esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Future Research Trends
### Theoretical Exploration  
In deep-learning-based channel estimation, strict mathematical proof is essential to define its performance limits. However, this approach is not backed by a solid mathematical foundation. Moreover, mathematical and physical theories explaining deep learning are still exploratory, making it unclear why certain network structures could achieve a good channel estimation performance [96]. Additionally, without advanced optimization theory, finding the optimal policy and loss function during training is challenging. Hence, overcoming the theoretical challenge is urgently needed and research-worthy.
## 1 Introduction
### Previous Research
They used a CNN network to classify three types of vehicles (sedans, microbuses, and SUVs) from input images. The authors trained the networks using image vectors normalized to a size of 32 \(\times\) 32 as input. The method used does not differentiate between large buses and trucks and sedans or SUVs by uniformly normalizing the vehicle region to a small area. Several studies have proposed methods for classifying vehicle types and colors. However, most of these studies focus on extracting features from parts of the vehicle that contain a relatively large number of features, such as the side or front of the vehicle, and use CNN learning algorithms. Transfer learning models have recently been proposed to extract feature information, and deep learning algorithms have been used to classify the type and color of objects of interest [18; 20; 25; 29; 30]. However, this research is limited to high resolution images and learning datasets obtained from the side or front of the vehicle, or from road images obtained by a fixed camera. Therefore, it is necessary to develop a method to determine the type and color of the preceding vehicle using only the rear and some side regions. This will enable the autonomous vehicle to effectively select the lane when changing lanes. To achieve this, a method for classifying vehicles by physical size, such as sedan, SUV, truck, and bus, is required. In addition, a reliable method for classifying vehicle color must be able to withstand variations in illumination, diffuse reflection from the vehicle surface, and adverse weather conditions.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 6 Conclusions  
This paper described an empirical dataset of different variables in a LoRaWAN deployment, including the collection methodology and possible applications. Our dataset contains the information of four ENs and one GW in Medellin, Colombia, in an urban environment, for four months. In addition to the traditional variables considered in other measurement campaigns, our experimental setup included sensors for measuring temperature, relative humidity, barometric pressure, and particulate matter PM2.5. These variables can be used for fitting accurate path loss and shadowing models that can be applied to different tasks like TPC or positioning. We also showed how our dataset could be used for CPLS modelling, including LDPLM and MLR models, indicating that the inclusion of the environmental variables helps improve the forecast of shadowing versus the traditional LDPLM from an R\({}^{2}\) of 0.85 to 0.91. Furthermore, our dataset also includes the energy consumption in each transmission, the SF, the SNR, and the frame size, so it can be used to study energy behavior in different transmitter configurations.  
**Author Contributions:** Conceptualization, M.G.-P., D.T.-V, L.M.S.-C., S.R., G.P. and L.B.L.; methodology, D.T.-V., L.M.S.-C. and M.G.-P.; software, M.G.-P. and S.R.; validation, M.G.-P., D.T.-V, L.M.S.-C., S.R. and L.B.L.; formal analysis, M.G.-P., D.T.-V, L.M.S.-C., S.R., G.P. and L.B.L.; resources, M.G.-P., D.T.-V. and L.M.S.-C.; data curation, D.T.-V. and L.M.S.-C.; writing--original draft preparation, M.G.-P.; writing--review and editing, D.T.-V., L.M.S.-C., S.R., G.P. and L.B.L.; visualization, M.G.-P. and G.P., supervision, D.T.-V. and L.M.S.-C.; project administration, M.G.-P. and L.M.S.-C.; funding acquisition, M.G.-P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was supported by the University of Medellin, Project No. 1039.  
**Institutional Review Board Statement:** Not applicable.  
Figure 14: Predicted versus measured CPLS for LDPLM and MLR models. It can be noticed that the MLR CPLS predictions are more concentrated around the regression line than those predicted by the LDPLM CPLS model.  
Figure 15: Box plots of relative errors of the LDPLM and MLR CPLS models versus distance. It can be noticed that the mean errors are around 1.5%.
## 2 Method
### Handling of Temperature  
First, it is assumed that there is no heat transfer between PM particles and that the gas temperature and PM particles are at the same temperature in the PM oxidation reaction. Furthermore, it assumed that radiation or convection is not affected during PM oxidation. This is because there are fine PM particles of several \(\upmu\)m sparsely distributed in the gas phase. Based on the above assumptions, the handling of heat will be explained.  
First, the heat capacity, \(Q\) [J/K], of the cell to be calculated is shown by the following formula.  
\[Q=C_{c}Vdc(1-\mu)+SC_{g}\]  
Cell deposition is \(V\) [mL], gas passage is \(S\) [mol], DPF wall transmittance is \(\mu\), the specific heat of the wall material (cordierite) is \(C_{C}\) [J/(kg)], density is \(dC\) [kg/m\({}^{3}\)], and  
Figure 6: Schematic of soot deposition in the DPF: (**a**) DPF structure with inlet, porous wall, and outlet, (**b**) segmented DPF wall for numerical analysis, and (**c**) soot deposition mechanism with bridge formation and accumulation [35].  
the specific heat of the exhaust gas is \(C_{g}\) [J/(gk)]. The \(PM\) reaction amount, \(R_{pm}\) [mol], is shown as  
\[R_{pm}=PM(t)=\int_{0}^{t}\frac{dPM}{dt^{\prime}}dt^{\prime}.\]  
The reaction temperature rise, \(\Delta T\) [\({}^{\circ}\)C], at that time is shown as  
\[\Delta T=\frac{R_{pm}}{Q}.\]  
At this stage, it is assumed that the PM particles retain their temperature, and the gas maintains its temperature as it moves between cells. When transitioning to the next cell, the wall temperature at the gas inflow side before the reaction is considered to be equal to the gas temperature. If the gas temperature exceeds approximately 650 \({}^{\circ}\)C, the following formula should be applied.  
\[T_{R}=T_{w}+(T_{PM})/Q\]  
The modified temperature is represented as \(T_{R}\) [\({}^{\circ}\)C], the wall temperature as \(T_{w}\) [\({}^{\circ}\)C], and the PM combustion temperature as \(T_{PM}\) [\({}^{\circ}\)C]. The temperature variation associated with PM oxidation is analyzed between unit cells j and j + 1 in the y-direction (Figure 6b). If PM is still present, oxidation generates heat, causing the gas temperature to rise. Conversely, if no PM remains, the gas temperature decreases. Additionally, in the x-direction, a temperature increase is anticipated between unit cells \(i\) and \(i+1\) due to the cumulative heat released by PM oxidation.
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
## 3 \(\alpha\)-Fluctuating Nakagami Fading Channel Model  
### Physical Model  
Considering the \(a\)-variate transformation procedure presented in [25], herein, it is assumed that the \(\alpha\)-root envelope [25] follows the Fluctuating Nakagami distribution [18]:  
\[R^{\alpha}=\sum_{i=0}^{m}U^{-\frac{1}{m_{s}}}X_{i}^{2}+\sum_{i=0}^{m}U^{-\frac {1}{m_{s}}}Y_{i}^{2}, \tag{4}\]  
where \(m\) multi-path clusters (composed of independent zero-mean Gaussian components \(X_{i},Y_{i}\) with equal variance \(\sigma\)) randomly fluctuate (i.e., \(U\sim(0,1)\)) with shadowing sever ity \(m_{s}>1\), and \(\alpha\), representing the nonlinear envelope distortion coefficient (due to nonhomogenity of the propagation environment [25]).
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 4 Conclusions  
The DA-DFE is widely used in underwater acoustic communications, but DA-DFE needs to carefully tune some parameters, such as the length of filters and forgotten factor, to achieve convergence. The CE-DFE's robustness has been verified in underwater acoustic SIMO communications. However, the traditional CE-DFE, including channel estimation and channel equalization, does not consider CoI when used in MIMO communication, and the communication performance is not improved. To address this issue, in this paper, we proposed a new structure for underwater acoustic MIMO communication. The proposed receiver consisted of channel estimation with IC, and channel equalization with IC. Specifically, in channel estimation, the interference was projected to a new subspace where the interference was free; thus, the channel estimation performance was improved, which provided more accurate channel information for the channel equalizer. In channel equalization, IC filters were added to traditional CE-DFE; thus, the interference was further eliminated. In addition, the traditional DA-DFE and our proposed method were compared in terms of output SNR and time. Finally, the sea trial experiment demonstrated that our proposed method outperformed the traditional DA-DFE and traditional CE-DFE. Since our proposed method did not rely on parameters, it was robust for different underwater acoustic channels. Our proposed method provided a new alternative to the design of underwater acoustic MIMO communication systems. In the future, we will consider implementing the proposed method in hardware, and design an underwater acoustic MIMO MODEM.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation F.T. and X.Y.; formal analysis, Y.Z.; investigation, X.Y.; resources, F.T.; data curation, Y.Z. and F.T.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision; F.T.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Fundamental Research Funds for the Central Universities (No. 20720210078), in part by National Key Research and Development Program of China (No. 2018 YFE0110000), and in part by the Science and Technology Commission Foundation of Shanghai (No. 21 DZ1205500).  
Not applicable.  
The data presented in this paper are available after contacting the corresponding author.  
The authors declare no conflict of interest.
## 3 Methodology
### In-Line Dimensional Inspection
#### 3.2.2 Device Calibration  
The calibration of the scanner involves the characterization of the laser triangulators and their relative positions regarding the axis of the rotating disk. The scanner has a total of two triangulators, with one and two cameras respectively (see Figure 4). Assuming that the reference system of the machine is in the center of the rotating disk, the calibration involves the estimation of the three camera poses.  
Both laser projectors have been mechanically positioned and aligned so that their intersection coincides with the axis of the rotating disk. During the construction of the scanner this alignment is verified with a gauge specifically designed for this task, and it does not need further adjustments. This alignment ensures that the points reconstructed by the cameras belong to the plane \(XZ\) of the reference system, which is defined by the rotation axis and the two laser lines.  
The pose of the cameras is estimated using a calibration object with a hollow revolution geometry, as shown in Figure 5. Such a calibration object has been measured using a CMM by a metrology laboratory certified by an ENAC (National Accreditation Entity).  
During the calibration process the object rotates around the axis of the turn table while the cameras observe the projections of the lasers on its surface. Each camera captures an image for each pulse of the encoder, which has been set to 360 pulses per complete revolution. From these images the intersection of the different segments (laser projections) are obtained.  
Since the geometry of the calibration object is known, it is possible to establish 3D-2D point matches that relate points from a common reference frame with their corresponding observations  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Power & 20 mW \\ Wavelength & 450 nm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the laser line projectors used to irradiate the warm workpiece surface.  
Figure 4: Setup of the 3D optical system designed to digitize the warm workpiece.  
in the camera images. In this way, it is possible to obtain the pose of the three cameras solving the homography matrices induced by the sets of correspondences [31].  
Experimentally, it has been found that the residual error of the homography evaluated in the intersection points after the calibration is under 0.01 mm. Figure 6 shows some dimensions measured in the calibration object. As it can be deduced from the results, the uncertainty of the scanner is better (less deviation) in the central area of the scanning volume. This effect can be attributed to the fact that the images of the cameras have better focus quality in this area, even after adding the Scheimpflug adapters.  
Figure 5: Front and top views of the calibration object geometry.  
Figure 6: Deviations (mm) after calibration in a cross-section of the scanned pattern (purple), through the \(XZ\) plane.
## 6 Conclusions  
Eliminating the magnetic interference of the aircraft platform is an important technical link of aeromagnetic survey and is a vital part of aeromagnetic measurement. Its compensation effect directly determines the quality of aeromagnetic survey data. The traditional compensation method is based on the T-L model, which establishes a linear relationship between the magnetic interference of the aircraft and the attitude of the aircraft. The compensation coefficients are solved by designing the calibration flight. At present, almost all aeromagnetic systems use the fluxgate magnetometer fixed to the aircraft to realize the attitude measurement of the flight platform. However, in fact, the larger the noise of the fluxgate magnetometer, the worse the compensation results. The equipment noise is also often unpredictable due to the complex working conditions and many interference factors. So, it is difficult to improve the compensation effect by adjusting the fluxgate magnetometer. So, this paper improves on the traditional compensation scheme based on fluxgate information and proposes a new conjunctive compensation method based on INS/GPS and fluxgate magnetometer information. The actual flight experimental data and the processing results show that the proposed method in this paper can significantly improve the quality of aeromagnetic measurement data by 30-60% compared with the traditional compensation method only based on fluxgate information and it is a real-time compensation method. Additionally, the compensation coefficients only based on INS/GPS information can also effectively compensate for the noise generated by the aircraft. The compensation effect is slightly lower than that only based on fluxgate magnetometer information, but the difference is not obvious. It is considered to cancel the fluxgate magnetometer and use INS/GPS information in some application scenarios of low power consumption and light load. In summary, the method proposed in this paper can significantly reduce the residual interference and improve the signal-to-noise ratio of magnetic anomaly signals, which is conducive to the development of subsequent magnetic anomaly detection algorithms.  
Conceptualization, B.C. and K.Z.; methodology, B.C.; validation, B.C.; data curation, B.C.; writing--original draft preparation, B.C. and K.Z.; writing--review and editing, B.Y.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2021 YFB3900202.  
Not applicable.  
The authors would like to thank the editors and reviewers for their efforts to help the publication of this work.  
Author declares no conflict of interest.
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 3 Challenges of Parallelizing Stream Joins on TEEs
### Join Methods  
Despite the large body of work on efficient join processing, join methods remain fundamentally different and hence, only contextually comparable in performance. Suggested optimizations need to be hardware-aware and able to generalize to arbitrary data flows. To that end, we consider in this study our own implementation of the Symmetric Hash Join (SHJ) [27,47]'s implementation of the Multiway Sort Merge (MWAY) as examples of Hash-Based and Sort-Merge join algorithms respectively. The essence in SHJ lies in interleaving the probe and build processes. It maintains a hash table for each stream. Each arriving tuple is immediately inserted into the corresponding hash table and probed in the opposite one. This process repeats until all arriving tuples are consumed. In contrast, the key idea in Sort-Merge algorithms is to first sort all relations by the join key before merging them. MWAY improves on the idea by efficiently partitioning the relations prior to independently sorting them. This enhances performance for NUMA systems. In the context of SGX, the inhibiting enclave size restricts the number of tuples that can loaded at once for sorting, hindering the effectiveness of the algorithm in the case of large datasets. Similarly, SHJ assumes both hash tables fit entirely in (secure) memory [31]. We would like to examine how these variables affect the overall system performance.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 2 Phoneme Clustering
### Confusion Matrix  
A confusion matrix is a table of comparison results for the predicted and actual values to analyze intra-class and inter-class performances. It is used to evaluate the performance of supervised learning, such as classification in machine learning [39]. After training the baseline phoneme classification model, on a test set we evaluate phoneme classification accuracies and create a confusion matrix. Figure 1 shows the obtained confusion matrix from baseline phoneme classification results for 39 mapped phonemes. The \(y\)-axis is the real labels of the given phonemes, and the \(x\)-axis is the predicted labels from the baseline RNN. Each element of the matrix, denoted by \(C(i,j)\) where \(i\) and \(j\) are row and column numbers, respectively, is the rate of predicting phoneme \(j\) when the actual one is \(i\). According to the definition of the confusion matrix, the diagonal elements (\(i=j\)) are the correct classification rates for individual phonemes and all off-diagonal elements (\(i\
eq j\)) are ratios of incorrect classification pairs. For example, the first column of the the third row, \(C(1,3)\), is brighter than the nearby components with the of the exception diagonal ones. It means that the phoneme [aw] is misclassified a lot to the phoneme [ae]. Among off-diagonal elements, ([uh], [ax]), ([uh], [ih]), ([n], [m]), and ([z], [s]) are examples of phoneme pairs with high misclassification rates.
## 5 Conclusions  
This paper introduces a deep learning model that integrates the YOLOv5s model, efficient channel attention, and a decoupled head structure, aiming to enhance the accuracy and speed of pavement crack detection, particularly for extra-long cracks on road surfaces. Furthermore, by employing binocular camera technology, this study expands the application scenarios for UAV-based crack analysis, overcoming the limitation of needing to preset the shooting distance inherent in previous UAV crack-detection technologies. The main findings and conclusions are as follows:  
1. An improved crack detection algorithm is proposed, significantly enhancing pavement crack recognition accuracy through the optimization of the detection network structure. This algorithm meets stringent accuracy requirements for crack detection in real-world applications, with notable increases in network recall to 86.82% and mAP to 86.32%.
2. This study describes a method to accurately measure the photographed depths of different portions of a long crack in a roadway surface using a binocular unmanned aerial vehicle capable of quantifying the widths of various sections of long cracks within images. The method successfully solves the errors arising from relying on approximate distances for depth measurements when UAVs detect pavement and bridge cracks.
3. The combined use of binocular UAV vision and deep learning algorithms for crack detection is effectively applied to the quantitative analysis of pavement cracks. For cracks wider than 2 cm, the absolute error does not exceed 3.53 mm, and the maximum relative error is maintained at 9.57%, remaining below the 10% standard.  
These outcomes furnish empirical support for the precision of crack measurement techniques.  
Conceptualization, H.X.; methodology, J.Z.; software, W.H.; validation, P.L. and K.Z.; investigation, P.L.; resources, R.G. and W.H.; writing--original draft preparation, J.Z.; writing--review and editing, H.X.; visualization, J.Z.; supervision, R.G.; project administration, K.Z.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 12262015.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.