esa-sceva/satcom-chunk-collection huggingface dataset:
# Four-Dimensional Thermal Analysis of 888 nm Pumped Nd:YVO4 Dual-Rod Acousto-Optic Q-Switched Laser
## 4 Results and Discussion  
### CW Condition  
For the steady CW state, time derivative terms in Equations (1)-(6) and Equations (12) and (13) in our theoretical model are zero. The pump and laser power distributions in two Nd:YVO\({}_{4}\) crystals can be simulated with a MATLAB procedure and the numerical results are shown in Figure 3a,b. The intracavity circular laser powers and pumps absorption processes are shown in Figure 3a,b. The input pump power on each crystal is 67 W and the total pump power is 134 W. As can be seen, Xtal 2 undertakes a higher forward laser power than Xtal 1 because it is near the OC. The pump absorptivity in each crystal is approximately equal due to the symmetry of the cavity. The simulated output power is 62.8 W, as can be obtained in Figure 3b, which is in accordance with our experimental result 61.6 W within the scope of tolerable error.  
Then, the heat generation can be calculated by using Equations (17) and (18) in our thermal model. The thermal power density and pump waist distributions within each crystal are depicted in Figure 3c,d. The pump waist radius for each crystal is 450 \(\upmu\)m at a distance of 1/3 crystal length from the incident end face for each crystal, as can be seen in Figure 3c,d. The profiles of heat generation distribution coincide approximately with the distributions of pump beam.  
The steady state temperature distributions obey the heat conduction Equation (19) without the time derivative term while with the spatial boundary conditions Equations (21)-(25). The temperature distributions within the two crystals are show in Figure 3e,f. In the numerical simulation above, we also considered how the interaction effect between spatial temperature and temperature induces cross sections changing by using the dynamic iteration method [23]. The maximum temperature within Xtal 1 is 57.6 \({}^{\circ}\)C. The maximum temperature of Xtal 2 is 58.1 \({}^{\circ}\)C, which is a little higher than that of Xtal 1. The average temperature of Xtal 1 on the left side and that of Xtal 2 on the right side are 41.2 \({}^{\circ}\)C and 41.6 \({}^{\circ}\)C, respectively, which coincide approximately with the measured value of about 43.8 \({}^{\circ}\)C via thermal imager.  
Figure 3: Power distributions along the \(z\)-axis in (**a**) Xtal 1; (**b**) Xtal 2, heat generation distributions of (**c**) Xtal 1 at face \(x_{1}=0\); (**d**) Xtal 2 at face \(x_{2}=0\) and temperature distributions within (**e**) Xtal; and (**f**) Xtal 2 for the continuous-wave condition.
# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 2 Materials and Methods
### Voice Recognition  
Voice recognition is a field that has evolved significantly over recent decades. Researchers have focused on various scientific techniques to enhance voice recognition sys  
Figure 1: Processes involved in the functionality of the system. The arrow represents the part flow from or toward the user. (**a**) The user asks the robot to bring a part. (**b**) The user asks the robot to store a part.  
tems. These techniques can be separated into two approaches, speaker-dependent and speaker-independent, each with distinct advantages and applications.  
Speaker-dependent recognition involves training the system with samples from specific people and adapting the system to recognize them accurately. Its primary drawback lies in its limited applicability, catering only to certain users. If a user who has not trained the model attempts to use it, there is a high likelihood of commands being misclassified. On the contrary, speaker-independent recognition aims to identify speakers without prior training, making it a versatile and general solution [41].  
Furthermore, neural networks have become a powerful tool for voice recognition. Deep Learning models, such as CNNs, have exhibited remarkable results in improving the accuracy of voice recognition systems. These networks excel at extracting meaningful features from audio data, thereby enhancing recognition performance and enabling the system to adapt and recognize diverse speakers more effectively [42].  
CNNs' approach involves representing one-dimensional (1D) audio signals as 2D spectrograms. Before any analysis of audio samples, raw audio data are transformed into 2D spectrograms. This representation offers a visual representation of audio signals, facilitating more robust feature extraction and classification [43].
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Analysis Method of Error Distribution Patterns in MLS Point Clouds
#### 3.4.5 Analysis Metrics  
The primary metrics analyzed in this study are the mean and standard deviation of the cloud-to-mesh (C2M) distances measured from the MLS point clouds to the reference planes derived from TLS point clouds. The representative error is defined as the mean C2M distance calculated between the reference planes and their corresponding MLS point clouds, and it is hereafter referred to as the mean error for clarity. This mean error corresponds to the peak of the Gaussian distribution fitted to the entire set of measured distances, indicating the most densely concentrated region of the MLS point cloud for a given plane.  
The standard deviation, on the other hand, quantifies the spread of C2M distances from the mean within each plane. It serves as an indicator of data precision, functionally analogous to the \"relative accuracy\" reported in Table 1. Therefore, analyzing the standard deviation offers additional insight into both the precision of MLS point cloud measurements.
## 4 Simulation and Analysis of Transmission Lines Foreign Object Detection
### Simulation Results and Analysis  
#### 4.2.1 Results under Different Foreign Object Samples  
The foreign object image dataset of balloons, kites, nests and plastic was constructed through transmission line inspection images and public images. To verify the effectiveness of target region extraction and overcome the problem caused by small samples, three types of samples including samples A, B and C were prepared. Sample A was the original images collected by inspection equipment and public images dataset; Sample B was the target region images extracted from the images in Sample A by the image preprocessing; Sample C added synthetic images on the basis of Sample B, the synthetic images blended the foreign object target with the transmission lines by Photoshop, and each type of foreign object images adds an additional nearly 50% of its original image number. The details of three types of samples are shown in Table 4.  
In order to verify the effect of image preprocessing and artificial synthetic images on improving the accuracy, the prepared samples were randomly divided into training set and testing set according to the proportion of 7:3. Then, the divided samples were transformed into the size of 224 \(\times\) 224 \(\times\) 3 and input to the selected networks such as GoogLeNet, EfficientNet-B0, DenseNet-201, ResNet-101 and AlexNet for feature extraction and the extracted features were used to train and test the random forest classifier. The detection results of three kinds of samples are compared and analyzed, as shown in Figure 8.  
According to the detection accuracy in Figure 8, the accuracy of each network adopting Sample B was much higher than that of Sample A, which can be concluded that the image of target region extracted from original images had reduced the influence of complex background factors in the inspection images and effectively improved the accuracy. Moreover, according to the comparative analysis of samples B and C, it can be seen that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{5}{c}{**Type of Foreign Object**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & **Balloons** & **Kites** & **Nests** & **Plastic** & \\ \hline Sample A & 40 & 67 & 95 & 44 & 246 \\ Sample B & 40 & 67 & 95 & 44 & 246 \\ Sample C & 60 & 100 & 143 & 66 & 369 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The distribution of foreign object samples.  
Figure 8: Detection accuracy of different samples based on different networks.  
adding synthetic images can further improve the fitting effect and generalization ability of random forest classifier, so as to deal with the actual complex background of the detection.
# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
## 4 Experiment and Analyses
The accuracy of the MCP status recognition method based on time domain features is the lowest, followed by the accuracy of the MCP status recognition method based on fuzzy entropy features; the MCP status recognition accuracy of on the SSAE-based method is up to 100%, and its effect is the best.
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation
### Solution of the Sensor Deployment
#### 5.2.1 Varying the Number of Pols  
Figure 4 shows the performance of deployed sensors when the number of Pols is varying. The results show that as the number of Pols increases, more sensors are deployed for both the GHDS solution and the GHDSAE solution, but the increase rate slows down because as the number of Pols increases, the possibility of one sensor monitoring multiple Pols gradually increases, which results in a slower increasing trend of the number of deployed sensors. Compared with the GHDS solution, the GHDSAE solution saves \(-0.04\)-\(0.47\%\) (with an average of \(0.19\%\)) sensors, but the GHDSAE solution consumes \(207.62\)-\(262.34\%\) (with an average of \(232.53\%\)) more time than the GHDS solution. Therefore, the number of sensors deployed by the GHDSAE solution is comparable to that deployed by the GHDS solution, and thus, the aggregation effect has no significant impact on the deployment of sensors. Although the GHDSAE solution takes longer than the GHDS solution, its execution time is still within seconds, making it acceptable. In summary, both algorithms are scalable as the problem scale grows.
## 5 Evaluation Results and Discussion
### Comparison of Network Deployment Scenarios  
Next, we evaluate relative gains (savings) in the overall network cost (denoted as \(\Delta\)) achieved in the Flex-O network deployment scenario in comparison to the reference scenarios described in Section 3, namely, Fix-O, Flex-D, and Fix-D.  
Figures 6-8 show the optimized network deployment costs (\(z\)) and the values of \(\Delta\) as a function of traffic load (\(\rho\)), assuming different WDM capacities (\(W\)) in the three networks. We can see that the application of OADMs (in scenarios Flex-O and Fix-O) allows to reduce the network deployment costs in all networks when compared to the scenarios with direct TPs (i.e., Flex-D and Fix-D). For instance, the cost gains of Flex-O can reach up to above 45% in network MESH-38. Moreover, the optimized application of OADMs and MUXs in Flex-O brings significant cost savings when compared to the Fix-O scenario, in which the optical devices of a fixed capacity are considered. In particular, the cost gains can reach up to 25% in MESH-9, 30% in MESH-20, and 35% in MESH-38 for \(W=8\).  
In general, the cost savings are higher in larger networks, which can be explained by the higher possibilities for the aggregation of traffic from remote sites. These savings decrease with the traffic load, which comes from higher amount of requested connections (wavelengths) at remote sites and, hence, lower possibilities for their aggregation on the TPs. Finally, the cost gains increase with the WDM system capacity, which is due to the higher number of wavelengths that a TP can carry.  
Figure 5: MILP solving times as a function of the number of candidate routing paths (\(k\)) and traffic load (\(\rho\)) in network MESH-38 assuming \(W\in\{4,8\}\) channels.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 5 Simulated Tactile Internet Model
The \(10\,\text{ms}\) restriction (\(t_{\text{hardware}}\leq 375\,\text{\SIUnitSymbolMicro s}\)) is achieved in specific cases where SD is a virtual environment and HSD is a high performance processor computer [53]. Thus this work aims to minimize the execution time in HMD, \(t_{\text{HMD}}\), and HSD, \(t_{\text{HSD}}\), using FPGA. In other words, the target is to achieve a \(t_{\text{hardware}}\leq 37.5\,\text{\SIUnitSymbolMicro s}\). This paper presents a hardware reference model for the FK-HMD, KFF-HMD, IK-HSD, FK-HSD, and FBF-HSD modules illustrated in \[\mathbf{\hat{a}}(n)=\left[\hat{x}^{OP}(n),\hat{y}^{OP}(n),\hat{z}^{OP}(n) \right]. \tag{54}\]1 The complete model that will be presented in detail in the next section makes use of a parallel implementation methodology in which high throughput is prioritized, i.e., the execution time of the modules \(t_{\text{FK}}\), \(t_{\text{KFF}}\), \(t_{\text{IK}}\) and \(t_{\text{FBF}}\), illustrated in \[\mathbf{\hat{a}}(n)=\left[\hat{x}^{OP}(n),\hat{y}^{OP}(n),\hat{z}^{OP}(n) \right]. \tag{54}\]2  
This work does not propose dedicated hardware reference models for the CPD-HSD, JPD-HSD, CPD-HMD, JPD-HMD and FCS modules as there are several techniques and algorithms that can be applied to them. However, considering the hardware time constraints, \(t_{\text{hardware}}\), it is noted that it is also important to use dedicated hardware structures with as FPGA-based circuits for these modules. Studies in the literature foresee the use of AI based techniques for these modules; however, it is essential to note that AI techniques and algorithms implemented on general purpose processor-based hardware platforms can lead to higher processing times [19; 20; 21; 22; 23; 24; 25].
# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 3 Simulation Study and Discussion of Results  
This section analyzes the impact of the proposed short-term wind power forecasting techniques on the performance of the microgrid. To this end, the Monte Carlo simulation is employed to test two different case studies for the daily load demand in the microgrid, namely constant load and fixed (empirical) load curve. For each case study, three scenarios are considered with respect to the wind power forecasts:  
* Scenario (a): naive approach (last value of wind power output used as forecast for the next period).
* Scenario (b): ANN-based wind power forecasting model (without PLM correction).
* Scenario (c): ANN-based wind power forecasting model adjusted by the PLM method.  
At this point, it is noted that the naive forecasting technique is used as a benchmark for the comparison. Moreover, Monte Carlo simulation is chosen as the method to reflect the stochastic events based on building load demand and wind power production in order to examine the required energy storage at any time [53]. The stochastic variation of wind power production along with the building load demand affect the SoC of the battery, which in turn affects the amount of electricity purchased (or sold) from (or to) the grid. In the context of pricing, the simulation study provides useful insight on the economic efficiency and performance of the system.  
Figure 1: Empirical curve of probability of lower misclassification (PLM): The \(x\)-axis shows the power classes obtained by the Fisher–Jenks method and the \(y\)-axis shows the probability of the associated lower misclassification.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### Kdd99  
The KDD99 dataset was created in 1999, using tcpdump, and was built based on the data captured by the DARPA 98 IDS evaluation program [11]. The training data are around four gigabytes of compressed TCP data from seven weeks of network traffic. The network traffic contains attack traffic and normal traffic. The capture of the network traffic was done in a simulated environment. The dataset contains a total of 24 attack types, which fall into four main categories: Denial of Service (DOS), Remote to Local (R2L), User to Root (U2R), and probing. KDD99 has been used extensively in IDS research. The report [12] showed that during 2010-2015, 125 published papers performed IDS evaluation using KDD99. While this dataset is considered inadequate for evaluation, such as a lot of redundant instances recorded, the main problem is that the dataset is not up to date with the recent situation and attack vectors. Although many researchers were already convinced with this information, studies from another group of researchers argued that this dataset is the most widely used for benchmarking [13] or to limit their study only for KDD99 [14].
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 1 Introduction  
### Biomechanics and Biometry  
Biomechanics is the field where the movements of a living body are studied from a mathematical and physics perspective for different purposes, such as ergonomy, healthcare, sports, among others [1]. Research in biomechanics in the sports science field has allowed the development of important applications for improvement in healthcare and human performance [2]. Meanwhile, Biometry refers to the development and implementation of statistical and mathematical methods for data analysis solutions to problems in the biological sciences field [3]. Biomechanics and biometry are similar in the sense that they analyze biological information with mathematical methods to generate a solution. The aforementioned areas have allowed to develop biomechanical tools for digital joint tracking [4], joint-range associated injury prevention and rehabilitation [5], athlete's technique assessment and correction [6], among others.  
For several years, biomechanical studies have assessed the existing risk of developing fractures and injuries generated from high-performance sports activities. Among the typesof fractures formed in sports activities, the formation of stress fractures stands out. A stress fracture is defined as a \"partial or complete fracture of bone due to its inability to withstand non-visible stress applied in a rhythmic, repeated, sub-threshold manner\" [7]. There is not a precisely defined mechanism for the initiation of stress fractures, although it is known that when surrounding muscles become fatigued, they concentrate excessive forces over the zones surrounding the bone, generating microcracks.
## 4 Conclusions and Recommendations
### Conclusions  
1. The specific gravity of the mixtures decreased as the percentage of fly ash content in the mixture increased. This could be because the specific gravity of the fly ash was lower than the specific gravity of the original soil. The specific gravity decreased as the fly ash content increased.
2. Based on the OMC and MDD values, the mixtures with 40% and 50% fly ash content both seemed to be acceptable in terms of providing maximum compaction. Therefore, the addition of Class **F** fly ash to clayey sand (A-7-5) improved the compaction properties of the soil.
3. Based on the UCS values, a mixture with 50% fly ash content seemed to be better in terms of providing strength as subgrade material for a curing period of 8 days. However, based on the trend analysis, the optimum fly ash content seemed to have a wide range of 32-46%, with an optimum curing period range of 16-19 days.
4. In accordance with PI values, the mixture with 40% fly ash content seemed to be better in terms of compactability. It can be concluded from the experimental data that a mixture of soil with 50% fly ash content seemed to be an optimum and better option for subgrade material for a curing period of 8 days, however, a mixture with 40% fly ash content could be used as a viable alternative for the same curing period. This did not match with the findings from the trend analysis and might change with the future recommended study experimental data. Until the new data are available, this finding could be used.
5. The volumetric swelling potential decreased with increased fly ash contents. Potentially, 32-50% fly ash can be used as a beneficial use that would otherwise go to landfill. Based on the C\({}_{\text{c}}\) and C\({}_{\text{s}}\) values, the soil-fly ash mixtures with 40% and 50% fly ash content both experience less settlement and volume change, ranging from 44% to 70%, compared to the original soil.
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 1 Introduction
This two-channel signal is then fed to a spatial filter (also known as a beamformer) that aims to amplify the target speech source presence in the mixture to then be enhanced by the Demucs-Denoiser model. This work is structured as follows: Section 2 describes the original Demucs-Denoiser model; Section 3 details the proposed target selection strategies; Section 4 describes the training of the Demucs-Denoiser model while using each of the target selection strategies; Section 5 details the evaluation methodology for both strategies; Section 6 presents the obtained results; Section 7 provides insights that were obtained from the results; and Section 8 concludes with potential future work.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 1 Introduction
As a result, in long-length system identification scenarios, the RLS adaptive filter using a third-order tensor (TOT) decomposition from [37] outperforms its previously developed NKP-based counterpart designed in [26]. There is a well-established relation between the RLS algorithm and the Kalman filter, as supported in [2]. While there is a (misleading) resemblance between the two algorithms, the Kalman filter allows for better control in time-varying environments [7]. Nevertheless, due to the specific features of the TOT decomposition, there is not a straightforward extension from the RLS-TOT [37] towards the corresponding Kalman filter. In this paper, motivated by the previous aspects and expected performance gain, we design a Kalman filter based on TOT decomposition. The main goals of our proposal target three basic directions. First, we aim to show that the developed Kalman filter based on TOT decomposition outperforms the conventional Kalman filter, in terms of both performance and complexity. Second, with the proposed TOT-based solution, we target a better performance as compared to the previously developed NKP-based version from [27], which exploits a second-order decomposition level. Third, the goal is to outperform the recently introduced RLS-TOT algorithm from [37], which applies the TOT decomposition in the framework of the RLS filter. In order to develop our proposal, the rest of the manuscript is organized as follows. Section 2 presents the necessary background concerning the system identification scenario, the conventional Kalman filter, and the TOT decomposition framework. Then, the proposed Kalman filter based on TOT is developed in Section 3. Simulation results are provided in Section 4, using echo cancellation as the experimental setup. Finally, conclusions and future works are summarized in Section 5.