esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Prediction Model
#### 3.5.2 Load Prediction Model  
In an edge computing system, wrong offloading decisions may lead to load imbalance if the server load is not considered. The monitoring system on the edge server records system data and performance metrics, such as CPU occupancy, network usage, and current execution of tasks. We can use the logged data to predict the future server load. We refer to the load model in the literature [30] to predict the edge server load level (the number of idle queues on the edge nodes) using historical records. The output value \(H_{t}^{\sim}\) is obtained from the trained LSTM load prediction model by using \(H_{1},H_{2},\cdots H_{t-1}\) as the input sequence in the edge server historical load sequence \(H_{t}\in\{H_{1},H_{2},\cdots H_{T}\}\). \(h_{t}^{\sim}\in H_{t}^{\sim}\) is used as the predicted idle server, which can be preferentially selected as the offload computing node when training the DRL. As shown in Figure 3, in the DRL training process, the actions are selected by the agent through a certain probability, i.e., the action with the largest Q value is selected with \(\varepsilon\) probability, and the action is selected randomly with \(1-\varepsilon\) probability. In this paper, when the original agent selects a random action, the size comparison between a random value \(\sigma\) and the probability \(\varepsilon\) is used to determine whether it is a Random Action or a Prediction Action, so as to avoid falling into a local optimum. By giving the pre-selected action (idle server in the actual scenario) through Prediction Action, it can reduce the number of explorations by the agent and improve the convergence speed of the algorithm.  
Figure 3: Illustration of offloading decision.  
Figure 2: Flow chart of task prediction.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 1 Introduction
Furthermore, recently, the idea of using deep learning to detect data bits in the OFDM-IM system was proposed [14]. The suggested detector has approximately the same performance as the ML detection, but significantly reduces the runtime of existing detectors in classical OFDM-IM. The detector in [15], inspired by the sphere decoding approach, was designed for subblock-design-aided OFDM with all-index modulation (SD-OFDM-AIM). In [16], by using the same set of active subcarrier indices in two distinguishable clusters and implementing coordinate interleaving for M-ary modulated symbols, the repeated index modulation OFDM with coordinate interleaving (RIM-OFDM-CI) was presented. In [17], a low-complexity hybrid number and index modulation (OFDM-HNIM) scheme was developed for transmitting additional information bits using the number and index of active subcarriers. However, all the above studies were proposed for conveying the bits to the indices of active subcarriers in complex subgroups, and they employed either look-up tables or combinatorial methods for index mapping and detection algorithms. In this study, a new index modulation scheme for OFDM systems is introduced. The modulation is performed at the positions of the nulled subcarriers in the frequency domain. Contrary to the existing OFDM-IM techniques, where the index modulation takes place on the active subcarriers, in this new modulation method, the information bits are conveyed by the indices of the nulled subcarriers. This new method significantly reduces the number of computations as compared to the existing OFDM-IM schemes. It is worth noting that, to the best of our knowledge, this is the first study where the index modulation process was carried out by utilizing null subcarriers without the need for using look-up tables or combinatoric algorithms for the index selection and detection at the transmitter and receiver, respectively.
## 2 Development of the Roots Pump
### Rotor Profile  
The rotor profile has a significant effect on the performance of the Roots pump. As shown in Figure 1a, the rotor profile is composed of five curves. At the tip of the rotor, there is a circular arc AB which has the same radius as the rotor for reducing the leakage between the rotor tip and the casing. The main part of the profile is an involute curve CD across the pitch circle of the rotor. Another circular arc BC is designed to smoothly connect the involute curve CD and the arc AB at the tip. The remaining two curves of the profile DE and EF are the conjugated curves with the two circular arcs BC and AB, respectively. The engage line indicated by the green curve and the whole profile for the Roots pump is shown in Figure 1b. It is illustrated by the shape of the engaged line that the profile continuously and smoothly meshes at all contact points.  
The lobe number and the wrap angle are two key parameters having great effects on the performance of the Roots pump. The number of intermediate chambers increases with the lobe number. Hence, the pressure difference decreases with the increment in the lobe number. A decrease in pressure difference results in a reduction of leakage and an increase in volumetric efficiency. Moreover, the manufacturing cost can increase with the enlargement in the lobe number, which should also be taken into account in the design. Based on these considerations, a rotor with three lobes is designed for the Roots pump. Regarding the value of the wrap angle, two aspects of issues need to be considered in the design, which are addressed in this paper. One is the variation in the axial velocity of the gas within the pump from the suction end face to the discharge end face. To eliminate either acceleration or deceleration of the gas, the wrap angle is designed to make the axial velocity of the gas equal to the flow velocity at the suction ports. Another consideration of the wrap angle is the increase in the area of the radial discharge ports. The flow resistance will be smaller with the larger value of the wrap angle since the area is proportional to the increase of the wrap angle.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Lobes of rotor & 3 \\ Center distance between the rotors(mm) & 43.4 \\ Outside diameter of rotor(mm) & 64.2 \\ Length of the rotor (mm) & 40 \\ Wrap angle of the rotor (\({}^{\circ}\)) & 95 \\ Theoretical capacity per rotation (L) & 0.135 \\ Rated rotation speed of the rotor (rpm) & 8000 \\ Tip peripheral speed of the rotor(m/s) & 26.9 \\ Theoretical volume flow rate (L/min) & 1082 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical parameters of the Roots pump.
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 3 Simulation Results and Discussion
### Simulation Parameters
Hence, the proposed system can also be applied to meeting rooms, hospitals, museums, hotels, police stations, and so on..., with different room dimensions and corresponding numbers of transmitters and receivers. The proposed system is considered environmentally friendly, cheaper, less power consumption, more secure, higher data rate compared to RF systems. Thus, the proposed system has advantage over the conventional systems [22]. Implementing a hardware simulation and producing patents for the proposed system are a very good attempt for more validation.
## 3 Results and Discussion
### Results in Poland
#### 3.1.3 Pulaskiego Street  
In Pulaskiego Street, a section with three consecutive raised crosswalks with short distances between them was evaluated. Measured speed values are exposed in Figure 6.  
When passing the raised crosswalk in the middle, RCW 2, low speeds were detected. In this case, as the distance to the next RCW is short, drivers did not speed up because they would have to decelerate in a short distance. Therefore, values collected in MP 2-3, when the distance between RCWs  
Figure 5: Speed values in the measurement points in Wschodnia Street, in Bialystok (Poland).  
Figure 6: Speed values in the measurement points in Pulaskiego Street, in Bialystok (Poland).  
is short (63 m), are very similar to the ones of the RWC 2. On the contrary, since the distance from RCW 1 to RCW 2 is longer, 114 m, higher values were registered. As a global assessment, since similar data would have been collected in the other raised crosswalks (RCW 1 and RCW 3), it can be said that all the stretch was really calmed because even the maximum speed was around 50 km/h. This area was aimed to be specially protected due to the presence of a school near a populated neighborhood of Bialystok. Consequently, placing successive vertical TCMs at short distances really achieves a pacified area.
###### Abstract  
At a time of increasing evidence for dose-effect relationships in radioembolization (RE) with \({}^{90}\)Y-microspheres, the general consensus is that there is an urgent need for accurate treatment planning and dose assessment in patients undergoing RE treatment. This work aimed at assessing the usefulness of \({}^{99\text{m}}\)Tc macroaggregated albumin (MAA) SPECT/CT imaging for personalized provisional RE dosimetry considering a 3D-printed patientlike phantom (AdboMan phantom). A homemade tool was developed in MATLAB for image analysis and absorbed dose calculation. Two dose calculaton methods were implemented and used to calculate dose volume histograms: (I) dose kernel method and (II) local energy deposition method. The accuracy of the two different dosimetric methods was evaluated by means of 3D \(\gamma\)-index (1%-1 mm and 2%-2 mm) implemented in the tool. Differences between the two dose calculation methods using the 3D \(\gamma\)-index are within 1%-1 mm and 2%-2 mm for all AbdoMan inserts, with a passing rate of 99.9% and 100%, respectively, proving a good agreement between the two calculation methods. The present study supports the use of \({}^{99\text{m}}\)Tc-MAA SPECT acquisition for provisional dosimetry along with the local energy deposition method to convert reconstructed SPECT data into absorbed dose maps. As long as \({}^{99\text{m}}\)Tc-MAA SPECT acquisitions are performed on liver lesions larger than 40 mm, the absorbed dose computed by means of the local energy deposition method can lead to results in line with those obtained by Monte Carlo calculations.  
osimetry; molecular radiotherapy; diagnostic imaging; radioembolization +
Footnote †: journal: Appl. Sci.
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 4 Experiments and Discussion
### Accuracy Metrics  
The evaluation metrics for method performance in this study include precision (_Prec_), recall (_Rec_), overall accuracy (_OA_), and \(F\)1 score. The \(F\)1 score is a comprehensive measure that takes into account both precision and recall, making it the primary metric for assessing the overall performance of the models.  
\[F1=\frac{2\times\textit{Pre}\times\textit{Rec}}{\textit{Prec}+\textit{Rec}} \tag{5}\]  
\[\textit{Prec}=\frac{\textit{TP}}{\textit{TP}+\textit{FP}} \tag{6}\]  
\[\textit{Rec}=\frac{\textit{TP}}{\textit{TP}+\textit{FN}} \tag{7}\]  
\[OA=\frac{\textit{TP}+\textit{TN}}{\textit{TP}+\textit{TN}+\textit{FP}+\textit {FN}} \tag{8}\]  
where, _TP_ represents the number of pixels detected as solid waste correctly, _TN_ represents the number of pixels detected as background correctly, _FP_ represents the number of pixels falsely detected as solid waste, and _FN_ represents the number of pixels falsely detected as background.
# Chaotic Evolutionary Programming for an Engineering Optimization Problem
## 6 Results and Discussion
\begin{table}
\begin{tabular}{l l r r r r} \hline \hline
**Test Function** & **Fitness** & **CEP-1** & **CEP-2** & **CEPPS-1** & **CEPPS-2** \\ \hline \multirow{3}{*}{Griewank function} & Worst & 4.61 & 1.08 & 11.05 & \(9.29\times 10^{-1}\) \\  & Average & 4.61 & 1.08 & 11.05 & \(9.29\times 10^{-1}\) \\  & Best & 4.61 & 1.08 & 5.48 & \(0.01\times 10^{-1}\) \\ \hline \multirow{3}{*}{Rastrigin function} & Worst & 21,893.57 & 305.44 & 40,041.02 & 97.11 \\  & Average & 21,893.57 & 305.44 & 40,041.02 & 61.70 \\  & Best & 21,893.57 & 305.44 & 15,691.13 & 61.70 \\ \hline \multirow{3}{*}{Rosenbrock function} & Worst & \(7.54\times 10^{8}\) & 222.46 & \(1.00\times 10^{10}\) & 43,304.03 \\  & Average & \(7.54\times 10^{8}\) & 22.36 & \(1.00\times 10^{10}\) & 7300.95 \\  & Best & \(7.54\times 10^{8}\) & 22.36 & \(2.91\times 10^{8}\) & 7300.95 \\ \hline \multirow{3}{*}{Schwefel’s 2.22 function} & Worst & 48.16 & 68.45 & 7.57 & 27.71 \\  & Average & 48.16 & 68.45 & 7.57 & 27.71 \\ \hline \multirow{3}{*}{Sphere function} & Worst & 13,094.38 & 8.23 & 50,471.00 & \(4.79\times 10^{-19}\) \\  & Average & 13,094.38 & 8.23 & 50471.00 & \(9.40\times 10^{-20}\) \\  & Best & 13,094.38 & 8.23 & 18,642.14 & \(9.40\times 10^{-20}\) \\ \hline \multirow{3}{*}{Step function} & Worst & 670.00 & 37.00 & 969.00 & 28.00 \\  & Average & 670.00 & 37.00 & 969.00 & 28.00 \\  & Best & 670.00 & 15.00 & 532.00 & 15.00 \\ \hline \multirow{3}{*}{Step 2 function} & Worst & 15,349.00 & 6.00 & 39,277.00 & 8.00 \\  & Average & 15,349.00 & 6.00 & 39,277.00 & 5.00 \\ \cline{1-1}  & Best & 15,349.00 & 6.00 & 17,381.00 & 5.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance analysis of the fitness value of generalized benchmark test functions [29]. The comparison of the results show that in Case 1, CEPPS-1 and CEPPS-2 had a lesser cost in comparison to all others, and both obtained a solution with a cost of 623.75 $/h. Similarly, in Case 2, the cost for the generation schedule by CEPPS variants was better than the others except CPSO, where it was comparable. The cost was a little bit higher by 0.05 $/h and 0.06 $/h than CPSO. Further, in Cases 4 and 5, the generation cost for both CEPPS variants was better than the other contenders. Moreover, in these two cases, CEPPS-1 had the advantage of obtaining a lesser generation cost schedule. Lastly, in Case 6, CEPPS-2 obtained a better quality generation schedule than SPPO, whereas CEPPS-1 was able to obtain a somewhat deteriorated solution. To summarize, both CEPPS variants obtained a power generation schedule either with lesser than or comparable generation cost as the others. In addition to this, the generation cost comparison between CEPPS-1 and CEPPS-2 shows that CEPPS-1 was able to obtain a generation schedule with a lesser cost.
## 4 Results  
The relative errors estimated for the different models are displayed in Figure 8. Larger standard errors were observed for the WAL datasets, probably due to their lower sample size. Overall, the three less performant models were PLSR-P, PLSR, and AVG-SHENK (except for TROP2 where AVG-SHENK was as performant as other averaging methods).  
For CV-, AIC-, and BIC-averaging, the effect of parameter \(\alpha\) (Figure 9) was dataset and method dependent, without showing a clear pattern. For instance, removing the 30% less performant models in Equation (2) (i.e., \(\alpha\) = 0.3) did not improve the predictions for BIC weighting (all the datasets), while this was more successful in other situations (e.g., for CV-weighting).  
For PLSR, lower dimensionalities \(a\) were selected for WAL datasets (\(a\leq 20\)) compared to the others, in relation to their lower size. As expected, PLSR-P also selected lower dimensionalities \(a\) than the usual PLSR (Table 5). Nevertheless, PLSR-P was always less efficient (Figure 8) than PLSR. This indicates that selecting high values \(a\) in PLSR (up to \(a\) = 50 LVs in certain repetitions; Table 5) did not generate overfitting on these data.  
Globally, averaging models showed better performances than stacking (Figure 8), even if both types of methods returned close relative errors. On our forage data, building a top model over the \((A+1)\) PLSR models was therefore not advantageous when compared to a direct averaging of the predictions.  
Within the averaging methods, computing model weights from CV-, AIC-, or BIC-error rates has tended to slightly increase the performances compared to a uniform average. Nevertheless, the differences were very low (e.g., for LUS2 in Figure 8, the relative error was 5.0% for AVG-BIC vs. 5.1% for AVG) with orders of magnitude that have no agronomic importance in practice. Differences between the weighting methods were also clearly dataset dependent (e.g., in TROP2, AVG was almost equal to other averaging). Even with a uniform weighting, it is important to note that averaging was always better than the usual PLSR.
## 1 Introduction
The wave packets of the defect reflection waves overlap due to the close distance between double defects, which makes it challenging to identify the defect reflection wave packets, and to determine the axial position of the defects based on the propagation mechanism of guided waves in the pipeline. Therefore, there is a need for new research methods to accurately determine the relative axial position of double defects. Therefore, this study focuses on double defects in layered pipeline structures, and decomposes the received sensor signal average, using the wavelet packet decomposition method to calculate the damage index values, and form a damage index matrix. Through the comparison of the difference in damage index values between single and double defects in the layered pipeline structure, the relative axial position of the defects can be determined. The research results show that using the damage index value as a parameter to locate the relative axial position of defects in layered pipeline structures provides a new approach and method for the localization of multiple defects in engineering applications.
# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 4 Discussion  
Although the SE-ResNet has better classification performance than the ResNet, our classifier still has several limitations. First, the F1 scores of AFL, PVC, SB, ST and FAB were much lower than those of the other sectors (Normal / AF) in our model. This was because of the model's inability to distinguish between FAB and SB, AF and AFL, FAB and PVC, and ST and PVC. This lower accuracy might be due to the insufficient data for AFL, PVC, SB, ST and FAB compared with the data provided for Normal and AF. In the future, we plan to upgrade our model to increase the F1 scores of AFL, PVC, SB, ST and FAB.  
Second, owing to the lack of ECG data, we created a classification model for only some often-observed arrhythmias. To expand this model to other arrhythmia conditions, we must accumulate additional arrhythmia data; e.g., junction rhythm, Supraventricular Tachycardia(SVT), Ventricular Tachycardia(VT), Wenckebach, etc., as presented in [PERSON] et al. [5] and [PERSON] et al. [6].  
Finally, we built the model and conducted various experiments to determine the best F1 score and accuracy, without considering the model's complexity. To achieve higher accuracy, we followed the deep-learning trend of making deeper and more complicated networks. However, many studies in the real world must be carried out on computationally limited platforms. In the future, we will consider the model's file size and computation speed, as well as its accuracy and F1 score.
## 2 Experiment Design
### Experimental Data Collection  
Calibrating the control components is crucial prior to using a driving simulator, involving several key aspects of optimizing the driving experience from an academic perspective.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device Serial Number** & **Equipment** & **Device Description** \\ \hline
1 & GIMI Z7X & Resolution of 1920 \(\times\) 1080 \\  & Logitech G29 & Steering wheel, Pedals and Shifter \\  & (Logitech, Newark, NJ, USA) & \\
3 & Computing unit & \\  & (Yokogawa Electric Corporation, Tokyo, Japan) & Intel i7, 32 Gb RAM, 8 Gb VRAM \\
4 & (Nvidia Corporation, Santa Clara, CA, USA) & NVIDIA GeForce RTX 2060 SUPER \\
5 & Camera & Intel RealSense D435i \\  & (Intel Corporation, Santa Clara, CA, USA) & \\  & Driver seat & \\  & (Logitech, Newark, NJ, USA) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptions of Main Components in Driving Simulator Tests.  
Firstly, calibration is required for the brake and accelerator pedals to adjust their minimum and maximum rotation angles as well as sensitivity based on the driver's habits. Such adjustments ensure that the driver achieves a sense of operation in the simulator that aligns with their individual driving habits, thereby enhancing the authenticity and reliability of the simulation experiments. Regarding the steering wheel, parameters such as sensitivity, maximum steering angle, force feedback strength, and damping also need to be adjusted to simulate the handling characteristics of real-world driving.  
During the experimental process, driving simulators like CARLA incorporate built-in data collection modules that can collect real-time driving data from the driver. This data includes driving speed, steering wheel angle, throttle, and brake pedal positions, among others, which are then saved for subsequent processing and analysis. The collected data from the simulator primarily encompasses aspects such as mechanical control data, vehicle status, and environmental conditions. Some useful data includes throttle pedal pressure, steering wheel angle, gear lever position, vehicle speed, acceleration, position, engine status, fuel consumption, road conditions, and weather information.  
Through these driving simulator experiments, a large volume of driving data can be gathered, covering crucial information, such as the driver's operational behavior, reaction time, and attention allocation. This data is crucial for assessing the driving ability and safety of drivers in different driving scenarios. By analyzing this data, it is possible to gain in-depth insights into the driver's behavior patterns and decision-making processes. Additionally, it enables the study of the driver's ability to respond to emergency situations, adapt to complex traffic environments, and interact with other road users.