esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### MFB Features  
The baseline MFB features achieves a DER of 15.76% when optimized with our adaptation strategy. Their primary strength lies in speaker counting accuracy (74.8%) and speaker turn detection (F1-score of 55.14%), outperforming both alternative configurations in these metrics. MFB features also demonstrate superior performance in overlap speech detection with an F1-score of 52.8%. The error component analysis reveals a balanced profile with a moderate miss rate (8.9%) and the lowest false alarm rate (2.1%) among the configurations. As shown in Table 19, MFB features show a modest but consistent cumulative improvement of 6.2% relative DER reduction from baseline to fully optimized configuration. The most significant gain comes from restricting the adaptation set to CH1 2-4 speakers (6.9% improvement), while dropout regularization actually causes a slight performance degradation for this feature type. This suggests that MFB features, being less  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & \multicolumn{4}{c}{**DER Components (\%)**} \\ \cline{2-6}
**Configuration** & **Miss** & **FA** & **Speaker Error** & **Total** \\ \hline MFB & 8.9 & 2.1 & 4.7 & 15.76 \\ ECAPA-TDNN \(\oplus\) MFB & 5.7 & 3.7 & 5.5 & 14.89 \\ GeMAPS \(\oplus\) MFB & 12.3 & 1.6 & 5.0 & 18.87 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Error component analysis for ECAPA-TDNN \(\oplus\) MFB and GeMAPS \(\oplus\) MFB with enhanced temporal resolution (subsampling = 5). All configurations use dropout = 0.3 and restricted adaptation (CH1 2–4 spk).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature** & \multicolumn{2}{c}{**Error Components (\%)**} & \multicolumn{2}{c}{**DER**} & \multicolumn{2}{c}{**Speaker Count**} & \multicolumn{2}{c}{**Turn Det.**} & \multicolumn{1}{c}{**OSD**} \\
**Configuration** & **Miss** & **FA** & **Spk. Error** & **(\%)** & **Acc. (\%)** & **F1 (\%)** & **F1 (\%)** \\ \hline MFB & 8.0 & 2.7 & 4.9 & 15.64 & 74.4 & 53.97 & 52.6 \\ ECAPA-TDNN \(\oplus\) MFB & 5.7 & 3.7 & 5.5 & 14.89 & 68.8 & 50.53 & 47.0 \\ GeMAPS \(\oplus\) MFB & 12.3 & 1.6 & 5.0 & 18.87 & 73.2 & 52.65 & 41.5 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Performance comparison of optimal configurations in terms of DER% for each feature type. All configurations use restricted adaptation (CH1 2–4 spk), dropout = 0.3, and subsampling = 5.  
additional 4.4% improvement, confirming the value of finer-grained analysis for capturing the subtle prosodic variations that GeMAPS features represent.
# A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning
## 5 Results and Analysis
### Ablation Studies  
In order to verify the effectiveness of global granularity reasoning and local multi-granularity reasoning in our model, we design a local multi-granularity reasoning model without fusing the global granularity feature information of the CLS position and using only the BERT model with convolutional pooling. The experimental results are shown in Table 3.  
From Table 3, it is clear that the model considering only local multi-granularity reasoning degrades in performance of by 1.52%, 0.961%, and 1.25% over the baseline BERT model  
Figure 4: Loss changes graph with epoch.  
Figure 3: Accuracy changes graph with epoch.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{C_{M}^{3}}\)_-Test_ & \(\mathbf{C_{D}^{3}}\)_-Test_ & \(\mathbf{C^{3}}\)_-Test_ \\ \hline BERT & 64.6 & 64.4 & 64.5 \\ Local Multi-Granularity Model & 63.08 & 63.439 & 63.25 \\ Local + Global-Granularity Model & 65.234 & 65.238 & 65.236 \\ \hline \hline \end{tabular}  
* Results are measured by accuracy, using percentages (%).  
\end{table}
Table 3: Results of the ablation studies *.  
on \(C_{M}^{3}\)_-test_, \(C_{D}^{3}\)_-test_, and \(C^{3}\)_-test_, respectively. This result indicates that only considering local granularity reasoning without the global scope information will cause a certain decrease in the performance of the model. This is consistent with the fact that if humans reason only in terms of the partial information when answering reading comprehension questions, it will lead to inaccurate outcomes; thus, further demonstrating the correctness of our multiple-choice MRC model that incorporates multi-granularity semantic reasoning not only considering local multi-granularity reasoning but also global granularity reasoning.
## 3 Results
### Determining the State of the Forest with GEDI Lidar Data
In the Amazon Basin, on the other hand, there are carbon sinks that absorb up to 2.5 Mg C ha\({}^{-1}\) per year. Overall, we found that the Amazon rainforest has a neutral carbon balance, with a total NEE value of \(-\)0.1 Pg C a\({}^{-1}\). In our calculations, no direct carbon emissions from deforestation were considered (carbon emissions from deforestation: approximately \(-\)0.46 Pg C a\({}^{-1}\); see [11]), but the degradation of forests and resulting carbon emissions were already included. The estimated carbon turnover time \(\left(=\frac{AGB}{NPP}\right)\) for our analysis was 42 years. The turnover time varied from 0 to 120 years. Especially in the northeast and along the border between Peru and Brazil, long turnover times of 70-80 years could be observed, while in the Rio Negro Campinarana area and the southeast, near the Arc of Deforestation, short turnover times of 15-20 years occurred (see Figures S1 and S2). Means and standard deviations for the different forest parameters, as well as Amazon-wide totals for the forest parameters, were calculated. The mean values were calculated from all waveforms located in a 1 km\({}^{2}\) cell, and the standard deviations were calculated accordingly. As an overview, the results are shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline  & \begin{tabular}{c} **Mean \(\pm\) Standard Deviation** \\ **at Lidar Footprint** \\ **Resolution (25 m Diameter)** \\ \end{tabular} & \
\begin{tabular}{c} **Mean \(\pm\) Standard Deviation** \\ **at Map Resolution (1 km\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Total Amazon** \\ **(5.4 Mio km\({}^{2}\))** \\ \end{tabular} \\ \hline \multicolumn{3}{c}{Number of GEDI waveforms per} & 1 & 27 \(\pm\) 17 & 109,696,420 \\ Area & 1 & 23.4 \(\pm\) 10.2 m & 23.4 m \\ RH95 & 23.7 \(\pm\) 12.4 m & 23.4 \(\pm\) 10.2 m & 23.4 m \\ AGB & 175.5 \(\pm\) 118.6 Mg odm ha\({}^{-1}\) & 163.4 \(\pm\) 93.1 Mg odm ha\({}^{-1}\) & 88.3 Pg odm \\ GPP & 21.5 \(\pm\) 11.2 Mg C ha\({}^{-1}\) a\({}^{-1}\) & 21.1 \(\pm\) 8.7 Mg C ha\({}^{-1}\) a\({}^{-1}\) & 11.4 Pg C a\({}^{-1}\) \\ NPP & 4.0 \(\pm\) 2.1 Mg C ha\({}^{-1}\) a\({}^{-1}\) & 3.9 \(\pm\) 1.4 Mg C ha\({}^{-1}\) a\({}^{-1}\) & 2.1 Pg C a\({}^{-1}\) \\ Turnover Time & 43.9 \(\pm\) 56.5 a & 42.1 a \\ Respiration & 17.5 \(\pm\) 9.1 Mg C ha\({}^{-1}\) a\({}^{-1}\) & 17.2 \(\pm\) 7.5 Mg C ha\({}^{-1}\) a\({}^{-1}\) & 9.3 Pg C a\({}^{-1}\) \\ NEE & \(-\)1.4 \(\pm\) 13.0 Mg C ha\({}^{-1}\) a\({}^{-1}\) & \(-\)0.3 \(\pm\) 5.3 Mg C ha\({}^{-1}\) a\({}^{-1}\) & \(-\)0.1 Pg C a\({}^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Mean values and standard deviations at a lidar footprint resolution of 25 m diameter, mean values and standard deviations at a map resolution of 1 km\({}^{2}\), and Amazon-wide totals (5.4 Mio km\({}^{2}\)) for RH95, AGB, GPP, NPP, turnover time, respiration, and NEE. Here, turnover time is defined as AGB/NPP, and respiration is defined as GPP minus NPP (autotrophic respiration).
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 5 Reinforcement Learning for Routing Optimization
### Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Scenario** & **Proposed Solution** & **Performance** & **Routing Policy** & **Deployment Mode** \\ \hline
[109] & SDN-based optical transport network & MPNN + DQN & The proposed architecture outperforms the state-of-the-art DRL algorithms on unseen network topologies. & Traffic volume routed through the network & Centralized \\ \hline
[112] & SDN-based 6G Network & GCN + Actor-Critic & The GCN-based multi-task DRL outperforms other learning-based algorithms for joint network slicing and routing tasks and is robust to diverse network environments. & link bandwidth utilization maximization, packet loss minimization, and SLA satisfaction ratio maximization & \\ \hline
[113] & Computer network & AutoGNN (MPNN + DRL) & AutoGNN improves the average end-to-end delay of the network by up to 19.7\% and presents higher robustness against topology changes. & Delay minimization & Centralized \\ \hline
[114] & Wireless sensor network & GRL-NET (MPNN + DDPG) & GRL-NET obtains a lower transmission energy consumption and shows a good generalization ability on unseen topologies. & Transmission energy consumption minimization & Transmission energy consumption minimization & Centralized \\ \hline
[110] & Elastic optical network & GCN + RNN + A2C & The proposed approach achieves a lower blocking probability and a better generalization ability. & Service blocking probability minimization & Centralized \\ \hline
[115] & Software-defined network & GraphNET (GNN + DQN) & GraphNET outperforms shortest path routing algorithms in terms of packet delivery success ratio and average packet delay time and is robust to network structures changes. & Delay minimization & Centralized \\ \hline
[116] & Computer network & GDDR (DNN + PPO) & GDDR achieves a lower maximum link utilization into the multilayer perceptron-based baseline and shortest path routing. & Link congestion minimization & Centralized \\ \hline
[117] & LEO satellite network & GRouting (MPNN + DQN) & GRouting outperforms four baseline algorithms in terms of throughput. & Throughput maximization with delay guarantee & Centralized \\ \hline
[48] & Computer network & DGATR (GAT + DQN) & DGATR (GAT + RL-based algorithms without GNNs in terms of packet transmission delay and affordable load. & Delay minimization & Centralized, federated, and cooperated \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of studies with reinforcement learning for routing optimization.
## 3 Adaptive Neural Fuzzy Model
### ANFM Learning Algorithm
#### 3.2.3 Pruning of Fuzzy Rules  
Pruning techniques are necessary for the identification of dynamic time-varying nonlinear systems in order to avoid overfitting. In this section, the error rate of decline method [62] is used to prune the fuzzy rules, and the basis for pruning is the importance of the fuzzy rules.  
Given n pairs of input/output data \((X_{i},o_{i})\), \(i=1,2,\cdots,n\), Equations (22) and (23) are used as a special case of the linear regression model.  
\[D=H\theta+E \tag{27}\]  
where, \(D=O^{T}\in\Re^{n}\) is the desired output, \(H=\Psi^{T}=(h_{1}\cdots h_{p})\in\Re^{n\times p}\), \(v=u\times(r+1)\) is called the regression vector, \(\theta=W^{T}\in R^{p}\) contains the parameters and \(E\in R^{n}\) is assumed to be an error vector uncorrelated with the regression quantity \(h_{i}\). For a matrix \(H\), by QR decomposition.  
\[H=QA \tag{28}\]  
Substituting Equation (28) into Equation (27) gives  
\[D=QA\theta+E=QG+E \tag{29}\]  
The linear least-squares solution for \(G\) is  
\[G=\left(Q^{T}Q\right)^{-1}Q^{T}D \tag{30}\]  
Or  
\[g_{i}=\frac{q_{i}^{T}D}{q_{i}^{T}q_{i}} \tag{31}\]  
where, \(i=1,2,\cdots,v\) and \(G\) and \(\theta\) satisfy the following equations.  
\[A\theta=G \tag{32}\]  
when \(i\
eq j\), since \(q_{i}\) and \(q_{j}\) are orthogonal, the sum of squares of \(D\) is given by  
\[D^{T}D=\sum_{i=1}^{v}g_{i}^{2}q_{i}^{T}q_{i}+E^{T}E \tag{33}\]  
After removing the mean, the variance of \(D\) is given by the following equation.  
\[n^{-1}D^{T}D=n^{-1}\sum_{i=1}^{v}g_{i}^{2}q_{i}^{T}q_{i}+n^{-1}E^{T}E \tag{34}\]  
Therefore, the rate of error decline can be defined as  
\[err_{i}=\frac{g_{i}^{2}q_{i}^{T}q_{i}}{D^{T}D} \tag{35}\]  
Substituting Equation (31) into Equation (35), we get  
\[err_{i}=\frac{\left(q_{i}^{T}D\right)^{2}}{q_{i}^{T}q_{i}D^{T}D} \tag{36}\]  
Let \(\phi_{i}\) be the angle between vector \(q_{i}\) and \(D\). Then  
\[cos^{2}\phi_{i}=err_{i}=\frac{\left(q_{i}^{T}D\right)^{2}}{q_{i}^{T}q_{i}D^{T}D} \tag{37}\]  
If \(\phi_{i}=0\) and \(err_{i}=1\), it means that the influence is maximum. Otherwise \(\phi_{i}=90^{\circ}\), if \(err_{i}=0\), the two vectors are orthogonal, indicating that \(q_{i}\) has no effect on \(D\).  
Rearrange the newly obtain \(err_{i}(i=1,2,\cdots(r+1)u)\) into a matrix \(\Delta=(\delta_{1},\delta_{2},\cdots,\delta_{u})\in\Re^{(r+1)\times u}\), the ith column \(\delta_{i}\) of \(\Delta\) is the \((\mathbf{r+1})\) error rate of decline associated with the ith rule. Further definition is  
\[\eta_{i}=\sqrt{\frac{\delta_{i}^{T}\delta_{i}}{r+1}} \tag{38}\]  
It can be seen that \(\eta_{i}\) reflects the importance of the ith rule: the larger the value of \(\eta_{i}\), the more important the ith rule is. If \(\eta_{i}<k_{err}\) then the ith rule can be eliminated. \(k_{err}\) is a pre-defined threshold value.  
The flowchart of ANFM algorithm is shown in Figure 4.
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 5 Simulated Data Analysis  
In this section, we apply the proposed procedure to simulated data. There are two cases: deterministic trend describing the degradation process mixed with (a) Gaussian and (b) non-Gaussian noise. The model is described as follows:  
\[HI(t)=\left\{\begin{array}{ll}a&t\leq 1000\\ b\cdot t&1000<t\leq 1600\\ c\cdot\exp(d\cdot t)&t>1600\end{array}\right\}+\left\{\begin{array}{ll}\sigma _{1}\cdot N(t)&t\leq 1000\\ \sigma_{2}\cdot t\cdot N(t)&1000<t\leq 1600\\ \sigma_{3}\cdot\exp(t)\cdot N(t)&t>1600\end{array}\right\}, \tag{4}\]  
where \(a,b,c,d\) are constant parameters that are related to deterministic parts, and \(\sigma_{1},\sigma_{2},\sigma_{3}\) are parameters responsible for the scales of noise parts. Moreover, \(\{N(t)\}\) is a noise.  
Internal and external Gaussian noise is present in every real system due to various reasons. Unfortunately, the case becomes much more complicated when additional non-Gaussian behavior is present in the signal. Such a model of the signal is inspired by real long-term data we collected from various machines. The source of non-Gaussian noise may be related to machine design, the process related to machine operation, electromagnetic interference, etc.
## 2 Three-Dimensional Image and Brightness Loss
### Three-Dimensional Image Brightness Loss  
The sources of the loss in brightness can be divided into two categories: (a) Those due to the half-mirror 3D camera rig used to film the 3D content; and (b) Those resulting from the 3D glasses during viewing of the 3D device. Note that the degrees of brightness degradation caused by polarization glasses and shutter glasses are almost identical, as both of these devices allow only half of the light to penetrate the lenses, whether in the spatial or temporal domain.  
The sources of the brightness differences between 2D and 3D images that can have a visual impact on the eye can also be divided into two categories: (a) Control during filming of a scene, with adjustments being made in accordance with the ambient light (for example, the aperture for the right-eye image may need to be increased so that the brightness of the images received by both eyes are within a similar range). This prevents the eyes from becoming rapidly fatigued, while also reducing the time and cost of dimming of the film during post-production. (b) The screen brightness may be too low or too high during 2D or 3D viewing. In this case, the eyes can begin to suffer from significant discomfort within a short period of time. This study focuses on the brightness of 2D and 3D images within the range where a viewer can experience the same level of brightness for both eyes, while not experiencing physical discomfort such as eye soreness, dizziness, vomiting, _etc._, during the viewing of the 3D images.
# Shear Properties of the Interface Between Polyurethane Concrete and Normal Concrete
###### Abstract  
Polyurethane concrete (PUC) is a promising candidate for structural repair materials due to its excellent mechanical properties and durability. However, the bonding performance between PUC and concrete interfaces may limit its broader application. This study examined the factors affecting the shear strength at the PUC-NC interface. A total of 16 oblique shear tests, varying by interface treatment methods (smooth--GH, roughened--ZM, and grooved--KC), adhesive application rates--NJJ (0, 0.2, and 0.3 kg/m\({}^{2}\)), and steel fiber contents--GXW (0%, 0.5%, 1%, and 1.5%), to evaluate their impact on the mechanical properties of the PUC-NC interface. The results demonstrated that roughening the interface significantly improved the shear strength, resulting in a 32% increase compared to a smooth interface and 15% compared to a grooved interface. A moderate adhesive application rate (0.2 kg/m\({}^{2}\)) enhanced the interface strength, while excessive adhesive did not further increase the shear strength. The optimal steel fiber content (1%) resulted in the highest shear strength, improving it by 22%, whereas excess steel fibers (1.5%) reduced the interface strength. This is due to fiber agglomeration, which weakens mechanical interlocking and introduces defects that impair interfacial bonding. Load-slip curve analysis revealed that roughened interfaces combined with the appropriate amount of steel fibers improved the interface toughness, delaying the failure process. This study presents a model for calculating the shear strength of steel fiber-reinforced PUC-NC interfaces, incorporating shear slip. Compared to existing models, it more accurately reflects the experimental data.  
polyurethane concrete (PUC); normal strength concrete (NC); interface; shear bond strength; oblique shear test; predictive model +
Footnote †: journal: Applied Sciences
# Online Track Vertex Reconstruction Method Based on an Artificial Neural Network for MPGD
## 3 Comparison of Results  
We used the traditional centroid method and FNN to process the same track data. The results of the two reconstructions were compared with the original incidence locations, as shown in Figures 9a and 10a. The resolution of the two methods for reconstructing the track vertexes was analyzed using statistical methods, as shown in Figures 9b and 10b. The blue histograms are the statistical distributions of \(\mathrm{P_{cen}}\)\(-\)\(\mathrm{P_{trk}}\) and \(\mathrm{P_{ANN}}\)\(-\)\(\mathrm{P_{trk}}\), and the red lines were fitted using a Gaussian distribution of the result.  
Figure 9: **(a)** Location distribution of the results of the centroid method reconstruction (the x direction is the position of the particle incidence in the simulation, the y direction is the position of the centroid method reconstruction); **(b)** The error distribution of the centroid method reconstruction results (\(\mathrm{P_{cen}}\) is the position of the centroid method reconstruction, \(\mathrm{P_{trk}}\) is the position of the proton incidence in the simulation).  
Table 2 shows the results of the statistical analysis of the two reconstruction methods.  
\begin{table}
\begin{tabular}{c c c} \hline
**Reconstruction Method** & **Sigma (mm)** & **Mean (mm)** \\ \hline Centroid method & 2.60 & 0.013 \\ ANN method & 0.35 & 0.0015 \\ \hline \end{tabular}
\end{table}
Table 2: The results of the statistical analysis of the two reconstruction methods.  
Figure 10: **(a)** Artificial neural network (ANN) reconstruction position distribution (the x direction is the position of the particle incidence in the simulation, the y direction is the position of ANN reconstruction); **(b)** the ANN reconstruction result error distribution (\(\text{P}_{\text{ANN}}\) is the position of the ANN reconstruction, \(\text{P}_{\text{trk}}\) is the position of the proton incidence in the simulation).  
The above results show that the incidence position of the particle can be reconstructed more accurately using the ANN algorithm. The sigma value in the error fit of the ANN reconstructed position is lower than the sigma of the centroid method, indicating that we can obtain better position resolution with the ANN algorithm. The centroid method gives biased results for some events because it takes the center of gravity of the track as the incident position of the particle. When a charged particle is not perpendicular to the detector drift region, the particle generates a longer track, which induces a charge on multiple readout strips. The center of gravity of the track will be off the incident position of the particle, resulting in a larger error in the result. Using an ANN to reconstruct the particle incident position improves accuracy, and the ANN can be implemented at the FPGA level.
# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 6 Result and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Classification** \\ **Algorithm** \\ \end{tabular} } & \multirow{2}{*}{**Actual Action**} & \multicolumn{6}{c}{**Predict Action (Number of Samples)**} \\ \cline{3-6}  & & **Standing** & **Sitting** & **Walking** & **Standing of HD** & **Sitting of HD** & **Gait of HD** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} SVM \\ \end{tabular} } & Standing & **73** & 0 & 2 & 0 & 0 & 0 \\  & Sitting & 1 & **74** & 0 & 0 & 0 & 0 \\  & Walking & 2 & 0 & **72** & 0 & 0 & 1 \\ \cline{2-6}  & Standing of HD & 0 & 0 & 0 & **75** & 0 & 0 \\  & Sitting of HD & 0 & 0 & 0 & 0 & **75** & 0 \\  & Gait of HD & 0 & 0 & 1 & 0 & 2 & **72** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} RF \\ \end{tabular} } & Standing & **72** & 1 & 0 & 2 & 0 & 0 \\  & Sitting & 0 & **75** & 0 & 0 & 0 \\ \cline{1-1}  & Walking & 0 & 0 & **75** & 0 & 0 & 0 \\ \cline{1-1} \cline{2-6}  & Standing of HD & 4 & 0 & 0 & **71** & 0 & 0 \\ \cline{1-1}  & Sitting of HD & 0 & 0 & 0 & 1 & **70** & 4 \\ \cline{1-1}  & Gait of HD & 0 & 0 & 2 & 0 & 1 & **72** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of classification algorithms. Figure 8: Algorithm accuracy. conclusion that when using the method described in this paper to distinguish the actions of patients with HD, the performance of SVM is better than that of RF.