esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Feasibility for Practical Use in Anaerobic Digestion Operation
#### 3.2.2 Quartz Crystal Microbalance Measurements of Anaerobic Digestate Samples
When \(x_{i\ org}\) is lower than 1, the mixture of palmitic acid and trirolein can be considered as organic liquid. On the basis of the experimental study on equilibria in fatty acid/triglyceride systems, the mixture of palmitic acid and trirolein can be considered an ideal solution ([PERSON] et al., 2016); thus, \(\gamma_{i}\) org is 1. From our recent studies ([PERSON] et al., 2016), \(\Delta H_{fus}\) and \(T_{m}\) of palmitic acid are 335.6 K and 55,840 J/mol, respectively. Therefore, the water-solubility of palmitic acid in the mixture of palmitic acid, triolein, and water is expressed as follows:  
\[C_{pal\ w}=\ 12.5\ x_{pal\ tri}\ \cdot\ C_{tot}^{sat} \tag{5}\]  
Figure 5: (**a**) Time courses of QCM frequency-shift rates (\(n=3\)) and LCFA concentrations of anaerobic digestate samples; and (**b**) Comparison of relationships between frequency-shift rates and LCFA concentrations in digestate and palmit samples. Total LCFA values in Figure 5 are the concentrations of diluted samples before measurements. This result suggests that even if \(x_{pal\ tri}\) is only 0.08, palmitic acid can maintain the same level of solubility when applied as a sole contaminant. Realistically it is found that the effect of organic contaminants on the solubility of LCFA can be ignored. These results confirm that the newly developed QCM sensor can detect the increasing trend in LCFAs prior to the inhibition of biogas production.
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 2 Methodology  
### Original YOLOv12 Algorithm  
YOLOv12 is a convolutional neural network-based object detection algorithm that employs an end-to-end approach for both training and inference [31,32]. In YOLOv12, the network architecture primarily consists of four components: the input layer, the Backbone [33], the Neck network [34], and the detection Head. The Backbone is responsible for feature extraction, utilizing a deep convolutional neural network architecture to extract features from the input images through multiple convolutional layers. The C3k2 and A2C2f modules are incorporated to further refine these features. The Neck is primarily used for multi-scale feature fusion, employing techniques such as the feature pyramid network (FPN) [35,36] and the Path Aggregation Network (PAN) [37] to enhance the detection of small objects. The Head handles the final object classification and bounding box regression. The advantage of YOLOv12 lies in its efficient detection capability and real-time performance, enabling high-precision object detection with relatively low computational cost. Compared with previous YOLO versions, YOLOv12 achieves improvements in both detection speed and accuracy, and its adoption of an Anchor-Free mechanism simplifies the traditional Anchor-Based design, thereby reducing inference latency.
## 4 Methods
### Main Processing  
#### 4.2.1 Endmember Selection  
Due to the low dimensionality of the multispectral data, only five endmembers were taken into consideration. Three endmembers represented the main distinctive lithologies in the area, the two primary volcanic lithologies (named hereafter \"rhyodacites\", representing acidic to intermediary lava compositions, and \"basalic andesites\", representing basic to intermediary lava compositions) and alluvial/beach deposits (named hereafter \"alluvial deposits\"). Two endmembers represented the argillic hydrothermal alteration materials (named hereafter \"hydroxyl-bearing alteration\" and \"sulfur\"). Scree and other loose materials were not represented by endmembers due to their mixed compositions that included, among others, the two primary volcanic lithologies. Lacustrine deposits and mudflows related to hydrothermal explosions were of mainly clay composition (illite, kaolinite, and montmorillonite) and were represented by the hydroxyl-bearing alteration endmember.  
For the endmember selection and extraction, two different approaches were followed: (1) pixel-based endmember selection [102] and (2) retrieval from the USGS spectral library. The pixel-based approach was followed for the retrieval of the four endmembers. More specifically, the endmembers \"rhyodacites\", \"basalic andesites\", and \"alluvial deposits\" were extracted from the images by the use of regions of interest (ROIs). More specifically, first, we identified from the 1:15,000 digitized geological map [68] the areas corresponding to the different lithologies and then, by superimposing them to each dataset, we selected 9x9 ROIs within these areas, so that they were as far as possible from hydrothermal field. The means of the spectral signatures of the pixels within the identified ROIs were then computed and used as the endmembers for the respective lithologies. For the \"hydroxyl-bearing alteration\", the endmember was retrieved by calculating the normalized hydrothermal alteration spectral index (NHASI) on each dataset, as shown in Table 2. The spectral signature of the pixel presenting the highest NHASI value was used as the \"hydroxyl-bearing alteration\" endmember for each dataset.  
Figure 3: Flowchart of the methodology followed in this study.  
Finally, the sulfur endmember was retrieved from the USGS Spectral Library online Version 7 (pure mineral sulfur sample \"Sulfur GDS94 Reagent BECKa AREF\"), resampled to the spectral bands of each one of the three satellites. Figure 4 shows the plots of the spectral signatures of the endmembers for each multispectral dataset.
## 4 Results
### Hybrid Machine Learning  
The extracted number of peaks (sidebands and characteristic frequencies) varies between the different faults, measures and sensors. After several tests, the number of used peaks is set to the 100 with the highest importance. The dataset contains 12 classes with 10 samples per sensor. One sample corresponds the FFT of one measurement.  
The results of the hybrid machine learning model with one LSTM layer are shown in Table 4. The algorithm performs very good on all sets. It is conspicuous that the set using all sensors is not the best. The reason for this is the very limited amount of data. The algorithm struggles to fit a function to that number of features with the existing data.  
Adding a second LSTM layer has different effects on the performance of the hybrid machine learning pipeline (see Table 5). To extract features with a higher complexity results in a better performance for the model trained on all sensors. The test accuracies of the two models trained on acceleration signals, radial and lateral, become slightly worse. This indicates that there is no advantage in extracting more complex features. The additional layer does not affect the algorithm trained on the microphone data.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Used** & **accX, accY** & **accX** & **accZ** & **mic** \\
**Sensor(s)** & **accZ, mic** & & & \\ \hline Train & 96.30\% & 99.07\% & 99.07\% & 98.15\% \\ accuracy & & & & \\ \hline Test & 91.67\% & 100.00\% & 100.00\% & 91.67\% \\ accuracy & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of the hybrid LSTM model (one LSTM layer).  
Figure 13: Confusion matrix of the conventional two-layer LSTM trained on mic.  
Using one single sensor signal, the model with one LSTM layer performs better and only using all sensors at the same time the additional layer has a positive influence on the accuracy.  
Figure 14 shows the confusion matrix of the two-layer LSTM trained on the data from the microphone. The wrong classifications of classes two and three can be explained with a small difference in the state of wear. All false classifications that include corrosion or rolling body marks are based on the fact that there is a damaged part on the inner and outer ring.  
The best model achieves an accuracy of 100% (all sensors) and the worst, 91.7%. Compared to the conventional models the best hybrid is 4.1% better. The difference between the worst models is 81.6%.
## 2 AMR Signals
### Communication Channel Model  
Information signals in communication systems suffer from quality degradation when they are propagated from the transmitter to the receiver via a communication channel. The transmitted signals are attenuated and distorted in the channel. The signal loses some of its energy due to channel impedance, causing it to be attenuated [23]. Distortion changes the shape or form of the signal. This condition occurs when more than one signal arrives with different frequencies at the receiver side and when channel impairments exist. Another source of transmission impairment is random noise, which originates from several natural and artificial sources. This type of noise is classified as random because it is unpredictable and a crucial issue in the study of transmission impairments in communication systems.  
The existence of these impairments in signal transmission makes it difficult to identify the modulation type used to transmit the information signal to the receiver. Additive white Gaussian noise (AWGN) is a common channel noise that usually affects the signal amplitude. Therefore, with the presence of noise, the received signal is expressed as [27]  
\[r(t)=s(t)+n(t) \tag{1}\]  
where \(r(t)\) is the received signal waveform. The received signal consists of two uncorrelated components, namely, modulated signal \(s(t)\) and noise \(n(t)\) (i.e., introduced by the channel during transmission).  
AMR algorithms introduced in the literature have utilized the information contained in the received signal, extracted from the baseband wave or intermediate frequency [28]. Generally, the signal received by the AMR for further processing can be expressed as  
\[r(t)=\tilde{s}(t)e^{-j(2\pi\ell+\phi_{c})}+n(t) \tag{2}\]where \(f_{c}\) is the carrier frequency, \(\varphi_{c}\) is the carrier phase, \(\tilde{s}(t)\) is the baseband complex envelope of real signal \(s(t)\) and is calculated as  
\[\tilde{s}(t)=a(t)e^{-j(2\pi f(t)+\phi(t))}+n(t) \tag{3}\]  
where \(a(t)\) is the instantaneous amplitude of the signal and \(f(t)\) and \(\varphi(t)\) are the frequency and phase of the signal, respectively [29; 30]. The modulated signal is represented through the real part of \(\tilde{s}(t)\), as follows:  
\[m(t)=\text{Re}[\tilde{s}(t)e^{-j2\pi f(t)}] \tag{4}\]
## 4 Materials and Methods
### Measurement Design
#### 4.3.3 Range of Scope Correctness  
We evaluated the CoMeS-SPL method in terms of the correctness and usefulness of obtained scope artifacts.  
* Perceived usefulness of scope obtained (PUSO): The degree to which a person thinks the defined scope will be used in the following activities of the product line development and will allow (or not) decision making about the production of the product line. The perceived usefulness is a subjective and dependent variable, and for measuring this variable an instrument was used that was supported in the proposals of [47; 48].
* Scope usefulness (SU): The degree to which the scope is used for decision making about SPL adoption and cost estimation.
* Range of well-defined scope (RWS): We assessed the scoping artifacts of the CoMeS-SPL method in terms of completeness and correspondence with the requirements. Furthermore, we evaluated the value of these artifacts for decision-making regarding the SPL's production and the subsequent development stages.  
Table 4 summarizes the indicators, metrics, and methods for data collection and instruments considered in the design of the case study in order to evaluate the CoMeS-SPL method. This study case combined qualitative and quantitative data to achieve a better understanding of the studied phenomenon.  
Three data collection methods were applied in this study, namely documentation analysis, observation, and survey. Additionally, the documents related to the artifacts produced in the scoping were analyzed. To measure the perceived ease of use (PEOU), perceived utility (PU), intention to use (ITU), perceived collaboration (CP), and perceived usefulness of scope obtained (PUSO) variables, we designed an instrument (survey) from the one proposed for the evaluation of the requirements modeling methods [49] and the Technology Acceptance Model [47]. The instrument has defined a set of items to measure each of the perception variables. The evaluation of these elements used a Likert scale of 6 points (the lowest value or disagreement 1, and the highest value or agreement 6). The items were randomly organized in the questionnaire to prevent the participants from giving systematic answers. Four open questions were included in the instrument to obtain suggestions and observations regarding the proposed method. In order to measure the variables participation degree (PD) and factor artifact history (HFar), these were measured by counting the contributions of each of the participants in the construction of the artifacts that make up the scope according to the CoMeS-SPL method. The metrics' scope usefulness (SU) and range of well-defined scope (RWS) were evaluated by the architect and the manager of the company.
# Attosecond Streaking Time Delays: Finite-Range Interpretation and Applications
## 4 Conclusions  
We have presented a review of theoretical studies leading to the finite-range interpretation of the attosecond streaking time delay. Classical analysis of the dynamics of the photoelectron in the streaking field following absorption of the XUV photon, along with a comparison with results of ab initio quantum calculations, led to the conclusion that the streaking time delay is accumulated until the streaking field ends over a finite range of the potential of the residual ion only. This analysis further gave rise to an analytical model formula that was used to provide insights into various aspects of the streaking time delay. For example, an alternative justification into the widely used decomposition of the streaking time delay into a Wigner-Smith time delay and a Coulomb-laser coupling term has been given. The role of the attochirp of the ionizing XUV pulse as well as the relevance of the first-half cycle of the streaking field for the observed streaking time delay have been studied and discussed. Finally, we have discussed the perspective of using the streaking time delay to resolve dynamical aspects, namely temporal changes of the potential explored by the photoelectron.  
Conceptualization, Investigation, Methodology, Validation, Formal Analysis, C.G., A.J.-B. and A.B.; Software, Visualization, C.G.; Writing, C.G., A.J.-B. and A.B.; Supervision, Project Administration, and Funding Acquisition, A.J.-B., A.B. This work was primarily supported (C.G., A.B) by a grant from the U.S. Department of Energy, Division of Chemical Sciences, Atomic, Molecular and Optical Sciences Program (Award No. DE-SC0001771). A.J.-B. acknowledges support by a grant from the U.S. National Science Foundation (Grant No. PHY-1734006). We acknowledge [PERSON] and [PERSON] for their work on the finite-range interpretation of the streaking time delay and time delay in two-photon ionization, which has been published in Refs. [11; 20; 21].  
The authors declare no conflict of interest.  
Figure 7: **(a)** Streaking delay as a function of the period of oscillation of the additional Gaussian potential (diamonds). The same potential and XUV pulse as in Figure 3 with a 11-cycle streaking pulse of 800 nm wavelength is used. Also shown is the time delay for the same parameters, but with the additional Gaussian potential remaining fixed (dotted horizontal line); **(b)** zoom-in of **(a)** for small oscillation periods.
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
## 5 Subjective Measurements
### Analysis of the First Measurement  
Figure 12 presents the differences across various music genres. Listeners evaluated rock music samples similarly across all sensory attributes--for transparency, the average rating was 0. In the case of pop music, the ratings were slightly lower; however, the average for dynamics was above 0. Jazz music showed greater variation in ratings across different sensory attributes. All observed differences were minor, and the confidence interval did not exceed 0.2.  
Figure 11: View of the equipment under testing from the listening posts.  
Figure 10: Photograph of the expert panel.  
Figure 9: Positioning of the tested loudspeaker systems.  
The chart illustrating the average differences for playback sequences shows variation across different configurations. However, the A-A test sequence was centered around 0 for all evaluated parameters. The visible discrepancies were minor and may have resulted from the listeners' emotional state or unconscious expectation of a difference.  
The statistical analyses shown in Tables 2 and 3 revealed no statistically significant effects. Specifically, [PERSON]'s test confirmed the assumption of homogeneity of variances, indicating that the variability within groups (new vs. used devices) is comparable. Furthermore, the ANOVA results showed no significant differences in mean scores across the evaluated variables. These findings suggest that the condition of the device--whether new or used--did not have a measurable impact on participants' perceptions in terms of clarity, dynamics, brightness of sound, or overall evaluation.  
This outcome supports the notion that both device types performed similarly from the users' perspective, at least within the scope of the tested parameters. It may also imply that other factors, such as user expectations, environmental conditions, or individual preferences, play a more substantial role in shaping subjective evaluations than the device's usage status alone.
## 3 Research Methodology
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Key Technologies** & **Contributions** & **Drawbacks** \\ \hline [PERSON] et al. (2018) [13] & SVM (98\%) & \begin{tabular}{l} Determined which model is suitable for which parameters among an SVM, an ANN, and GMDH. \\ \end{tabular} & Accuracy was reduced significantly for predicting pH. \\ \hline [PERSON] et al. (2017) [21] & FS-MNN (93.4\%) & \begin{tabular}{l} Combined two neural networks (forward selection and backward elimination) for better WQI. \\ \end{tabular} & BOD and COD parameters were excluded. \\ \hline [PERSON] et al. (2019) [15] & WDT-ANFIS (98\%) & \begin{tabular}{l} An augmented wavelet denoising technique (WDT-ANFIS) was introduced. \\ \end{tabular} & Optimal selection of hyperparameters still required. \\ \hline [PERSON] et al. (2019) [22] & MLP (85.07\%) & \begin{tabular}{l} Both WQI and WQCs calculated in a quicker and inexpensive way. \\ \end{tabular} & In trying to make their solution inexpensive, they used only four parameters. \\ \hline [PERSON] et al. (2022) [23] & XGB-SHAP (95.6\%) & \
\begin{tabular}{l} An additive explanation method was introduced to format the XGB mode. \\ \end{tabular} & Only monthly (no long-term data) readings were used in this study. \\ \hline [PERSON] et al. (2019) [24] & IGA-BPNN (97.34\%) &
\begin{tabular}{l} Adjusted integration of an improved genetic algorithm for water quality for prolonged periods. \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing works with key technologies and drawbacks. InfluxDB is the repository for all the raw data and predicted values. The database can also contain additional information about the sensors, such as their uptime and whether or not they require maintenance. * Visualization and monitoring: Grafana is employed to create interactive dashboards that visualize both raw water quality data and the generated predictions. These dashboards provide a user-friendly interface for monitoring water quality trends and patterns. The visualization process is hosted on AWS, enabling access to dashboards and data from any location with an internet connection. By combining IoT, InfluxDB (version 3), ML, Grafana (version 10.0), AWS, and Terraform, this system offers a robust and scalable solution for automated water quality classification and monitoring.
## 5 Numerical Simulations
### Simulation Results
#### 5.2.5 Tolerance Parameters and Robustness  
We investigated the relationship between the tolerance parameters and the ADN robustness when the proportion of distributed generators was 0.2.  
Figure 11: \(R_{\text{PF}}(\infty)/R_{\text{IF}}(\infty)\) of different network topology under different \(\Theta_{P}\) and proportion of distributed generators = 0.8.  
Figure 10: \(R_{\text{PF}}(\infty)/R_{\text{IF}}(\infty)\) of different network topology under different proportion of distributed generators and the unintentional-random-initial-failure-rate \(\Theta_{P}\)= 0.002.  
The relationship between the final expected proportions \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) of remaining normal operational nodes and the tolerance parameters \(\alpha_{P}\) and \(\alpha_{I}\) is shown in Figure 12. Of the nodes, 1.188% survived in the PN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=1.5\) under the ER topological structure. Of the nodes, 93.37% survived in the PN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=2.0\) under the ER topological structure. Of the nodes, 1.188% survived in CN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=1.5\) under the ER topological structure and 93.37% of nodes survived in the CN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=2.0\) under the ER topological structure. Accordingly, 0% of nodes survived in the PN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=1.5\) under the BA topological structure and 1.386% of nodes survived in the PN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=2.0\) under the BA topological structure. None of the nodes survived in the CN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=1.5\) under the BA topological structure and 1.386% of nodes survived in the CN for \(\Theta_{P}=0.01\) with the tolerance parameters \(\alpha_{P}=\alpha_{I}=2.0\) under the BA topological structure.  
As shown in Figure 12, \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) with the tolerance parameter 2.0 were greater than that of the tolerance parameter 1.5. Therefore, this agreed with our fifth conclusion. That is, the robustness of the ADN was positively correlated with the tolerance parameters. The robustness of the ADN was improved when the values of the nodes tolerance parameters \(\alpha_{P}\) in the PN and \(\alpha_{I}\) in the CN were greater. This also shows that the larger the tolerance parameters, the smaller the impact of load redistribution on the robustness of the ADN.