esa-sceva/satcom-chunk-collection huggingface dataset:
# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 2 Materials and Methods
### Classification Methods
In the five-class emotion classification task on the GEES corpus, the kNN classifier achieved the average recognition accuracy of 91.3% after LDA feature reduction and with k = 9. The lowest class recognition rate was obtained for joy (83.6%) and anger (86.8%). Regarding fear, neutral and sadness, higher class recognition rates were achieved--93.7%, 95.9% and 96.3%, respectively. Employing LDA, kNN achieved the average accuracy almost equal to the best result in our experiments (91.5%). In the case of linear Bayes classifier, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Recognized Emotion Class (\%)**} \\
**True Emotion Class** & **Anger** & **Fear** & **Joy** & **Neutral** & **Sadness** \\ \hline Anger & 88.8 & 0 & 11.2 & 0 & 0 \\ Fear & 1.4 & 92.5 & 1.4 & 3.2 & 1.4 \\ Joy & 14.9 & 0.6 & 84.2 & 0.3 & 0 \\ Neutral & 0 & 1.1 & 1.2 & 97.1 & 0.6 \\ Sadness & 0 & 1.2 & 0 & 4 & 94.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Normalized confusion matrix for linear Bayes classifier. there were no improvements after LDA feature reduction probably due to good linear separability between classes in the original feature space. Using both classification methods in our SER experiments, lower recognition results obtained for joy and anger may be explained with the observed tendency in human perception tests to misclassify anger and joy from the GEES corpus [16]. In our earlier study, the SER experiments on the same GEES corpus were performed using a multilayer perceptron (MLP) with one hidden layer [41]. The number of neurons in the input layer was equal to the number of extracted features (same feature vector as described in Section 2.2), and the number of neurons in the output layer was equal to the number of emotion classes (5). MLP was trained using standard backpropagation (BP) algorithm with varying number of neurons in the hidden layer. The highest recognition rate was achieved with 15 neurons in the hidden layer. Further increase in neurons in the hidden layer resulted in insignificant improvement of the recognition rate at the cost of increased computational complexity and thus longer processing time. The average recognition accuracy achieved in emotion classification experiments with MLP was 90.4%. Joy was recognized with 82.5% and anger with 86.5% recognition rate. In the case of these two emotions, results of MLP underperform results of the linear Bayes classifier approximately by 2%. Sadness and neutral are emotions with the highest recognition rates of 97.7% and 93.7%, respectively. Fear is recognized with 91.7%. It can be noted that two emotions with the lowest recognition rates, namely joy and anger, have a lower recognition accuracy compared to the experimental results with the linear Bayes classifier. This is an additional reason why we decided to use the linear Bayes classifier in the proposed system, besides it is a fast classification method.
## 3 The Considered Architecture
### Protocols and Service Classes  
As mentioned in Section 2, in ISPNs, all flows can receive different QoS, which must be obtained when the sessions start, in order to verify if the QoS-aware relay nodes are able to support them. In particular, the MRSVP active/passive policy (described in the following) and the MRSVP service classes are used, while the soft state signaling approach [17; 23] is the same of the NSIS scheme, because in the network nodes the \"non-permanent\" control state expires unless refreshed, while the considered level of bandwidth may vary between a minimum and a maximum level.  
In this way, NSIS Signaling Layer Protocols (NSLPs) can signal for any QoS model (as the IntServ). In addition, MRSVP uses UDP for transport mechanism and discovery: signaling message delivery are combined into a single protocol step (it does not provide a solid security framework). Instead, all NSIS nodes necessarily do not support all signaling applications, because signaling message transport and signaling applications are strictly separated (by the NSIS Transport Layer Protocol--NTLP and NSIS Signaling Layer Protocols--NSLPs). Decoupling of discovery and transport of signaling messages is re alized in NSIS through the introduction of a session identifier. For details see [39]. We based our work on the service classes provided in [5; 6] and, as described in Section 1, the QoS state is maintained through the NSIS protocol, so the prefix NSIS is used in order to identify the presence of the NSIS protocol for signaling operations. In [5; 6], three service classes are provided:  
1. NSIS Mobility Independent Guaranteed (NSIS-MIG, which provides intolerant applications, with very stringent guarantees on packet delays and jitter);
2. NSIS Mobility Independent Predictive (NSIS-MIP, dedicated to elastic real-time applications, able to work with some resource bounds);
3. NSIS Mobility Dependent Predictive (NSIS-MDP, which can be compared with the best-effort class, subject to continuous QoS degradations and/or droppings).
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 6 Conclusions  
Through experimental and FE analyses of UHPC-RC composite decks with different UHPC layer thicknesses for steel-concrete composite bridge structures, the main conclusions are summarized as follows:  
1. A UHPC-RC composite deck can effectively increase the anti-cracking capacity of steel-concrete girder bridges within the hogging moment region.
2. With increasing UHPC layer thickness, the anti-cracking capacity of bridge decks gets better; with economic cost consideration, the UHPC layer ratio of the bridge deck can be taken as 0.3.
3. The developed FE models could reasonably predict the behavior of the four specimens, and they can be used to design UHPC-RC composite decks of steel-concrete composite bridge structures.  
These tests were focused on the observation of cracking on the deck surface. Further study needs to focus on the inner cracks in the RC layer and crack width development. In addition, it was found that the construction process has some effect on the behavior of UHPC-RC composite decks during the test. Further study is needed on the construction process, details of the connection between the UHPC and RC layers, and shrinkage and creep of UHPC and RC.  
Conceptualization, H.M., G.L. and X.S.; methodology, R.Z., G.X. and S.E.; software, R.Z. and S.E.; validation, H.M., G.L. and R.Z.; formal analysis, G.L. and R.Z.; investigation, G.L., H.M. and R.Z.; resources, H.M., G.L. and X.S.; data curation, R.Z., G.X. and S.E.; writing--original draft preparation, H.M., G.L. and R.Z.; writing--review and editing, H.M., G.L., R.Z. and S.E.; visualization, R.Z.; supervision, H.M., G.L. and X.S.; project administration, G.L. and H.M.; funding acquisition, G.L. and H.M. All authors have read and agreed to the published version of the manuscript.  
The Open Project of Shandong Key Laboratory of Highway Technology and Safety Assessment (SH202001).  
Not applicable.  
The authors declare no conflict of interest.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### CP-OFDM  
One CP-OFDM scheme is a multicarrier modulation scheme [72]. CP-OFDM schemes are deployed for designing well known 3 GPP based LTE networks. The digital input data in CP-OFDM systems are mapped using modulation schemes such as _M_-PSK or _M_-QAM in order to form complex symbols which are mapped into orthogonal sub-carriers by frequency to time domain transformation using IFFT. Guard band (or CP) is added to the transmitting symbols in order to give extra robustness to the propagating signals. The inserted CP is used to immune the propagating signals against the unwanted effects of inter-symbol interference (ISI) and inter-carrier interference (ICI) which can arise from multipath propagation as the signals travel over the wireless communication channel to the receiver. The added CP is simply a copy of the tail of the transmitted symbols that is eventually appended to its beginning. The added CP leads to poorer system spectral efficiency due to the added CP overhead. In the emerging 5G networks and beyond, FBMC schemes is considered as a pre-eminent waveform to replace the cyclic prefix-long term evolution (CP-LTE) systems due to the bandwidth wastage incurred from CP insertion as experienced in the design of OFDM systems. The principles of operation and structure of CP-OFDM scheme is well documented in the literature. Readers are referred to [73; 74].
## 2 Data and Methodology
### Geometry Model and Observation Parameters  
To analyze the spatiotemporal characteristics for Earth observation, the geometrical relationships between a halo orbiter and the Earth are the preconditions. As depicted in Figure 1, which is in the ITRS reference frame, the relative position of the halo orbiter determines the observation conditions at some point. Thus, the following are derivations about several intermediate parameters related to observation characteristics to be used for further analysis.  
The zenith angle, i.e., the incidence angle, is the angle between the direction of the observer and the local zenith, which is regarded as the normal vector of the ground plane, can be expressed as:  
\[\theta=\Psi\Big{(}R_{h}^{GCRS}-R_{P}^{GCRS},R_{P}^{GCRS}\Big{)}, \tag{6}\]  
where \(\Psi(R1,R2)\) generates the angle between vectors \(R1\) and \(R2\) by the law of cosines.  
The azimuth angle is the angle between the reference direction, such as the north direction, and the projection vector of the viewing direction on the horizontal plane. For the intuitiveness of presentation for later results, here, the east is set as the reference direction. Then, the azimuth angle can be expressed as:  
\[\phi=\Psi\Bigg{(}\Big{(}R_{h}^{GCRS}-R_{P}^{GCRS}\Big{)}-\Bigg{(}\Big{(}R_{h} ^{GCRS}-R_{P}^{GCRS}\Big{)}\cdot\frac{R_{P}^{GCRS}}{\big{|}R_{P}^{GCRS}\big{|}} \Bigg{)}\frac{R_{P}^{GCRS}}{\big{|}R_{P}^{GCRS}\big{|}},\Omega_{E}\times R_{P} ^{GCRS}\Bigg{)}, \tag{7}\]  
where \(\Omega_{E}\) is the direction of angular momentum of the earth's rotation.
# Triangular Mesh Surface Subdivision Based on Graph Neural Network
## 2 Related Work
### Graph Neural Network  
Mesh data are often classified as 3D spatial data and are widely used in 3D deep learning. However, compared with one-dimensional signal data and two-dimensional image data, the representation form of three-dimensional mesh data is closer to graph data. They are composed of nodes, node feature vectors, and connection relationships between nodes. Graph data use adjacency matrices to represent internode connections, while 3D mesh data use vertex indexes to represent faces, which can also be regarded as a relationship between nodes. Therefore, using graph neural networks for deep learning tasks relating to 3D meshes is reasonable and feasible.  
The success of the graph convolutional neural network lies in the fact that it provides a fusion strategy based on topology and node features to learn node embedding, and an end-to-end learning framework supervises the fusion process. Some research on graph neural networks focuses on static graph structures. To improve the ability of graph convolutional neural networks to fuse to node features and topological structures, [PERSON] et al. [27] extracted embeddings from node features, topologies, and combinations at the same time and used the attention mechanism to learn the adaptive weights of different embeddings. [PERSON] et al. [28] proposed a Cluster-GCN suitable for SGD training using a graph clustering structure. Recent studies on dynamic graphs [29; 30] have shown that dynamic graphs can achieve better results. [PERSON] et al. [31] dynamically constructed the graph structure by connecting adjacent nodes on each layer. [PERSON] et al. [32] used k-nearest neighbors (KNNs) to construct node neighbors.  
In addition to selecting static and dynamic graph networks, the correspondence between the mesh and graph data must be considered when applying a graph neural network to the mesh subdivision task. Some methods choose to convert mesh triangles to nodes in graph data, but this can result in complex mesh refinement and post-processing. Therefore, some researchers choose to directly correspond the mesh's vertices to the graph's nodes. [PERSON] et al. [33] generalized CNNs to the field of non-Euclidean data processing to learn the characteristics of a specific task. [PERSON] et al. [34] used projections on a set of anisotropic diffusion kernels instead of classical CNN convolutions, generalizing CNNs to non-Euclidean domains. In the method of GNS, tensor voting is used to align the small graph structure generated by local mesh patches. Among them, the patches' vertices correspond to the graph nodes one by one.  
This study uses the advantages of graph neural networks and their adaptability to 3D mesh data to improve the existing neural subdivision algorithms.
# Defect Detection and Error Source Tracing in Laser Marking of Silicon Wafers with Machine Learning
## 3 Results
The external causes of laser marking defects could be unstable laser power, a dirty laser head, platform shaking, and voltage fluctuation of the electrical power. Unstable laser power causes excessive or insufficient marking depth, dirty laser heads induce missing marks, platform shaking causes improper alignment of marks, and voltage fluctuation of the electrical power creates blurred or illegible marks. Another 591 images gathered from the qualified product and reoriented randomly are blended to the system validation schema. A total of 1000 synthetic images were inspected by the proposed method; with 25 as the pixel value differencing index, 579 images passed the examination standard, and 421 images had been ruled as a defect product. The confusion matrix of this classified result is shown in Table 3. The accuracy of defect classification is \((570+400)/(591+409)=97.0\), and the F\({}_{1}\) score is (2 \(\times\) 570)/(2 \(\times\) 570 + 21 + 9) = 0.974. This outcome reveals the high accuracy in inspecting the defect laser marking on wafers with this inexpensive hardware and near-real-time handling process. The error causes determined by the proposed methods are listed in Table 4; there are four kinds of defects that have been detected. Four different phenomena have been reported, such as improper alignment of marks, missing marks, excessive or insufficient marking depth, and blurred or illegible marks. The number of classified results is listed in the corresponding table. The random forest algorithms with the training model can trace the error source with an overall accuracy of 375/409 = 91.69% and kappa = \([409\times 375-41,\!831]/[409^{2}-41,\!831]\) = 0.889. This technique also detects the defect product with an accuracy of 97% using an inexpensive hardware configuration, and  
Figure 5.0 Contour of overlap ratio. (**left**) Laser power 10 W, (**right**) laser power 8 W. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Product** & \\ \cline{2-3}
**Result** & 570 & 9 \\ \cline{2-3}  & 21 & 400 \\ \hline \end{tabular}
\end{table} 
Table 3: The confusion matrix of classification result. the high kappa value suggests the capability of separating the error caused by the photo of the wafers.
# The Use of Augmented Reality for the Management of Equipment Ageing with a Virtual Sensor
## 2 Architecture of the Virtual Sensor
### Methodology for Ageing Monitoring and Prediction  
The methodology for the ageing monitoring and prediction consists of the three steps, shown in Figure 1. In the first phase, the data for the definition of the ageing conditions of the equipment are collected from various sources (e.g., thickness measurements of the material's equipment, accelerating and slowing down factors with respect to the ageing, etc.). After being acquired, these data are pre-processed (second phase) to be ready for the subsequent phase (processing). The data processing (third phase) consists of the execution of the above-mentioned models.  
The outputs of these models can be displayed by using a PC, which is the common way to view the results, or by means of a smartphone, a tablet, or finally, by a wearable device, such as smart glasses. A QR code (placed on the equipment) is used as marker for the equipment identification and to associate the elaborated outcomes. Smart glasses allow an increased view of the produced information to be displayed by overlapping the iso-level corrosion maps on the surface of the corresponding equipment (augmented reality); other outputs are accessible through tables and graphs and shown on the sides of the equipment. The third phase includes the data storage.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 4 Results and Discussion
### Class B Polling Results  
Class B polling in wireless networks introduces some randomness due to the way slots are selected for nodes at the beginning of each beacon period. This mechanism, designed to improve interference resilience, means that polling periods in Class B represent maximum theoretical latencies rather than deterministic intervals.  
With the described ABPC mechanism over the Class B scheduler, the maximum polling time is derived from the beacon period, but it can be shorter. Table 3 shows the calculated beacon guard duration, according to (6), using the modified ABPC.  
Figure 10 shows stepped or discrete evolution in the max polling time duration when network size changes. This behavior is directly related to how the Class B scheduler configures beacon period durations, proportional to powers of 2 and fixed for intervals of network sizes. As expected, using the multicast solution reduces the maximum polling time by a factor of \(n_{ch}\) against the unicast. This can be seen in better detail in Figure 11, which shows the impact of \(n_{ch}\) in the maximum polling latency for a fixed network size of 100 nodes.  
The ratio in maximum latency between devices with different SFs becomes more significant when more channels are available. This is another point to consider when deciding to operate with a given number of channels and when having variable SFs.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(N\) & Number of devices & 1...100 \\ \(SF_{u}\) & Spreading factor uplink & SF7–10 \\ \(SF_{d}\) & Spreading factor downlink & SF9 \\ \(M_{down\_payload}\) & Message size (up/down) & 51 Bytes \\ \(DC_{d}\) & Duty cycle downlink limit in g3 sub-band & 10\% \\ \(DC_{u}\) & Duty cycle uplink limit in g1 sub-band & 1\% \\ \(n_{ch}\) & Number of channels available in GW & [1-8-16-32] \\ \(I_{TX/RX}\) & Radio current consumption during & 11 mA \\ \(I_{deep\_slorp}\) & CPU current consumption in deep sleep & 7 \(\mu\)A \\ \(I_{CPU}\) & CPU current consumption active (during & 50 mA \\ \(I_{CPU}\) & radio operation) & 50 mA \\ \(ToA_{bacon}\) & Time on air to receive beacons & 160 ms \\ \(ToA_{downlink}\) & Time on air listening for empty slots & 328,704 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario and simulation configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SF** & **Beacon Guard Duration (s)** \\ \hline SF7 & 0 \\ SF8 & 17 \\ SF9 & 31 \\ SF10 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Beacon guard duration to adapt to uplink ToA.
## 2 Computational Model
### Deformation and Force Mapping  
During the MBD simulation, the computational grids of contact surfaces overlap. The ideal shape of contact bodies such as a ball, cylinder or plane, is a myth. In fact, the contact bodies have a true shape determined by the manufacturing process. In addition, operational steady-state temperature deformation can also be a contributor to the true shape. The solution to elastohydrodynamics requires knowledge of the deformation and the true shape of the contact bodies. Therefore, mapping algorithms are inevitable. The Cubic Hermite spline is sufficient for this task. After the evaluation of hydrodynamic and asperity contact forces, they must be mapped to the structural grids (used for the evaluation of the deformation) of the contact bodies. If a cubic spline is used for the force effects mapping, the MBD solver struggles to find a converged solution and unacceptable computational time occurs. This is the result of not guaranteeing that the pressure distribution of the source field is statically equivalent to the pressure distribution of the target mapped field.  
Thus, the force effects are mapped using analytic integration, and the discrete field of the pressures \(p_{i,j}\) is replaced by the continuous function \(p\), defined by shape functions \(N\):  
\[p(v,w)=p_{00}N_{00}+p_{01}N_{01}+p_{10}N_{10}+p_{11}N_{11}. \tag{23}\]  
The shape functions are bilinear:  
\[\begin{split} N_{00}&=(1-v)(1-w)\\ N_{01}&=(1-v)w\\ N_{10}&=v(1-w)\\ N_{11}&=vw\end{split} \tag{24}\]The function \(p\) is then integrated for each half grid element of the structural grid:  
\[\widetilde{p}_{IJ}=\int\limits_{v_{0}}^{v_{1}}\int\limits_{w_{0}}^{w_{1}}p(v,w) \mathrm{d}w\mathrm{d}v \tag{25}\]  
Numerical integration is trivial, since the function \(p(v,w)\) is analytically defined as bilinear. This way, the pressure distribution of the source field is statically equivalent to the pressure distribution of the target mapped field. Mapping through analytical integration is advantageous for different structural and hydrodynamic grid sizes. The deformation of the contact body is well represented by the coarse mesh. Nevertheless, the hydrodynamic grid requires a fine mesh. For simulations where the same fine mesh is used for the structural and hydrodynamic grid, the computational time becomes excessive. Therefore, it is effective to use different structural and hydrodynamic grid sizes and map data between them.
## 4 Frequency Response Function (FRF)
### Laplace Transform  
Let us denote \(G(p)\) as the Laplace transform of the time function \(g(t)\), \(G(p)=L\{g(t)\}\). The Laplace integral transform is defined by the relation:  
\[G(p)=\int\limits_{0}^{\infty}g(t)\cdot\mathrm{e}^{-p\cdot t}\cdot\mathrm{d}t. \tag{9}\]  
In this case,\(p=\sigma+\mathrm{i}\cdot\omega\) is a complex number.  
The function \(g(t)\) and its derivatives over time are transformed as follows:  
\[\begin{split} L\Big{\{}g^{(n)}(t)\Big{\}}=p^{n}\cdot G(p)-\sum \limits_{i=1}^{n-1}p^{n-1-i}\cdot g^{(i)}(0+),\ \ \text{for}\ n=1,\ 2,\ \dots\dots\\ g^{(n)}(t)\to p^{n}\cdot G(p)-p^{n-1}\cdot g(0+)-p^{n-2}\cdot \dot{g}(0+)\dots\dots-g^{(n-1)}(0+),\\ \dot{g}(t)\to p\cdot G(p)-g(0+)\,g(0+)=\underset{t=0+}{\lim}(t),\\ \ddot{g}(t)\to p^{2}\cdot G(p)-p\cdot g(0+)-\dot{g}(0+).\end{split} \tag{10}\]
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 6 ML Performance Review in IDS
### Multiclass Classification
#### 6.3.3 Trends and Insights, Tables 10 and 11  
While RF dominates multiclass tasks, models based on neural networks such as LSTM [52; 58] demonstrate growing potential, particularly for sequential data. Tailored preprocessing and feature engineering are critical, as evidenced by studies [38; 61]. Standardized reporting practices remain essential for ensuring fair comparisons and reproducibility.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 Score**} & **AVT** & **ADT** \\  & & **[\%]** & & **[\%]** & & **[\%]** & **[ms]** & **[ms]** \\ \hline
[45] & 99.985 & 99.996 & - & 99.997 & 0.0026 & - & RF \\
[52] & 99.97 & 99.98 & 99.97 & 99.98 & 0.05 & - & Hybrid Deep Learning (LSTM + DENS + GRU) \\
[82] & 99.95 & 99.95 & 99.95 & 99.95 & - & - & RF \\
[67] & 99.9 & 99.9 & 99.9 & 99.9 & 0.0011 & - & RF \\
[30] & 99.8 & - & - & - & - & 36.9 & RF \\
[62] & 99.6 & - & - & - & - & - & MLP algorithm \\
[63] & 99 & 99 & 99 & 99 & - & - & KNN \\ \hline \hline \end{tabular}
\end{table}
Table 10: Multiclass classification performance for BoT-IoT (sorted by accuracy); AVT: Average Validation Time, ADT: Average Detection Time.