consideration involves expanding the existing dataset by incorporating additional classes of engineering vehicle images, thereby further enhancing the model's applicability and effectiveness.  
Conceptualization, H.L. and Y.Z.; Methodology, H.L.; Software, H.L. and Y.Z.; Validation, M.L.; Investigation, H.L. and Y.Z.; Resources, Y.Z.; Writing--original draft, H.L. and T.Z.; Writing--review & editing, Y.Z. and M.L.; Visualization, T.Z.; Project administration, Y.Z. and M.L.; Funding acquisition, Y.Z. and M.L. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The predictions of different models.  
The National Natural Science Foundation of China (Grant Number: 62373360, 62473368), the Science and Technology Project of Xuzhou (Grant Number: KC22020), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant number: KYCX23_2718), and the Graduate Innovation Program of China University of Mining and Technology (Grant number: 2023 WJJCRCZL118).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 5 Blueprint Template
### Implementation Artifacts Section  
This section is important for the application developer who is responsible for the deployment and provisioning of a blueprint. It encompasses the following information: an artifact id for uniquely identifying an artifact, an artifact name, an artifact type indicating whether this artifact is a software binary, a composition script, a database startup file or some other kinds of configuration files, an artifact location for downloading, and some artifact dependencies pointing to the other artifacts that have to be executed before executing this one.  
Examples of implementation artifacts can be found in Figure 5. _TaxiTransmitter.war_ is the ID of a deployable war file that implements parts of the Taxi application SaaS. However, it can work only after another artifact _TaxiServiceProvider.zip_, which is a zip file containing the BPEL scripts for the taxi application, has been deployed properly.
## 4 Federated System
### Simulation Results
#### 4.3.1 Test 1--Single System  
The first test was focused on testing the federation process. The scenario implemented in this test is shown in Figure 6, where two clients participate during all aggregation rounds. Two hundred thousand training timesteps were implemented in each of the five rounds of local training managed by the aggregation server.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Slice Type** & **CPU Cores** & **RAM (GB)** & **Storage (GB)** & **BW (Mbps)** \\ \hline
1 & 128 & 512 & 5000 & 2000 \\ \hline
2 & 128 & 512 & 5000 & 2000 \\ \hline
3 & 128 & 512 & 5000 & 2000 \\ \hline
4 & 128 & 512 & 5000 & 2000 \\ \hline
5 & 64 & 256 & 2000 & 1000 \\ \hline
6 & 64 & 256 & 2000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum resources reserved per slice.  
During the five rounds of individual local training for each client, the behavior of the reward obtained was monitored, observing its relationship with the exploration ratio. Figure 7 shows the results for each client during the individual local training. It can be seen that each client, given that it experienced a different environment, presented a different behavior during the first round of training. After aggregating the parameters from both clients, the server constructed a global model, which it sent to each near-RT RIC before initiating the next round of local training. The results indicate that, starting from round 3, with the selected hyperparameters and slice request generation configuration, two rounds of aggregation combined with local training of 200,000 timesteps were sufficient to achieve convergence in the global model.  
Figure 6: Federated system scenario (Test 1).  
Figure 7: Reward obtained in Test 1 by Client 1 (a) and Client 2 (b).  
Across all simulation rounds, each training episode was capped at 100 slice requests, meaning that the maximum cumulative reward achievable per episode is 100, as illustrated in Figure 7a,b. When the model reached the maximum reward by accurately processing 100 demands, it is assumed that this behavior validates the efficacy of the slice admission control.
## 2 Mathematical Formulation
### Objective Functions  
The optimization problem seeks to minimize the following composite function (\(E_{\text{loss}}\)), concerning power losses due to the Joule effect:  
\[E_{Loss}=\min(f_{\text{loss}})=\min(\sum_{h\in\Omega_{H}}\sum_{l\in\Omega_{L}} R_{l}{|I_{l}|}^{2}\Delta h\right), \tag{1}\]  
\[\{\forall h\in\Omega_{\mathcal{H}},\forall l\in\Omega_{\mathcal{L}}\}\]  
In this expression, \(R_{l}\) represents the resistance of the distribution line \(l\), and \(I_{l}\) is the magnitude of the current flowing through the line.  
Lastly, the emissions function (\(E_{missions}\)) seeks to reduce the environmental impact of the system:  
\[E_{missions}=\min(f_{\text{CO}_{2}})=\min(\sum_{h\in\Omega_{H}}\sum_{i\in\Omega_ {\mathcal{N}}}P_{i,h}^{cg}CE_{i}^{cg}\Delta h+\sum_{h\in\mathcal{H}}\sum_{i\in \Omega_{\mathcal{N}}}P_{i}^{dg}CE_{h}^{dg}\Delta h), \tag{2}\]  
\[\{\forall h\in\Omega_{\mathcal{H}},\forall i\in\Omega_{\mathcal{N}}\}\]  
In this equation, \(P_{i,h}^{cg}\) represents the active power generated by a conventional generator at node \(i\) during period \(h\), and \(CE_{i}^{cg}\) is the corresponding emission coefficient. This coefficient depends on the type of energy source used, such as electricity from the main grid, diesel-based distributed generation, or natural gas, and is introduced into the model as a constant factor representing CO\({}_{2}\) emissions per unit of energy (in this particular case, kg/kWh). The period duration \(\Delta h\) defines the time interval in which electrical variables remain constant. Similarly, \(P_{i,h}^{dg}\) denotes the active power supplied by the DG at node \(i\) during period \(h\), while \(C_{h}^{\text{dg}}\) refers to the expected electricity production curve of the DG. The emission coefficient \(CE_{i}^{\text{cg}}\) is also applied here to estimate CO\({}_{2}\) emissions from distributed generators.  
It is important to note that in the proposed model, the emission factor for each generation source can be adjusted depending on its characteristics. For instance, distributed generators that rely on fossil fuels (e.g., diesel) will have a positive emission coefficient, while clean sources, such as photovoltaic (PV) systems, are assigned a zero emission factor since they do not emit CO\({}_{2}\) during operation [32]. In the context of this paper, PV generation is the only distributed source used, and therefore, it does not contribute to emissions in the simulation results.  
The sets \(\mathcal{N}\), \(\mathcal{L}\), and \(\mathcal{H}\) refer to the nodes, lines, and hourly periods considered in the microgrid.