esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 4 SCoRe Design
### Recursive \(N_{\text{dslot}}\) Scheduling  
Scheduling is similar to gathering, but needs one more scheme to avoid slot violations. SCoRe schedules relative transmission timeslots layer-by-layer based on \(N_{\text{dslot}}\) information obtained via recursive \(N_{\text{dslot}}\) gathering. When (and only when) disseminating a command, SCoRe root builds a relative time schedule with enough timeslots to cover the total amount of \(N_{\text{dslot}}\), and allocates timeslots to each 1-hop child according to their \(N_{\text{dslot}}\). The assigned timeslot information is carried into and disseminated via command messages, where 4 bytes is used per 1-hop child; 2 bytes is the IEEE 802.15.4 short address and 2 bytes for the allocated aggregate timeslot length. Then, every node will redistribute the assigned slots to its 1-hop children recursively.  
A node receiving a command finds the relative timeslot offset (from the position of its own short address in the sequence) and length (2 bytes) assigned to itself in the message. In Figure 2, for example, suppose \(M\) is 1 and the routing tree has been established through which \(N_{\text{dslot}}\) has been gathered. SCoRe root knows that node A and B require 10 and 4 timeslots, respectively, thus it assigns timeslots according to their requirements sequentially. Node B knows that it can start its transmission (including its subtree members) after A's reservation of 10 timeslots, and use 4 timeslots for itself and its subtree.  
Figure 2: SCoRe’s scheduling process overview.  
Each node uses a subset of the assigned timeslots to transmit its own response message (to the command), and also to forward commands. The remaining timeslots are rescheduled for its subtree nodes. For instance, node A in Figure 2 uses 2 timeslots from the 10 assigned to itself, and the remaining 8 timeslots are rescheduled, 6 for node C, and 2 for D.  
\(N_{\text{dslot}}\) scheduling is processed recursively similar to \(N_{\text{dslot}}\) gathering, but has a critical difference that a node should never use more than what it receives. Suppose node D has recently joined the network and the root is unaware of D's \(N_{\text{dslot}}\) yet as shown in Figure 3. Node A's \(N_{\text{dslot}}\) would have been 8 in the past, and thus the root will allocate only 8 slots for node A despite it needs 10 slots total to support D's responses. If node A uses 2 more slots to cover node D, then the transmissions for delivering [PERSON]'s response packet (D\(\rightarrow\)A, A\(\rightarrow\)root) will disrupt node B's transmissions (response and dissemination) which will result in packet collisions. Therefore, node A should not allocate timeslots to node D in this case.
# Road Obstacle Detection Method Based on Improved YOLOv5
## 4 Results
### Experimental Results
#### 4.5.3 Comparative Experiments on the Performance of Different Algorithms  
To verify the performance advantage of the YOLOv5-EC3F algorithm, the comparison experiments of YOLOv5-EC3F with the YOLOv3, YOLOv5, YOLOv8 [45], YOLOX [46], and SSD algorithms on the same dataset are carried out on the test set. The experimental results are shown in Figure 10.  
This can be known based on Figure 10:  
1. The YOLOv5-EC3F algorithm shows significant improvement in both R and mAP values compared to YOLOv5. The R-value is increased by 7%, and mAP is improved by 3%. Although there is a slight decrease of 0.1% in the P (precision) value, the algorithm still meets real-time demands, despite a slight reduction in FPS (frames per second). Furthermore, the YOLOv5-EC3F algorithm outperforms YOLOv8 across all evaluation metrics.
2. The YOLOX algorithm is 4.6% better than the YOLOv5-EC3F algorithm in precision (P) but is 2% and 2.3% lower in recall (R) and mAP, respectively. Additionally, the FPS value of YOLOX is only 34.6. YOLOv3 achieves the highest R-value and FPS, but it performs inadequately in precision and mAP. The SSD algorithm outperforms the YOLOv5-EC3F algorithm by 0.7% in precision, but it falls short in other important aspects. Overall, the YOLOv5-EC3F algorithm proposed in this paper is the most effective across the evaluated metrics.  
1. Comparison of experimental results between Group A and Group C shows that Group A misclassifies car as stone, while Group C misclassifies pothole as stone. The YOLOv5-EC3F algorithm defines the target more clearly, which effectively solves the problems of obstacle detection error and unclear feature expression.
2. Comparison of experimental results between Group B and Group D shows that broken trees are missed in Group B, while potholes are missed in Group D. The YOLOv5-EC3F algorithm can locate the obstacle target more accurately and solves the problems of inaccurate localization and missed detection.  
To more intuitively demonstrate the detection performance of the proposed algorithm, this study compares the obstacle detection results before and after the improvement of YOLOv5 and visualizes them, as shown in Figure 11.  
Figure 10: Comparative experiments of different algorithms.
## 2 Previous Works
### Post-Process Welding Quality Control
Using image processing and deep learning techniques, [PERSON] et al., 2019 [18] proposed a framework to detect defects such as Lack of Fusion (LoF) in mass-produced welded cylinder parts for flame sensors. They developed 1D-pDFT and Gabor filters that help highlight features related to the defect. Finally, a deep learning model utilized the output of the mentioned filters to detect defects. [PERSON] et al., 2016 [19] developed a weld inspection system that relies on image processing techniques to extract features such as weld edges. After feature extraction, the welds were evaluated by neural networks. They ran the developed system on a single-board computer (SBC) and employed a cloud-based solution to store the system output. Exploiting the laser triangulation technique, [PERSON] et al., 2020 [20] proposed a deep-learning-based inspection system to monitor the quality of weld seams. The laser scanner was mounted to a robot providing profile data of the weld seam surface. After preprocessing, such as gamma correction and normalization, a network based on Inception-v3 architecture [21] is responsible for the classification task on the weld quality. [PERSON] et al., 2019 [22] used structured light to capture the weld surface profile. The 3d model of the weld seam was reconstructed by applying image processing filters, extracting the center of the laser stripe, and curve-fitting. After 3d model reconstruction, the weld geometry was measured and compared with the manual measurement. [PERSON] et al., 2021 [23] developed an unsupervised approach to inspect weld quality in the aerospace industry using unannotated real and synthetic X-ray images as input. The unsupervised process is based on the U-Net [24] segmentation encoder-decoder and a k-means clustering technique. Finally, the random-forests (RF) classier was trained on the features extracted by the encoder. In all the previously mentioned research projects, the goal was to monitor the existing or in-process welding quality. However, the present research does not focus on the quality of existing welds. The purpose of this research is to detect missing welds in specific areas of prefabricated joists (i.e., nodes) that require precise assessment. By leveraging detailed engineering drawings and the expertise of the industrial partner's quality control team, we aim to specifically determine if any segment of a weld is absent. This approach is essential for accurately identifying incomplete welding in these critical areas, thus ensuring the reliability and safety of the construction. Additionally, none of the available open datasets was suitable for the target elements of this study, hence, a targeted dataset needs to be created.
## 2 Related Work
Realizing this in practical deployments could be tricky. A new energy-efficient clustering approach (EECS) [17] for single-hop wireless sensor networks is presented which is more suitable for the periodical data gathering applications. EECS extends LEACH algorithm by dynamic sizing of clusters based on cluster distance from the base station. However, EECS does not consider the structural characteristics of network topology and thus cluster heads are only elected on the basis of residual energy. Energy-efficient cluster formation protocol (EECF) [18] selects cluster heads following a three-way message exchange between each sensor and its neighbors. Cluster head election is mainly based on sensors' respective residual energies and degrees. The message exchange complexity of EECF is \(O(1)\) and a worst-case convergence time complexity is \(O(N)\). In [19], a novel energy efficient cluster formation algorithm based on a multi-criterion optimization technique is presented, which uses multiple individual metrics in the cluster head selection process as input while simultaneously optimizing on the energy efficiency of the individual sensor nodes as well as the overall system. However, the multi-criterion optimization technique involves some complex matrix operations. As sensor nodes are significantly constrained in computational capacity, it is difficult for them to execute complex matrix operations.
# Controlling Traffic Congestion in a Residential Area via GLOSA Development
## 2 Materials and Methods
### Simulation Scenario  
The algorithm controlling traffic congestion in residential areas (CTCRA) is shown in Figure 4, which was developed. To implement the algorithm in a simulation environment like VISSIM, the algorithm collects information about all drivers (vehicles) who communicate with navigation applications and plan to take a shortcut through the residential area. The algorithm determines a range of air quality levels (pollution levels) within the residential area, which, if the minimum of these levels is reached, will allow the driver to take a shortened route (this limit is often outside peak times). Therefore, the algorithm only affects vehicles that intend to take the shortened route at peak time.  
A scenario was implemented in which the percentage of users of the navigation application changed. An evolution in travel times was observed with the increase in users of the application. While the travel time remains constant for the main road, the travel time increases through the residential area for the first 25% of application users. The reason behind the increase in travel time is the presence of the majority of drivers on the main road. When using the app, drivers take the shortcut through the residential area. Whenever the percentage of drivers using the application is high, the route through the residential area becomes very crowded.
## 3 Results and Discussion
### Effluent Characterization at Optimal Conditions  
Under optimal conditions (EC I: 4.0 A; RPM: 100; pH: 7; EO NaCl: 1.0 g L\({}^{-1}\); D: 0.75 cm. I: 2 A; pH 7), the generated effluent was characterized and the results are shown in Figure 9. The COD, DOC, color, and NH\({}_{3}\)-N removals for the EC process were 63%, 69%, 94%, and 50%, respectively, and the EO system generated 82%, 86%, 99%, and 81%, respectively. The application of the EO process as an additional treatment to the EC system allowed leaching with a higher level of treatment, achieving increases in COD, DOC, color, and NH\({}_{3}\)-N removal of 19%, 17%, 5%, and 31%, respectively. An increase was achieved for IB. An IB of 0.26 (BOD\({}_{5}\): 300 \(\pm\) 10 mg L\({}^{-1}\)) was obtained for EC. An IB of 0.48 (BOD\({}_{5}\): 280 \(\pm\) 10 mg L\({}^{-1}\)) was obtained for the EO process.  
In the EC process, the removal of COD (63%) and DOC (69%) could be explained by the destabilization of the colloidal material caused by the formation of Fe hydroxides. The removal of COD (82%) and DOC (86%) in the EO process could explained by the complete oxidation of organic material to CO\({}_{2}\) owing to the action of \(\bullet\)OH radicals formed in the DDB anode and indirect EO mediated by the presence of Cl\({}^{-}\). The EO process removed 94% of the color; in contrast, the EO process only generated an additional 5%  
Figure 9: Effluent characterization under optimal treatment conditions. COD—chemical oxygen demand; BOD\({}_{5}\)—biochemical oxygen demand; BI—biodegradability index; DOC—dissolved organic carbon; NH\({}_{3}\)–N—ammonical nitrogen; Pt-Co U—platinum—cobalt units.  
removal (from 94% to 99%). EC had a good removal effect on humic substances responsible for color (HA and FA) [88]; however, EO further decreased the color from 200 to 20 Pt Co U, indicating that EO removed humic substances that were unaffected by the EC process. The removal of 50% of NH\({}_{3}\)-N in the EC process could be explained by two different processes. During the first stay, agitation favored the elimination of NH\({}_{3}\)-N in a gaseous form, in addition to an increase in pH and temperature, which decreased the solubility of NH\({}_{3}\)-N. The second process was indirect EO in the EC reactor, which was favored by the presence of Cl\({}^{-}\) in the raw leachates (6700 \(\pm\) 200 mg L\({}^{-1}\)), which were transformed electrochemically to hypochlorous acid (EC process removed 46% of Cl\({}^{-}\), from 6700 \(\pm\) 300 mg L\({}^{-1}\) to 3600 \(\pm\) 100 mg L\({}^{-1}\)). The 31% additional removal of NH\({}_{3}\)-N in the EO process (from 50% to 81%) could be explained as follows: Cl\({}^{-}\) was oxidized at the anode in the presence of an electrical current (electrons) and chlorine gas was formed (Cl\({}_{2}\)). Water and Cl\({}_{2}\) reacted immediately to produce HClO and hypochlorite (ClO\({}^{-}\)), which were responsible for the oxidation of NH\({}_{3}\)-N to N\({}_{2}\) (EO process removed 59% of Cl\({}^{-}\), from 4600 \(\pm\) 100 mg L\({}^{-1}\) to 1900 \(\pm\) 100 mg L\({}^{-1}\)) [89].
# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 3 _Ekf_ Based Model
### Self-Adaptive Model  
The \(EKF\) based self-adaptive method (\(SAM\)) is used to gain the safety range of the current curve. In \(EKF\), according to mathematical model of \(TM\) driven by \(SS\), the setting profile is chosen as the prediction value, and the drive feedback current is chosen as the measurement value. At first, the \(SAM\) should ensure the absence of foreign bodies, and then injection machine mold close and open automatically according to self-adaptive time \(n\). Every \(EKF\) current curve will be saved in a \(m\times n\)-dimensional matrix, \(A_{m\times n}\). \(m\) is the time of sample, and the position of every sampled point is saved in a \(m\)-dimensional matrix, \(P\). Equation (17) records the data to calculate the standard current curves.  
\[\left\{\begin{array}{l}A_{m\times n}=[A_{1},A_{2}\ldots A_{k}\ldots A_{n}],\\ P=[p_{1},p_{2},\ldots,p_{m}]^{T},\end{array}\right. \tag{17}\]  
where \(A_{k}=[i_{k1},i_{k2},\ldots,i_{km}]^{T}\) is one time current curve optimized by \(EKF\).  
Then, we can get the safety range of current curve as follows.  
\[\left\{\begin{array}{l}U=\mathcal{L}_{max}(A_{1},A_{2}\ldots A_{k}\ldots A_ {n})+\Delta,\\ L=\mathcal{L}_{min}(A_{1},A_{2}\ldots A_{k}\ldots A_{n})-\Delta,\end{array}\right. \tag{18}\]  
where \(\mathcal{L}_{max}\) and \(\mathcal{L}_{min}\) are functions to get the maximum and minimum values of every sampled point, respectively. \(U\) and \(L\) are upper deviation current curve and lower deviation current curve, respectively; and the dimension of \(U\) and \(L\) is \(m\). \(\Delta\) is true error.  
Regarding Equation (18), the safety range at every position should be between the maximum and minimum currents within \(n\) self-adaptive processes. Note that \(\Delta\) is necessary because the self-adaptive time \(n\) is limited. However, because of the unknown of the true error value, the error value is calculated by the Bessel formula below.  
\[\Delta=\sqrt{\frac{\sum_{k=1}^{n}(A_{k}-\bar{A})^{2}}{n-1}}, \tag{19}\]  
where \(\bar{A}=\frac{\sum_{k=1}^{n}A_{k}}{n}\) is the arithmetic mean of \(n\)-time curves.  
When the \(IMM\) is running in \(MP\) checking area, it will be checked whether the actual current curve is between the two curves. If not, the machine will stop closing and then open to protect the mold.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 4 Neural Networks
### Relevant Papers
The GCN captures the topological state of the network, while the GAN is used to predict future traffic bursts. An additional Simple Feedforward Neural Network (SFNN) is used to predict the most probable future network state. One-week-long network snapshots were used in the GCN-GAN model to tackle short-term prediction, while three-month-long snapshots were used for long-term prediction. The model was validated with real traffic data from the Telus Fiber Network. The results showed that the GCN-GAN model was more effective in predicting burst traffic when compared with the LSTM model in terms of both lower mean squared error values and lower overestimation of traffic volume.
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
## 2 Materials and Methods  
This research focused on studying plasma combustion technology for MGT engines using biodiesel fuel resources. We focused on the external integration of a hybrid plasma-rich fuel mixture in the combustion chamber intake manifold of the proposed fabricated 50 kW (67 hp) testbed MGT engine. We used several biofuel mixtures for the operation of the MGT engine under three different loading conditions. We fabricated the MGT engine system in our laboratory using individual engine components, such as compressor systems, a plasma torch unit, and GTE system assembly modifications. Other operations involved fuel system design, air intake system construction, and atomization construction for the testbed engine. We investigated the viability of adopting plasma technology and alternative renewable biodiesel fuel (from animal fat waste) in MGT engines. The findings should be useful in enhancing effective engine performance and reducing operating costs and GHG emissions.
## 5 Experimental Methodology
### Performance Metrics  
We tracked the following performance indicators:  
1. Time complexity: * Trie construction: \(O(N\cdot L)\) where \(L\) is address length; * Path length calculation: \(O(N)\); * Statistical analysis: \(O(K)\) where \(K\) is unique path lengths.
2. Space complexity: * Peak memory usage; * Node count; * Storage overhead per address.  
These metrics provide a quantitative basis for validating our theoretical predictions against experimental results, presented in Section 5.  
All experimental code and data are available in our public repository [28] for reproducibility.
# Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect
## 3 Verification and Validation
### Boundary Conditions  
The boundary conditions applied in the simulation are summarized in Table 4. A key aspect of the boundary conditions is the control of pressure at the hot outlet. In experimental setups, the hot outlet opening ratio is adjusted by controlling a valve, which alters the pressure. As the hot outlet opening ratio decreases, the pressure increases, reducing gas flow through the hot outlet and increasing the discharge through the cold outlet. This refers to the ratio of the surface-averaged mass flow rate discharged through the cold outlet (\(\dot{m}_{c}\)) to the inlet mass flow rate (\(\dot{m}_{i}\)). However, implementing this control method in simulations is challenging. Therefore, following the approach of [PERSON] [27], the pressure at the hot outlet was controlled at 1.4, 1.5, 1.6, and 1.7 atm to replicate the experimental cold mass fraction. Figure 5 compares the experimentally measured cold fraction with the values obtained from simulations using the standard k-\(\epsilon\) model and grid systems described in Section 3.2, with varying outlet pressures. The results show good agreement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Boundaries** & **Type** & **Variable** & **Value** \\ \hline \multirow{3}{*}{Inlet} & \multirow{3}{*}{stagnation inlet} & pressure & 200 kpa \\  & & temperature & default (300 K) \\  & & turbulent intensity & 5\% \\ \hline \multirow{3}{*}{Cold outlet} & \multirow{3}{*}{pressure outlet} & pressure & 1 atm \\  & & temperature & default (300 K) \\ \hline \multirow{3}{*}{Hot outlet} & \multirow{3}{*}{pressure outlet} & pressure & 1.4, 1.5, 1.6, 1.7 (atm) \\  & & temperature & default (300 K) \\ \hline \multirow{3}{*}{Others} & \multirow{3}{*}{wall} & shear stress & \multirow{3}{*}{no-slip} \\  & & spectral specification & \\ \cline{1-1}  & & thermal specification & adiabatic \\ \hline \hline \end{tabular}
\end{table}
Table 4: Boundary conditions.  
Figure 4: Representative grid system.
## 3 Dynamic Response Analysis of RPC Columns Under Coupled Fire and Explosion
### Fire Duration  
Fire durations of 0 min, 30 min, and 120 min were selected to analyze the effect of fire duration on the blast resistance of RPC columns. The mid-span displacement curves of the columns subjected to different fire durations are shown in Figure 6, from which it can be seen that as the fire duration increases, the ultimate deflection and deformation rate of the RPC columns in the mid-span increase significantly, thus proving that their blast resistance decreases.  
The maximum deformation and residual deformation in the span of the RPC columns with fire duration are shown in Figure 7, from which it can be seen that the maximum deformation and residual deformation in the span of RPC columns increase with fire duration. Under the effect of the explosive load, the maximum deformation of the RPC column is 7.71 mm and the residual deformation is 6.36 mm when the fire duration is 120 min, which is 29.9% and 37.4% greater than that of the RPC column with a maximum deformation of 5.93 mm and residual deformation of 4.63 mm during a fire duration of 30 min, with 4.58 mm and 3.94 mm in the residual deformations in the RPC column with a fire duration of 0 min. The maximum deformation of 4.58 mm and the residual deformation of 3.94 mm increased by 68% and 61.4%, respectively. The above results indicate that the blast resistance of RPC columns under high-temperature conditions is greatly weakened.  
Figure 5: RPC column numerical model.  
Figure 6: Horizontal displacement–time course curves in RPC column spans with different fire times.
## 2 Materials and Methods
### Molecular Docking  
The three-dimensional structures of the ligands were generated with UCSF Chimera 1.11.2 [31] through their SMILES string. The structures were energy-minimized using Chimera default parameters (which include MMTK, Amber, and Amber's Antechamber parameters) [32]. Rotatable bonds and atomic charges from the ligands were defined with AutoDock Tools 1.5.6. [33]. The crystallographic structure of the AChE receptor (PDB ID:1 EVE) in complex with donepezil was downloaded from the Protein Data Bank ([[https://www.rcsb.org/](https://www.rcsb.org/)]([https://www.rcsb.org/](https://www.rcsb.org/))). The preparation for the receptor with AutoDock Tools included emotion of the co-crystallized ligand and water molecules, hydrogen addition and charges calculation. For the docking, AutoDock Vina [34] was employed, with a grid box of 30 \(\times\) 30 \(\times\) 30 A with center coordinates x = 2.27731, y = 63.7499 and z = 65.4998. Five docking runs with 10 poses each were made per ligand, with an exhaustiveness of 8. Visualizations of the docked poses for their analysis were made with Chimera. To validate the docking protocol, a re-docking of co-crystallized donepezil was made and the resulting pose was compared to the crystallographic one through their RMSD.