esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 2 Related Work
### Loss Function of Singing Voice Separation Models  
To measure the error between the ground truth and the predicted spectra, there are many distance metrics which provide scale-invariance. One of such metrics for the STFT spectra is Itakura-Saito divergence [21], and it was applied to nonnegative matrix factorization (NMF) [22; 23] with successful results in music sound analysis [22]. \(\beta\)-divergence also provides scale-invariant distance, and it was applied to NMF as well [24; 25]. However, in DNN learning, it is preferred to use simple metrics that are easy to differentiate to derive a learning algorithm and compute gradients efficiently. In our paper, we use mean squared error (MSE, squared L2 loss) between ground truth and predicted spectrum that was adopted to singing voice separation recently [7; 8]. The MSE between two arbitrary STFT vectors \(\mathbf{x}\) and \(\mathbf{y}\) is defined as follows:  
\[l_{2}(\mathbf{x},\mathbf{y})=\frac{1}{F}\left\|\mathbf{x}-\mathbf{y}\right\| _{2}^{2}=\frac{1}{F}\sum_{f=1}^{F}\left(x(f)-y(f)\right)^{2}\,, \tag{2}\]  
where \(f\) is a discrete frequency index, and \(F\) is the total number of frequency bins. The advantage of the squared L2 loss is that it is differentiable and has smoother convergence around 0. For the given ground truths in the powerspectral domain at time \(t\), \(\mathbf{y}_{1,t}\) and \(\mathbf{y}_{2,t}\), and their approximates, \(\hat{\mathbf{y}}_{1,t}\) and \(\hat{\mathbf{y}}_{2,t}\), the prediction error is defined by the MSE averaged over all the frequency bins:  
Figure 2: Structure of stacked recurrent neural networks.  
\[E_{p}(t) = l_{2}(\mathbf{y}_{1,t},\tilde{\mathbf{y}}_{1,t})+l_{2}(\mathbf{y}_{2,t},\tilde{\mathbf{y}}_{2,t}) \tag{3}\] \[= \frac{1}{F}\sum_{f=1}^{F}\left[(y_{1,t}(f)-\tilde{y}_{1,t}(f))^{2}+ (y_{2,t}(f)-\tilde{y}_{2,t}(f))^{2}\right].\]  
The objective function should be designed to reduce the prediction error between the ground truth and the approximate at all time and frequency units [4; 5; 6]:  
\[J = \frac{1}{T}\sum_{t=1}^{T}E_{p}(t) \tag{4}\] \[= \frac{1}{TF}\sum_{t=1}^{T}\sum_{f=1}^{F}\left[(y_{1,t}(f)-\tilde{y }_{1,t}(f))^{2}+(y_{2,t}(f)-\tilde{y}_{2,t}(f))^{2}\right]\;,\]  
where \(T\) is the number of samples in time. Learning using this loss is expected to yield a high signal to interference ratio (SIR) [26].
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 4 Experiment Results
### Experimental Results on Resnet50 with CIFAR-100
#### 4.3.1 Experimental Results on Case 1  
We utilized the 'Case 1' network and updated the branch new network with the remaining 30 classes. Then we set the threshold accuracy degradation value to 2% less than the baseline accuracy. Figure 5a shows the result of the selected optimal number of sharing layers through BayesOpt. Observing the result, we could find out that the 39 th layer was the optimal number of sharing layers with six iterations. This means it could converge to the solution that satisfied the proposed objective function with only six layer-attempts of the experiment. Additionally, since BayesOpt followed the global optimization scheme, the L2 distance of the consecutive points was also very high with updating the best calculated sample value as shown in Figure 5b,c. The combined classification accuracy value of the corresponding sharing layer was 67.84%, while the baseline of classification accuracy was 69.73%, as listed in Table 1. Additionally, we proceeded to experiment with a different threshold value of the combined classification accuracy degradation, such as 3% less than the baseline accuracy, as depicted in Figure 6a-c. Thus, we could get the optimal sharing configuration number of layers to be 47, having an accuracy of 67.03% with only six iterations.  
Figure 4: Visualization results of the optimal sharing layer configuration through BayesOpt using ‘EI’ as the acquisition function. (70 classes/30 classes) (**a**) The result of the selected optimal number of sharing layers. (**b**) The L2 distance of the consecutive observed points. (**c**) The value of the calculated best-selected sample for every iteration.
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
###### Abstract  
The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X-Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF-K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages.  
intrusion detection system; supervised learning; Kohonen self-organizing maps; CAN bus
# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 3 Methodology
### Input Configurations  
To study the behavior of LiDAR odometry, six different input configurations are proposed. In each configuration, a new set of filtered data is generated, which is later fed to the odometry algorithm for evaluation. The different experimental configurations and their motivation are presented below:  
1. Raw: Raw original point cloud from the KITTI dataset. This configuration serves as the baseline for the comparison.
2. Dynamic: Remove all dynamic objects from the point cloud, i.e., vehicles and pedestrians. As stated in other works (e.g., [8]), dynamic objects are a source of spurious correspondences in the matching, leading to erroneous estimations.  
3. Dynamic Vehicles: Remove only dynamic vehicles from the point cloud. The only difference between this configuration and the previous one is that pedestrians are not removed from the point cloud. The hypothesis that motivates this configuration is that, due to their smaller size and lower velocity, the contribution of pedestrians to the error may be negligible.
4. Far: Remove all points that are far from the vehicle. All points \(p\) with a distance \(|p|>30\) m are removed from the point cloud. This configuration is motivated by the fact that LiDAR point clouds are dense and rich in details in close distances, but become more sparse over the distance, thus far points may introduce more noise to the system.
5. Ground: Remove the ground points. Removing the ground from the point cloud may have some advantages, but also some disadvantages. On the one hand, the rings of the LiDAR sensor that lay on the ground (assuming it is planar) will always have the same appearance. This should result in a misconceiving of the translation part because all those points will have the same coordinates in different frames, even if the vehicle was moving. On the other hand, ground points may be helpful for the rotation part, particularly for pitch and roll angles.
6. Structures: Leave only points from structures, such as buildings, and objects, such as poles and traffic signs. This test case is proposed to analyze what would happen if only the truly static and structured objects from the scene are kept. Everything is removed from the point cloud, including static vehicles, vegetation and the ground.  
These new sequences generated after filtering the original point cloud are used as the input of the LiDAR odometry algorithm. As a result of this filtering, the total size of the point cloud is reduced. The total percentage of downsizing from the raw point cloud is visible in Figure 2, where values are averaged over all the test sequences.
# Design and Evaluation of Vacuum Central Drum Seed Metering Device
## 5 Discussion  
To improve the level of lightened and simplified mechanization of hybrid rice seeding in China, this paper adopted a combination of theoretical calculation, parametric design, and a bench test to design a vacuum central drum seed-metering device. The test results show that the developed metering device can meet the design objectives, reducing the number of conventional seed-metering devices, and high demanding for vacuums.  
1. The technical requirements of the hybrid rice vacuum drum seed-metering device were determined according to the agronomic characteristics of hybrid rice, \(2\pm 1\) seeds per suction hole was defined as qualified, 0 seeds as missed, and \(\geq\)4 seeds as reseeding. The suction performance of the vacuum central drum seed-metering was studied, and the suction amount performance of the drum seed was studied for different rotational speeds, different negative suction pressures, and different positive pressures.
2. The double drum design allows for easy replacement of the outer drum with different types of holes, which helps to improve the adaptability to different varieties of rice or other crops and provides a basis for further adaptability studies.
3. The use of distributed seed guide tubes can be adapted to the agronomic requirements of different row spacing. The form of distribution of the seed guide tubes, their angle, and the avoidance of sliding friction coefficients with the seeds all affect the distribution of the seeds in the field, which is the focus of future research.
## 3 PITEC Protocol  
From the perspective of verification process, the major participants are controllers, Verifiers, and standby-ers. The depletion rate \(D\) is assumed to be _constant_ in this protocol. The controllers regulate the traffic and are selected randomly among the participants. The user's role changes as time evolves. The controllers regulate the traffic via _periodically_ injecting unverified transactions to the verifiers. Associated with pulse period, \(T\), there are a fixed cost \(A\) and a variable cost \(v\) per cycle. The fixed cost \(A\) is irrespective of the volume of injected transactions. The variable cost \(v\) is associated with exposing the transactions to any vulnerability. \(v\) is proportional to the time a transaction remains as a tentative before it joins the DAG as a leaf node. Let \(Q^{*}\) be the cost-optimum volume of the total unverified transactions available at the beginning of each cycle. For a theoretical simulation purpose, the _exponential smoothing_ approach is used. Exponential smoothing is a well-known technique for analysis of time-series data. It predicts the number of unverified transactions released into the system. Transactions that failed the verification or had to be returned represent at least an opportunity cost to the system. They must be reflected in the model.  
The total cost, \(C\), per unit time \(T\) is  
\[C=\frac{A}{T}+\frac{Q\times v}{2}, \tag{1}\]  
where \(\frac{1}{2}Q\times T\) is the average volume of transactions vulnerable during the cycle. Since the total expected vulnerable time for \(Q\) transactions is \((1/2)(QT)\), the expected total cost associated with vulnerability is \((1/2)(QvT)\). Therefore, the total cost is  
\[C_{total}=A+\frac{1}{2}Q\times v\times T. \tag{2}\]During the cycle time \(T\) and a fixed consumption rate \(D\), the number of consumed transactions is \(Q=D\times T\). Therefore, the cost per unit time is  
\[C=\frac{A\times D}{Q}+\frac{1}{2}Q\times v. \tag{3}\]  
This leads to  
\[Q^{*}=\sqrt{\frac{2A\times D}{v}},\quad T=\sqrt{\frac{2A}{v\times D}}. \tag{4}\]
## 2 Physical Model and Control Equation
### Solution Method and Boundary Condition Setting  
In this paper, Fluent software is utilized for conducting simulation experiments. The solver type is configured as pressure-based, the velocity format is set to relative, and a transient environment is selected for the simulation. The solution method is set to coupled, and the initialization method is configured as hybrid initialization. This initialization approach can enhance the convergence of the calculation results by adjusting the relaxation factor. The boundary conditions are set as follows:  
1. The RNG model in the \(k\)-\(\epsilon\) (2 eqn) model was selected. The wall function selects the standard wall function.
2. The model constants were Cmu = 0.0845, C1-Epsilon = 1.42, C2-Epsilon = 1.68.
3. The material is liquid water, the nozzle outlet is the inlet of the fluid domain, the inlet is the velocity inlet, the velocity is 25 m/s, and the turbulence intensity is 5%. Considering the influence of gravity, the initial velocity of the \(y\)-axis is set to \(-\)9.81 m/s.
4. The outlet condition is the pressure outlet, the initial gauge pressure is 0, the reflux turbulence intensity is 5%, and the reflux turbulence viscosity ratio is 10.
5. Set the wall as a non-slip solid wall.
6. The time step method is set to \"Automatic\". The length scale method is set to \"Conservative\". The time scale factor is set to 1. The number of iterations is set to five hundred. The report interval is set to 0.5. The data update interval is set to 1.  
Because it is a turbulence model, the pressure and velocity schemes select the Coupled scheme. The spatial discretization is based on the least squares cell, and the second-order gradient pressure is selected. The momentum is second-order upwind, and the turbulence dissipation rate is one-section upwind. The value of \(y+\) is between 30 and 60. Since the model used in this paper is the \(k\)-\(\epsilon\) model, which is a high Reynolds number model, the value of \(y+\) meets the calculation requirements within this interval.
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 4 Test and Verification of Seed Metering Device
### Single Factor Test Results  
#### 4.4.1 Effect of the Rotational Speed of the Metering Wheel on Each Index  
Figure 10 shows the single-factor test results of the influence of the rotation speed of the metering wheel on seeding performance. The results showed that when the hole diameter was 11 mm and the hole depth was 6.5 mm, the variation coefficient of seeding uniformity decreased first and then increased with the increase in rotational speed of the metering wheel. The seed metering quantity increased with the rise of the rotation speed of the metering wheel. The seed metering quantity was 152.22-176.01 g/min at the rotation speed of 80-100 r/min, which met the seed metering quantity index of 150.10-178.29 g/min.  
When the rotation speed of the metering wheel is low, the stirring effect of the metering wheel on the seed is poor, which leads to the seed being still in the accumulation state and cannot be separated from the seed pile. The irregular movement between populations is small, and the probability of the seed filling the hole is reduced, resulting in leakage and uneven distribution of seeds. When the rotation speed of the metering wheel is too large, the disturbance of the metering wheel on the flow layer of the population is substantial. It lead to the seeding before the number of seed filling in a single pore reaches the standard, leading to the problem of missing seeding. The high-speed rotation of the metering wheel causes the amplitude of the metering device to become more extensive, and the seeds are prone to offset when falling to the conveyor belt, which affects the uniformity of seed distribution. Therefore, the appropriate rotation speed of the metering wheel can effectively improve seeding performance.
## 1 Introduction
Based on existing research, formulas were established for assessing the risk of river blockages caused by landslides, the stability of landslide dams, and the peak flood discharge after dam breakage. A risk assessment model of the hazard chain of landslide-river blockage-outburst flood was proposed, with explicit formulas. The case of the Baige landslide was investigated to verify the applicability of the model.
# eHealth Integrity Model Based on Permissioned Blockchain +
## 1 Introduction
### Contributions  
In this paper, a Blockchain-based eHealth Integrity Model (BEIM) is proposed that ensures information integrity in eHealth systems and uses permissioned blockchains with off-chain information storage. In contrast to existing solutions, the paper shows how to allow for information removal, as it is a legal requirement to have such an option in many countries. Several security problems related to blockchains that must be considered during blockchain development in eHealth systems are also discussed. The proposed model can easily be integrated with systems using service-oriented architectures.  
Blockchain technology can be used to store complete medical data or only security-related data. In the BEIM, a blockchain is mainly used to implement a data-integrity service. The blockchain provides a solution that does not require trusted third parties, and enables the easy synchronization of a common history of events coming from multiple sources. Furthermore, it allows to link together records related to one patient that are simultaneously produced in many healthcare sites. As is discussed above, one of the most important applications of blockchain technology in EHRs concerns data-integrity preservation and the unified logging of medical records. However, in many cases, a blockchain is proposed to store all EHRs (e.g., [PERSON] et al. [19]). In the BEIM, the blockchain is used as designed, i.e., to create rigorous and tamper-resistant data registers and transactions related to them. Furthermore, the BEIM uses a blockchain that is not related to cryptocurrency.  
The main research question was to find if it is possible to design a model allowing to achieve transactional transparency in eHealth systems that does not require trusted third parties and supports document removal. The model should preserve medical-record confidentiality.  
The present work is the full version of our paper. In comparison to the conference version [20], it provides an extended introduction, new sections on security assumptions and adding and removing records, an improved and extended discussion, results from a performance test, and additional information.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### Overview of UAVs
Information exchange protocols and other technical details will also be specified in control and communication operations. UTM is the mechanism that manages airspace to facilitate and permit UAV operations performed outside the beyond visual line of sight (BVLoS) where standard air services are unavailable. As a result, UAV operators and the FAA will work together to determine and report the state of the airspace in real-time. The FAA now imposes several restrictions on UAV operators to ensure safe management operations. FAA and UAV operators mostly communicate through a distributed network of highly automated systems via the Application Programming Interfaces (APIs). They do not coordinate through verbal communication, as pilots and air traffic controllers do.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
###### Abstract  
In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian Process Regression and obtain a sparse representation of each time series in the dataset with a logarithmic (in the original length \(T\)) number of inducing data points. In the second stage, we apply \(k\)-means with DTW Barycentric Averaging (DBA) to the sparsified dataset using a generalization of DTW, which accounts for the fact that each inducing point serves as a representative of many original data points. The asymptotic running time of our Sparse Time-Series Clustering framework is \(\Omega(T^{2}/\log^{2}T)\) times faster than the running time of applying \(k\)-means to the original dataset because sparsification reduces the running time of DTW from \(\Theta(T^{2})\) to \(\Theta(\log^{2}T)\). Moreover, sparsification tends to smoothen outliers and particularly noisy parts of the original time series. We conduct an extensive experimental evaluation using datasets from the UCR Time-Series Classification Archive, showing that the quality of clustering computed by our Sparse Time-Series Clustering framework is comparable to the clustering computed by the standard \(k\)-means algorithm.  
time series; clustering: Dynamic Time Warping; sparse Gaussian processes +
Footnote †: journal:  
0000-0001-5000-0000-0000-0000-0000-0000-00000-0000-0  
Tables 2 and 3)), with most dis(similarity) measures being computationally demanding. For example, computing (or approximating) the DTW distance of two time series requires quadratic time in their length, making it time-consuming for time series with more than a few thousand observations.
## 5 Data Products, Processing and Distribution
### Data Processing System
#### 5.2.2 Scanning LiDAR Data  
Riegl's RiPROCESS software is used for managing, processing, analyzing and visualizing data acquired with the ALS system. RiPROCESS ingests raw laser scanner data and pre-processed GPS-INS data, applies calibration information, transforms the scan data into geographic coordinates and exports return data in LAS file format [22]. For mapping projects, individual swaths are co-aligned for small differences (typically \(<\)0.5 m) in elevation due to differences in position uncertainty, which is typically an effect of different satellite configurations and atmospheric conditions. A cross-track swath is typically collected at the conclusion of a mapping project to serve as a reference point for swath co-alignment.  
Figure 10: Data processing workflow for downwelling irradiance and upwelling image spectroscopy data. GLT, Geometric Look-up Table; BIL, Band Interleaved by Line.  
LiDAR returns are resampled and processed as smaller data volumes (\(\sim\)1 km tiles for mapping and \(\sim\)7 km segments for sampling transects) for efficient processing. Tiles are buffered to prevent edge artifacts during ground classification and creation of the Digital Terrain Model (DTM). Classification of ground returns is performed with a progressive morphological filter [23]. Delaunay triangulation is used to create a Triangulated Irregular Network (TIN) of ground hits, and the TIN is used to linearly interpolate DTM elevations on a 1 m raster grid. Additionally, the TIN is used to interpolate the base elevation of every non-ground return, and vegetation heights are computed by difference. A Canopy Height Model (CHM) is created by selecting the greatest return height in every 1 m grid cell, using these points to create a TIN and interpolating canopy heights on a 1 m raster grid. An example of the DTM and CHM models are shown in Figure 11. For mapped areas, tiles are mosaicked, and terrain slope and aspect, canopy rugosity (standard deviation of height) and common LiDAR metrics [24,25] are derived from the resulting data at the scale of a US Forest Service Forest Inventory and Analysis (FIA) field subplot (7.32 m radius).