esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Eye Movement  
The eye movement data from the experiment were collected and used to assess the degree of various speed-calming measures' impacts on the number and duration of drivers' visual fixations. Table 1 shows the descriptive statistical findings.  
The experimental results demonstrate that not all of the four speed-calming measures had a significant impact on the duration and number of visual fixations (\(p<0.1\)). Regarding the duration of fixations, with the exception of Treatment 5, the remaining three measures had remarkable effects on the duration of fixations. Only the diversion line had a substantial influence on the number of fixations.  
It can be seen from the distribution of subjects' fixation points that drivers' visual fixations vary when they pass through areas with different velocity-stabilizing measures. In the case of Treatment 1, wherein no speed-calming measures were taken, the drivers' fixation points showed greater dispersion and a broader range. On the contrary, in the case of Treatment 2, which only made use of one speed-calming measure (anti-collision barrels), the drivers' visual attention was too concentrated. These observations suggest that anti-collision barrels have greater visual appeal for drivers, causing them to ignore the broader environment of the road in front of and on both sides of the vehicle.  
In the case of Treatment 3, which only featured traffic-channelizing lane markings, the drivers' fixation points were distributed in the front and left-front areas, and the scope of the direction of the drivers' gaze was expanded horizontally. It is worth noting that in Treatment 4, when traffic-channelizing lane markings were combined with anti-collision barrels, the drivers' visual focus was more focused on the area directly in front of them; this result was more significant than in Treatment 2. Finally, in the case of Treatment 5, which combined speed bumps and cross-hatched intersection markings, the distribution of drivers' visual fixations was very similar to that in Treatment 1, which involved no  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent** & **Control Group** & **Treatment** & **Mean Difference** & **_p-Value_3 ** & & 2 & \(-\)2450.7 (\(-\)4355.5, \(-\)546.0) & 0.035 \\ Duration of & \(1\) & 3 & \(-\)1924.3 (\(-\)3829.1, \(-\)19.6) & 0.097 \\ fixation & & 4 & \(-\)2038.9 (\(-\)3943.7, \(-\)134.2) & 0.079 \\  & & 5 & \(-\)1132.9 (\(-\)3037.7, 771.9) & 0.325 \\ \hline  & & 2 & \(-\)3.8 (\(-\)11.3, 3.7) & 0.406 \\ Number of & \(1\) & 3 & \(-\)8.6 (\(-\)16.1, \(-\)1.1) & 0.060 \\ fixations & & 4 & \(-\)4.4 (\(-\)12.1, 3.2) & 0.328 \\  & & 5 & \(-\)3.0 (\(-\)10.5, 4.5) & 0.509 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of significance of impacts on duration and number of fixations.  
Figure 5: Distribution of the direction of drivers’ gazes.  
speed-calming measures; this indicates that the cross-hatched intersection markings at the center of the intersection had the smallest effect on the drivers' visual attention.
# Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection
###### Abstract  
Automated quality inspection has been receiving increasing attention in manufacturing processes. Since the introduction of convolutional neural networks (CNNs), many researchers have attempted to apply CNNs to classification and detection of defect images. However, injection molding processes have not received much attention in this field of research because of product diversity, difficulty in obtaining uniform-quality product images, and short cycle times. In this study, two types of dual-kernel-based aggregated residual networks are proposed by utilizing a fixed kernel and a deformable kernel to detect surface and shape defects of molded products. The aggregated residual network is selected as a backbone, and a fixed-size, deformable kernel is applied for extracting surface and geometric features simultaneously. Comparative studies are conducted by including the existing research using the Weakly Supervised Learning for Industrial Optical Inspection dataset, which is a DAGM dataset. A case study reveals that the proposed method is applicable for inspecting the quality of injection molding products with excellent performance.  
Keywords:aggregated residual learning network; deformable convolutional network; defect classification; injection molding process; surface inspection +
Footnote †: journal: Applied Sciences
## 3 System Design and Implementation
### Software Design
#### 3.3.4 Implementation of the Host-Side Software  
Python 3.12.0 boasts mature Modbus open-source libraries (e.g., pymodbus) that can be directly utilized for Modbus communication implementation, significantly reducing the development cycle. Furthermore, Python 3.12.0's rich graphics libraries (e.g., Matplotlib and Tkinter) facilitate user interface design, while its scientific computing ecosystem (e.g., NumPy and Pandas) supports efficient integration with data-processing tools. Consequently, the Modbus data transmission, reception, and processing modules on the host computer are implemented using Python 3.12.0. Figure 17 shows the flowchart of the host computer program. The host sends read register requests to the gateway, and the device returns current data values upon response. The system continuously cycles through the'send request-receive response-update interface' process. The user interface, implemented in Python 3.12.0 and running on the host computer, updates and displays the retrieved contents of holding registers in real-time. For example, when the host computer sends a Modbus read holding register request via Python 3.12.0, the slave device receives the request, generates a response frame, and transmits it back to the host, after which the interface dynamically reflects the acquired data. When monitoring is manually stopped, the system safely terminates communication threads and closes the network interfaces to release resources.
# Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
## 4 Implementation Details
### ECG Data
Please note that the ECG records for patients 102, 104, 107, 217 have been excluded from this study because these recordings do not contain any arrhythmic beats. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Categories** & **N** & **S** & **V** & **F** \\ \hline \multirow{3}{*}{**Definitions**} & Normal beat & Atrial premature beat & \multirow{3}{*}{Premature ventricular contraction} & \multirow{3}{*}{} \\ \cline{2-2} \cline{4-5}  & Left bundle branch block beat & & Aberrated atrial premature beat & \\ \cline{2-2} \cline{4-5}  & Right bundle branch block beat & & Nodal (functional) premature beat & \\ \cline{2-2} \cline{4-5}  & Atrial escape beats & & Supraventricular premature beat & \\ \cline{2-2} \cline{4-5}  & Nodal (junctional) escape beat & & & \\ \hline
**Annotations** & 90,585 & 2781 & 7235 & 802 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Beat classification categories and total number of annotations included in the Arrhythmia Laboratory of Boston’s Beth Israel Hospital (MIT-BIH) dataset for each category.
## 5 Algorithm
### System Model
These include the following:  
* Total capacity \(c_{ij}^{\psi}\) measured with no traffic between \(v_{i}\) and \(v_{2}\): \[c_{ij}^{\psi}=maxflow(v_{i},v_{j})\] (5)
* Aggregate of GFBRs allocated to flows \(b_{ij}^{\psi,GFRR}\): \[b_{ij}^{\psi,GFRR}=c_{ij}^{\psi}-maxflow(v_{i},v_{j})\] (6)
* Peak aggregated bandwidth (aggregate of MFBRs) \(b_{ij}^{\psi,MFRR}\) that can be consumed by allocated flows: \[b_{ij}^{\psi,MFRR}=\sum_{(u,w)\in SP(v_{i},v_{j})}b_{uw}^{MFRR}\] (7)
* Utilisation of overlay link \(u_{ij}^{\psi}\) between \(v_{i}\) and \(v_{j}\): \[u_{ij}^{\psi}=(c_{ij}^{\psi}-b_{ij}^{\psi,GFRR})/c_{ij}^{\psi}\] (8)  
The delay and packet error rate are approximated by calculating the shortest path obtained above and aggregation/multiplication over the edges belonging to the path:  
\[d_{ij}^{\psi}=\sum_{(u,w)\in SP(v_{i},v_{j})}d_{uw} \tag{9}\]  
\[\hat{\psi}_{ij}^{\psi}=1-\prod_{(u,w)\in SP(v_{i},v_{j})}(1-\delta_{uw}) \tag{10}\]  
Each traffic flow is modelled by a tuple \(f=(v_{src},v_{dst},t_{0},t_{d},q)\), where \(v_{src}\)--source, \(v_{dst}\)--sink, \(t_{0}\)--arrival time, \(t_{d}\)--duration, and \(q_{i}\)--an SDN-level QoS class. Following the simplified 3 GPP 5 QI definition, \(q_{i}\) is modelled as a tuple \(q_{i}=(b^{\text{GFRR}},b^{\text{MFRR}},d^{\text{max}},\delta^{\text{max}})\), where \(q_{i}\)--a QoS class identifier (common across SDN domains/strata), \(b^{\text{GFRR}}\)--GFBR (corresponding to the bandwidth reserved by SDNC/M-SDNC), \(b^{\text{MFRR}}\)--MFBR, \(d^{max}\)--Packet Delay Budget (PDB) (cf. [48], clause 5.7.3.4), and \(\delta^{max}\)--Packet Error Rate (PER) (cf. [48], clause 5.7.3.5). Hereby, each flow is considered as unidirectional, due to commonly different proportions of uplink (UL) and downlink (DL) traffic. In such cases, allocation of two-way exchange requires using different \(q_{i}\) values for UL and DL flows. Each allocation of flow \(f\) results in either acceptance or rejection decision \(f_{d}\in 1,0\).
## 4 Experimental Results and Analysis
### Analysis of the Baseline Classifier Results  
The performance of each classifier with the PSO feature selection algorithm has been measured and is detailed in Table 4 whereas Table 5 gives the performance of each classifier without the PSO feature selection algorithm. The result of the performance measurements shows that the proposed MLP classifier with PSO feature selection generates a greater accuracy result when likened to the other classifiers. When comparing the two proposed classifiers, it is discovered that the PSO + MLP classifier outperformed that of PSO + SVM for the diagnosis of breast cancer.  
Figure 4 presents the precision of each classifier. Note that the classifier (PSO + MLP) performs the best with an accuracy of 97.2% in diagnosing breast cancer cells. Improvement in inaccuracy is a result of the introduction of the PSO algorithm, which optimizes the problem of classification accuracy by finding the best fit. PSO + MLP performs the best as well with a ROC curve value of 0.972. Therefore, it can be recommended that our proposed model based on the IoT PSO + MLP is used to recognize breast cancer.  
\begin{table}
\begin{tabular}{c c c} \hline
**Measures** & **SVM (\%)** & **MLP (\%)** \\ \hline Recall & 97.0 & 97.8 \\ \hline Specificity & 95.7 & 96.3 \\ \hline F1-score & 97.0 & 97.6 \\ \hline Precision & 97.0 & 97.8 \\ \hline Accuracy & 96.5 & 97.2 \\ \hline \end{tabular}
\end{table}
Table 4: Performance assessment for proposed classifiers with PSO.  
\begin{table}
\begin{tabular}{c c c} \hline
**Measures** & **SVM (\%)** & **MLP (\%)** \\ \hline Recall & 97.0 & 97.0 \\ \hline Specificity & 95.7 & 95.7 \\ \hline F1-score & 97.0 & 97.0 \\ \hline Precision & 97.0 & 97.0 \\ \hline Accuracy & 96.5 & 96.5 \\ \hline \end{tabular}
\end{table}
Table 5: Performance assessment for proposed classifiers without PSO.
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.1 Comparison with Other Object Detection Models
As shown in the results, YOLOv4-Tiny, CenterNet, and SSD exhibit very low \(F1\) values for detecting incomplete penetration defects, making these models unsuitable for this type of defect detection. Compared to YOLOv3 and the original YOLOv4 model, YOLOv4-cs1 demonstrates a significant increase in \(F1\) values for pore and slag inclusion defects. Furthermore, YOLOv4-cs2 possesses higher-level semantic information and retains more defect edge information, resulting in higher \(F1\) values for pore, slag inclusion, and incomplete penetration defects compared to YOLOv4-cs1, as illustrated in Figure 10.  
A missed alarm rate is another metric for evaluating the model. It represents the proportion of positive samples that are incorrectly classified as negative samples out of all positive samples. The calculation formula is given in Equation (7). \[Missing\ alarm=\frac{FN}{TP+FN} \tag{7}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Pore F1** & **Slag Inclusion F1** & **Incomplete Penetration F1** \\ \hline YOLOv4-cs2 & 0.87 & 0.92 & 0.65 \\ YOLOv4-cs1 & 0.82 & 0.89 & 0.62 \\ YOLOv4 & 0.72 & 0.83 & 0.68 \\ YOLOv3 & 0.78 & 0.88 & 0.64 \\ YOLOv4-Tiny & 0.63 & 0.78 & 0.15 \\ Centernet & 0.32 & 0.63 & 0.002 \\ SSD & 0.6 & 0.77 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(F1\) values of different models. Figure 10: \(F1\) values of different models. The comparison results of different models regarding the missed detection rates are presented in Figure 11.  
As shown in Figure 11, the missed detection rates of the YOLOv4-cs2 model for pores and slag inclusion are relatively low, demonstrating that the improved model has advantages in detecting these types of defects. Two other important metrics for evaluating the model are _AP_ (Average Precision) and _mAP_ (mean Average Precision). _AP_ measures the average precision for a specific defect type, while _mAP_ represents the average precision across all defect types. The calculation formulas for _AP_ and _mAP_ are provided in Equation (8) and Equation (9), respectively. \[AP_{j}=\frac{\sum_{i=1}^{N_{j}}precision_{i}}{N_{j}} \tag{8}\]  
\[mAP=\frac{\sum_{j=1}^{j}AP_{j}}{j} \tag{9}\]  
In Equation (8), _AP_ represents the ratio of the sum of all detection accuracy rates for a specific type of defect to the total number of instances of that defect. In Equation (9), _mAP_ represents the average of the _AP_ values across all types of defects. The results of a comparison between different models are presented in Table 5. Figure 12 illustrates a comparison of the _AP_ and _mAP_ results of various models. The results indicate that the YOLOv4-cs2 model performs better, demonstrating an improved detection performance. Figure 11: Missed detection rates of different models. Figure 12: Comparison of AP and mAP for varied models.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 5 Experimental Results
### Experimental Environment  
The hardware used in this experiment was a 16-channel LiDAR sensor, as shown in Table 7. The LiDAR sensor has a horizontal scanning angle of 360 degrees and a vertical scanning angle of +15 degrees to -15 degrees. The LiDAR sensor emits 16 infrared rays at a horizontal angle. According to the speed of the LiDAR sensor, the resolution of the horizontal angle is determined. The speed of the light is adjustable from 5 Hz to 20 Hz, and the corresponding horizontal angle is 0.1 to 0.4 degrees. Our experiment used the initial setting to simulate a rotational speed of 10 Hz and a horizontal resolution of 0.2 degrees. The LiDAR sensor can measure distances up to 100 m and sweep out 300,000 points per second.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Channel & 16 \\ Measurement Range & 100 m \\ Accuracy & \(\pm\)3 cm \\ Laser & 903 mm Wavelength \\ Horizontal Angle & 360\({}^{\circ}\) \\ Vertical Angle & 30\({}^{\circ}\) (+15\({}^{\circ}\) to -15\({}^{\circ}\)) \\ Rotation Rates & 5–20 Hz \\ Power Consumption & 8 Watt \\ Operating Temperature & \(-\)10\({}^{\circ}\) to +60\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: LiDAR sensor specification.  
Figure 21: The \(Z\)-axis circuit of the coordinate conversion calculator.
## 1 Introduction
A method for estimating the residual life of lithium-ion batteries based on data hybrid drive is proposed in the literature [28]. The literature [29] defines the main parameters affecting life decay, selects the suitable stress conditions for retired power cells, and identifies the life decay characteristics of retired power cell modules using an accelerated life test method based on an inverse power law model. The literature [30; 31] is based on the accurate measurement of energy storage lifetime losses based on experimental data, and the method is again too complex and computationally overwhelming. The literature [32; 33] limits the number of cycles of energy storage and the charging and discharging time, and the model's accuracy is not accurate enough. Therefore, constructing a suitable energy storage lifetime decay model and using it to evaluate the lifetime decay of energy storage during operation is crucial in the process of the optimal scheduling of integrated energy systems. In the process of solving search and optimal scheduling problems, the most common approach is to use optimal scheduling algorithms for solving them, where particle swarm algorithms show good performance in multi-objective, multi-constrained, and nonlinear optimization problems and can be applied to nonlinear systems, computer science, robotics, engineering, and many other fields in real-world applications [34; 35]. They have been widely used with the advantages of few constraints on the fitness function, simple structure, easy coding, and fast convergence. The literature [36] uses a particle swarm algorithm to optimize the scaling factor of a PID controller, thus improving the performance of a permanent magnet DC motor for a photovoltaic wire feeding system. The literature [37] uses a particle swarm algorithm combined with an MPPT controller to control the PV output power near the maximum point. The literature [38] uses a particle swarm algorithm to optimize the coefficients of a dynamic regulator for the purpose of increasing the power of a solar PV feeder system. The literature [39] evaluates different PSO approaches in the scheduling and control of different residential energy sources such as smart appliances, electric vehicles (EVs), heating/cooling equipment, and energy storage, so that particle swarm optimization algorithms can be widely used for distributed energy scheduling and control in real demand response applications. In summary, this paper establishes a life decay model of energy storage, measures the life decay of energy storage by studying the changes in the capacity and charge state of energy storage during system operations, and introduces it to the changes in objective function and constraints in the process of system optimal scheduling, solves the integratedenergy system optimization and dispatch model by the particle swarm algorithm, and finally verifies the reasonableness and the validity of the model by arithmetic analysis.
# A Gap Waveguide-Based Compact Rectangular Waveguide to a Packaged Microstrip Inline Transition
## 2 Transition Concepts
### Dielectric-Filled Ridge and Groove Gap Waveguide Based Transition  
The first structure consists of a Chebyshev transformer implemented using ridge and groove gap waveguide sections, which are partially filled by the microstrip line dielectric substrate. This transformer matches the standard rectangular waveguide to the ridge groove gap waveguide. To complete the transition to microstrip line, a tapered microstrip line section connects the ridge gap waveguide section to the microstrip line. The 3D perspective view of the structure is presented in Figure 1a. The rectangular waveguide would directly connect to the first section of the Chebyshev transformer.  
This transition has the advantage of being suitable for wide substrates, since the substrate, which fills partially the gap between the bed of nails and the lid, is included in the design of the ridge and groove gap waveguide sections.
###### Abstract  
Different location and incidence of lower extremity injuries have been reported in rearfoot strike (RFS) and forefoot strike (FFS) recreational runners. These might be related to functional differences between the two footstrike patterns affecting foot kinematics and thus the incidence of running injuries. The aim of this study was to investigate and compare the kinematic patterns of foot joints between naturally RFS and FFS runners. A validated multi-segment foot model was used to measure 24 foot kinematic variables in long-distance recreational runners while running on a treadmill. These variables included the three-dimensional relative motion between rearfoot, midfoot, and forefoot segments. The footstrike pattern was identified using kinematic data and slow-motion videos. Functional analysis of variance was used to compare the time series of these variables between RFS (n = 49) and FFS (n = 25) runners. In FFS runners, the metatararsal bones were less tilted with respect to the ground, and the metatararsus was less adducted with respect to the calcaneus during stance. In early stance, the calcaneus was more dorsiflexed with respect to the shank and returned to a more plantar flexed position at push-off. FFS runners showed a more adducted calcaneus with respect to the shank and a less inverted midfoot to the calcaneus. The present study has showed that the footstrike angle characterizes foot kinematics in running. These data may help shed more light on the relationship between foot function and running-related injuries.  
strike pattern; rearfoot strike; forefoot strike; running; multi-segment foot kinematics
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### Infineon 3 DB6830 UWB Ic  
The Infineon 3 DB6830 (Infineon Technologies Switzerland AG, Zurich, Switzerland) is a low-power IEEE 802.15.4f [38]-compatible UWB transceiver with an integrated security layer that makes it more suitable for distance measurement for security applications. The IC measures the distance based on direct ToF measurement and can be driven by any host processor or microcontroller that can execute random number generation and authentication routines. It works within the upper UWB bands from 6 to 8 GHz. It can cover up to 120 m in LoS communication conditions, with \(\pm\)10 cm accuracy [39].
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 4 The Proposed Detection Scheme
### Step 3: Multivariate Normality Test  
The goal of this stage is to verify the conformity of the dataset to the multivariate normal distribution by applying the Rao-Ali multivariate statistical test. Indeed, the multivariable samples obtained from the previous step are transformed to univariate samples for each time window. The resulting datasets are one dimension that will be used as input by the UVN normality test, namely the Ryan-Joiner test. Hence, the Ryan-Joiner is employed to compute the R-J correlation coefficient in each time window continuously. Based on the values of this correlation coefficient, the attack detection step is performed.