esa-sceva/satcom-chunk-collection huggingface dataset:
Therefore, hardware conversion gateways still have an irreplaceable role to play. Modbus gateways predominantly support protocol interconversion between Modbus TCP and RTU. From the above discussion, it is clear that under weak network conditions, TCP can exhibit higher latency compared to UDP and cannot accommodate certain high real-time scenarios, while UDP itself lacks reliability. Modbus gateways based on these transport protocols face the same challenges. This study presents a novel gateway design, enabling bidirectional conversion between Modbus UDP and Modbus RTU protocols. Leveraging the low-latency advantages of UDP, the proposed architecture introducesapplication-layer reliability mechanisms to enhance data transmission robustness, thereby achieving deterministic real-time performance while mitigating the inherent reliability limitations of UDP. The specific objectives encompass the following:  
* Utilizing the connectionless nature of UDP to dramatically reduce data transfer delays on the Ethernet side of the Modbus gateway in weak network conditions. * Adopting Markov chain model to analyze Modbus UDP transmission in depth, and proposing an improved UDP protocol, AUDP, i.e., add retransmission, data checking, and confirmation mechanism in the UDP application layer to improve the reliability of UDP transmission itself appropriately. * Develop a Modbus AUDP and Modbus RTU protocol conversion module, design a Modbus AUDP to Modbus RTU experimental gateway, and conduct data transmission verification tests. * Analyze and compare the test data using the Markov chain model to verify the performance of the experimental gateway, especially under weak network conditions. The subsequent sections of this paper are organized as follows: Section 2 first introduces the current status of Modbus TCP gateways to highlight issues related to UDP applications. Then, we conduct a theoretical analysis of the real-time performance of Modbus TCP and Modbus UDP, demonstrating the necessity and feasibility of improving Modbus gateways. Finally, we briefly introduce the current applications and principles of Markov chains, which provide the theoretical foundation for the subsequent analyses in this paper, such as supporting the necessity of certain mechanisms in AUDP (Section 3.3.2) and offering theoretical support for the in-depth analysis of the final experimental results (Section 4.3). Section 3 introduces the methodology, including hardware design and software design, and the hardware design includes the design of the overall system framework and hardware architecture, and the software design mainly includes the design of the AUDP mechanism and protocol conversion with the help of Markov chain. Section 4 carries out the experimental validation and analyzes the experimental results based on the Markov chain. Section 5 summarizes the whole paper and looks forward to the direction of future research.
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 2 Related Works
### Isolation Forest Algorithm  
The Isolation Forest algorithm represents a significant advancement in anomaly detection, particularly suited for concept drift scenarios. [PERSON] et al. (2008) [4] introduced this approach, demonstrating its unique ability to identify anomalies through isolation rather than density estimation. The algorithm's efficiency stems from its tree-based structure, which naturally handles high-dimensional data while maintaining linear time complexity.  
Recent work by [PERSON] and [PERSON] (2013) [14] has shown how Isolation Forest's characteristics make it particularly effective for streaming data environments. The algorithm's ability to operate with partial models and its efficient sub-sampling capabilities provide significant advantages for real-time applications. [PERSON] et al. (2021) [15] further demonstrated its robustness in handling evolving data patterns, while [PERSON] et al. (2023) [16] showed its effectiveness in detecting both gradual and sudden drift patterns. Our work does not really look at whether a drift is gradual or sudden; this work just determines any drift, be it gradual or sudden. Given that our data deals with attacks in network traffic data, the drift could be gradual or sudden.
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 3 Proposed MPPT Algorithm and Irradiance Estimator
### Estimation of the Irradiance  
Aside from the described MPPT approach, it is of high importance to also tackle the problem of irradiance sensors, since the irradiance information must be obtained.  
We hereby introduce a simple irradiance estimator that relies on the previously identified NN model of the photovoltaic, with the main advantage of only a few additional computational operations being required. Motivated by work in [41], the proposed estimator relies on the immersion and invariance principle, and, therefore, it will be referred to as the NI&I. Considering that \(I\) and \(T\) are measurable, we can define the error between the measured and predicted module current as:  
\[e_{k}=I-\hat{I}=I_{k}-net\left(\hat{G}_{k},T_{k},V_{k}\right). \tag{13}\]  
The immersion and invariance estimator:  
\[\hat{G}(k+1)=\hat{G}(k)+\gamma e(k) \tag{14}\]  
with \(\gamma>0\) will ensure:  
\[\lim_{k\to\infty}\hat{G}_{k}\to G,\]  
for all initial conditions \(G_{0}\) and all positive signals \(I_{k},V_{k},T_{k}\). The necessary condition for the convergence is that function \(e(k)\) monotonically increases with \(G\), which is analytically proved in [41]. On the other hand, it is a known fact that the module current is increasing with increase in irradiance.  
The faster estimator (neural gradient estimator (NGE)) can be provided if the cost function is defined as a square of \(e(k)\), and then minimized by the gradient descend method:  
\[\hat{G}(k+1)=\hat{G}(k)-\delta\frac{\partial e^{2}(k)}{\partial\hat{G}(k)} \tag{15}\]  
Since \(e(k)\) is monotonically decreasing, \(e^{2}(k)\) will be strictly convex and will have one minima.  
By differentiation of (6) with respect to irradiance, then substituting into (15), the update rule of the NGE becomes:  
\[\hat{G}_{k+1}=\hat{G}_{k}+\delta\left(\mathbf{W}_{1,1}^{T}\odot\mathbf{W}_{2 }\right)\tanh^{\prime}\left(\mathbf{W}_{1}\mathbf{u}_{k}+\mathbf{b}_{1}\right) e_{k}, \tag{16}\]  
where \(\mathbf{W}_{1,2}\) is the second column of the matrix \(\mathbf{W}_{1}\), while \(\delta\) is the positive parameter.
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 4 Case Study
### Truss Structure
#### 4.1.1 Finite Element (FE) Modeling  
The truss structure model is shown in Figure 3, numerically modeled by the 'Matlab' programming software. To simply to verify the feasibility of the method, each beam is discretized into a beam element, there are 21 discrete beam elements and 12 nodes, each node contains translational degrees of freedom (DOFs) in \(X\) direction and \(Y\) direction. Due to the Node 1 is fixed and Node 12 is fixed on the hinge support, so the translational DOFs \(x_{1}\) and \(y_{1}\) in \(X\) direction and \(Y\) direction of Node 1 and translational DOFs \(y_{12}\) in \(Y\) direction of Node 12 are locked, so the total DOFs of the truss structure is 21. It is assumed that the DOFs of the target model that need to be updated and the FE model are matched, so there is no problem of model reduction and modal expansion. Therefore, the degrees of  
Figure 2: Model updating implementation steps.  
freedom of both the FE model and the target model is 21. The elastic modulus of all beam elements in the initial FE analysis is \(E=70\) Gpa, the density of the truss-element material is \(\rho=2700\) kg/m\({}^{3}\), the length of each beam is \(L=1\) m, and the cross-sectional area of each truss element is \(A=1\times 10^{-4}\) m\({}^{2}\).  
It is assumed that the FE model of the truss structure has modeling errors, and the elastic modulus of beam element 1, 6, 11, 14, and 21 of the target model is 1.1, 1.2, 1.2, 1.2, and 1.1 times of the FE model, which is bold in red in Figure 3. It is assumed that the simulation experiment is a single point excitation, and the excitation point and response point in the target model is the same one. Using the above data for FE analysis, the frequencies of the first 7 order and FRFs of Node 4 and Node 10 are selected as the target value for model updating. Because the planar truss system is modeled in undamped mode, the influence of damping on FRFs at resonance peak need not be considered in the updating progress.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 3 The DBMIST-US Algorithm
### Core Stage  
After cleaning the majority class with DBSCAN, a graph-based approach is used to compute a candidate core of the set \(C^{\prime-}\). A weighted complete graph \(G_{w}=(V(G),E(G))\) is built from the set of majority class instances \(C^{\prime-}\) given by the DBSCAN-based stage as follows:  
* \(V(G)=\{i\mid p_{i}\in C^{\prime-}\}\) a set of vertices.
* \(E(G)=\{\{v,u\}\mid v,u\in V(G)\}\) a set of edges.
* \(\forall e=\{v,u\}\in E(G)\), \(w(e)=d(p_{v},p_{u})\) where \(d(p_{v},p_{u})\) is the Euclidean distance between \(v\) and \(u\).  
The purpose of computing an MST is to build a subgraph that connects all vertices in \(G_{W}\) without forming cycles, that is, there is not a path from a vertex \(x\) to a vertex \(y\) such that \(x=y\) and whose sum of edge weights is the smallest. Figure 1 shows an illustrative example of an MST (b) computed from a weighted graph (a).  
Figure 1: Illustration of weighted graphs. The graph on the right depicts an MST of the graph on the left. (**a**) weighted graph; (**b**) MST.  
To build an MST, an incidence matrix \(M_{G}\) (line 6) computed by means of [PERSON]'s algorithm [45] is used. This process is as follows:  
1. Choose an initial vertex \(v\) at random.
2. Select the edge \(e=\{v,u\}\) with the lowest weight in \(M_{G}\) and mark \(v\) as visited. Now, the next vertex to be analyzed is \(u\).
3. Repeat Step 2 now taking \(u\) as the initial vertex of the edge \(e\) and while there exists a vertex \(z\) such that \(z\) has not been visited yet, e=[u,z].
4. The MST will be built doing backtracking on the already marked vertices until each vertex has been marked.  
By definition, an MST contains each vertex of the graph. Our proposal takes only a subset of the MST: the first \(S\)-vertices to build the new majority class \(C^{\prime-}\), where \(S\) denotes the maximum imbalance ratio required (lines 8-10). To validate the 95% of confidence in the imbalance ratio, the values chosen are \(Z=1.96\), \(\sigma=0.5\), and \(e=0.05\) according to the procedure given by [PERSON] et al. [46].
# An Algebraic Approach to Identifiability
## 5 Discussion  
The problem of deciding global or local identifiability of a system has been addressed by viewing the parameters as additional state variables and testing the observability of these. In contrast to the ordinary observability problem, various kinds of state variables exist in the extended system that may be handled differently. Algebraic criteria for global and local identifiability, with or without known initial conditions, have been given. These can be tested for each individual physical parameter. For locally yet not globally identifiable or observable systems, the critical points in the state and parameter space can be computed as well. This information may be used to select appropriate initial conditions or input signals for identification experiments, such that these points are avoided. Furthermore, parameter constellations that prevent their identification can be computed in advance.  
The approach used herein differs from algebraic methods for identifiability tests that eliminate the system state. While our method introduces additional variables, we can entirely avoid calculations in a differential ring. Nonetheless, computations in the ordinary or differential polynomial ring may require a large computational effort even for systems that appear very simple, as the expressions swell very quickly. Which criterion performs better may depend on the system itself as well as on the underlying algorithms used. In addition, the monomial ordering as well as the ranking of the differential monomials have a high impact on the computation time and size of the intermediate results. No comparison of the different approaches has been carried out until this point.  
Currently, the manageable systems must allow a state-space representation with a polynomial vector field in both the state and the parameters, as opposed to the differential algebraic methods that naturally support rational vector fields, and can thus handle a greater system class. One can possibly overcome this limitation.  
Conceptualization, D.G. and K.R.; methodology, D.G. and K.R.; software, D.G.; investigation, D.G.; writing, D.G. and K.R.; supervision, K.R.; funding acquisition, K.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deutsche Forschungsgemeinschaft (DFG) under the research grant RO2427/5-1.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Glottal Gap Segmentation and Facilitative Playbacks
### Glottal Gap Segmentation Techniques
#### Assessment of Vocal Folds Segmentation  
Assessing the glottal segmentation is not trivial due to the huge amount of frames to evaluate and the need to take into account the spatial-temporal information of the video sequences. The evaluation is even more complicated having in mind that there are neither standard metrics to evaluate the distinct algorithms nor public databases that could be used for benchmark and comparison purposes [109].  
A simple way to evaluate the glottal segmentation is by visual inspection. However, it requires a frame by frame intensive evaluation over a large set of images, including the contribution of several experts to minimize the inter evaluation bias. The literature proposes subjective ways to evaluate the segmentation by grading the quality of the glottal gap delineation from 0 to 5 points [73; 90; 99]. A more complete evaluation combines segmentation quality, readability of the playbacks (GVG, PVG, GAW) and shape similarity between VFT and VKG [86].  
The literature also refers to different objective evaluations to compare the segmented image against a Ground-Truth (GT). The degree of similarity between the human and machine-generated images determines the quality of the segmentation. Among the most extended metrics used to evaluate the glottal gap segmentation are: Dice Coefficient (DICE), Mean Square Error (MSE), Accuracy (ACC), Mean Area Error (MAE), object-level consistency error (OCE), Pratt, F-measure(F-M), True negative rate (TNR), Pixel Error Rate (PER), Overlapping ratio (OR), Rand Index (RI), Mutual Information (MI), Cohen Kappa coefficient (KAP), Intersection Over Union (IOU), Hausdorff distance (HAD) and Adjusted Rand Index (ARI) [74; 86; 88; 90; 93; 94; 95; 96]. The main drawback of objective evaluation is to manually generating a ground-truth since it is a subjective and time-consuming task. Table 3 summarize the main studies carried out to objectively and subjectively assess the vocal fold segmentation.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 1 Introduction  
With the advent of high definition TV, 3D video and mobile devices, spatial audio technologies have gained great popularity in recent years. Speaker sets have evolved from the classic stereo systems into many channels, not only considering 2D configurations, but also height speakers. The variety of formats (from 5.1 to 22.2 and also headphone systems) and the reproduction techniques (Vector Base Amplitude Panning (VBAP) [1], Wave Field Synthesis (WFS) [2], Ambisonics, _etc._) open up many possibilities for the recreation of acoustic environments and especially the creation of new musical experiences. Audio reproduction systems based on loudspeakers are the most popular, but the headphone-based systems are increasing in popularity because of the private hearing they provide in any type of environment, as well as the widespread use of mobile devices nowadays. Headphones are commonly employed to reproduce stereo recordings, but binaural material represents a step forward.  
The reproduction of binaural sound over headphones uses the principles of the human auditory system [3]. It assumes that, if we are able to reproduce in the listener's ears with headphones the same pressures that the listener experiences in a natural environment, a realistic acoustic immersion can be simulated [4].  
To have a correct sense of spatial immersion, high quality microphones should be employed in conjunction with acoustic mannequins. In addition, high quality headphones should be used for playback. However, low end headphones are widely used in most cases, either for economic reasons or simply because they are included with mobile devices. It is generally known that low cost headphones usually provide a poorer sense of immersion, but the degrading factors that cause such a loss in quality have not been sufficiently studied, as well as the level of their effects. In this research, we are laying the groundwork for a strategy to study the factors that affect the spatial sensation on listening with headphones and their relationship to perceived quality.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 5 Simulation of Field Tests
### Distance Independent Consecutive Packet Loss  
To measure the distance independent losses, we used our testbed to monitor the number of consecutive losses observed in 3000 experimental samples when measuring packet loss rates. To do this, we attached a number in each packet and extrapolated the number, and this allowed us to see the number of packets lost consecutively. We chose to do this over a 100-m distance because typical stopping distances up to 70 mph fall within this distance range. We took our samples and compiled a normal distribution plot showing our spread of consecutive losses. This can be seen in Figure 3 and shows most consecutive losses are in the range of 1-11. It can also be seen that the most common occurrence of consecutive loss is 2 packets, and the mean is approximately 5. Using these results, we selected a range of values through 1-18 because 1 is our minimum consecutive loss and 18 is our highest. We produced results for 30 mph, 50 mph, and 70 mph and conducted this over the full range of data rates we utilized in our testbed, i.e., 10 to 140 packets per second.  
Table 5 represents the remaining stopping distance available after a number of consecutive losses at 30 mph and 10 pps. The table is used to give an example of what the results look like after applying our model.  
As can be seen at 0 losses, this is the full stopping distance required at 30 mph. The distance shown on the right is the remaining stopping distance that would be available after consecutive loss, shown on the left. The results for 11 packets and higher show a negative value. This negative value means the distance remaining to stop has reached a point whereby a vehicle could have collided or overtaken the lead vehicle. This highlights the importance of reliability for communication when autonomous vehicles become more widespread. There are variables in the model, and, in these tests, the three variables are:  
* Consecutive Losses, which have been set to--0, 1, 4, 6, 8, 9, 11, 13, 15, 17, 18;
* Data Rate in packets per second, which is configured as--10, 25, 50, 75, 100, 120, 140; and
* Speed in mph or (km/h), which is configured as--30 (48.27), 50 (80.45), 70 (112.63).  
The following set of figures are used to show the results for distance independent measurements, and these are for each speed and data rate. Figure 4 shows 30 mph, Figure 5 shows 50 mph, and Figure 6 is for 70 mph.  
Figure 3: Packet loss distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Consecutive Packet Loss** & **Equation (4). Remaining SD (m)** & **Equation (5). Remaining SD (m)** \\ \hline
0 & 13.1474 & 13.1474 \\
1 & 11.7675 & 10.3488 \\
2 & 10.13876 & 8.96891 \\
4 & 7.62782 & 6.20911 \\
6 & 4.86803 & 3.44932 \\
8 & 2.
## 2 Materials and Methods
### Data Analysis Methods
#### 2.5.4 Spectral Data Fusing Method  
Differences in the performance of the two spectrometers resulted in different spectral reflectance acquired in the spectral overlap region (1000-1050 nm). Therefore, it is necessary to splice the two wavebands in the overlapping region to make a contiguous curve. First, the spectra with low signal-to-noise ratio at the edges were truncated, and the spectral data of FieldSpec HandHeld 2 and SW2520 in the range of 400-1050 nm and 1000-1650 nm were preserved, respectively. The spectral reflectance of the two spectrometers at 51 overlapping wavelengths was subsequently calculated by the interpolation algorithm [27]. Finally, the two datasets were stitched together by averaging method, this is a low-level data fusion of visible/short-wave and long-wave NIR spectral data. The formula for calculating the spectral reflectance after fusing as Equations (2) and (3) [28]:  
\[e=\frac{1}{n}\sum_{i=1}^{z}R_{1,i}\ -\ \frac{1}{n}\sum_{i=1}^{z}R_{2,i} \tag{2}\]  
\[R_{i}=\frac{1}{2}(R_{1,i}+R_{2,i}+e) \tag{3}\]  
where \(e\) represents the difference between the averages of the two datasets of spectral reflectance in the overlap region; \(z\) is the number of 51 wavelengths obtained after interpolation of the overlap region; \(n\) represents the total number of samples; \(R_{1,i}\) is the spectral reflectance at the \(i^{th}\) wavelength acquired by FieldSpec HandHeld 2; \(R_{2,i}\) is the spectral reflectance at the \(i^{th}\) wavelength acquired by SW2520; \(R_{i}\) is the spectral reflectance at the \(i^{th}\) wavelength after data fusion.
# Advances in Computer-Aided Medical Image Processing
## 4 Neural Network Design for Medical Imaging
### Reuse of Low-Level Features in Medical Images
#### 4.1.1 U-Shaped Structure Reuse Low-Dimensional Edge Features
The decoder is also composed of a series of convolutional layers but with upsampling layers (or transposed convolutions), which help the network to precisely locate and delineate the features learned by the encoder. U-Net [77] proposed extracting high-dimensional features by downsampling first and supplementing some image information by upsampling, but the information supplement must be incomplete, so it needs to be connected with the image with higher resolution on the left. This is equivalent to making a direct connection between high-resolution and more abstract features because the extracted features are more effective and abstract as the number of convolutions increases. The upsampled image is formed after multiple deconvolutions, which is different from the original image. At this time, the upsampled image can be directly connected with the original image of the same dimension and high resolution to obtain more valuable information. The U-Net network can be divided into three parts. The first part of the U-Net network is the backbone feature extraction part. The backbone feature extraction part consists of a convolution+ maximum pooling layer. The convolution layer is used to initially extract the effective features, and then the maximum pooling layer is used to conduct downsampling to obtain the effective feature layer. The second part of the U-Net network strengthens the feature extraction part. In this paper, the preliminary effective feature layer obtained from the backbone feature extraction part is used for upsampling; the resolution of the feature map is restored to the resolution of the original image by deconvolution, and then it is fused with the effective feature layer obtained from the backbone part so that a final effective feature layer with all features can be obtained. The third part of the U-Net network is the prediction part, which uses the final effective feature layer to classify each feature point, equivalent to the classification of each pixel point.