esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Audio Delay and Quality in Network Music Performance +
## 6 Discussion
### Audio Quality  
Our study found statistically significant results on the impact of delay on the QoE of musicians, but not on the audio quality. The lack of statistical significance could be due to several reasons:  
* Limitations of the subjective evaluation: The questionnaires used in the study were not explicitly designed to capture the emotions of musicians during the performance of a musical piece. Therefore, there may be a gap in understanding how varying audio quality affected the emotional dimensions of musical performances (e.g., dynamics, articulation, and timbre) [32]. The impact of audio quality on these elements could, in turn, significantly influence the emotional connection among musicians.
* Nature of NMP: In NMP, real-time interaction and coordination are more critical than audio quality. Musicians could adapt to less-than-perfect audio quality as long as they could maintain the necessary temporal coordination for their performance.
* Sensitivity to delay vs. audio quality: Musicians are typically more sensitive to timing issues than audio quality. While high audio quality is desirable, the ability to play in time with a peer is more critical for a successful performance [33]. Therefore, variations in delay are likely to have a more significant impact on QoE than audio quality.
* Focus and adaptation: Musicians focusing on timing and coordination may be less attuned to audio quality. Also, they may adapt to the variable audio quality while performing, reducing its perceived impact.
* The perceptual threshold for audio quality: The changes in audio quality were within what musicians considered acceptable. The quality degradation did not cross the threshold of what musicians would find troubling, leading to insignificant findings related to its impact on QoE.
## 3 Methodology
### Experimental Setup
#### 3.7.2 Evaluation Metrics  
We use the following metrics to evaluate our frameworks:  
**Prediction Performance:** We use the Area Under the ROC Curve (AUC) as our primary performance metric. AUC measures the model's ability to discriminate between positive and negative classes across all possible classification thresholds. AUC ROC for a binary solution is as follows [63]:  
\[\text{AUC}=\frac{1+\text{TPR}-\text{FPR}}{2}, \tag{23}\]  
where TPR (True Positive Rate) and FPR (False Positive Rate) are defined as  
\[\text{TPR}=\frac{TP}{TP+FN},\quad\text{FPR}=\frac{FP}{FP+TN}, \tag{24}\]  
with TP, FP, TN, and FN representing true positives, false positives, true negatives, and false negatives, respectively.  
**Fidelity:** We measure how well the white-box explainer model mimics the behavior of the black-box predictor model using AUC\((f_{\theta}.g_{\phi})\).  
**Point Fidelity:** This measures the agreement between the explainer and predictor for individual instances:  
\[\text{PointFidelity}(\mathbf{x}_{i})=\mathbb{1}(\hat{y}_{f,i}=\hat{y}_{g,i}) \tag{25}\]  
**Neighborhood Fidelity:** This extends point fidelity to consider agreement within local neighborhoods:  
\[\text{NeighborhoodFidelity}(\mathbf{x}_{i})=\frac{1}{k}\sum_{\mathbf{x}_{j} \in\mathcal{N}_{k}(\mathbf{x}_{i})}\mathbb{1}(\hat{y}_{f,j}=\hat{y}_{g,j}) \tag{26}\]  
**Stability:** This measures the consistency of explanations, calculated as the total variation of fidelity scores:  
\[\text{TV}(\mathcal{F})=\frac{1}{N-1}\sum_{i=1}^{N-1}|f_{i+1}-f_{i}| \tag{27}\]  
**Computational Efficiency:** We measure the computational cost of generating explanations, including training time and explanation generation time.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Electrical Test
#### 4.1.2 Gain and Front-End Stage Linearity  
In these tests, we studied the behavior of the stages that precede the SAMV71 ADC, where the analog signal is digitized: the amplifier and the peak hold. For these measures, we used a 40 ns pulse with variable amplitude to simulate the shape of the pulse compatible with a typical SiPM signal (Figure 5). The maximum signal output of LNA and the voltage level of the peak hold were measured for different amplitudes of the input pulse. As you can see in the diagrams in Figures 8 and 9, the dependence is rather linear for both stages, and the coefficient of determination for the linear regression is very high (\(R^{2}\) > 0.9998).  
Figure 7: Comparator output and analog output for 42 MHz (left picture) and 43 MHz (right picture) of square pulse input. The blue signal is the output of the LNA stage (amplitude scale 100 mV/div) and the green one is the square TTL output from the comparator (amplitude scale 5 V/div) The time scale of the horizontal grid was 20 ns/div.  
The linear interpolation of data allows one to evaluate the gain (given by the gradient of the line) of the amplification stage, which is 4.48. An offset of a few mV is present in both stages. It is bigger at the output of the peak hold, which by its nature is affected by noise since it locks the maximum noise value. Since the peak-hold output is the signal converted by the ADC, this offset affects the dynamic range of the system. It is possible to compensate for this offset thanks to a 10-bit DAC made available to the SAMV71 analog blocks. The measured value of peak-hold offset can be used to calculate the value for the DAC.
## 3 Some Numerical Results and Discussions
### Dynamic Convergence Study  
The FGM constituent material 1 located at lower position is stainless steel (SUS304); the FGM constituent material 2 located at upper position is silicon nitride (\(Si_{3}N_{4}\)) used with \(h_{1}=h_{2}\) under applied heat temperature \(\overline{T_{1}}\) and \(T\). The convergence of center displacement amplitude \(w(a/2,b/2)\) (unit mm) are studied for the thick FGM plates at \(t=6\) s with \(a/b=1\), \(c_{1}=0.925925/\)mm\({}^{2}\), \(h_{1}=h_{2}=0.6\) mm, \(T=100\) K and \(\overline{T_{1}}=100\) K. The calculated values of \(w(a/2,b/2)\) for \(a/h^{*}=10\) with applied heat flux \(\gamma=0.2618004/\)s and \(a/h^{*}=5\) with \(\gamma=0.2618019/\)s, respectively, are obtained and listed in Table 1 by using the advanced nonlinear varied \(k_{a}\) and \(\omega_{11}\) under three values of \(R_{n}\). For \(a/h^{*}=10\), the values are calculated and used in case 1: \(\omega_{11}=0.059249/\)s and \(R_{n}=0.5\); in case 2: \(\omega_{11}=0.061595/\)s and \(R_{n}=1\); in case 3: \(\omega_{11}=0.064255/\)s and \(R_{n}=2\). For \(a/h^{*}=5\), the values are calculated and used in case 1: \(\omega_{11}=0.018034/\)s and \(R_{n}=0.5\); in case 2: \(\omega_{11}=0.032166/\)s and \(R_{n}=1\); in case 3: \(\omega_{11}=0.032121/\)s and \(R_{n}=2\). The error accuracy of the amplitude \(w(a/2,b/2)\) is 2.702918E-05 for \(R_{n}=0.5\) and \(a/h^{*}=10\). The grid point 13 \(\times\) 13 could be considered in enough grid numbers to provide the acceptable convergence for the amplitude \(w(a/2,b/2)\) and used continuously in the next time response of calculations. The advanced nonlinear \(k_{a}\) values for \(a/b=1\) under \(T=100\) K are shown in Table 2, they are typically varied with the three parameters: \(c_{1}\), \(R_{n}\) and \(T\). The typical \(\omega_{mn}\) (unit 1/s) vs. mode shapes \(m\) and \(n\) (from 1 to 9) of the vibrating plate are reported in Table A1 and listed in Appendix A.
## 2 Results
### Validation of RNA-Seq Results by RT-qPCR  
Eight DEGs associated with Okaramine B biosynthesis, regulatory factors, tryptophan metabolism, and ABC transporters were selected for RT-qPCR (Table 2). These results were consistent with the RNA-Seq data (Figure 8), confirming a comparable expression pattern of up- and down-regulated genes. These findings can be utilized for subsequent studies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene ID** & **Primer Name** & **Primer Sequence 5\({}^{\prime}\)\(\rightarrow\)3\({}^{\prime}\)** \\ \hline
4.104 & actin-F & ACAGTCCAAGCCGTGATACC \\  & actin-R & CACACGAGCTCCGTGTAG \\ \hline
3.16 & okaA-F & CTTGATCCGAGCGAGGTTAG \\  & okaA-R & CGGCTATCGAAAGCACATTAGG \\ \hline
3.15 & okaB-F & CAAACACAACAAGGCCAAAGT \\  & okaB-R & TATACCCTCGACCCTCGTTG \\ \hline
1.1084 & reg2-F & ACCAGATCAGCTCACCAAA \\  & reg2-R & CGATCAAAGCCTGGAAACG \\ \hline
1.213 & reg19-F & TCAGTCACACAAGTTCCAGT \\  & reg19-R & CTTCCTGATCCTCCTCGCT \\ \hline
7.557 & try7-F & TGACTCTGGTCCCCCAAG \\  & try7-R & GCTGCCTTAGAGTTACCCCA \\ \hline
5.320 & try9-F & CTCTGTGAAACTCGCTG \\  & try9-R & ACAAACCACAATGGAAGCCC \\ \hline
2.1634 & trans4-F & GCACTGTGTAGCGAAAAGCAA \\  & trans4-R & TCTTCAGGTGGTGGCCATG \\ \hline
2.896 & trans5-F & TCCTTCGCTCTCTCTTCTT \\  & trans5-R & AGTACACACAGACTGCCAA \\ \hline \hline \end{tabular}
\end{table}
Table 2: RT-qPCR primers used in this study.  
Figure 8: The verification of relative expression levels of genes by RT-qPCR during the submerged fermentation (**a**) and semi-solid fermentation (**b**) processes. Gene 3.16, the non-ribosomal peptidesynthease gene _okaA_; Gene 3.15, the flavin-dependent monooxygenase gene _okaB_; Gene 1.1084, transcriptional regulatory protein Reg2; Gene 1.213, transcriptional regulatory protein Reg19; Gene 7.557, indoleamine 2,3-dioxygenase; Gene 5.320, putative kynureinase; Gene 2.1634, transporter gene; Gene 2.896, transporter gene. The level of significance was established at \(p\) < 0.05 (labeled as *), \(p\) < 0.01 (labeled as **), and \(p\) < 0.001 (labeled as ***). NS indicates no significant difference.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 3 Results
### Datasets  
In our experiments, we utilized the CHN-CUG Roads Dataset, a remote-sensing road dataset that was created and made available by [PERSON]'s team at the China University of Geosciences. Figure 7 shows the specific location where the remote-sensing image information is selected.  
Figure 7: The dataset was selected from six representative Chinese cities with specific locations.  
The six cities, namely Beijing Chaoyang District, Shanghai Yangpu District, Wuhan City Center, Shenzhen Nanshan District, Hong Kong Shatin District, and Macau, exhibit varying levels of development, characterized by differences in their history, culture, and urban structure. The dataset includes marked roads, both covered and uncovered, comprising railroads, highways, and urban and rural roads, among others.  
The dataset consists of 4511 labeled data points, with 3608 images being used for training purposes and 903 images for testing and validation. The resolution of the images is 50 cm/pixel. Figure 8 shows the original and labeled graphs in the dataset. To enhance the credibility of the DANet network structure's segmentation results, we randomly divided 1/4 of the training set into a test set. The training set comprised a total of 2706 images, while the test set consisted of 1805 images.  
The dataset contains road information with complex shapes and textures, including urban and irregular roads as well as occlusions caused by buildings. As a result, the remote-sensing image road semantic segmentation algorithm needs to be robust and generalizable. The dataset is a suitable choice for testing the effectiveness of the DANet network structure.
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 2 Materials and Methods  
This section presents a detailed description of how probabilistic ED forecasting handles uncertainty. It is challenging to produce a suitable adequate dataset for the parametric algorithm's training under anomalous events such as the COVID-19 pandemic because of the unprecedented changes in consumption patterns and the quantity of data. As parametric approaches optimize multiple parameters, they need a lot of data to detect relative trends. Therefore, it is not suitable in this scenario because only limited ED and mobility observations have been available from the beginning of the pandemic. Parametric distributions for the random variables are generally expected [47]. However, conditional predictive densities are not guaranteed to follow the same distribution, even when observations form a known and well-behaved marginal distribution [47]. Incorrect distributional assumptions potentially affect analyses and interpretation of the findings [47]. This work trains a model using a non-parametric GPR-KDE method, which does not require prior knowledge or assumptions about data distribution [39].  
The forecasting set-up for electricity demand time series data is represented as follows: at time \(t\), electricity demand at time \(t+k\), \(k\) is referred to as the lead time, and \(y_{t+k}\) is the random variable which is electricity demand at time \(t+k\). The historical electricity demand data \([y_{t-1},\ldots y_{t}]\) and the training set (\(x_{t}\)) include different selected features, such as mobility data and temperatures. The objective of ED is to develop a model \(f(.)\) for \(y_{t+k}\) based on the gradient gained from optimizing the loss functions.  
Probabilistic EDF typically involves three phases. Initially, the influential factors which are relevant to electricity demand variability should be identified from the historical dataset through a feature selection process. Next, the GPR algorithm is utilized to predict the electricity demand on different kernels. Finally, the probability density function can be obtained by KDE. The flowchart of the proposed GPR-KDE-based probability density forecasting method is shown in Figure 2. The evaluation metrics are also discussed in this section.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 4 Prototype Implementation  
This section lays out the tools and hardware used in building our proposed ECG patch. The hardware prototype consists of four major components: A microcontroller with a wireless core for BLE communications, an analog frontend interface (AFE) for acquiring analog ECG signals using a high-performance analog to digital converter and a micro-SD card module for data logging, as shown in Figure 4.
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 5 Discussion
### Model Accuracy and Operation Speed  
For Transformer, when the decoder is used for prediction, the subsequent overload curve is obtained by masking the subsequent input through the upper triangular mask matrix. This approach is similar to that of the traditional RNN network, but for the RNN network, it is difficult to correlate the input information at the current time with the input information at the early time. When using RNN to predict the time series of a long series, it is easy to forget the first half of the information, and Transformer will integrate all the data not covered by the mask matrix to make the prediction, effectively ensuring the accuracy of the model. Because the model uses KL divergence to calculate attention, the computational complexity is changed from O (L ^2) to O (LlogL), which greatly reduces the computational difficulty. Time series prediction that can output 48 us within 20 us has been emphasized many times in this paper. First of all, from the perspective of sensor sampling, it is customary to take 20 us as a time period to sample the overload value, so after obtaining the overload value of 20 us, we hope that we can predict longer overload data through the model within 20 us. All the calculations in this paper have been integrated and expanded into large matrix calculations. Take encoder as an example to estimate the number of model parameters. Encoder includes word embedding layer, multi-head attention layer, fully connected layer and layer normalization layer. According to the hyperparameter settings of the model, the number of parameters used in the word embedding layer is about 2,304,000, and the number of parameters used in the multi-head attention layer is 1,048,576. The formula of the fully connected layer is as follows:  
\[\text{FFN}(\text{x})=\text{max}(0,x\text{W}_{1}+\text{b}_{1})\text{W}_{1}+b_{2} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **PF-Informer** & **GRU-ODE-BAYES** & **GRU** & **LSTM** & **ARIMA** & **CNN** & **N-BEATS** \\ \hline MSE & 0.221 & 0.366 & 0.464 & 0.452 & 0.932 & 0.822 & 0.343 \\ MAE & 0.252 & 0.387 & 0.520 & 0.502 & 1.012 & 0.942 & 0.369 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training results of different models.  
The parameters of the fully connected layer are 2,097,152, the layer normalization layer is 17,408, and the total parameters of the encoder are 3,393,536. Each head in each encoder is parallel, and the Q, K, V matrices of each header are parallel, so most matrix calculations can be performed synchronously. The floating-point performance of the computing resources used is 40 TFLOPS. Therefore, it is possible to achieve the prediction of 48 us or even longer, within 20 us.
## 3 Results and Discussion
### EPR Spectroscopy Investigation
#### 3.2.2 The Spin Density  
Spin concentration is a measure of the number of unpaired electrons in a given amount of sample. Overall, the EPR results show that the spin density of the maltene fraction, in both unaged and aged samples, is around 5% of that determined in the asphaltene phase. This is a further confirmation that the main contribution to the organic radicals detected in bitumen samples comes from unpaired electron in non-localized \(\pi\) system stabilized by resonance in extended polyaromatic macromolecules.  
Interestingly, natural antioxidant additives affect the bitumen spin density even in the absence of the aging treatment, just as an effect of mixing. This is specifically true for LCS, the addition of which results in a lower radical content in the asphaltene fraction, ascribable to an effective scavenging action (see Figure 6).  
Figure 6: Spin density of asphaltene samples mixed with 2% \(w/w\) of antioxidants, as determined from the X-band EPR spectra registered at room temperature. CTRL indicates the control sample with no antioxidant added. Full bars: unaged samples; striped patterned bars: samples artificially aged through the Rolling Thin Film Over Test (RTFOT) method.  
The RTFOT treatment causes a significant increase of the spin density in the CTRL asphaltene sample, and, to a lower extent, in the corresponding maltene fraction. This indicates aging causes an increase in the average size of the polyaromatic asphaltene molecules, thus resulting in a further stabilization of the unpaired electrons in the extended \(\pi\) systems.  
The anti-aging effect of natural antioxidant additives can be evidenced by observing the differences in concentration relative to the aged CTRL. In fact, upon the aging treatment, the addition of LCS, VC and RH leads to a reduction in the density of free radicals in asphaltene fraction of 39%, 36% and 34%, respectively (see Table 2).  
In particular, among all the additives tested, LCS is the only one able not only to lower the free radical content of the asphaltenic component in response to the heat treatment to which the bitumen is subjected, but also to keep the spin density unchanged before and after aging stress. The relative antioxidant effect recorded on the maltene fraction is much more pronounced compared to the aged CTRL, showing a strong reduction in the concentration of paramagnetic radicals for bitumens modified, respectively, with LCS (53%) and VC (49%) while the effect of RH is somewhat weaker (17%).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### AI-Enabled Interference Mitigation  
To address the increasing complexity and dynamic behavior of B5G networks, the 3 GPP has initiated efforts to incorporate AI into the standardization of IMI techniques. AI techniques such as supervised learning, DL, and RL are gradually being considered for integration into 3 GPP's SONs and RICs frameworks.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Microbial Degradation of Plastics
#### 2.1.5 Biodegradation in Different Environments  
In the marine environment, plastics undergo photodegradation and biodegradation, leading to weight loss and changes in their properties. Microorganisms form biofilms on the surfaces of these plastics, which facilitates degradation [69]. In a study conducted by [PERSON] et al. [70], the degradation potential of _Aspergillus_ sp. (a fungal species) and _Vibrio_ sp. (a bacterial species) isolated from the marine environment was investigated. Over a six-week period, the bacterial strain demonstrated 35% degradation of plastic bottle waste, while the fungal strain achieved 22% degradation. This study also evaluated various influencing factors, such as temperature, pH, and inoculum concentration, to optimise the degradation conditions. These findings suggest that bacteria, specifically _Vibrio_ sp., are better than fungi at breaking down plastic waste under the conditions tested, making bacterial treatment a more efficient approach for plastic disintegration in marine environments [68].  
In soil, the degradation of plastics varies significantly. Conventional plastics like PE and PVC can persist for decades [71; 72]. However, some bacteria, such as _Lysinibacillus_ sp. isolated from soil, have demonstrated the ability to degrade PE and PP [73]. On the other hand, biodegradable plastics can degrade much faster than conventional plastics. For example, PHB shows 99% degradation in soil after 136 days, while PCL also degrades by 99% in soil within 136 days. PBS degrades by 85% after 150 days in soil. The degradation rates of these plastics in aqueous environments are also noteworthy. After 117 days, PBS degraded by 2%, PCL degraded by 77.6%, and PHB degraded by 83.0%. These examples illustrate that the same type of plastic can degrade at significantly different rates in different environments [35; 36; 38].  
Despite these studies, challenges and limitations exist in microbial plastic degradation. Reliable reports are mostly limited to specific synthetic polymers, with fewer studies focusing on bioplastics despite their persistence in the environment for an extended time. This issue can be potentially mitigated by applying microorganisms, plastic-eating insects, and enzymes to accelerate their degradation. Some researchers have argued that the microbial degradation of plastics has been overstated [74]. These researchers point to the lack of evidence for the substantial degradation of unadulterated PE, PP, PS, or PVC, which represent most of the global plastics production. Table 3 details the various plastic-degrading bacterial species reported and their respective biodegradation patterns for different plastics. The table summarises the advancements in identifying and cultivating microorganisms under laboratory conditions based on published reports from the ten year period 2014 to 2024.