esa-sceva/satcom-chunk-collection huggingface dataset:
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 9 Conclusions  
In this paper we have proposed a D2D-based V2P resource allocation based on the distance between vehicles and road-side pedestrians. We have performed two separate tasks after classifying the users on the basis of their vulnerability; firstly, we have performed a Q-learning-based reinforcement learning for power allocation. A one-to-many bipartite matching has been performed for pair assignment. The matching allows the users to form the connections based on their weights (depending on distance) and the reinforcement learning allocates the optimum power by giving a higher discount factor to the most vulnerable pedestrians. Users outside the D2D range are treated as normal cellular users with a minimum guaranteed quality of service. The results have shown that our method provides a higher throughput to the most vulnerable links while taking other factors such as threshold SINR, vehicular speed, and number of users into account.  
**Author Contributions:** U.A.K. proposed the idea, implemented it in software, and wrote the manuscript. S.S.L. supervised the entire research and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Technology Innovation Program (10062375, Development of Core Technologies Based on V2X and In-Vehicle Sensors for Path Prediction of the Surrounding Objects (Vehicle, Pedestrian, Motorcycle)) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Enhanced Label Noise Filtering with Multiple Voting
According to the adopted voting mechanism, EnFilter consists of two types: single-voting based (SVFilter) and multiple-voting based (MVFilter).The SVFilter detects noises only based on one-time voting of multiple classifiers, and therefore, it has a potential instability problem. To solve this instability problem, an MVFilter was proposed in our previous work [40]. In essence, an MVFilter consists of a set of SVFilters (assume this number is t). For the training data, if at least m (\(m\leq t\)) SVFilters treat it as noisy, then the MVFilter regards it as noisy. The internal mechanism of the SVFilter makes comparisons between each SVFilter, therefore through their fusion, the MVFilter can improve the noise detection stability and accuracy compared to the SVFilter. In the design of the MVFilter, one of the key issues is how to define the value of m (called the decision point), which actually defines the noise detection rule. An optimal decision point (ODP) could maximize the performance of an MVFilter. In [40], the decision points were empirically explored with different representative values. However, a systematic approach to determining the ODP is lacking. To this end, a novel approach is proposed in this work to compute the ODP for an MVFilter. Instead of only considering the number of errors, our approach takes cost information into account because many applications have unequal costs for various errors. When a cost matrix containing various cost values is given, the ODP selected by our approach is able to identify the noises that minimize the expected cost. The core idea of our approach is as follows: firstly, estimating the mislabeled data distribution in the noisy training dataset; secondly, estimating the expected costs of each possible decision point; and finally, the optimal decision point determined by minimizing the expected cost. We tested our approach based on a set of MVFilters. The experimental results show that our approach can significantly improve the performance of existing MVFilters. Our approach consistently works well for different datasets and different cost matrices. In addition, our approach is effective and straightforward. Only a few predefined parameters and prior knowledge are required. In the next section, we will briefly review ensemble learning based noise filters. Section 3 analyzes the performance of the MVFilter when costs are considered. Our novel approach is presented in Section 4. The experimental evaluations are presented in Section 5. Section 6 concludes this work and presents future work.
## 3 Results of Cyclic Loading Tests  
### Crack Pattern and Failure Mode  
Figure 6 shows the crack patterns and failure modes of specimens. In specimens with masonry infills, the initial cracks occurred in the diagonal direction at the corners of the infill wall. In specimens with thin walls (IF-0.5 and IFS-0.5), the diagonal tensile cracks were combined with horizontal bed joint sliding cracks, while in specimens with thick walls (IF-1.0 and IFS-1.0), almost all cracks occurred only in the diagonal direction. In specimens without a gap (IF-0.5 and IF-1.0), after the occurrence of the main diagonal crack, a number of short cracks in the parallel direction appeared above and below the main diagonal crack. On the other hand, in specimens with a gap (IFS-0.5 and IFS-1.0), a short diagonal crack occurred only below the main diagonal crack. Additionally, in the case of IF-0.5 and IF-1.0, a number of cracks with various angles occurred at the corner of the infill wall, showing a more similar pattern of corner crushing failure compared to that of the specimen with a gap. In general, specimens without a gap have widespread but small cracks, whereas a small number of large cracks was observed in specimens with a gap.  
In all the specimens, the crack pattern in masonry walls indicates diagonal tensile failure combined with bed joint sliding cracks. In IF-0.5 and IFS-0.5, the tendency of bed joint sliding was high, whereas in IF-1.0 and IFS-1.0, the tendency of diagonal tensile crack was high. In IFS-0.5, at a drift angle of around 5%, lateral buckling occurred at the top of the masonry wall, resulting in a drop-off in the out-of-plane direction. This is seemingly because the top boundary of the masonry wall was not constrained properly. The length-to-thickness ratio, \(l/t\), of the masonry wall in IFS-0.5 was 24.4 (2200/90). In IFS-1.0 with \(l/t\) of 2200/190 = 11.6, out-of-plane buckling was not observed. Additionally, in IFS-1.0, the gap between the bottom of the beam and the top of the masonry started to decrease as the drift ratio increased and eventually closed, probably due to the curvature shortening of columns. Figure 7 shows the gap closure due to lateral displacement.  
In the IF-0.5 specimen, the final failure of the frame was presumed to be'shear failure after flexural yielding', in which yielding in flexure precedes shear failure. In the IF-1.0 and IFS-1.0 specimens, a large diagonal crack developed at the end of the column. In all specimens, the test was terminated due to the spalling of cover concrete and detachment of transverse reinforcements, followed by the buckling of longitudinal rebars. Figure 8 shows the failure of columns.  
Figure 5: Test setup and loading protocol. (**a**) Test setup. (**b**) Loading protocol.  
Figure 6: Crack patterns and failure modes. (**a**) BF. (**b**) IF-0.5. (**c**) IFS-0.5. (**d**) IF-1.0. (**e**) IFS-1.0.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### T-Junction Estimation
#### 4.4.3 T-Junction Distance and Length Estimation  
The T-junction distance is estimated directly based on the branch radius and points used for estimating it. As we can observe from Figure 13, among three points used for branch radius estimation, the point in \(C\)-band with the highest intensity can be projected to the \(X_{T}\)-axis to get the end point \(E_{T}\). The start point \(O_{T}\) is \(2r_{B}\) distance before the \(E_{T}\) on the \(X_{S}\)-axis. By knowing \(O_{T}\), the distance \(\Delta x_{T}\) to the T-junction can be obtained as defined in Figure 3. On the other hand, the T-junction length \(l_{T}\) is obtained directly as the branch diameter \(l_{T}=2r_{B}\).
## 5 Experimental Results and Discussion
### Discussions  
Regarding the problem of the classification accuracy of the SDTSVM algorithm being highly dependent on the parameters and the classification order, an improved SDTSVM algorithm was proposed. In the proposed model, the classification order of the ISDTSVM algorithm was determined by the sum of the Euclidean distances between the multi-class samples centers and the parameters were optimized by the inertia weight PSO algorithm.  
As presented in Figures 6 and 16, the classification accuracy of the trained data sets was higher than 94% for the UCI data sets and the steel cord conveyor belt defects signals using the inertia weight PSO algorithm. As presented in Tables 2 and 4, for the Vowel, the Zoo and the Wine data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the conventional SDTSVM algorithm.  
In order to verify the impact of the imbalanced samples on the classification of the steel cord conveyor belt defects, experimentation was performed on the steel cord conveyor belt defects samples with a different imbalanced ratio (IR) [25]. The positive class was chosen for the fewest samples and the rest of the samples was a negative class. For the imbalanced classes classification evaluation, metrics such as accuracy, balanced accuracy and F1 were used [26]. A half of the samples were used for training and the other half of samples were used for testing. The steel cord conveyor belt defects samples with the different imbalanced ratio (IR) were classified using the ISDTSVM algorithm and the corresponding results are presented in Table 6. As can be observed, the lower IR samples had a higher accuracy, balanced accuracy and F1. The results showed that the imbalanced samples could decrease the classification accuracy of the ISDTSVM algorithm.  
On the whole, the SVM parameter \(C\) and \(\gamma\) could be optimized well using the inertia weight PSO algorithm and the optimal classification order could be determined by the sum of the Euclidean distances between the multi-class samples centers in the ISDTSVM algorithm. The ISDTSVM algorithm could efficiently improve the classification accuracy of the steel cord conveyor belt defects. It was noted that the imbalanced samples could reduce the classification accuracy when using the ISDTSVM algorithm. The ISDTSVM algorithm is recommended for further improvements, when there are imbalanced data sets.
## 2 Materials and Methods
### Statistical Procedures  
To perform statistical analyses, the SPSS statistical package (IBM SPSS Statistics Version 29.0, IBM Corporation, Armonk, NY, USA) was used. Descriptive values for each variable were calculated. The normality of data distribution was confirmed using the Kolmogorov-Smirnov test. One-way ANOVA (group) was conducted separately for FL and ACSA to examine differences between age groups. When a significant main effect or interaction was observed, [PERSON]'s post hoc test was conducted (\(p<0.05\)). Effect sizes (ES) for the ANOVA were determined by partial eta squared (\(\eta^{2}\)) (small: 0.01 to 0.059, moderate: 0.06 to 0.137 and large: \(>0.138\)). [PERSON]'s \(d\) statistical index determined ES for pairwise comparisons (trivial: 0-0.19, small: 0.20-0.49, moderate: 0.50-0.79 and large: 0.80 and greater) [34]. To assess test-retest reliability, the intra-class correlation coefficients (ICCs) were used. [PERSON]'s correlation coefficient [35] examined linear associations between the examined variables and was considered trivial (\(r<0.10\)), small (\(r=0.10\)-0.29), moderate (\(r=0.30\)-0.49), large (\(r=0.50\)-0.69), very large (\(r=0.70\)-0.89) and almost perfect (\(r\geq 0.90\)) [36].
## 6 Conclusions  
In this paper, we propose a new collision-free hybrid MAC protocol based on pipeline parallel transmission for distributed multi-channel underwater acoustic networks. Three new techniques are adopted in the proposed protocol. First, a collision-free hybrid MAC framework is proposed, where the handshaking and data transmission are implemented as a pipeline on multiple acoustic channels. Handshaking is implemented using the TDMA technique in a dedicated control channel to allow multiple successful handshakes in a transmission cycle and avoid collision. Second, the replication computation technique is employed in the data packet scheduling to reduce the control overhead in distributed environments. Third, greedy algorithms for TDMA time slot assignment and data packet scheduling is proposed to optimize the control and data packet transmission using the underwater time domain parallelism. Simulation results show that the proposed protocol outperforms SFAMA, ROPA and DSCT in throughput, packet delivery rate, and average energy consumption in the price of longer end-to-end delay introduced by the TDMA based handshaking.  
Conceptualization and methodology, J.Z., G.N.; software and validation, Z.H.; formal analysis and data curation, Z.H., Y.X., and J.Z.; writing--original draft preparation, Y.X. and J.Z.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61871191, the Science and Technology Planning Project of Guangzhou, grant number 201804010209, and the Key Program of Marine Economy Development, Department of Natural Resources of Guangdong Province, grant number YZRZHI[2020]009.  
The authors declare no conflicts of interest.
## 4 Conclusions  
This paper presented a new load optimization framework for Connected Smart Green Townhouses (CSGTs). It outperforms existing methods in terms of load reduction, cost savings, emission reduction, and accuracy, with lower MAE and higher \(R^{2}\). The results obtained show improvements of up to 15% in load efficiency, 15-20% in cost savings, and 10-15% in carbon emission reduction. Thus, it is effective in improving performance and supporting sustainability goals. It can adjust to real-time conditions, ensuring efficient and reliable operation for practical real-time applications in dynamic environments.  
Future work will examine the implementation of an integrated Machine Learning (ML) and Internet of Things (IoT) system. The goal is to improve adaptability and reduce manual intervention for greater efficiency and sustainability. Validation of the proposed framework will also be considered for various building configurations and locations to assess its robustness in urban and suburban settings. Smart grid scenarios such as buildings with high renewable energy penetration and battery storage will be examined. In addition, the proposed framework will be extended to large scale residential and commercial developments.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Proposed Model** & **[9]** & **[10]** \\ \hline Load Savings & 15\% & 10\% & 12\% \\ Cost Savings & 15\% & 8\% & 10\% \\ Carbon Emissions Reduction & 15\% & 9\% & 10\% \\ MAE (kWh) & 50 & 60 & 55 \\ \(R^{2}\) & 0.98 & 0.92 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Model Performance.  
**Author Contributions:** Conception and design, S.M.M., T.A.G., I.T.C. and H.T.; preparation and analysis, S.M.M., I.T.C. and T.A.G.; writing--original draft, T.A.G., S.M.M. and I.T.C.; writing--review and editing, T.A.G., S.M.M., I.T.C. and H.T.; supervision, T.A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Activation-Based Pruning of Neural Networks
## 4 Experiment Settings  
### Common Setting  
We implemented activation-based pruning on a 4-layer feedforward network with the total number of neurons in each layer as 300, 200, 100, and 50, respectively. We used the Rectified Linear Unit (ReLU) as the nonlinear activation function for all neurons. We used Stochastic Gradient Descent (SGD) as the optimization method, where the loss function is sparse categorical cross-entropy. To train the network, we used the Fashion MNIST database, publicly available through the Keras framework. The target training accuracy for each run was set to 98%. We allocated a part of the data for validation testing. We implemented early stopping by monitoring the validation loss so that the model did not overfit to the training data. If validation and training accuracy are improving, but validation loss is increasing, it signifies that the model might be overfitting to the training data. The stopping criterion was set at 0.1% so that training would stop when the validation loss exceeded 10% of the minimum validation loss for three consecutive times.  
In our study on activation-based pruning, we conducted an in-depth analysis of importance-based scoring. This evaluation was carried out using two distinct methods for assigning importance, which we termed _Neurons_ and _Neurons & Weights_. In the first case, we pruned \(k\%\) of the neurons with the lowest activation counters. In the second case, we computed the product of the activation counter of each neuron and the absolute value of the corresponding incoming weights and pruned \(k\%\) of the weights with the smallest product values. More precisely, consider a neuron with an associated activation counter \(a\). If the incoming weights associated with the neuron are given by the vector \(\mathbf{w}\), we computed the product of the absolute value of the weight vector with the activation counter, \(|\mathbf{w}|a\). We performed this operation for each neuron in the network and pruned \(k\%\) of the weights with the smallest product values.
# FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle
## 2 Design Specifications for the HEV
### Thermal Model and Cooling System  
Electric machine thermal analysis is usually performed using lumped equivalent circuits or 2D/3D finite element analysis [15; 42; 43]. The literature shows that these models can predict the average temperature of the different parts in an electric machine with acceptable accuracy. The Motor-CAD 2022 R2 software is a thermal analysis package which employs both lumped circuit and FEA modeling. The boundary conditions and material characteristics are adjusted based on empirical equations [44; 45]. This paper uses Motor-CAD for modeling of the proposed cooling system.  
Similar cooling jackets with spiral cooling channels are assumed for both the IM and IPMSM stators as shown in Figure 2. The literature shows that this provides good heat removal from the stator and produces a reasonably uniform temperature distribution [46]. The height and width of the water channels are 5 mm. The coolant information was presented earlier in Table 1.  
The rotor bars of induction machines have losses which are normally comparable in magnitude to the stator winding losses and so require a suitable cooling system [47]. In this paper, a spiral groove is assumed to be embedded into the IM shaft to remove these rotor losses [1; 48].
## 3 Results  
### Comparison of Cost Function Weights  
Figure 3 illustrates the results of the cost function weights comparison, wherein the weights of the main lobe width \(\beta_{1}\) and the side lobe level \(\beta_{2}\) in the Equation (12) were varied. Figure 3a illustrates three examples of element arrays that have been optimized with different combinations of weights. The element array on the left was optimized with a relatively large \(\beta_{1}\) and a relatively small \(\beta_{2}\), resulting the elements positioned relatively farther from the origin in both the \(x\)- and \(y\)-directions. The element array in the center was optimized with a relatively small \(\beta_{1}\) and a relatively large \(\beta_{2}\), showing a high degree of element concentration. The element array on the right exhibited the greatest SNR in the US imaging of the wire scatterers. It is observed that the sizes of the elements are larger than those of the other two arrays, and the distances of the elements from the origin are between the other two arrays. The average element sizes of the left, center, and right arrays are 0.27, 0.27, and 0.39 mm\({}^{2}\), respectively. And the average distances of the elements from the origin are 12.3, 9.8, and 11.0 mm, respectively. The changes in the element array throughout the optimization process are shown in Video S1.  
Figure 3b shows the US images of the wire scatterers acquired with three optimized arrays depicted in Figure 3a, under conditions of a \(20^{\circ}\) angular error around the \(z\)-axis and a \(3\) mm positional error in the \(y\)-direction. In the image on the left, the noise level in regions other than the wires is relatively high. In the center image, the upper-left wire is blurred. The right image demonstrates the visualization of all wires with a high SNR.  
Figure 3c depicts the average SNR of the nine wires, each imaged under nine different positional and angular error conditions, for each combination of weights. The average SNR was maximized when \(\beta_{1}=10^{-2}\) and \(\beta_{2}=10^{0}\), with SNR declining as the weights deviate from this combination. The element array optimized with this weight combination was used as the optimized element array in the subsequent results.
## 2 Materials and Methods  
### Proposed Wireless Network  
Figure 1 depicts the multi-hop network described in this work. There are four sensor nodes: a transmitter, relay 1, relay 2, and a receiver, where the receiver is linked to the computer as the monitoring center [11]. The wireless sensor node is a SparkFun Thing Plus XBee3 micro-module with an on-board chip antenna [13]. It is built on the IEEE 802.15.4 2.4 GHz ZigBee standard, with a data rate of 250 kbps for radio transmission and 1 Mbps for serial data transfer. The RF transmit power is set to +8 dBm, or 6.3 mW, which is the maximum power for this task, and the supply voltage ranges from 2.6 VDC to 3.6 VDC. More specification details are also provided in Table 2.  
The protocol for communication between the nodes [11] is explained here, as shown in Algorithm 1. When the computer, acting as the monitoring center, wishes to gather sensing data from the source node (in this case, the transmitter), the receiver first transmits a command message to the source node via relays 2 and 1, respectively. This command packet also includes the desired transmit power (i.e., 8 dBm) and sampling rate (i.e., 200 ms) for the sensing data transfer. As a result, we can adjust the transmission power and sampling rate to meet the needs of the network. Following the receipt of the command packet, the transmitter sends a data packet containing sensing data to the receiver via relays 1 and 2, respectively. Using our solution, the packet sequence number of the sensing data, node IDs, time stamps at each node, and RSSI information from each hop are then sent to the computer via a serial interface for tracking. During data transmission, an acknowledgment message among the hops is also used to confirm the successful data transmission. For example, when relay 1 receives data from  
Figure 1: A multi-hop WSN with the communication protocol among the nodes. the transmitter, it will send back the acknowledgment message to the transmitter, and it then forwards the data to the next hop. Further details can be found in [11].