esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 3 Overview of DCASE Dataset  
The DCASE dataset used in this article is TUT Sound Events 2017, which is a subset of TUT Acoustic Scenes 2017 [1]. It is made up of acoustic audio recordings that include artificial and natural sounds from different streets with varying amounts of traffic and other activities in urban and suburban environments. Each audio recording is between three and five minutes long, and they are collected in different locations. In daily life, there are six different types of focused sound classes (occurrences) that are quite prevalent. The following sound classes were chosen for the tasks: brakes squeaking, car sounds, children, large vehicles, people speaking, and people walking. Each class was further subdivided into distinct sub-classes such as car engine running, car passing by, and car running; all of these noises are collectively referred to as \"car\". Similarly, children streaming and children chatting were classed as \"children\", and \"large vehicle\" sounds included sounds from buses, trucks, etc. When listening to these audio samples, it can be challenging to distinguish the \"person speaking\" class of sounds, as voices are quickly masked by background noise from different levels of traffic and other activities. It is very difficult to hear \"people walking\", as the recordings include other noises that sounds similar. Due to its low sample count and short average length compared to other types of sound occurrences, \"brakes squeaking\" is difficult to detect in this situation. Neither in the training audio set nor in the test audio set is possible to control how many overlapping sound occurrences occur at each instant. A Roland Edirol R-09 wave recorder with a sampling rate of 44.1 kHz and resolution of 24 bits was used in conjunction with a Binaural Soundman OKM II Klassik/studio A3 electret in-ear microphone to capture the audio events.  
Based on the number of examples available for each sound event, the development (Dev-Set) and evaluation datasets (Eval-Set) were separated in the given DCASE dataset. Each recording in the Dev-set was utilized precisely once as test data, and it was made up of four folds, each comprising training and test subsets. Table 2 lists the number of instances for each event subclass in the Dev-Set and Eval-Set.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 3 Generating Density Maps from Raw AIS Data
### Data Cleaning
This filter can also be used for excluding messages referring to a time before the dataset's specifications, caused by delays during their transmission. 7. _Noise-filter_: In some cases, consecutive AIS messages of a single vessel indicate an invalid transition between the two points [54; 55]. More precisely, if the distance between consecutive messages is so large that it would not be possible for a vessel to cover in the corresponding time frame, this transition is considered noise in our data and the second AIS message is removed. A transition is considered to be improbable if the calculated speed for the vessel to cover the distance in question exceeds the threshold of 92 km/h (approximately 50 knots). 8. _Insignificant tracks_: For the purpose of processing only meaningful trajectories, all data regarding vessels that have less than 10 AIS messages after the merging step of our processing are discarded. This threshold can be adjusted depending on the use case. Although these filters should be applied in most scenarios, the appropriate thresholds for some heavily rely on the input dataset quality, as well as the nature of the desired analysis for each use case. The filters considered--along with all steps of the suggested framework--are suitable for all types of vessels. Nevertheless, since some studies focus solely on specific types of vessels, a selection mechanism that keeps only vessels of a specific type can also be incorporated for more targeted results.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 2 Materials and Methods
### Methods
#### 2.2.1 Pre-Processing  
All BraTS 2017 & 2021 MRI scans undergo conversion to the NIfTI file format [36], co-registration to the same anatomical template (SRI24) [37], resampling to a consistent isotropic resolution (1 mm\({}^{3}\)), and finally, skull-stripping. In this study, we transformed 3D cube data into 2D images (slices) using ImageJ software to reduce the computational cost and ease the segmentation problem from 3D to 2D images. The central slices in the brain were used for further processing and tumor detection.
## 2 Data Overview and Pre-Processing
### Data Pre-Processing  
As shown in Figure 2a, the co-channel interference is produced in most of the raw radar images, which seriously affects the quality of these radar images. During the preprocessing of these radar images, the correlation method in spatial domain is used to detect and eliminate the co-channel interference noise. Firstly, the threshold is determined by the constant false alarm rate (CFAR) method, and the position of the interference noise line in the radar image is detected by the correlation method. Then, the modified Laplace linear template is used to locate the noise points on the interference line. Finally, the noise points detected by the piecewise interpolation method are repaired to filter out the noise. The radar image after co-channel interference removal is shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Radar Parameters** & **The Performance** \\ \hline Antenna Height & 45 m \\ Radar Antenna Speed & 26\({}^{+2}_{-6}\) rpm \\ Pulse Power & 25 kw \\ Antenna Gain & 31 dB \\ Radar Frequency & 9375 mhz \(\pm\) 30 mhz \\ Polarization & HH \\ Horizontal Beam Width & 0.9\({}^{\circ}\) \\ Vertical Beam Width & 21\({}^{\circ}\) \\ Range Resolution & 7.5 m \\ Pulse Repetition Frequency & 2000 Hz \\ Pulse Width & 0.07 us \\ Grazing Angle & \textless{}5\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Configuration of the X-band Marine Radar.  
Figure 1: The experimental site and radar equipment.  
After removing the co-channel interference from these radar images, the time resolution is set to 10 min by the moving average method during the experiment. It is assumed that the spatial distribution of rainfall is uniform in a short period of time. The average value of RZE parameters of 32 radar images in a group of radar image sequence is taken as the RZE parameter of this sequence during this period, and the rainfall of this sequence is the accumulated value of rainfall during this period.  
Since the acquisition time of a radar image sequence is about 1.5 min and then pauses for 1.5 min, there are about three complete radar image sequences collected in 10 min. The RZE parameter of three adjacent radar image sequences within 10 min is calculated by the Equation (1). The processed value is approximately the RZE parameter within 10 min.  
\[S^{\prime}(i)=\frac{S(i-1)+S(i)+S(i+1)}{3} \tag{1}\]  
where \(S(i)\) and \(S^{\prime}(i)\) are the data values to be processed and processed, and \(S(i-1)\) and \(S(i+1)\) are the adjacent data values to be processed.  
The rainfall intensity of three adjacent radar image sequences within 10 min are calculated by the Equation (2). The processed value is the accumulated rainfall intensity within 10 min.  
\[I^{\prime}(i)=I(i-1)+I(i)+I(i+1) \tag{2}\]  
where \(I(i)\) and \(I^{\prime}(i)\) are the data values to be processed and processed, and \(I(i-1)\) and \(I(i+1)\) are the adjacent data values to be processed.
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 4 The Proposed Adaptive HARQ Q-Learning Model
### Model Description
#### 4.1.4 Reward Calculator
Therefore, the reinforcement reward \(R_{\text{P}}\) is set to help the agent to find the optimal strategy and is defined as in Equation (6):  
\[R_{\text{P}}=\left\{ \begin{array}{c}0,BLER_{\text{AHARQ}}\geq BLER_{\text{ HARQ}}\\ R_{\text{CQI}}+R_{\text{Distance}}+R_{\text{RBs}},BLER_{\text{AHARQ}}<BLER_{\text{ HARQ}}\end{array} ,\right. \tag{6}\]  
where \(R_{\text{CQI}}\), \(R_{\text{Distance}}\), and \(R_{\text{RBs}}\) are reinforcement reward values corresponding to the CQI, UE distance, and available resource blocks, respectively. They comprise reward coefficients and their normalized systems, as shown in Table 3.  
According to the eight different optimal transmission schemes of A-HARQ, the proportions of the CQI, distance, and available resource blocks are different. For example, the channel quality is considered more important when the decision is made to use the action of delay transmission. However, the transmission distance is more important when the repetition transmission action is chosen. Accordingly, the coefficient weights of the more focused factors are the largest. On the contrary, the channel quality is an important factor to consider in the length of the time delay. And the distance is an important consideration when considering the number of repetitive transmissions. At the same time, the amount of available resource blocks is another important factor to be considered to postpone the sending time and/or to reduce the number of repetitive transmissions. Specifically, for the CQI factor, the worse the CQI is when the \(T\)-delay scheme is selected, the bigger the reward that will be obtained, while the better the CQI is when the \(K\)-repetition scheme is selected, the bigger the reward that will be obtained. And the opposite is true for the factors of distance and the number of resource blocks. The greater the distance and the number of resource blocks, the greater the reward of repeated transmission, while the smaller the distance and number of resource blocks, the greater the reward of the delayed transmission behavior.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Proposed Fusion Network for Change Detection with Panchromatic Imagery  
In general, a change detection system involves a pre-processing step to reduce geometric and radiometric distortions for better results. A radiometric correction is applied to remove atmospheric effects for a time-series image analysis. In this study, a radiometric correction was applied by converting digital numbers (DNs) into a radiance value. Then, the top-of-atmosphere (TOA) reflectance values were computed using the gain and offset values provided by a satellite provider. In addition, to ensure that the pixels in the image were in their proper geometric position on the Earth's surface, a geometric correction was applied. The parameters (polynomial coefficients) of the polynomial functions were estimated using least square fitting with ground control points (GCPs) identified in an unrectified image and corresponding to their real coordinates. A digital elevation model (DEM), namely, shuttle radar topography mission DEM (SRTM DEM), was then used to correct the optical distortion and terrain effect. The corrected images were then incorporated into the proposed network to detect changes.  
To achieve a change detection, the proposed network employs a fusion network by fusing the FDN and BDN architectures. Dual outputs were generated to solve low-level differential and high-level differential problems. For the training stage, a contrastive loss function and a weighted binary loss function were combined to optimize the proposed fusion network parameters. In addition, a pre-processing step was applied to validate and ensure false changes during the inference stage. Intersection and union operations were then applied from the dual outputs of the proposed network. According to the proposed change detection, the false-positive and false-negative rates could be reduced, resulting in high sensitivity and specificity for a proper change detection. Symbols used in the proposed method are tabulated in Table 1.
## 4 Parameters Analytical Calculation Model
### Air Flow Calculation Model  
Related to the bellmouth instrumentation method presented above and according to the established measurement parameters, the engine air flow calculation model is defined by the following steps:  
\[p_{s}=p_{0}-DP \tag{14}\]  
\[M=\sqrt{\frac{2}{k_{a}-1}\cdot\left[\left(\frac{\sigma_{dat}\cdot p_{0}}{p_{s} }\right)^{\frac{k_{a}-1}{k_{a}}}-1\right]} \tag{15}\]  
\[T_{s}=T_{0}/\left(1+\frac{k_{a}-1}{2}\cdot M^{2}\right) \tag{16}\]  
\[\rho_{s}=p_{s}/(R_{a}\cdot T_{s}) \tag{17}\]  
\[V=M\cdot a=M\cdot\sqrt{k_{a}\cdot T_{0}\cdot R_{a}} \tag{18}\]  
\[A=\pi\cdot D^{2}/4 \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain: T = 600–1700 (K)** & \multicolumn{2}{c}{**Domain: T = 600–1700 (K)**} \\ \hline
**Symbol** & **Value** & **Symbol** & **Value** \\ \hline \(a_{1,g}\) & 37.668695 & \(a_{1,g}\) & 5.7625165 \\ \(a_{2,g}\) & 8.3071487 \(\times\) 10\({}^{-1}\) & \(a_{2,g}\) & 5.1685108 \(\times\) 10\({}^{-3}\) \\ \(a_{3,g}\) & 3.5077944 \(\times\) 10\({}^{-4}\) & \(a_{3,g}\) & \(-\)4.8471337 \(\times\) 10\({}^{-6}\) \\ \(a_{4,g}\) & \(-\)1.2905428 \(\times\) 10\({}^{-7}\) & \(a_{4,g}\) & 3.170584 \(\times\) 10\({}^{-9}\) \\ \(a_{5,g}\) & 3.5650596 \(\times\) 10\({}^{-11}\) & \(a_{5,g}\) & \(-\)1.1469426 \(\times\) 10\({}^{-12}\) \\ \(a_{6,g}\) & \(-\)5.272937 \(\times\) 10\({}^{-15}\) & \(a_{6,g}\) & 1.7227014 \(\times\) 10\({}^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gas enthalpy and entropy polynomial coefficients.  
\[M_{af}=\rho_{s}\cdot V\cdot A\ \ \ \Rightarrow\ \ M_{af} \tag{20}\]  
In this case, the calculation model uses a total pressure loss coefficient (\(\sigma_{da.t}\)). On the test bench for a bellmouth inlet engine, the coefficient is estimated to be in the range \(0.990\div 0.995\).  
In the case of the second method, only the following equations change in the calculation algorithm; otherwise, the calculation is the same as above.  
\[p_{s}=p_{t}-DP \tag{21}\]  
\[M=\sqrt{\frac{2}{k_{a}-1}\cdot\left[\left(\frac{p_{t}}{p_{s}}\right)^{\frac{k_ {a}-1}{k_{a}}}-1\right]} \tag{22}\]
## 3 Stability Analysis under Resilient Event-Triggered Rule and DoS Attacks
### Feasibility Criterion  
In this subsection, the main objective is to find the minimum update rate, which aims to avoid Zeno behavior with an unlimited number of transmission instants in a finite time period. The maximum downtime of (8) under DoS attacks is calculated.  
Theorem 2: _For the closed-loop MJCPSs (12) with resilient event-triggered rule (8) and dual-mode security controller (10), Zeno behavior is strictly excluded if the inter-execution interval \(\Delta_{k}\), \(k\in\mathbb{N}_{0}\) is greater than or equal to_  
\[\Delta_{\text{min}}=\min\Bigl{\{}\Delta_{\text{min}}^{1}\Delta_{\text{min}}^{2 }\Bigr{\}}, \tag{29}\]  
_where_  
\[\Delta_{\text{min}}^{1}=\frac{1}{\theta_{1}}\text{ln}\left[\frac{\mu_{1}}{ \mu_{2}}+1\right],\,\Delta_{\text{min}}^{2}=\frac{1}{\theta_{1}}\text{  
Proof.: See the Appendix A.  
**Remark 3**.: _In view of the proposed resilient event-triggered rule (8), the purpose is to characterize two possible data update policies generated by intermittent DoS attacks through random variable \(\alpha(t)\), where \(\alpha(t)=1\) and \(\alpha(t)=0\) represent dormant and active DoS attacks, respectively. Here, the existence of \(\mathcal{J}_{d}\) makes the triggering threshold when \(\alpha(t)=0\) greater than that when \(\alpha(t)=1\). This implies that a higher triggering threshold may determine fewer packets to be transmitted. Note that it is quite possible to transmit a large number of packets in a short period of time because of an inappropriate event-triggered rule. Therefore, regardless of whether the triggering threshold is low or high, Zeno behavior needs to be strictly avoided. Moreover, the exogenous disturbance can direct the trajectory of \(\|e_{\alpha}(t)\|\) in the resilient event-triggered rule (8). In order to prevent the influence of the disturbance signal on the estimation of \(\|e_{\alpha}(t)\|\), it is necessary to develop an improved triggering inequality when discussing the feasibility of resilient event-triggered rule (8)._  
**Remark 4**.: _Note that the unreasonable event-triggered rules may also lead to singular triggering, which is another abnormal behavior that cannot transmit for a long time after a successful transmission attempt. By solving the maximum inter-execution interval between two consecutive transmissions, it is concluded that singular triggering can be avoided to guarantee the validity of the developed event-triggered rules. Due to intermittent DoS attacks, a large number of packets cannot be transmitted within the active DoS subintervals. Thus, it is necessary to characterize the maximum downtime between two adjacent successful updates to estimate the impact of intermittent DoS attacks on control performance. However, few results discuss this critical issue, and it is difficult to obtain an explicit representation for the maximum downtime caused by DoS attacks. Furthermore, similar to the procedures for avoiding Zeno behavior, we also study the potential impact of disturbance signal on calculating the maximum downtime._
## 3 Analysis of Failure Modes in Multiple Physical Fields
### Thermal Field Load Simulation and Verification  
In the thermal field simulation process, the loss values calculated under the electromagnetic field are first imported into the thermal field as a heat source, and the parameters are set, such as the thermal conductivity and specific heat capacity of each motor component. The finite element model is carried out by setting the boundary condition of the heat dissipation coefficient on the outer surface of the stator rotor structure.  
As shown in Figures 7 and 8, the motor produced a temperature rise in all parts when operating under the test conditions, with the highest temperature of 87.481 \({}^{\circ}\)C occurring in the winding section. This is due to the high Joule heat generated by the three AC currents passing through the winding coil and the poor heat dissipation conditions around the coil. The stator core temperature is higher than that of the rotor area because the losses in the stator core are much higher than in the rotor core.  
As shown in Figure 9, the highest temperature of the rotor occurs near the magnets, with a maximum temperature of 70.405 \({}^{\circ}\)C. Due to the relatively large eddy current losses in the permanent magnets, the rotor temperature decreases from the inside to the outside along the permanent magnet area, while the rotor core losses are mainly concentrated near the magnets. The temperature of the outer surface of the rotor is lower than the temperature of the entire rotor. With the change in rotation speed, the outer surface of the rotor will have a better air convection environment to dissipate heat, indicating that the temperature rise of the stator has little effect on the rotor due to the air gap between the stator and the rotor. Figure 10 shows the temperature change curve at the magnetic bridge.  
Figure 8: Stator winding temperature distribution diagram.  
Figure 7: Motor temperature distribution diagram.  
Figure 11: Bench test diagram.  
Figure 10: Temperature variation at the rotor spacer bridge.  
Figure 9: Rotor temperature distribution diagram.  
Figure 12 displays a comparison of the measured stator temperature with the simulated temperature. The figure indicates that the simulated temperature and the measured temperature follow the same trend in the time domain, but with a certain error. This is due to simplifying the number of stator winding meshes and omitting the winding ends in the simulation process, as well as using an empirical formula for the boundary heat dissipation coefficient of the stator and rotor. The error between the simulated combination and the actual temperature is within 5%, which is acceptable and verifies the accuracy of the thermal field simulation model.
## 2 Theories
### Cartographer  
The Cartographer [23] stands as an open-source real-time 2D and 3D mapping library and SLAM system, with its primary development carried out by Google. The original source code was introduced in 2016 [23] and has been updated subsequently. Consequently, we are presently utilizing the latest release version, which received its most recent update in 2021.  
The Cartographer system employs a dual approach, incorporating both local and global optimization techniques to rectify errors and estimate poses. These two optimization methods are relatively independent and align with the distinct mapping processes: the front end, responsible for converting scans into submaps, and the back end, tasked with linking submaps into the global map. The latter can also be viewed as managing loop closures.  
1. Scans-to-submap (Local map)  
Initially, the LiDAR raw data were transformed into scans without any pose estimation. A sequence of consecutive scans was then employed to construct a submap, represented as a probability grid. To enhance the quality of the scans, a bicubic interpolation method was utilized for filtering and smoothing noisy data. This refined scan output is referred to as a grid scan. The scan matching process, utilizing a Ceres-based [24] scan matcher, was employed to perform this task, simplified as a nonlinear least squares problem.  
\[\text{argmin}_{\xi}\sum_{k=1}^{K}\left(1-M_{\text{smooth}}(T_{\xi}h_{k})\right) ^{2} \tag{6}\]  
where \(T_{\xi}\) transforms \(h_{k}\) from the scan frame to the submap frame according to the scan pose. The function \(M_{\text{smooth}}\): \(\mathbb{R}^{2}\rightarrow\mathbb{R}\) is a smooth version of the probability values in the local submap. Consequently, this process enabled the estimation of the poses, with the grid scan subsequently incorporated into the submap.  
2. Submaps-to-map (Global map)  
As the front-end process gradually accumulates errors, the back end characterizes this optimization as a nonlinear least squares problem, akin to the local mapping step, termed sparse pose adjustment (SPA). This challenge involves handling scan poses and submap poses, incorporating constraints related to relative poses and their associated covariance matrices. To prevent an excessive computational burden when exploring the entire domain, the Cartographer employs a Branch-and-Bound (BAB) scan matching approach. Its algorithm can be summarized as a method for refining the search process to obtain an optimal pose estimate.