esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Perceptual Evaluation of Speech Quality  
ITU-T P862 [14] recommended the PESQ measure [15] as the standard for the speech quality evaluation of test speech signals. This measure better correlates with subjective listening tests than most objective measures. Table 2 presents the PESQ comparisons. The quality of denoised speech becomes better if this denoised speech obtains a larger value of the PESQ score.  
The maximal PESQ score corresponds to the best speech quality. One can find that the proposed method outperforms the other two methods in most conditions. In the cases of heavy noise corruption for helicopter-cockpit and car noise, the FB_MCRA is superior to the MCRA and the proposed methods. This attributes to the selection of larger magnitude in forward and backward noise estimation for the FB_MCRA method, enabling a great quantity of interference noise to be removed by speech denoising. In the cases of babble (speech-like) noise corruption, the proposed method cannot outperform the other methods. The reason is that the background noise is wrongly regarded as weak vowels. The level of interference noise is underestimated, and therefore interference noise cannot be removed effectively by the process of speech denosing. Although the proposed method does not outperform the other methods in some cases, the performance of the proposed approach is very close to that of the FB_MCRA or MCRA approach. In the cases of middle and slight noise corruptions (5 dB and 10 dB), the proposed approach outperforms the other methods. This is due to that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{2}{c}{**Average SegSNR Improvement**} \\ \cline{2-5}  & **(dB)** & **MCRA** & **FB\_MCRA** & **Proposed** \\ \hline \multirow{3}{*}{White} & 0 & 6.95 & 7.15 & 7.83 \\  & 5 & 4.44 & 4.70 & 5.64 \\  & 10 & 1.57 & 2.08 & 3.44 \\ \hline \multirow{3}{*}{F16} & 0 & 5.81 & 5.73 & 5.98 \\  & 5 & 3.78 & 3.83 & 4.53 \\  & 10 & 1.44 & 1.76 & 2.83 \\ \hline \multirow{3}{*}{Factory} & 0 & 5.41 & 5.35 & 5.62 \\  & 5 & 3.43 & 3.47 & 4.17 \\  & 10 & 1.14 & 1.46 & 2.53 \\ \hline \multirow{3}{*}{Helicopter} & 0 & 6.22 & 6.29 & 6.34 \\  & 5 & 4.13 & 4.32 & 4.99 \\  & 10 & 1.76 & 2.25 & 3.28 \\ \hline \multirow{3}{*}{Car} & 0 & 7.87 & 10.08 & 9.86 \\  & 5 & 5.70 & 8.19 & 9.08 \\  & 10 & 3.10 & 5.97 & 7.05 \\ \hline \multirow{3}{*}{Babble} & 0 & 4.26 & 4.23 & 4.22 \\  & 5 & 2.79 & 2.83 & 3.26 \\ \cline{1-1}  & 10 & 0.94 & 1.27 & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of SegSNR improvement for the denoised speech in various noise corruptions.  
harmonic structure of noisy speech does not been destroyed by interference noise. The harmonic structure is preserved by the underestimate of noise magnitude by which the segment length increases according to (15). The denoised speech using the proposed noise estimator results in less distortion. Therefore, the proposed method obtains higher scores of the PESQ than the other two methods in most noise corruptions.
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
## 3 Results and Discussion
### Parallelogram Module  
Following, the typical surface temperature curves of batteries in parallelogram modules are shown in Figure 10. Similar to the results of rectangular modules, the thermal failure propagation in parallelogram modules also exhibited three phases. As displayed in Figure 11, the two batteries nearest the heater took failure at first, then the other two batteries failed and the battery in the corner caught fire in the end. For the module with discharging treatment, the time to the three phases of failure were 377 s, 533 s, and 626 s, respectively. As for the module without discharging treatment, they were 399 s, 554 s, and 651 s, respectively. From the heater to the battery of the third phase, the propagation distance was 46.8 mm. Hence, the propagation speed was 0.075 mm/s and 0.072 mm/s, respectively. Apparently, the thermal failure propagation in modules with discharging treatment was relatively  
Figure 8: The domino effect of thermal failure propagation in rectangular module.  
Figure 9: The typical curves of mass loss for rectangular modules in tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **With Discharging** & **Time to the** & **Time to the** & **Time to the** & **Propagation** & \multirow{2}{*}{**Mass Loss (g)**} \\  & **Treatment** & **First Phase (s)** & **Second Phase (s)** & **Third Phase (s)** & **Speed (mm/s)** & \\ \hline
1 & Yes & 368 & 440 & 568 & 0.063 & 51.6 \\
2 & No & 389 & 480 & 583 & 0.062 & 50.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detailed information of rectangular modules in tests.  
faster. In addition, Figure 12 shows the mass loss process of parallelogram modules and the detailed information of failure propagation in parallelogram modules is listed Table 4.  
Figure 11: The domino effect of thermal failure propagation in parallelogram module.  
Figure 10: The typical surface temperature curves of batteries in parallelogram modules: (**a**) with discharging treatment; (**b**) without discharging treatment.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 3 Experiment
### Evaluation Criteria  
To prove the effectiveness of our proposed method, we evaluated the performance of the saliency detection methods using three popular evaluation criterions: the average precision-recall curve, F-Measure, and mean absolute error (MAE).  
Figure 9: Saliency performance for different parameter settings. Left: precision-recall curves; Right: precision, recall, and F-measure at adaptive threshold. (**a**) \(\sigma_{x}\), \(\sigma_{y}\), (**b**) \(M\), (**c**) \(\varepsilon\), \(\tau\).  
For a saliency map, it can be converted to a binary mask \(M\) and _Precision_ with _Recall_ can be computed by comparing \(M\) with ground-truth GT:  
\[Precision=\frac{|M\cap\text{GT}|}{|M|} \tag{31}\]  
\[Recall=\frac{|M\cap\text{GT}|}{|GT|} \tag{32}\]  
For each method, a pair of the precision and recall scores can be obtained with the threshold ranging from 0 to 255. Using the sequence of precision-recall pairs, the precision-recall curve can be plotted.  
F-measure is a harmonic mean of precision and recall, as in [47], we defined it as:  
\[F_{\beta}=\frac{(1+\beta^{2})\cdot precision\cdot recall}{\beta^{2}\cdot precision +recall} \tag{33}\]  
Following [47], we set \(\beta^{2}=0.3\) to emphasize the precision. The F-measure curves can be drawn with the threshold sliding from 0 to 255. Additionally, we applied an adaptive threshold \(T_{a}\) to the saliency map, which is defined as twice the mean saliency of the image:  
\[T_{a}=\frac{2}{W\cdot H}\sum_{i=1}^{W}\sum_{j=1}^{H}S(i,j) \tag{34}\]  
where \(W\) and \(H\) are the width and height of the saliency map \(S\).  
To further evaluate the methods, we adopted the mean absolute error (MAE) as another evaluation criterion. The MAE score calculates the average difference between the saliency map \(M\) and the ground truth GT, it is defined as:  
\[\text{MAE}=\frac{1}{W\cdot H}\sum_{i}^{W}\sum_{j}^{H}|S(i,j)-GT(i,j)| \tag{35}\]  
MAE is more meaningful in evaluating the applicability of a saliency model in vision task such as object segmentation.
# Timbre Preferences in the Context of Mixing Music
## 3 Sound Manipulations
### Timbre Manipulations (FX)
#### 3.1.2 Odd to Even Ratio (OER) Manipulation  
A second manipulation changes partial amplitudes, based on their harmonic number, by multiplying the amplitudes of even-number partials by the OER manipulator \(\beta\). The slider interface for this was flipped, however, so the slider at its minimum was \(\beta=1\), and at its maximum it was \(\beta=0\). If perfectly harmonic partials were in phase synchrony, with a slope-value of \(\alpha=1\) and the multiplier for even harmonics set to \(\beta=0\), the resulting sound would have no even-number harmonics and would approximate a square-wave oscillation; a similar sound but with the multiplier set to \(\beta=1\)  
Figure 1: Screenshot of the mixing interface. Sliders from left to right were affecting Slope, OER (odd/even ratio), Distortion, Inharmonicity. The ’X’ buttons below the sliders toggle the sliders’ output between zero and its’ set value for an easy A/B comparison to the start setting.  
would approximate a sawtooth oscillation. This manipulator would obviously be most closely related to the OER audio descriptor [14].  
The range given for the OER slider in the experiment was: \(\beta=0\) to 1.  
This is the maximum range available for this particular manipulation. Negative values would be almost identical to their positive counterparts (only with flipped phases), and values past one (one being the same level for odd and even harmonics) would result in emphasizing a sawtooth sound an octave up with an additional sub-octave.
## 2 System Description
### Filters
#### 2.3.2 Emitter Filter
In the proposed system, the filter choice is limited by the receiver analog implementation; thus, a matched filter should be digitally designed in the emitter. Therefore, the emitter pulse shaping filter, \(p(t)\), should be generated from the impulse response of the receiver filter, \(g(t)\). Note that the peak of \(p(t)\) should be aligned with the peak of the cosine and sine terms of Equations (2) and (3), to avoid losses in the signal energy.
## 2 Related Work
However, DBSCAN and other algorithms in this class have difficulties in finding clusters with varied densities, as they use a global density threshold for the formation of clusters [19]. To overcome this weakness, many variants of the initial DBSCAN algorithm have been proposed such a hierarchical DBSCAN [20], which will be employed in this work. The idea behind HDBSCAN is to produce many DBSCAN outcomes through increasing density thresholds. A dendrogram is built based on these outcomes to yield the hierarchical cluster structure. To decide the final clustering, it extracts a set of \"significant\" clusters at different levels of this dendrogram. HDBSCAN was chosen over other alternatives as it is performance efficient, easy to configure, and has not been used in the context of the optimization of 5G networks, to the best of our knowledge.
# Path Loss Measurements of Indoor LTE System for the Internet of Things
## 2 Principles
### Path Loss Model  
The path loss is defined as the ratio of the transmit power to the receive power. In a link budget, this refers to the largest transmit power that the transmitter can send and the smallest receive power at which the receiver can recover the original information. Then, the greatest path loss can be calculated from the measured largest transmit power and smallest receive power. A path loss model relates the path loss to the distance between the transmitter and the receiver. By combining the link budget with a suitable path loss model, we can estimate the coverage range of the base station.  
Using the ITU-R indoor path loss model [15], the path loss between a femtocell eNB and an UE separated by a distance \(d(m)\) in a given cell is:  
\[PL(d)=\ 20\log_{10}(f)+10N\log_{10}(d)+L_{f}(n)-28\ \ (dB), \tag{1}\]  
where the carrier frequency\(f\) (MHz) is set as 2350 MHz with 20 MHz bandwidth in the experiment. \(N\) is the path loss exponent, where the nominal value in the indoor office is set to 3 [15]. \(L_{f}(n)\) is the penetration loss between the floors, where \(n\) is the number of floors. The penetration loss is not considered in the simulations.
## 8 Techniques for Crispness Measurement
### Ultrasonic Measurement of Crispness  
The ultrasonic pulse-echo technique has been used to study the crispness of bisciuits [86], ciabatta crust [87], chicken nuggets [88], cornflakes [89], and apples [90; 91]. It has been found that the velocity of longitudinal sound and sensory crispness has a strong correlation [76]. It is a non-destructive and rapid method. The key challenge for this technique is to standardize the numbers of the acoustic peak, force peak, and sound pressure level, as these parameters may vary for individual products [92].
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 5 SA Applied to Aircraft Design
### Results and Discussion
The dimensional sum is the summation of the three maximum dimensions in the three axes \(x\), \(y\), and \(z\). The dimensional sum has an associated design constraint: the \"dimensional constraint\". It cannot be larger than a specific value defined by the competition. By observing the graphs, it can be seen that the larger the dimensional sum, the higher the payload. Figure 13a shows the largest concentration of solutions at the higher payload. Figure 13b shows an intermediary exploration. Figure 13c a higher exploration of the full domain. One possible heuristic that can be deduced from these results is to always keep the maximum dimensional sum. Figure 10: Crystallization factor evolution according the three positive feedback strategies. (**a**) Enhancing the refinement. (**b**) Good balance between refinement and exploration. (**c**) Enhancing the exploration. The \(x\) and \(y\) dimensions can be freely decided, but the \(z\) dimension is determined based on a new rule. The \(z\) dimension must be the maximum dimensional sum reduced by the \(x\) and \(y\) dimensions. Using this empirical rule, the optimization will search in a domain with higher scores, and this will be faster (as one constraint is always satisfied by construction). Figure 14 shows the best aircraft for each positive feedback strategy. The aircrafts from positive feedback strategies I and III are similar, while the aircraft from positive feedback II is different than the others. It can be seen that strategies I and II reached the highest scores and strategies I and III reached similar designs. Figure 11: The accepted solution ratio for each parameter is shown for all the three positive feedback strategies. (**a**) The refinement is enhanced. (**b**) There is a balance between refinement and exploration. (**c**) The exploration is enhanced. Figure 12: Total number of accepted solutions for each temperature. Figure 13: The influence of some intermediary parameters with the final score: the dimensional sum and payload. The score is represented in colors. (**a**) Strategy I, (**b**) Strategy II, (**c**) Strategy III.
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 5 Prediction Model for Subclinical Mastitis in Dairy Cows Based on IGOOSE-XGboost
### Building the IGOOSE-XGBoost Regression Model for Subclinical mastitis in Dairy Cows
#### 5.3.3 Building the IGOOSE-XGBoost Regression Model
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Evaluation Indicators** & **SSA** & **WOA** & **ESOA** & **GOOSE** & **IGOOSE** \\ \hline Average R\({}^{2}\) & 0.585 \(\pm\) 0.015 & 0.623 \(\pm\) 0.014 & 0.598 \(\pm\) 0.015 & 0.642 \(\pm\) 0.013 & 0.663 \(\pm\) 0.012 \\ Average MAE & 0.675 \(\pm\) 0.019 & 0.585 \(\pm\) 0.016 & 0.667 \(\pm\) 0.018 & 0.531 \(\pm\) 0.014 & 0.526 \(\pm\) 0.013 \\ Average MSE & 0.910 \(\pm\) 0.022 & 0.799 \(\pm\)models' strong stability and dependability. In particular, the IGOOSE-XGBoost model has the smallest standard deviation (\(\leq\)0.014) for all the indicators, which further confirms that the model has the best stability and reliability. In summary, our study created an IGOOSE-XGBoost-based model to forecast the subclinical mastitis of dairy cows, predicting whether cows were sick using their SCS. To predict whether cows had subclinical mastitis, feature selection was performed using six intelligent optimization algorithms, and XGBoost was used as a classifier. To further improve prediction accuracy, to improve the XGBoost parameters, each of these six algorithms was applied. According to the results, the IGOOSE-XGBoost classification model performed the best (ACC = 0.87; REC = 0.88; PRE = 0.89; F1 score = 0.88; fitness value = 0.14523). For predicting the SCS of dairy cows, the DHI data used started with 33 features, and after deleting irrelevant attributes and correlation analysis, the four most important features were selected. We trained and compared four machine learning models, and selected the model with the best prediction performance, XGBoost, to which five intelligent optimization algorithms were added to improve its parameters. The IGOOSE-XGBoost regression model produced the greatest results, judging by the results (RMSE = 0.693; MSE = 0.511; MAE = 0.526; R2 = 0.663). The integration of these approaches enhances prediction accuracy and comprehensiveness, offering valuable insights for targeted prevention and treatment strategies.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
###### Abstract  
The proliferation of IoT using heterogeneous wireless technologies within the unlicensed spectrum has intensified cross-technology interference (CTI) in wireless local area networks (WLANs). As WLANs increasingly adopt time-triggered transmission methods to support real-time services, this interference affects throughput, packet loss, and latency. This paper presents a CTI-aware rate adaptation framework designed to mitigate interference in WLANs without direct coordination with heterogeneous wireless devices. The framework includes a CTI identification model and CTI-aware rate selection algorithms. Leveraging short-time Fourier transform, the identification model captures the time-frequency-power characteristics of CTI signals, enabling the estimation of the average power of various heterogeneous wireless technologies employed by interfering devices. The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. Experimental results demonstrated that the lightweight CTI identification model accurately estimated the average power of each type with an error margin of \(\pm\)1.414 dBm, achieving this in under 1 ms on the target hardware. Additionally, applying the proposed framework to explicit interference mitigation enhanced goodput by 20.67%, reduced packet error rate by 2.38%, and decreased the probability of packets exceeding 1 ms latency by 0.932% compared to conventional methods.  
IoT; cross-technology interference; rate adaptation; WLANs; Bluetooth; Zigbee +
Footnote †: journal: Applied sciences  
1
###### Abstract  
Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WRTPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.  
Keywords:5G IoTs; WRTPCA; multi-attribute; data recovery Article
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Non-Negativity and Constraints of the Extended Entropy \(\overline{S}\)
#### 3.6.1 Variational Principle and Lagrange Multipliers  
The Lagrange multipliers \(\mathbf{a}\) and \(\mathbf{b}\) are determined by maximizing \(\overline{S}\) under constraints of conserved quantities (e.g., total momentum \(\mathbf{P}=\sum_{a}\mathbf{p}^{(a)}\) and angular momentum \(\mathbf{L}=\sum_{a}\mathbf{r}^{(a)}\times\mathbf{p}^{(a)}\)). This aligns with the **maximum entropy principle**:  
\[\delta\overline{S}-\lambda\delta\mathbf{P}-\mu\delta\mathbf{L}=0, \tag{13}\]  
where \(\lambda,\mu\) enforce conservation. Solving this yields \(\mathbf{a}=\lambda\), \(\mathbf{b}=\mu\), ensuring that the additional terms \(\mathbf{a}\cdot\mathbf{p}^{(a)}+\mathbf{b}\cdot(\mathbf{r}^{(a)}\times\mathbf{ p}^{(a)})\) are _bounded_ by the system's conserved quantities. These terms refine microstate selection without violating non-negativity, as their magnitudes are controlled by the variational principle.