esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multiple-Access Reservation Receiver Initiation MAC (MAR-RiMAC)
### Collision Resolution
In the aforementioned discussion and, in particular, in the random sequence discussion, when determining the Bernoulli distribution parameter, we have assumed that \(K\leq N^{3}\). For greater \(K\), a different Bernoulli distribution parameter should be chosen [55]. Remark II: Even though, at first glance, it might seem that the ASC procedure is identical to the CSS mechanism, it is important to note that the two procedures are different, not least because the latter relies on correlators, while for the former, it is sufficient to rely on energy detection, which identifies which minislots are \"Busy\" and which are \"Idle\". Note that the cross-correlation between different sequences in the ASC scheme is not negligible. Trying to correlate a sequence of a non-transmitting device with the outcome will not produce a near-zero outcome. On the other hand, it is sufficient that only one bit of a non-transmitting device is 1, while the corresponding minislot at the outcome is \"Idle\", i.e., none transmitted at this minislot, indicating that the device was not transmitting. Figure 6: MAR-RiMAC collision resolution procedure illustration for \(K=4\). After the sink’s “Ready to Receive” (RtR) beacon, five devices (more than \(K=4\)) attempt transmission. The sink sends a “Ready to Receive Retransmissions” (”RtRR”) to resolve the conflict, which is replied to by three devices (less than \(K=4\)). The other two devices transmitted after the sink finished polling the three devices and sent a new RtR.
## 3 Preliminary and Model
### Corruption in Vehicle Detection
#### 3.1.2 Synthetic Corruption in Vehicle Detection  
We design three severities for each corruption and use image enhancement techniques by using the Imgaug [48] library to synthesize corruption images. the specific methods are shown in Table 1 and the synthesized images in Figure 2. More details on parameter design can be found in Appendix A.2. From multiple datasets, we selected 854 original images of densely packed vehicles from a vehicular perspective. These corruptions are categorized into 14 classes, each with three levels of severity. For each image, we synthesized 14 * 3 damaged images reflecting various types and degrees of corruption.  
A discussion of the gap between synthetic corruption and real corruption: Corruption in real life can come from a variety of different sources. It is possible, for instance, for a vehicle to encounter adverse weather conditions and unusual objects simultaneously, causing much more complex corruption. In consideration of the fact that it is impossible to provide a comprehensive list of all corruption types that exist in the world, we systematically created 14 corruption types that are divided into three levels to serve as a practical testing ground for evaluating robustness in a controlled manner. However, we were able to verify that the model's performance in synthetic weather conditions was consistent with its performance in real-world data. A more detailed discussion can be found in Appendix A.5.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Corruption Type** & **Methods** \\ \hline Snow & Predefined severities \{1, 3, 5\}, add a \{10\%, 20\%, 30\%\}-opacity gray mask layer, reduce its brightness by 20\%. \\ Fog & Predefined severities \{1, 3, 5\}, add a \{10\%, 20\%, 30\%\}-opacity gray mask layer, reduce the brightness by 20\%. \\ Rain & Add raindrop (size = (0.025, 0.05), speed = (0.25, 0.05)), add splatter with predefined severities \{1, 2, 3\}, add motion blur (k = 3), reduce the brightness by 20\%. \\ Frost & Predefined severities \{1, 3, 5\}, add a \{10\%, 20\%, 30\%\}-opacity gray mask layer, reduce the brightness by 10\%. \\ Brightness & Predefined severities \{1, 3, 5\}. \\ Dark & The brightness is multiplied by (0.7, 0.5, 0.3), add Gaussian Noise with (1–5, 5–10, 10–20), adjust hue and saturation with (\(-\)10\(+\)10, \(-\)20\(+\)20, \(-\)30\(+\)30). \\ Saturate & Predefined severities \{1, 3, 5\}. \\ Gaussian noise & Predefined severities \{1, 3, 5\}. \\ Speckle noise & Predefined severities \{1, 3, 5\}. \\ Zoom blur & Predefined severities \{1, 3, 5\}. \\ Jpeg compression & Predefined severities \{1, 3, 5\}. \\ Motion blur & Predefined severities \{1, 3, 5\}. \\ Elastic transform & Predefined severities \{1, 3, 5\}. \\ Occlusion & Randomly delete (30\%, 50\%, 70\%) of small areas in the image, with each area approximately covering (0.01–0.02%) of the total image area. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The methods for synthesizing corruption images.
## 3 Overview of the Inhibitors Targeting the OxPhos Pathway
### Menaguinone (MK) Biosynthesis
#### 3.2.1 MenD  
Studies by [PERSON] et al. identified two compounds, methyl succinylphosphonate (2) and succinylphosphonate (1), that inhibit _M. tb_ MenD at a Ki of 0.7 and 16 uM, respectively [115]. These inhibitors are structural analogues of the substrate 2-oxoglutarate and bind covalently to MenD. The resolution of the three-dimensional structure of _M. tb_ MenD by NMR will support target-based drug design [119].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Class** & **Represented by** & **Structure** & **Ref** \\ \hline \hline \multicolumn{4}{c}{**MenD inhibitors**} \\ \hline methyl succinylphosphonate & 2 & & [115] \\ \hline succinylphosphonate & 1 & [115] \\ \hline \hline \multicolumn{4}{c}{**MenE inhibitors**} \\ \hline vinyl sulphonamide & 3 & [50] \\ MeOSB-AVSN & & & \\ \hline \hline \multicolumn{4}{c}{**MenA inhibitors**} \\ \hline benzophenone O-methyl oxime & (R)-13 & & [49] \\ derivatives & & & \\ \hline
4-bromophenyl)[2-fluoro-4- & & & \\
[[6-methyl-2-propenylamino) & Ro-48-8071 & & [46] \\ hexyl]oxylphenyl]-methananone & & & \\ \hline aminoalkoxydiphenylmethane & CSU-20 & & [46,116] \\ \hline bicyclic inhibitors & NM-4 & & [117] \\ \hline \hline \multicolumn{4}{c}{**MenG inhibitors**} \\ \hline diphenylborinic acid quinoline & 4b & & [118] \\ esters & & & \\ \hline biphenyl amide & DG70 & & [51] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Menaquinone (MK) biosynthesis inhibitors discussed in this review.
## 2 Numerical Model
### Model Setup
#### 2.2.4 Coupling of the Two Domains  
It is assumed that there is no temperature jump between the porous-medium side of the metal plate and the free-flow side, that is, the influence of the metal plate between the bulk domain and the HTF channel is neglected. At the interface between the two domains, the heat fluxes sum up to zero:  
\[q_{e,HTF}=q_{e,bulk}=q_{e}. \tag{12}\]  
The heat flux in the porous bulk is purely convective:  
\[q_{e,bulk}=\lambda_{eff}\frac{T_{W}-T_{bulk,y=\Delta y/2}}{\Delta y/2}, \tag{13}\]  
with the wall temperature \(T_{W}\) at the interface, the temperature in the bulk grid cell closest to the HTF channel \(T_{bulk}\), and the hight of the bulk grid cell \(y\).  
In the free flow, forced convection determines the heat flow:  
\[q_{e,HTF}=\alpha_{HTF}(T_{HTF}-T_{W}), \tag{14}\]  
with \(T_{HTF}\) the temperature in the middle of the HTF channel. The heat-transfer coefficient \(\alpha_{HTF}\) is crucial for correct representation of the heat flux. It depends on the fluid properties, which change with pressure and temperature; however, it is common to assume \(\alpha_{HTF}\) as constant over the given surface [39]. It is estimated based on the Nusselt number \(\text{Nu}=\frac{\alpha D}{\lambda_{g}}\), with D, the hydraulic diameter. Reference [36] lists the value \(\text{Nu}=8\) for a flat-plate cross section with a fully developped boundary layer and laminar flow. With the gas properties of air at 773 K [40] and the thickness of the HTF channel, the heat-transfer coefficient is in the range of \(\alpha_{HTF}=440\)\([\text{W}/\text{m}^{2}\text{K}]\). Reference [20] follows the instructions of Reference [37] and yield \(\alpha_{HTF}=250\)\([\text{W}/\text{m}^{2}\text{K}]\) for heat transfer between the steel casing to the heat-transfer flux. As a correct value of \(\alpha_{HTF}\) is important to determine the heat flux between reactive bulk and HTF channel correctly, this parameter will also be calibrated by inverse modeling.  
The two Equations (13) and (14) allow for eliminating the unknown temperature at the wall \(T_{w}\). The coupling heat flux is then only a function of the known temperatures \(T_{HTF}\) and \(T_{bulk}\). The locations of the unknown variables are indicated in Figure 1 on the bottom right.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
###### Abstract  
The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model's parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased [EMAIL_ADDRESS] by 3.9% and [EMAIL_ADDRESS]-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections.  
YOLOv8n; small-target detection; dangerous-object detection; PConv lightweight module; vehicle safety +
Footnote †: journal: applied sciences
## 5 Limitations and Future Research  
A key limitation of this study was its reliance on self-reported data, as students reflected on a hypothetical i-AIDA rather than engaging with a fully implemented system in a real learning environment. While participants in Study 3 were shown screenshots and dialogues of a potential i-AIDA, they did not interact with a live version integrated into their studies. This raises questions about whether the key constructs identified remain relevant once an i-AIDA is operational. Additionally, while this study confirmed the stability of student perspectives over time, it remains unclear whether actual adoption rates would align with the stated preference of 56.4% of students.  
Future research should explore pilot implementations of i-AIDAs to assess whether student expectations translate into actual usage patterns. Additionally, understanding educator and institutional perspectives on i-AIDAs are critical, as feasibility and acceptance by teaching staff will significantly influence its adoption. Further studies should also investigate how demographic factors, prior education levels, and GenAI experience shape attitudes toward AI in education. By engaging students, educators, and institutional leaders, future research can ensure that i-AIDA solutions are inclusive, ethical, and aligned with the needs of a diverse student population.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 6 Control Overhead and Computational Complexity  
_Control Overhead:_ In addition to the information sent in the RREQ, RREP, RERR and Hello packets in the standard AODV protocol, each node in the proposed TC-Pipe routing scheme includes the following information in its Hello packet:  
* Its UAV ID (7 bits), its current location (18 bits with 10 m resolution), next-waypoint cell (12 bits for cell ID, assuming 60 \(\times\) 60 cells of 100 m \(\times\) 100 m resolution in a 6 km \(\times\) 6 km area), and a local pheromone map (6-bit pheromone values in the 5 \(\times\) 5 cells centered on the UAV's current cell, for 150 bits total). It also includes the hop count of the shortest route to the BS (4 bits), and the pheromone mask flag/cell ID (12 bits).
* Its \(En\) and IL values (1 to 2 bytes).
* If the node is a one-hop neighbor of a node that participates in data packet forwarding, it broadcasts the LLT, \(En\), IL, and isLinkActive values for each of its one-hop neighbor nodes (variable size depending on neighbor density).  
The destination node periodically sends the Notify_Source packet to the source node, which carries the two-hop neighborhood information (i.e., UAV ID and their \(En\), IL, and LLT values) of all intermediate nodes on the route (variable size depending on two-hop neighbor density along the route).  
_Computational Complexity:_ In the TC-Pipe routing scheme, the source node uses the BFS algorithm to discover all possible routes to the destination node within the pipe. The worst-case time complexity of this algorithm is \(O(TV_{p}^{2}(E_{p}+V_{p}))\), where T represents the number of route computations, and \(V_{p}\) and \(E_{p}\) denote the number of nodes and links within the pipe, respectively. Additionally, intermediate nodes use the Bron-Kerbosch algorithm to compute cliques within their two-hop neighborhood. The worst-case time complexity of this algorithm is \(O(3^{V_{p}/3})\). Note that the clique computation is optional but aids in reducing control overhead.
# A Weighted Turbo Equalized Multi-Band Underwater Wireless Acoustic Communications
## 1 Introduction
The experimental results showed that, as the number of multi-bands increased, the performance was improved. In addition, when four bands were used, the threshold values based on the preamble error rate achieved the best performance.
# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 5 Conclusions  
This paper takes intelligent city bus as the platform and uses the EKF strategy to estimate the vehicle mass and the road slope. Then, combining the MPC strategy to design a multi-objective optimized control strategy to balance vehicle tracking ability, driving safety, driving comfort, and energy saving. The MATLAB/Simulink simulation and HIL experiments were carried to implement and verify the control strategy. The conclusions are as follows:  
1. The EKF strategy was used to realize the real-time estimation of vehicle mass and road slope. The estimation error of vehicle mass can be reduced to within \(\pm\)5% in 40 s, and the estimation error of road slope can follow the actual value well. Real-time estimation for mass and slope provides key parameters for ACC implementation;
2. In this paper, we established the cost functions about speed, acceleration, impact, and SoC. Based on cost functions, we proposed the nonlinear MPC control algorithm. By adjusting the cost coefficients, we realized the multi-objective optimization of ACC;
3. Through simulation and HIL experiments, the feasibility of the EKF+MPC control strategy was verified. On the basis of ensuring safety and tracking ability, the maximum impact of the current vehicle was reduced to 5 m/s\({}^{3}\), the consumption of the _SoC_ was reduced by about 10%, and the vehicle comfort and energy efficiency are well improved.  
[PERSON]-[PERSON] conceived the underlying idea of the paper. [PERSON]-[PERSON], [PERSON]-[PERSON], [PERSON] and [PERSON]-[PERSON] performed the numerical calculations. [PERSON]-[PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Foundation of China (No. 51890881).  
Not applicable.  
The authors declare that there is no conflict of interest.  
Figure 23: The adjustment results of driving torque.
# Nonsurgical Correction of Anterior Crossbite with Extra-Radicular Miniscrews--A Case Report
## 2 Case 1
### Treatment Progress  
The treatment started with full mouth bonding of 0.022-in self- ligating brackets. (Damon 2, Ormco company, Orange, CA, USA) A bite turbo was bonded on the lingual surface of the mandibular right canine to prevent bracket interference and help anterior crossbite correction. A 0.014-in thermal nickel-titanium (NiTi) archive was used as the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Norms** & **Pretreatment** & **Posttreatment** \\ \hline Skeletal Analysis & & & \\ SNA (\({}^{\circ}\)) & \(81.5\pm 3.5\) & \(80.0\) & \(80.0\) \\ SNB (\({}^{\circ}\)) & \(77.7\pm 3.2\) & \(82.0\) & \(82.0\) \\ ANB (\({}^{\circ}\)) & \(4.0\pm 1.8\) & \(-2.0\) & \(-2.0\) \\ SN-MP (\({}^{\circ}\)) & \(33.0\pm 1.8\) & \(34.0\) & \(34.0\) \\ Hyts (mm) & \(-2.8\pm 3.3\) & \(-11.8\) & \(-8.9\) \\ Dental Analysis & & & \\ U1 TO NA (mm) & \(3.9\pm 2.1\) & \(4.5\) & \(6.5\) \\ U1 TO SN (\({}^{\circ}\)) & \(108.2\pm 5.4\) & \(104.2\) & \(104.2\) \\ L1 TO NB (mm) & \(6.6\pm 2.8\) & \(5.0\) & \(2.5\) \\ L1 TO MP (\({}^{\circ}\)) & \(96.8\pm 6.4\) & \(87.9\) & \(74.6\) \\ FACLA ANALYSIS & & & \\ E-LINE UL (mm) & \(-1.1\pm 2.2\) & \(-1.0\) & \(-0.5\) \\ E-LINE LL (mm) & \(0.5\pm 2.5\) & \(4.0\) & \(0.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Case 1, Cephalometric measurements.  
Figure 2: Case 1, Pretreatment panoramic radiograph and lateral cephalogram.  
initial archive on both arches. A few segments of NiTi open coil springs were inserted around the maxillary lateral incisors in the second month to create space for maxillary arch alignment. Both arches were well aligned after 4 months of treatment. Short Class III elastics were prescribed for Class III correction in the fifth month, with 0.016 \(\times\) 0.025-in NiTi wires on both arches. We shifted to long Class III elastics in the seventh month with 0.019 \(\times\) 0.025-in stainless steel wire on the maxillary arch. The occlusion looked almost like a solid Class I dental relationship after 8 months of treatment (Figure 3). As planned, we reevaluated the patient's facial profile after correcting the anterior crossbites.  
A progressive record in the tenth month showed a borderline protrusive profile with very acceptable occlusion (Figure 4). After a thorough discussion with the patient and her parents, considering the facial profile, we decided to extract her four first premolars to reduce the protrusion. After extractions, space closure was accomplished on both arches with 0.019 \(\times\) 0.025-in stainless steel wire. The fixed orthodontic appliances were removed after 25 months of active treatment (Figure 5). The patient and her parents were satisfied with her facial profile and occlusion.  
Figure 4: Case 1, Facial and intraoral photographs after 10 months of treatment.  
Figure 3: Case 1, Treatment progress at (**A**) 2 nd month (**B**) 4 th month (**C**) 8 th month shows correction of anterior crossbite and Class III malocclusion.
## 2 Related Work
For example, [PERSON] et al. [27] used two types of deep belief network (DBN) to achieve the selection of VNFs and chain them. For each type of SPC request, they trained a DBN. However, this method requires a large number of labeled data to train the DBN, and the samples used for training a DBN are generally difficult to obtain. To cope with this challenge, in [7; 28], the authors proposed DRL-based approaches to solve SFC orchestration. However, the authors of [7] did not consider the resource consumption cost and [28] did not optimize the traffic acceptance rate. [PERSON] et al. [29] and [PERSON] et al. [30] studied the problem of VNF forwarding graph (VNF-FG) embedding and proposed different DRL algorithms. [PERSON] et al. [30] used DDQN to solve the problem of VNF placement and the shortest path first protocol to achieve traffic routing. However, their method considers neither resource usage on the link and node or network load balancing. Some research only considers VNF placement, without addressing traffic routing [8; 31; 32]. For example, a serialization and backtracking method was proposed by [PERSON] et al. [32] to address the challenge of handling large discrete action space for the placement of SFCs. Although DRL can quickly make optimal decisions for the current task and scenario, each time the IoT-MEC environment changes, many DRL algorithms suffer from low sample efficiency and slow convergence issues, i.e., when confronted with a new learning environment. Such DRL-based algorithms require a long retraining of the model. To address this challenge, meta learning has attracted the attention of many researches and is widely used in task offloading and caching problem [33; 34; 35; 36; 37; 38]. A general framework was proposed by [PERSON] et al. [33] to combine meta learning with hierarchical reinforcement learning, enabling rapid adaptive resource allocation for dynamic vehicular networks. To improve scheduling robustness, [PERSON] et al. [34] design a meta-gradient reinforcement learning algorithm for time-critical task scheduling. Refs. [36; 38] solved the task offloading problem using dynamic MEC environment-based deep meta learning and multiple parallel DNNs;the latter considered mobile cloud computing, whereas the former did not. The offloading decision process was transformed into a sequence prediction process by [PERSON] et al. [37], who proposed a custom seq2 seq neural network to model the offloading policy. However, few works have applied meta-RL to the SFC placement problem in an IoT-MEC environment. Inspired by the literature mentioned above, we consider the dynamics of MEC scenarios and apply meta-RL to the dynamic SFC placement problem.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 2 Preparatory Knowledge
### Feature Parameter Selection Based on the Improved Relief Algorithm
#### 2.3.3 SIG-ReliefF  
By using Standardized Interaction Gain (SIG), the interaction gains between different features can be standardized to enhance comparability and better reflect their contributions to the improvement of model performance. The definition is as follows:  
\[SIG(f_{i};f_{j};C)=\frac{IG(f_{i};f_{j};C)}{H(f_{i})+H(f_{j})} \tag{3}\]  
By combining Equations (1) and (3), we can derive the improved weight formula for feature \(X_{i}\) as follows:  
\[\overline{w}(X_{i})=\frac{1}{n}{\sum_{j=1}^{N}SIG(X_{i};X_{j};C)}+w(X_{i}) \tag{4}\]  
By using Equation (4) as the basis for weight updates, we define it as the SIG-ReliefF algorithm. Compared to the traditional Relieff algorithm, the SIG-ReliefF algorithm (Algorithm 1) can eliminate the influence of different scales among features, improve the accuracy of feature weight calculation, and take into account the interaction between features more comprehensively. This leads to improved feature selection and ultimately enhances the performance of the model.  
```
Input: Construct feature data set \(S=\{f_{1},f_{2},f_{3},\cdots,f_{n}\}\), category set \(C=\{C_{1},C_{2},C_{3},\cdots,C_{m}\}\), and the threshold is \(k\) Output: Target set \(G\)
1. Initialize the target set
2. For \(i=0\) to \(m\)
3. Calculate the SIG weights among all feature parameters in Equation (3)
4. Calculate the weights of all feature parameters in Equation (1)
5. End for
6. For \(i=0\) to \(m\)
7. The total weight values of the feature parameters are retained according to Equation (4)
8. End for
9. The weights of all the calculated feature parameters in the target set were sorted in descending order and combined with expert experience. The feature parameters with greater influence on the target state and greater weight were selected to build a new sample set
```  
**Algorithm 1** SIG-ReliefF algorithm flow