esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adaptability to Specific Scenarios
#### 3.4.3 Broken Glass Detection  
In the case of broken glass detection, we worked with a data set of 300 recordings, 150 of breaking glass and 150 of other familiar sounds in commercial environments. Table 10 presents the results when using the algorithm to detect the sound of breaking glass. An accuracy of 97.7% is observed, indicating that, in most cases, the model can accurately differentiate between the noise of breaking glass and other surrounding sounds. The sensitivity, which stands at 96.9%, demonstrates the system's effectiveness in capturing a vast majority of glass breakage events, leaving very few undetected. Furthermore, the specificity, at an impressive 98.4%, underscores the algorithm's ability to accurately rule out sounds that do not correspond to breaking glass, thus minimizing false alarms. These results emphasize the robustness of the model, especially in scenarios such as in commercial establishments, where early detection can be crucial.  
\begin{table}
\begin{tabular}{l l} \hline
**Metrics** & **Valor** \\ \hline Precision & 98.5\% \\ Sensitivity & 97.8\% \\ Specificity & 99.2\% \\ \hline \end{tabular}
\end{table}
Table 8: Results in detecting a baby’s cry.  
\begin{table}
\begin{tabular}{l l} \hline
**Metrics** & **Valor** \\ \hline Precision & 96.3\% \\ Sensitivity & 95.0\% \\ Specificity & 97.5\% \\ \hline \end{tabular}
\end{table}
Table 9: Results in the detection of emergency sirens.
# QoS-Aware Resource Management in 5G and 6G Cloud-Based Architectures with Priorities
## 2 Scheduler Priority Queue
### Model Description  
We consider an IP-based multi-service network over LAN with two dominant service classes, p and q, respectively [23]. Service class p has a real-time rigid class with low-latency characteristics and strict delay constraints, high radio scheduling priority (high-priority scheme and pre-emption capability (high priority) attribute), and high DiffServ and Pbit priorities over IP-based transmission network. Service class q has mostly flexible bandwidth packet-switched characteristics with integrity and throughput constraint on errors rather than on delay, low DiffServ, and Pbit transmission priorities and pre-emption vulnerability (low priority) attributes. Already established service class q connections, mostly due to their bandwidth flexibility and delay in relaxing constraint conditions, can easily be pre-empted from service class p traffic in blocked conditions through appropriate Capacity Management (CM) and Flow Control Management (FCM) functionality performance as an overall performance enhancement. However, to preserve service class q QoS traffic metrics in pre-emptive conditions, a Q length buffer could be used to store service class q pre-empted packets for a specified (operator-determined) period of waiting time before they could be offered again as a service into an empty available resource or be permanently blocked and released.  
The analytical model Is based on a two-dimensional Markov chain with C available cell resources, where state \((I,j)\) denotes specifically that there are \(i\) occupied resources from service class p users and \(j\) occupied resources from service class q users in the system. Without packet queue implementation it is always considered that \((i+j)\leq C\), while when a finite queue buffer of size Q is considered for service class q, then \((i\leq C)\cup(i+j)\leq C+Q\). In this model, \(P_{ij}\) denotes the probability that the system is in state \((i,j)\). It is also assumed that the arrival of p service requests follows Poisson arrival processes with rates \(\lambda_{n}\) for new services and \(\lambda_{h}\) for handoff services, while service class q requests follow the Poisson arrival process with rates \(\lambda_{d}\). Moreover, p service times are assumed to be exponentially distributed with a mean of \(1/\mu_{n}\), while for service class q they are also considered to be exponentially distributed with a mean of \(1/\mu_{d}\).
## 4 Optimal Design
### Optimal Process  
In order to avoid the wrong solution caused by improper setting of initial conditions, this paper uses exhaustive method to solve all the test schemes. The specific optimization flow chart is shown in Figure 5. The specific steps are as follows:  
1. According to the historical data, engineering experience and other information, the parameters of the model, the degradation threshold \(D\) of the motorized spindle performance [37], the initial stress \(S_{1}\) and the maximum stress \(S_{L}\) of the motorized spindle test, the number of test stress levels \(L\), the total test cost \(C_{1}\), the test cost per unit time \(C_{0}\), the unit price of motorized spindle sample \(C_{d}\) and the inspection interval \(\Delta t\) are determined;2. The test stress level combination \(S_{il}\), sample ratio \(n_{l}\) and monitoring times \(m_{l}\) under each stress were solved which satisfied the constraints, then the results of them constitute test scheme set \(\Omega_{\xi}\): \(\xi_{q}=\Big{\{}[n_{1},n_{2},\ldots,n_{l}];\,[S_{11},S_{12},\ldots,S_{i(l-1)},S_{ il}];\,[m_{1},m_{2},\ldots,m_{l}]\Big{\}}\), \(q=1,2,\ldots,N_{\xi}\), \(N_{\xi}\) is the total number of schemes in the scheme set;
3. Select a scheme \(\xi_{q}\) from scheme set \(\Omega_{\xi}\);
4. According to Equation (13), the performance degradation data \(Y_{qw}\) of motorized spindle is simulated using the Monte Carlo method for \(N_{m}\) times;
5. The information matrix \(M(\xi)\) of degradation data \(Y_{qw}\) of each simulation is calculated;
6. According to Equation (20), the objective function value \(\big{|}M^{11}(\xi)\big{|}\) of the scheme \(\xi_{q}\) is calculated;
7. Repeat steps 3-6 to calculate the objective function for all schemes in the scheme set;
8. The scheme \(\xi^{*}\) with the minimum objective function \(\big{|}M^{11}(\xi)\big{|}\) is selected as the optimal scheme.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 7 Results  
### Ablation Tests on the Configuration of Sinc Convolution  
We empirically study the impacts of the placement of the Sinc convolution layer, the input length, the number of Sinc filters, and the kernel size of the Sinc convolution. Table 2 demonstrates the configuration of five ablation tests, and Table 3 shows their results across the criteria introduced in Section 6.2. After making a comparison between these experiments, we can draw the following conclusions:  
* Increasing the number of Sinc filters degrades the system performance, since more filters introduce more system complexity, and the training becomes more difficult, accordingly.
* Decreasing the kernel size of the Sinc convolution deteriorates the performance since smaller kernel size limits the ability to extract representative speech clues.
* Systems benefit from longer input length due to more context information being included.
* The addition architecture outperforms the substitution architecture as the former is deeper.  
These experimental results explain why we choose the length of input signals as 1 s, the number of Sinc filters as 64, and the kernel size of Sinc convolution as 251.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Experiment** & **A** & **B** & **C** & **D** & **E** \\ \hline Architecture & sub & sub & sub & sub & add \\ Input length & 1 s & 1 s & 1 s & 250 ms & 1 s \\ Number of Sinc filters & 64 & 80 & 64 & 64 & 64 \\ Kernel size of Sinc convolution & 251 & 251 & 101 & 251 & 251 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The demonstration of different configurations of five ablation tests. _substitution_ is shortened to _sub_, and _addition_ is shortened to _add_.
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
###### Abstract  
Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a \(\sim\)40,000 km\({}^{2}\) western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a \"calibrated model\", which required ground-measured yield and weather data for calibration, and (ii) an \"uncalibrated model\", which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the \"calibrated\" approach captured a significant fraction (R\({}^{2}\) between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the \"uncalibrated\" approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the \"uncalibrated\" approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.  
smallholder farms; yield; Africa; remote sensing; maize; agriculture +
Footnote †: journal: Remote Sensing
# Effect of Fuel Composition on Carbon Black Formation Pathways
## 2 Materials and Experimental Methods  
### Feedstocks  
The two carbon blacks used in this study are derived from different feedstocks, described briefly as follows.  
CT-CB is derived from a coal-tar based feed. Coal tar is an extremely complex mixture, containing 4, 5, 6, and even 7-ring aromatics, as well as methylated and poly-methylated derivatives with SARA analysis differentiating saturates and aromatic. Other components can include heterocyclic compounds such as pyridine, benzene-toluene-xylene (BTX) compounds, cumenes, and naphthalenes [10].  
FCC-CB is derived from fluid catalytic cracking of decant oil. SARA analysis results highlight the key differences relative to the CT material as higher saturate and lower aromatic content. FCC decant oils can contain an aromatic distribution ranging from single to 4-ring compounds, potentially including some naphthenic rings. Reflecting catalytic origins, the lighter aromatic fraction may bear varied alkyl groups as reflected by the SARA analysis [11].  
Table 1 summarizes the results of the analysis for saturates, aromatics, resin, and asphaltene content (SARA) for these feedstocks. The sulfur content for the CT was 0.609 wt.% and 3.15 wt.% for the FCC material.  
These carbon blacks, formed with different fuel sources are compared using the following analytical techniques for their morphology and nanostructure.
## 2 Technical Background
### Machine Learning Models
#### 2.3.1 Linear Regression  
Linear regression is one of the simplest algorithms applied to solve regression problems. It assumes that the input variables have a linear relationship with the target variable. A straight line represents such a relationship in a two-dimensional space, i.e., when there is only one input variable, or it can be thought of as a hyperplane when the problem consists of more than one input variable. The mathematical representation of the linear regression model is presented in (13) [39]:  
\[\hat{y}=w_{0}+w_{1}x_{1}+w_{2}x_{2}+\cdots+w_{p}x_{p}, \tag{13}\]  
where \(\hat{y}\) is the predicted value, \(w_{i}\) denotes the coefficients of the model, and \(x_{i}\) denotes the variables or features. Therefore, the Linear Regression (LR) algorithm consists of finding the coefficients of the model through an optimization process that aims to minimize a loss function, \(J(y_{i},\hat{y}_{i})\), defined as the sum of squares of the difference between the target and predicted values. This optimization technique is known as Ordinary Least Squares and is represented by (14):  
\[J(y_{i},\hat{y}_{i})=\sum_{i=0}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{14}\]  
Despite being simple and efficient, such a model can become sensitive to inputs and unstable, especially for problems with a small number of samples or fewer samples than features. One way to overcome this problem is to adopt a modified loss function that includes additional costs when there are many input variables. The additional portion of the modified loss function is called a penalty, and different types of penalty can be used, each of them defining a new algorithm included in the class of penalized linear regression models. These algorithms include the LAS, RDG, and BAY. The LAS refers to the penalization based on the sum of the absolute coefficient values, the so-called \(L_{1}\) penalty, seen in (15). On the other hand, the RDG considers the \(L_{2}\) penalty, which is defined as the sum of the squared coefficient values as seen in (16).  
\[L_{1, penalty}=\sum_{j=0}^{p}|w_{j}| \tag{15}\]  
Figure 5: Normalized strength as predicted by the ROM. Adapted from [4].  
\[L_{2_{penalty}}=\sum_{j=0}^{p}(w_{j})^{2} \tag{16}\]  
Therefore, the modified loss functions for the RDG and LAS are defined in (18) and (17), respectively:  
\[J_{Lasso}=J+\lambda\cdot L_{1_{penalty}} \tag{17}\]  
\[J_{Ridge}=J+\lambda\cdot L_{2_{penalty}}, \tag{18}\]  
where \(\lambda\) is a hyperparameter that weights the penalty applied to the loss function. The \(\lambda\) values range from 0 to \(\infty\), where 0 cancels the penalty, and large values leads to simpler models with fewer parameters. Furthermore, the BAY is similar to the classical RDG, with the difference that the regularization parameter is now tuned based on the dataset under analysis. Hence, the Bayesian model has the advantage of adapting to the data at hand, but with the drawback of the inference being possibly time-consuming.
## 3 Results and Discussion
### Analysis of the Process Stability Based on the Classical Parameters
[PERSON] and [PERSON] [23] reported that despite the high VFAs concentration in co-digestion reactors of the cotton stalk with swine manure in comparison with single digestion of a cotton stalk, the anaerobic co-digestion assay showed higher stability with high biogas generation, indicating the role of swine manure on neutralizing VFAs in the medium. In the same way, [PERSON] et al. [50] studied semi-continuous anaerobic digestion of the solid cattle slaughterhouse (SB) and SB co-digestion with manure and various crops. They reported a failure of the individual digestion of SB. However,  
Figure 7: The classical parameters related to the process stability in the three SSTR. (**A**) The total alkalinity, (**B**) the acidity/alkalinity ratio, (**C**) the propionic acid concentrations, and (**D**) the propionic to acetic acid ratio. Values below the blue line refer to suitable values, values above the orange line indicate the critical threshold for proper operation, and values between both lines refer to alarming values that could distort the digesters. The red line refers to the threshold limit for the HPr/HAc ratio, as values above 1.4 were considered as critical for proper operation. stable performance, operating with higher loading rates, was observed for co-digestion with different manure sources. The authors highlighted the established synergy effect by mixing substrates with different characteristics. In a study of [PERSON] et al. [18], it has been found that AD of PM has given the highest methane yield in comparison with dairy, chicken, and rabbit manure at the same operating conditions. These findings are in agreement with the results of the present study. In addition to macronutrients such as carbon, nitrogen, phosphorus, and sulfur, trace elements are crucial for effective biogas production due to the microbial demand for these elements [51]. In a study of [PERSON] et al. [52] using continuous mesophilic digesters of slaughterhouse wastewater, the authors reported that the addition of trace elements to the digester resulted in higher biogas production and higher degradation efficiency, by improving the process stability. Moreover, higher OLR and lower HRT could be used in comparison to digesters with deficiencies in trace elements. Similarly, [PERSON] and [PERSON] [49] highlighted that micronutrients availability plays a crucial role in the process performance and stability of the AD of agricultural substrates. Moreover, authors have pointed out that the lack of trace elements is likely the main reason for poor process efficiency. In this sense, the supply of trace elements from the animal manure studied in this research could justify the high performance of reactors in comparison with the reactor of SBB digested individually.
## 3 Results  
### Time Series of Vegetation Indices and the Detection of Disturbance  
Savanna clearing and fire produced a great variety of changes in surface reflectance that depended on the intensity of disturbance, the disturbed savanna physiognomy, and the soil background composition before and after disturbance. For instance, when a woodland savanna area was cleared in the study area, we observed a reflectance increase in the visible and SWIR spectral intervals and a reflectance decrease in the NIR region (Figure 4a). This response was a combination of the soil reflectance with the reflectance of non-photosynthetic vegetation left on the ground after clearing (e.g., litter, branches, and twigs). In contrast, the effect of fire on shrub savannas was to decrease the reflectance of the NIR and SWIR-1 OLI/Landsat-8 bands (Figure 4b). Recently burned areas over savanna grasslands had low reflectance for the sensor, which gradually increased from the visible and NIR (VNIR) to the SWIR.  
Both events of disturbance reduced the VIs response. Because the reflectance after savanna clearing increased in the red band (640-670 nm) of OLI/Landsat-8 and decreased in its NIR band (850-880 nm) (Figure 4a), this type of disturbance produced a significant change in the temporal profile of NDVI (Figure 5a). To be labeled as a disturbance (\"break\" in the time series) by the CCDC algorithm, such NDVI modifications for a given pixel should be distinct from the seasonal NDVI variations produced by vegetation phenology. NDVI decreased from the rainy to the dry season of each year, reaching the lowest values toward September under maximum water stress for the plants. As demonstrated in Figure 5a, clearing can be also followed by a quick vegetation regrowth that may produce an increase in NDVI and a new pattern of seasonal profile (see NDVI after October 2019 in Figure 5a).  
For detecting disturbance by fire, VIs like NBR were especially important when used with CCDC because they tracked the reflectance changes over time in the NIR and SWIR spectral intervals (Table 1; Figure 4b). In our study area, disturbance by fire over the savannas generally transformed positive NBR values into negative ones. Therefore, \"breaks\" in the NBR time series were created because of the NIR reflectance decrease and the SWIR reflectance increase caused by biomass burning (Figure 5b).  
Figure 4: (a) OLI/Landsat-8 surface reflectance spectra of woodland savanna before and after a clearing event in 2019. (b) Spectra from a shrub savanna area affected by fire in the same year.
# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 4 Experimental Results and Analysis
### The Effect of \(\alpha\) and \(\lambda\)  
To verify the effects of and on the experimental results, experiments are conducted for and taking values 0 to 1, respectively, and the results are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **IFLYTEK (\%)** & \multicolumn{2}{c}{**THUCNews (\%)**} & \multicolumn{2}{c}{**Fudan (\%)**} \\ \hline Model & ACC & ACC & F1 & ACC & F1 \\ Exp1 & 56.35 & 87.78 & 82.80 & 86.53 & 80.42 \\ Exp2 & 58.78 & 91.53 & 84.53 & 91.04 & 86.03 \\ Exp3 & 53.94 & 86.53 & 82.74 & 86.21 & 80.02 \\ Exp4 & 55.73 & 89.04 & 84.89 & 89.63 & 82.79 \\ KGBGCN (Ours) & 60.65 & 92.25 & 86.83 & 91.99 & 86.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy and F1 values of ablation experiments.  
Figure 5: Comparison of the accuracy of ablation experiments.  
Figure 6: The general framework of Chinese long text classification technology based on knowledge graph. (**a**) Effect of \(\lambda\) values on accuracy for the IFLYTEK dataset; (**b**) effect of \(\alpha\) values on accuracy for the IFLYTEK dataset; (**c**) effect of \(\lambda\) values on accuracy for the THUCNews dataset; (**d**) effect of \(\alpha\) values on accuracy for the THUCNews dataset; (**e**) effect of \(\lambda\) values on accuracy for the Fudan dataset; (**f**) effect of \(\alpha\) values on accuracy for the Fudan dataset.  
As shown in the figure, the predictive interpolation model performs best at 0.7 on the IFLYTEK's long text dataset and achieves the highest accuracy at 0.8 on both THUCNews and Fudan University Chinese corpus datasets, which is consistent with the experimental verification of the best model performance in the literature. The experimental results further verify the effectiveness of the graph structure mask, which is 0.3 for the THUCNews dataset and 0.4 for the IFLYTEK's long text and Fudan University Chinese corpus datasets, i.e., the best value is between 0.3 and 0.4. This is because when the weight of the graph structure mask is too small, the dependency and entity-relationship types cannot be fully learned and trained by the model, instead, redundant information is added and the model does not perform optimally. The large weight of the graph structure mask reduces the contribution of the graph neural network and attention mechanism to the performance improvement of the model, and also makes the node information too complex, leading to the problem of gradient explosion and gradient disappearance of the model.