esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 4 Results and Analysis
### Comparative Results of Different Models  
Other advanced models, including FCN, PSPNet [33], U-Net [34], U-Net++ [35], SegNet, and R2U-Net [36] were chosen for a comparison. Table 4 presents the diverse performance of these eight models across four evaluation metric categories, along with the average inference time for each model on a single image. Our proposed method was outstanding in terms of each metric category. To illustrate the superiority of the proposed method more intuitively, four images were randomly selected from the test set, and the detection results of the above methods for rebar image segmentation were compared, as shown in Figure 8. It was found that our proposed model showed better segmentation results than the other methods when the images contained shadows, occlusion, and rebar intersection at the rebar edges.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Serial** & **IB *** & **AT *** & **AC *** & **Param (M)** & **mIoU (\%)** & **Precision (\%)** & **Recall (\%)** & **F1\_Score** \\ \hline
1 & & & & 42.1 & 89.66 & 97.20 & 91.91 & 94.48 \\
2 & \(\surd\) & & & 26.9 & 88.58 & 96.16 & 90.43 & 93.21 \\
3 & \(\surd\) & \(\surd\) & & 27.4 & 92.98 & 97.32 & 95.29 & 96.29 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & 27.4 & 94.62 & 97.42 & 96.95 & 97.18 \\ \hline \hline \end{tabular}  
* IB *: improved backbone network; AT *: addition of an efficient channel attention module; AC *: adjustment of atrous convolution.  
\end{table}
Table 3: Ablation experiments of improved module.  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mIoU (\%)** & \
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & **Recall (\%)** & **F1\_Score (\%)** &
\begin{tabular}{c} **Time** \\ **(s/Item)** \\ \end{tabular} \\ \hline U-Net & 92.88 & 96.42 & 95.98 & 96.20 & 2.37 \\ SegNet & 86.72 & 90.87 & 88.52 & 89.68 & 2.14 \\ FCN & 82.30 & 88.43 & 85.19 & 86.78 & 2.72 \\ U-Net++ & 92.81 & 96.56 & 95.67 & 96.11 & 2.11 \\ PSPNet & 81.97 & 91.81 & 87.42 & 89.56 & 2.25 \\ Deeplab v3+ & 89.66 & 97.20 & 91.91 & 94.48 & 1.86 \\ R2U-Net & 92.98 & 96.68 & 95.48 & 96.08 & 1.42 \\ Ours & 94.62 & 97.42 & 96.95 & 97.18 & 1.21 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation of different models for rebar detection.
## 2 Experiments
### Image Processing Method  
A series of time-dependent laser ablation plume images of the composite propellant were obtained using an established high-speed photogrammetry system. The plume features were extracted using an image segmentation method, as shown in Figure 4. We introduced the length, area, expansion rate, and relative luminosity of the plume to evaluate its characteristics. The plume length was defined as the extension of the plume front along the normal direction of the ablated surface. The plume area was the scaled luminous region in the plume image, and the expansion rate was the variation in displacement per second of the downstream plume. The relative luminosity referred to the ratio of the observed luminosity to the maximum luminosity.  
To calculate the plume area, a binarization method was employed to distinguish the plume from the background. We first collected a large number of background images in the experimental environment and extracted the maximum grey value by greyscale transformation, which served as the ambient value. For each plume image, comprising a finite number of pixels, the presence of any plume in a region was determined by comparing the grey value of the pixel with the ambient grey value. Subsequently, the plume area was computed by accumulating all the pixels in the image with a grey value greater than the ambient value. Furthermore, the relative luminosity was obtained by calculating the ratio of the grey value in the plume region to its maximum value.  
The aforementioned plume parameters are intimately concerned with the working performance of the propellant in LAPPTs. Variation in plume length as a function of time can directly reflect the expansion rate of the plume along the normal direction of the propellant surface. When the plume length exhibits a nearly linear increase, it indicates that the plume expansion rate is approaching a constant value. If there are abrupt changes or oscillations associated with the growth of the plume, then the generated plume is more  
Figure 4: Plume feature extraction.  
Figure 3: Preparation of composite propellant samples.  
volatile, which can also affect the discharge success rate of the thruster. The plume area can partially reflect the initial plume divergence. The larger the length-to-width ratio of the plume along the normal direction of the propellant surface, the smaller the diffusion rate in the non-principal propelling direction, which would help to improve propellant utilization. In addition, the higher the plume expansion rate, the larger the quantity of neutral gas that enters the discharge channel in a single pulsed discharge. This is also expected to augment propellant utilization, while the relative luminosity of the plume can partially reflect ionizability and electron density [35; 36].
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 4 Proposed Autonomous Scheduling Scheme for Convergecast
### Channel Offset Selection Mechanism
```
1:procedureGet current operation and channel offset(\(ASN\), \(i\))
2:\(ts\leftarrow(ASN+H_{v_{i}})\) mod \(L(S_{Conv})\)\(\triangleright\) relative timeslot number in current \(S_{Conv}\)
3:\(op\gets OP(S_{Conv},ts)\)\(\triangleright\) get operation for \(ts\) from \(S_{Conv}\)
4:if\(op\
eq idle\)then
5:if\(op=TX\)then\(CO\leftarrow(H_{v_{i}}-1)/2\)
6:elseif\(op=RX\)then\(CO\gets H_{v_{i}}/2\)
7:elseif\(op=BT\)then\(CO\gets(H_{v_{i}}-1)/2\)
8:elseif\(op=BR\)then\(CO\gets H_{v_{i}}/2\)
9:endif
10:endif
11:\(ts^{\prime}\leftarrow(ASN)\) mod \(L(S_{Base})\)\(\triangleright\) relative timeslot number in current \(S_{Base}\)
12:\(op^{\prime}\gets OP(S_{Base},ts^{\prime})\)\(\triangleright\) get operation for \(ts^{\prime}\) from \(S_{Base}\)
13:if\(op^{\prime}\
eq idle\)\(\triangleright\) if the operation of \(S_{Base}\) is not idle
14:\(op\gets op^{\prime}\)\(\triangleright\) the operation of \(S_{Conv}\) is suppressed
15:\(CO\gets 0\)
16:endif
17:return\(op,CO\)
18:endprocedure
```  
**Algorithm 2** Decide current timeslot operation and channel offset selection of the convergecast slotframe. Figure 5 shows an example of a schedule for the convergecast slotframe. For a network consisting of seven nodes as shown in Figure 4(a), Figure 4(b) is a schedule for each node of the convergecast slotframe with \(L(S_{Conv})=20\) (i.e., \(N=10\)). Figure 4(c) shows a schedule of the convergecast slotframe with the selected channel offsets. In Figure 4(c), links with Ranks 0 and 1 use Channel Offset 0, while links with Rank 2 use Channel Offset 1. Notice that we can see at Timeslots 5, 10, 11 and 19 that two or more communications in one timeslot are executed concurrently using different channel offsets. The convergecast slotframe provides a schedule for delivering packets generated from all nodes in the network to the sink with minimal delay. Using the schedule shown in Figure 4(c), a node can transmit packets to the sink using the same number of consecutive timeslots as the hop count of the node. The packet at node \(v_{7}\) is delivered to the sink using three consecutive timeslots (green cells at Timeslots 11, 12 and 13 of Figure 4(c)). For packet delivery to the sink, nodes \(v_{3}\) and \(v_{9}\) use only one timeslot based on their hop counts as shown in Timeslots 5 and 17 of Figure 5c, respectively. Nodes \(v_{1}\), \(v_{4}\) and \(v_{6}\) each use two consecutive timeslots based on their hop counts. Our proposed channel scheduling scheme was named Escalator because the packets generated in the nodes are sent up the stairs along the autonomously-generated consecutive timeslot based on the network topology.
## 4 Performance Evaluation
### Simulation Results  
In this section, we describe the simulation results and analyze the improvement in performance as a function of mobility speed and traffic load. The former demonstrates the resiliency of the proposed scheme, while the latter showcases its adaptability to increasing traffic loads.
# Effect of Platinum Doping on the Morphology and Sensing Performance for CuO-Based Gas Sensor
###### Abstract  
Pristine and Pt-doped CuO nano-flowers were synthesized by a simple water bath heating method in this paper. Highly sensitive hydrogen sulfide (H\({}_{2}\)S) gas sensors based on Pt-doped CuO nano-flowers were fabricated. Scanning electron microscopy, X-ray diffraction, inductively coupled plasma atomic emission spectrometry, and energy dispersive X-ray spectroscopy were used to examine the characteristics and morphology of materials. The sensing performances of sensors with different concentrations of Pt dopants were evaluated at different operating temperatures. The results indicated that the CuO sensor doped with 1.25 wt % Pt exhibited the highest response (R\({}_{g}\)/R\({}_{a}\), where R\({}_{g}\) is the resistance in gas, and R\({}_{a}\) is the resistance in air) of 135.1 to 10 ppm H\({}_{2}\)S at 40 \({}^{\circ}\)C, which was 13.1 times higher than the response of a pure CuO sensor. Pt doping also plays an important role for the enhancement of H\({}_{2}\)S selectivity against C\({}_{2}\)H\({}_{5}\)OH, NH\({}_{3}\), H\({}_{2}\), CH\({}_{3}\)COCH\({}_{3}\), and NO\({}_{2}\).  
CuO; gas sensor; Pt doping; low temperature Article
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 5 Implementation
### Framework Structure  
The general structure of the framework is shown in Figure 2. simulator.cc is the main file of the project, in which the NDN-specific parameters and objects, as well as the underlying ns-3 objects, are set up. First, simulator.cc calls config-file-reader.cc to read the network parameters defined by the user. The config-file reader reads the files parameters.param, defaults.cnf, and a user-defined input file, which we call input.cnf here.  
parameters.param defines key-rule pairs, where the key corresponds to a parameter name, and the rule indicates the allowed values for the parameter. A rule is a set of numbers or strings, e.g., [1:1000] indicates numbers between 1 and 1000. After that, the program reads defaults.cnf, in which key-value pairs can be defined, serving as default values for each parameter. A value is an element from a rule as defined above. The default values are supposed to ensure that no parameter value is undefined. Finally, the config-file reader reads input.cnf, which specifies key-value pairs like defaults.cnf, but for specific simulations. The values from input.cnf overwrite those in the defaults file.  
After reading the input files, assuming they have not caused a runtime exception, the parsed parameters are passed to graph-generator.cc. This file generates a graph, which is, however, not guaranteed to be connected. The program either generates new graphs until a connected one is created or until a predefined number of attempts is exceeded. In the latter case, the program terminates with an exception. If the graph creation succeeds, the connected graph is written into graph.txt and, together with the input parameters, serves as input for simulator.cc. This file creates the actual NDN network from the given graph text file. It also handles global network parameters like the caching scheme, caching strategy, and forwarding strategy. In order to measure the energy consumption of each node, the simulator installs so-called device energy models, which are described in more detail in Section 5.4. After executing the simulation, the energy consumption results are written into results.csv.
## 2 Materials and Methods
### Evaluation Metrics  
To determine the usefulness of this method, evaluation was performed using the holdout method for 42 cases (grades II and III: 12 cases; grade IV: 30 cases) on the BraTS2018 dataset, which was not used for training. The tumor region was extracted from the 42 cases previously mentioned, although there were 4 cases with 2 lesions. Therefore, grading was performed on 13 VOIs of grade II/III and 33 VOIs of grade IV.  
The Dice similarity coefficient (DSC) was used to evaluate the tumor-region-extraction accuracy [28]. It is used to quantitatively evaluate the similarity between two sets and is expressed by Equation (3), where A and B denote the results of tumor-region extraction and ground truth, respectively.  
\[\text{DSC}= \frac{2|\text{A}\cap\text{B}|}{|\text{A}|+|\text{B}|} \tag{3}\]  
Figure 3: 3D CNN architecture used for grading. This model is extension of ResNet, and it was designed and trained by us.  
In addition, accuracy, sensitivity, and specificity were used to evaluate the grading accuracy. They are expressed by Equations (4)-(6), which are defined as:  
\[\text{Accuracy}=\frac{\text{VOIs classified correctly}}{\text{All VOIs}} \tag{4}\]  
\[\text{Sensitivity}=\frac{\text{VOIs classified as grade IV correctly}}{\text{VOIs classified as grade IV}} \tag{5}\]  
\[\text{Specificity}=\frac{\text{VOIs classified as grade II and III correctly}}{\text{VOIs classified as grade II and III}} \tag{6}\]  
They were used for performance evaluation. Furthermore, the receiver operating characteristic (ROC) curve was created by changing the threshold to classify from probability of grades II and III or grade IV from 0 to 1, and the area under the curve (AUC) was calculated [29].
# Optimal Energy Management within a Microgrid: A Comparative Study
## 2 Model for Microgrid Components
### Feeders  
Each feeder is modeled as a line of transmission, as is illustrated in Figure 1, where \(I_{i}\) and \(E_{i}\) are the phasors of the injected electricity and voltage, respectively, on the node \(i\) (\(i=k\), \(m\)) of the microgrid. \(R\), \(L\), and \(B_{c}\) represent the parameters of resistance series, inductance series, and shunt susceptance, respectively, of the feeder. The relationship electricity-voltage of the equivalent circuit is given by Equation (5) [21].  
Admittance matrix elements are evaluated in Equations (4) and (5).  
\[\left[\begin{array}{c}I_{k}^{t_{z}}\\ I_{m}^{t_{z}}\end{array}\right]=\left[\begin{array}{cc}Y_{kk}&Y_{km}\\ Y_{mk}&Y_{mm}\end{array}\right]\left[\begin{array}{c}E_{k}^{t_{z}}\\ E_{m}^{t_{z}}\end{array}\right];\forall t_{z}\in T \tag{3}\] \[Y_{kk}=Y_{mm}=(y_{km}+j\!\cdot\!0\!\cdot\!5\!\cdot\!Bc)=G_{kk}+jB_ {kk};Y_{km}=Y_{mk}=-y_{km}=G_{km}+jB_{km}\] (4) \[G_{km}=R/R^{2}+(\omega L)^{2};B_{km}=-\omega L/R^{2}+(\omega L)^{2} \tag{5}\]  
Starting from Figure 1 and Equation (3) it is possible to obtain expressions that model the injection of active and reactive power--Equations (6) and (7), respectively--on the node \(i\) (\(i=k\), \(m\)) at each moment \(t_{T}\in z\)[21] where \(j=k\), \(m\), being \(j\
eq i\). The variables \(V_{n}^{t_{z}}\) and \(\theta_{n}^{t_{z}}\) (\(n=i\), \(j\)) represent the magnitude and the angle of the nodal voltage phasor \(E_{i}\):  
\[P_{iny\;i}^{t_{z}}=\left(V_{i}^{t_{z}}\right)^{2}\!G_{ii}+V_{i}^{t_{z}}V_{j}^{t _{z}}\!\left[G_{ij}\cos\left(\theta_{i}^{t_{z}}-\theta_{j}^{t_{z}}\right)+B_{ ij}\sin\left(\theta_{i}^{t_{z}}-\theta_{j}^{t_{z}}\right)\right] \tag{6}\]  
\[\mathcal{G}_{iny\;i}^{inj\;t_{z}}=-\left(V_{i}^{t_{z}}\right)^{2}B_{ii}+V_{i}^ {t_{z}}V_{j}^{t_{z}}\!\left[G_{ij}\sin\left(\theta_{i}^{t_{z}}-\theta_{j}^{t_ {z}}\right)-B_{ij}\cos\left(\theta_{i}^{t_{z}}-\theta_{j}^{t_{z}}\right)\right] \tag{7}\]  
The decision variables associated to these feeders correspond to the nodal voltages in its terminals \(\left[V_{k}^{t_{z}},V_{m}^{t_{z}},\theta_{k}^{t_{z}},\theta_{m}^{t_{z}}\right] \in y_{RD}\) (\(\forall t_{z}\in T\)).
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 4 Results
### Validation maneuvers  
In order to validate the central non-linear model-based predictive control algorithm, two driving maneuvers are utilized. First, the double lane change driving maneuver is used [31]. For this purpose, the vehicle is first accelerated from standstill to the target velocity before performing the double lane change at constant velocity. The first lane change is performed counterclockwise. The target velocity within this contribution equals 50 km/h. The track limits for the double lane change are defined in the ISO standard. By combining the acceleration phase and the dynamic lane changes, the central predictive control can be validated with regard to all control objectives. The second driving maneuver for validation is a sinusoidal steering. This maneuver also involves accelerating the vehicle to a velocity of 50 km/h and then performing sinusoidal steering, according to [32]. Three periods with a steering wheel angle amplitude of 68\({}^{\circ}\) and a frequency of 1 Hz are performed. In addition, a significantly reduced friction coefficient of 0.4 is used for this driving maneuver.
# Few-Shot Knowledge Graph Completion Model Based on Relation Learning
## 1 Introduction
By considering relations, our FKGC model can infer missing facts more effectively. This approach enhances the model's generalization capability and allows for the utilization of more contextual semantics to handle few-shot relations. To improve the accuracy of link prediction, this paper proposes a few-shot knowledge graph completion model (FRL-KGC), which makes the following contributions:  
1. We introduce the FRL-KGC model, which incorporates a gating mechanism to extract valuable contextual semantics from the head entity, tail entity, and neighborhood information, specifically addressing high-order neighborhood information in the knowledge graph. Furthermore, we leverage the correlation between entity pairs in the reference set to represent relations, reducing the dependency of relation embeddings on the central entity. 2. We effectively utilize both the structural and textual information of the knowledge graph to capture features related to few-shot relations. 3. Experimental evaluations are conducted on two publicly available datasets, and the results demonstrate that our proposed model outperforms other KGC models. Additionally, ablation experiments validate the effectiveness of each key module in our model.
## 4 Experiments
### Ablation Studies
#### 4.3.4 Ablation study on RFEM  
To demonstrate that the RFEM can improve the detection performance of medium-scale and large-scale objects by enlarging the receptive field, we conducted ablation experiments on both the VisDrone and DOTA datasets, and the experimental results are shown in Table 6.  
As evident from the experimental results, the RFEM improves the overall detection accuracy of our model. This is achieved by leveraging global information modeling to expand the receptive field, thereby boosting the model's performance.
## 2 Mathematical and Physical Models  
Table 1 lists two different thermodynamic models and three chemical kinetic models commonly used in the previous studies to characterize the internal energy excitations and the chemical reactions of air species. Every chemical kinetic model contains three models with different number of components, they are 5-species (N\({}_{2}\), O\({}_{2}\), N, O, NO), 7-species (N\({}_{2}\), O\({}_{2}\), N, O, NO\({}^{+}\), e\({}^{-}\)), and 11-species (N\({}_{2}\), O\({}_{2}\), N, O, NO, NO\({}^{+}\), e\({}^{-}\), \(N_{2}^{+}\), O\({}^{+}\)). Any one thermodynamic model together with any one chemical reaction model of 5-, 7- or 11-species can be chosen from Table 1 to calculate the thermochemical nonequilibrium effects for the hypersonic flow simulation, and thus there are eighteen combinations in all for comparison. For the two-temperature model, \(T_{tr}\) represents the equilibrium temperature corresponding to the translational energy mode and the rotational energy mode, and \(T_{vc}\) corresponds to the equilibrium temperature of the vibrational energy mode and the electronic energy mode; The one-temperature model indicates that all the internal energy modes are equilibrated in one temperature, that is \(T_{tr}=T_{vc}=T\). In the present study, the governing equations of hypersonic flow are still based on the Navier-Stokes (N-S) equations under the continuum assumption, closed with the models of source terms and transport properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Therodynamic Model** & **Chemical Kinetic Model** \\ \hline One-temperature & Park [9] & 5-species 7-species 11-species \\ \hline Two-temperature [8] & Gupta [10] & 5-species 7-species 11-species \\ \hline Dunn-Kang [11] & 5-species 7-species 11-species \\ \hline \hline \end{tabular} The chemical reactions of each chemical kinetic model are shown in Appendix A..  
\end{table}
Table 1: Thermodynamic models and chemical kinetic models employed in the study.
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Shape Similarity
#### 2.1.2 Shape Feature Extraction by Hausdorff Distance  
The Hausdorff distance reflects the proximity of positions between curves, and similarity is calculated by measuring the distance between discrete point sets. The basic idea is as follows:  
Assuming point sets \(A=\{a_{1},a_{2},\dots\ a_{m}\}\) and, \(B=\{b_{1},b_{2},\dots\ b_{n}\}\) the calculation using the Hausdorff distance calculation formula is as shown in Equation (2):  
\[H(A,B)=\max(h(A,B),h(B,A)) \tag{2}\]  
where \(h\) (\(A\), \(B\)) and \(h\) (\(B\), \(A\)) are one-way Hausdorff distances, representing the maximum of the minimum distances between all points in a set of points and another set of points. The details are shown as Equation (3).  
\[\left\{\begin{array}{l}h(A,B)=\max(a\in A)min(b\in B)\parallel a-b\parallel \\ h(B,A)=\max(b\in B)min(a\in A)\parallel a-b\parallel\end{array}\right. \tag{3}\]
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 3 Feature Transformation
### Classification  
After feature transformation, the proposed method performs final classification using the KNN approach. A set of class prototypes is maintained to support the classification process. For base classes, features of all training samples are extracted using the best-performing feature extractor obtained during training, and the mean feature vector of each class is computed and stored as its class prototype. For incremental classes, the prototype is calculated as the mean of the features extracted from the support set samples. During inference, the test sample and all class prototypes are passed through the same feature transformation module. Classification is then performed based on cosine similarity: the test sample is assigned to the class whose prototype has the highest cosine similarity with the test feature.