esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Ablation Study  
#### 3.3.1 Component Ablation  
To validate the effectiveness of each module in our proposed method, we conducted ablation experiments on the DanceTrack validation set. Using the ByteTrack tracking  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **HOTA (\%)** & **MOTA (\%)** & **IDF1 (\%)** & **DetA (\%)** & **AssA (\%)** \\ \hline SORT & 47.9 & 91.8 & 50.8 & 72.0 & 31.2 \\ DeepSORT & 45.6 & 87.8 & 47.9 & 71.0 & 29.7 \\ CenterTrack & 41.8 & 86.8 & 35.7 & 78.1 & 22.6 \\ FairMOT & 39.7 & 82.2 & 40.8 & 66.7 & 23.8 \\ QDTrack & 45.7 & 83.0 & 44.8 & 72.1 & 29.2 \\ TransTrack & 45.5 & 88.4 & 45.2 & 75.9 & 27.5 \\ TraDes & 43.3 & 86.2 & 41.2 & 74.5 & 25.4 \\ ByteTrack & 47.3 & 89.5 & 52.5 & 71.6 & 31.4 \\ OC-SORT & 54.6 & 89.6 & 54.6 & 80.4 & 40.2 \\ Our method & **57.5** & **92.5** & **57.3** & **82.3** & **40.3** \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison with the state-of-the-art methods on the dataset DanceTrack test set. The best results are shown in **bold**. ByteTrack, OC-SORT, and our method share detection results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **HOTA (\%)** & **MOTA (\%)** & **IDF1 (\%)** & **DetA (\%)** & **AssA (\%)** \\ \hline FairMOT & 54.6 & 61.8 & 67.3 & 54.7 & 54.7 \\ TransMOT & 61.9 & 77.5 & 75.2 & **64.0** & 60.1 \\ ByteTrack & 61.3 & **77.8** & 75.2 & 63.4 & 59.6 \\ OC-SORT & **62.1** & 75.5 & 75.9 & 62.4 & 62.0 \\ Our method & **62.1** & 75.1 & **76.0** & 62.0 & **62.3** \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison with the state-of-the-art methods over the dataset MOT20 test set. The best results are shown in **bold**. ByteTrack, OC-SORT, and our method share detection results.  
algorithm as the baseline, we evaluated the performance of different methods across five metrics: HOTA, MOTA, IDF1, DetA, and AssA. Table 3 summarizes the progression from the ByteTrack baseline to our proposed method, showcasing the improvements achieved at each stage.  
As shown in Table 3, the experimental results highlight the effectiveness of each module. Comparing the first and second rows, we see that the CEF method significantly enhances HOTA, MOTA, IDF1, and DetA metrics, indicating that the CEF method, which integrates predictive information, improves tracking performance. When we compare the first and third rows, we observe substantial gains across all metrics, confirming that adjustments to the KF state space combined with adaptive measurement noise positively impact the tracking of objects exhibiting complex motion. Similarly, the comparison between the first and fourth rows reveals significant improvements across all metrics, demonstrating that the enhanced affinity matrix facilitates more effective matching in MOT scenarios. Finally, when comparing the other rows with the last row, we note that all metrics achieve their highest values, providing strong evidence that our proposed improvements collectively contribute to enhanced tracking performance in multi-object tracking.
###### Abstract  
The vibrations caused by tunnel blasting strongly affects the construction safety and progress of the tunnel itself. The arch vibration attenuation law, structural energy response, and safety criterion were systematically investigated using blasting vibration monitoring in the Jiaohuayu Tunnel. The peak particle velocity (PPV) at the vault was always larger than that at the arch waist and was greater than that at the sidewall, regardless of the direction. The arch waist was where the initial lining had the highest risk of damage. Existing safety criteria can be supplemented and improved using the maximum instantaneous input energy to measure the first passage damage, the hysteretic energy consumption to measure the cumulative damage, and the input-hysteretic energy criterion to judge the structural failure. The energy threshold of the first passage damage of the initial lining structure was 200 J, and the plastic cumulative damage was 3000 J of the test section. This study is important when evaluating the safety of a tunnel's initial lining structure.  
blast vibration; tunnel arch; energy response spectrum; maximum instantaneous input energy; hysteretic energy consumption
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 1 Introduction
In the first response, in addition to the existing temperature and smoke sensors, camera-based flame recognition and accident vehicle tracking using the radar are performed simultaneously for accurate detection. The information collected is propagated through the optic fiber cable in the tunnel, but it also suggests an ad hoc network technique that modifies the 'better approach to mobile Ad hoc networking (BATMAN)' protocol for stable communication, even when the infrastructure has collapsed due to an accident. In the second response, if the internal situation can not be confirmed accurately despite the first response, the robot enters on behalf of the firefighter and an additional internal image is acquired. The robot not only ensures the safety of firefighters from the risk of further explosions, but also helps them acquire information faster than firefighters themselves can gather it. In the third response, after the firefighter completes the judgment of the internal situation, real-time location tracking of firefighters is performed using ultra-wideband (UWB) devices. For this purpose, in addition to the above-mentioned IoT sensors, UWB anchors are also installed at intervals of 40 m, and firefighters also have mobile devices with UWB embedded therein. The rest of this paper is organized as follows. In Section 2, we review the existing search and rescue equipment that can be used in fire sites and analyze the challenges related to addressing tunnel fires. In Section 3, we introduce various techniques and equipment for precise accident location tracking, and we consider their application in detail in an accident environment based on the above assumptions. We propose a concrete scenario for how the proposed search and rescue equipment can be linked with current firefighting response practices. Section 4 presents the performance analysis of the proposed equipment and applications. Section 5 presents additional discussion of the proposed system operations. Section 6 summarizes the proposed technologies and services, and concludes with directions for future work.
###### Abstract  
The aim of this study was to evaluate color inconstancy of dental ceramics under the white light-emitting diode illuminants recently proposed by the CIE. From spectral reflectance factors of 18 dental ceramics (VST, NSP, and IEC; shades A1, A2, A3, A3.5, B2, and C2), the corresponding colors under illuminant D65 and CIE 1931 Standard Colorimetric Observer were computed for all samples, using the chromatic adaptation transform CIECAT16. CIEDE2000 color differences between dental ceramics illuminated by CIE D65 standard illuminant and different white LED illuminants were calculated. Perceptibility and acceptability thresholds (PT\({}_{00}\) and AP\({}_{00}\)) in dental ceramics were used to analyze color changes. Color gamut size was within the same range for all illuminants and for all ceramics, since MCDM computed values were 4.1-4.4 for VST, 4.0-4.2 for NSP, and 4.3-4.6 for IEC. For all ceramics and shades, the color inconstancies were higher than 50:50% PT\({}_{00}\) and, in general, lower than 50:50% AT\({}_{00}\). The effect of CIE-proposed LED illuminants on dental ceramic is perceptible and slightly lower than on natural teeth. Dental clinicians should consider these lighting effects on the visual appearance of dental ceramics.  
dental ceramics; white light-emitting diode; color; CIEDE2000 +
Footnote †: journal: Applied Sciences
## 2 Formation Mechanisms of Effective Reservoir in Ultra-Deep
### Sedimentation Lays the Foundation for Mineral Fabric and Primary Porosity of Reservoirs
#### 2.2.2 Inhibitory Effect of Overpressure on Compaction  
As the thickness of overlying sediments increases, effective stress on the reservoir rises, enhancing mechanical compaction and reducing porosity. However, the development of pore fluid overpressure can mitigate effective stress and slow the rate of mechanical compaction, thereby preserving primary porosity in sandstone reservoirs. The main mechanisms are as follows: (1) Reduced mechanical compaction: overpressure counteracts effective stress, helping to maintain pore space; (2) Inhibition of intergranular overpressure solution: overpressure limits the dissolution of grain contacts, reducing the supply of silica ions and slowing quartz cementation; (3) Restricted ion exchange: overpressure creates a relatively closed system, significantly decreasing ion exchange with external fluids and effectively slowing cementation [39; 49; 50].  
The preservation of primary pores in reservoirs due to overpressure depends on the mechanical properties of sandstone and the timing of overpressure formation. Sandstone reservoirs with high plastic content have greater pore preservation potential, as compaction primarily controls reservoir quality. Researchers analyzed effective stress and intergranular pore content in core samples from oil fields such as the North Sea and the Gulf of Mexico. Their findings indicate that when effective stress decreases from 80 MPa to 10 MPa, intergranular porosity increases from approximately 15% to 30%, with a maximum of 35%. A similar correlation between porosity and pressure coefficient is observed in the deep layers of major domestic basins, where abnormal high-pressure zones are associated with high-quality reservoir development (Figure 5). For example, in the Zheng-10 well of the Junggar Basin, the high-quality reservoir zone of the Triassic Kelamayi Formation at \(\sim\)6500 m depth has a pressure coefficient of 1.8 (Figure 5a). In the Kuqa Depression's Cretaceous main production layer, the pressure coefficient exceeds 2.0, with reservoirs exhibiting abnormally high porosity beneath the overpressure zone (Figure 5b). These observations highlight the critical role of overpressure in preserving reservoir porosity [4; 51]. Overpressure formation is closely linked to hydrocarbon generation and under-compaction caused by the sealing effects of high-quality gypsum salt caprocks [52; 53]. In foreland basins, rapid compaction, tectonic compression, and other geological processes can also contribute to abnormal high-pressure conditions. Therefore, for future deep-basin exploration, identifying formations and zones with efficient source-caprock configurations within hydrocarbon-generating sags will be crucial in locating high-quality reservoirs.
## 3 Proposed Method
### Mobile Anchor Route Scheduling Method (MARS)
When applying the ISP algorithm, more reference points are needed for positioning. When \(M\) moves to location \(w_{1}\) on the path segment \(\{y\)\", \(E_{3}\}\), it detects the unknown node \(w\) and receives the signal of node \(x\) again. It is at this point that \(M\) is able to determine the actual position of node \(x\). After obtaining the position of node \(x\), node \(e\) collects two position-aware neighbor nodes \(\{x,y\}\). On the basis of the reference point \(e_{1}\), [PERSON] is able to locate node e, shown in Figure 3c. As \(M\) continuously moves toward the location \(E_{3}\) on the line segment \(\{y\)\", \(E_{3}\}\), node \(s\) receives a third reference point at \(s_{2}\). When the position of node \(s\) has been determined, the unknown node \(w\) will have three position-aware neighbors, and its position can be reported to the mobile anchor by its connected neighbors. Therefore, the mobile anchor does not need to schedule a route to locate it. When \(M\) reaches location \(s_{2}\), there are no other unknown nodes in the line segment from \(E_{3}\) to \(s_{2}\) in the region \(\square E_{2}E_{3}E_{9}E_{a}\) waiting to be positioned. Thus, \(M\) moves directly from \(s_{2}\) to \(E_{4}\) in order to start scanning the line segment \(\{E_{4},E_{b}\}\). Furthermore, the mobile anchor \(M\) detects no unknown nodes when it moves from \(E_{4}\) to \(E_{b}\). Therefore, it moves directly from \(E_{b}\) to \(E_{d}\), skipping the line segment \(\{E_{c},E_{5}\}\), and starts to scan the line segment \(\{E_{d},E_{6}\}\), shown as a dashed line in Figure 3a, because it is certain that there is no unknown node located in the region \(\square E_{4}E_{b}E_{c}E_{5}\). As \(M\) reaches \(E_{6}\), the mobile anchor \(M\) has already scanned the total deployment area, and the positioning procedure is terminated.
## 7 Scalability Study
### Impact of Transaction Load
#### 7.2.1 Varying Invoices per Client and Keeping Clients Constant  
In this set of experiments, there were 10 clients issuing and paying invoices at the same time. The results illustrated in Figure 9a,b show that performance drops instantly from 10 to 20 invoices per client. From there, performance (in terms of throughput and latency) drops smoothly as the number of invoices increases. This shows that the Smart Money DLT (i.e., the MTS) scales appropriately with increasing numbers of payments (issued and paid) by holding the number of clients constant and increasing the number of transactions per client. This is also observed when the number of invoices increases further, from 200 to 1000 per client, see Figure 9c,d. Enabling VAT split payments impacts performance in this case as well, since transaction throughput with split payments is always lower with an associated higher latency.  
7.2.2. Varying Number of Clients and Keeping Invoices per Client Constant  
In this set of experiments, each client was configured to submit 100 invoices both for issue and payment. The results presented in Figure 10a,b show that throughput drops and latency increases linearly with increasing numbers of clients (and consequently, the total number of invoices). This is observed for all types of payments, either with or without split payment. It is therefore shown that the MTS is scalable for increasing transaction load when the increase is caused by adding more clients to the network.
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 4 ACO&CM Algorithm  
In order to overcome the drawback that the ACO algorithm may easily fall into local optimum, we designed our solution methodology to solve the QoS multicast routing problem. Since solving this problem is rather complicated, this can lead the solution method into more difficult conditions. High quality solutions should require us to design the algorithm elaborately. To enhance the performance of the ACO algorithm, we present the hybrid ACO algorithm, namely, the ACO&CM algorithm, which can effectively integrate the solution building mechanism of the ACO algorithm and CM. Meanwhile, the cloud model embedded in the ACO framework can avoid overuse of some nodes and the algorithm easily getting trapped in a local optimum by adjusting the pheromone trail. This method has the outstanding advantage of the ACO algorithm, which can find better performance solutions. It also has the advantage of CM, which is capable of searching different solution spaces in order to get better solutions. To deal with the probability of forming a loop in the search path, we devise a memory detection search (MDS) strategy, and incorporate the MDS strategy into the path construction process. Furthermore, in order to obtain high quality solutions, we adopt improvement strategies by deleting an edge with the maximum cost to create a neighboring solution of the original tree. The framework of proposed algorithm contains a memory detection search strategy, path construction, tree construction, pheromone trail updating, and solution improvement.
# Environmentally Friendly Anticorrosive Polymeric Coatings
## 3 Protective Mechanism of Anticorrosive Coatings  
Anticorrosive coatings are generally classified in accordance with the mechanisms by which they protect a metal against corrosion. Each form of corrosion may differ between systems, environment, material design and engineering, and other variables [38; 39; 40]. Typically, a protective coating is composed by several layers where the primer is in direct contact with the metal, the intermediate coats are located in between, and the topcoat is exposed to the external environment. The three basic protective mechanisms of anticorrosive coatings are [41]:  
1. Barrier coatings or impermeable coatings: They act by blocking the transport of aggressive species into the surface such as water, gases (i.e., CO\({}_{2}\), SO\({}_{2}\) in industrial atmosphere), ions (CI' in marine atmosphere), or electrons. This can be obtained by a chemical conversion layer, or by addition of pigments to the coating. This type of coating may be used as primer, intermediate, or topcoat, and are often applied on immersed structures.
2. Inhibitive coatings: In contrast with coatings based on impermeability, inhibitive coatings avoid corrosion by reacting with the environment to provide a protective film or barrier on the metallic surface. The inhibitive pigments are generally inorganic salts, which are slightly water soluble. This type of coating is primarily applied as primer because they are solely effective if dissolved constituents can react with the metal. They are mainly applied in industrial environment when the risk of atmospheric corrosion is high and are generally not recommended for immersion in water or burial in soil.
3. Sacrificial coatings: They rely on the principle of galvanic corrosion for the protection of metals against corrosion. The substrate is coated by a metal or an alloy that is electrochemically more active than the substrate itself. Coatings formulated with metallic zinc powder are extensively employed for corrosion protection of steel structures. This type of coatings is only applied as primers because they need an electrical contact between the substrate and the sacrificial metal to be effective.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## Appendix A Tree-Based ML Algorithms
A2 = subset of dataset where feature > split_value
10. return A1, A2
11. # Function to find the best split (feature and split value) for a given node
12. function find_best_split(dataset, target_values):
13. best_feature = None
14. best_split_value = None
15. min_cost = infinity
16. for each feature in dataset:
17. for each split_value in unique values of feature:
18. # Split the dataset
19. A1, A2 = split_data(dataset, feature, split_value)
20. # Compute SSE for both subsets A1 and A2
21. SSE1 = compute_SSE(target_values corresponding to A1)
22. SSE2 = compute_SSE(target_values corresponding to A2)
23. # Calculate the weighted cost of this split
24. k1 = number of instances in A1
25. k2 = number of instances in A2
26. k = number of instances in original dataset
27. cost = (k1/k) * SSE1 + (k2/k) * SSE2
28. # Check if this is the best split so far
29. If cost < min_cost:
30. min_cost = cost
31. best_feature = feature
32. best_split_value = split_value
33. return best_feature, best_split_value
34. # Recursive function to build the Decision Tree
35. function build_tree(dataset, target_values, max_depth, min_samples_leaf, current_depth):
36. # Check stopping criteria: max depth or min samples per leaf
37. if current_depth >= max_depth or len(dataset) <= min_samples_leaf:
38. return create_leaf_node(mean(target_values))
39. # Find the best split
40. best_feature, best_split_value = find_best_split(dataset, target_values)
41. # If no valid split, create a leaf node
42. if best_feature is None:
43. return create_leaf_node(mean(target_values))
44. # Split the dataset into two subsets
45. A1, A2 = split_data(dataset, best_feature, best_split_value)
46. target_A1 = target_values corresponding to A1
47. target_A2 = target_values corresponding to A2
48. # Recursively build the left and right subtrees
49. left_child = build_tree(A1, target_A1, max_depth, min_samples_leaf, current_depth + 1)
50. right_child = build_tree(A2, target_A2, max_depth, min_samples_leaf, current_depth + 1)
51. # Return a decision node with the best split and its children
52. return create_decision_node(best_feature, best_split_value, left_child, right_child)
53. # Function to create the leaf node (terminal node)
```  
**Algorithm A1** Pseudocode for the Decision Tree regression algorithm (CART)
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Rescue  
The rescue service incorporates various functionalities to optimize resource allocation, provide medical assistance, monitor disease symptoms for epidemics, aid evacuation and fire extinguishing, and ensure the well-being of first responders during their operations.  
This service facilitates optimal distribution of resources by the UAVs, which can deliver essential supplies and aid packages to areas of interest (AoIs) without landing or upon landing via parachute. For medical care during emergencies, specialized UAVs are equipped with an electrocardiogram (ECG), automated external defibrillator (AED), and temperature and respiratory sensors. These UAVs analyze patients' conditions before the arrival of ambulances. Moreover, life-saving vaccines can be delivered through cold transport drones. This service also employs UAVs equipped with thermal and visual image sensors to remotely detect disease symptoms, including respiratory infections, through thermography and machine vision algorithms. This enables effective epidemic monitoring and prevention.  
Moreover, UAVs assist in evacuation efforts using megaphones to guide and facilitate the evacuation of stranded individuals within AoIs. In extinguishing operations, water-based fire extinguishing agents are deployed by drones to combat forest or building fires.  
Lastly, an indispensable facet of this service involves the surveillance of first responders' well-being. UAVs gather crucial data from wearable sensors affixed to their uniforms, encompassing GPS location, temperature, radiation levels, hazardous chemicals, and live video feed. These data play a vital role in identifying first responders in need of immediate assistance and expediting timely support during disaster scenarios.
## 4 Discussion
### Transmissibility  
Overall, the findings of this study suggest that \(T\) decreases as the vibration stimulus is transmitted from AN to HE; however, \(T\) showed amplification values (\(>\)2) at AN and KN in most of the frequencies and postures. The main effect on LVP was a strong decrease in the \(T\) at HI (\(<\)0.5) that was maintained to the HE. However, previous studies showed amplifications at AN and the attenuation turning point at KN [21; 22; 24; 25; 31]. In line with previous investigations [17; 20; 21], \(T\) on the LVP increased clearly at AN along with increases in the \(f_{\text{set}}\), however, this effect was reversed at KN, especially at HS, DS, and LU, where \(T\) decreased as the \(f_{\text{set}}\) increased. This behaviour could be attributed to an increased muscle activation as the lower-leg muscles above the AN react to the vibration stimulus [17; 20; 21; 33]. Regarding posture comparisons, by adopting TS the \(T\) drops on the LVP across the human body, as previous studies have shown [24; 25]. This effect might be relate to the dampening capability of the foot arch [40]. Other postures showed higher \(T\) values, especially at AN and KN when HS, DS, and LU were adopted. However, these postures showed a large \(T\) attenuation from HI to HE. In the HE, the ER showed the highest \(T\) (0.6) versus other postures (\(<\)0.2). Thus, although the vibration is largely attenuated from AN to HE, it is recommended to avoid the ER posture because the trunk and head are less tolerant to the vibration stimulus than the legs [7]. All these \(T\) values support the feasibility of the LVP transmission to the human body.