esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study aimed to investigate the impact of CNN parameter distributions on imbalanced data and improve the classification performance of minority class samples by optimizing the CNN model parameters. Quantitative evaluation metrics included accuracy, recall, F1 score, G-mean, and P-mean. We found that parameter selection significantly influence imbalanced data and specifically optimized additional hyperparameters of the CNN model for the CIFAR-10 dataset with a maximum imbalance ratio of 25:1 and the Fashion-MNIST dataset. The results demonstrate that the learning rate and kernel size notably affect the performance of the CNN model for imbalanced data. In CIFAR-10, adjusting the learning rate to 0.013 and setting the kernel size to 3 \(\times\) 3, and, in Fashion-MNIST, adjusting the learning rate to 0.0031 and setting the kernel size to 3 \(\times\) 3, led to improvements in accuracy by 14.20% and 3.87%, respectively, compared to traditional undersampling methods. For CIFAR-10, the recall, F1 score, G-mean, and P-mean of the minority class samples improved by 8%, 34%, 28%, and 42%, respectively, compared to the undersampling methods. For Fashion-MNIST, these metrics improved by 12%, 9%, 10%, and 4%, respectively. These findings underscore the importance of parameter selection in handling imbalanced data, particularly kernel size and learning rate adjustment, which play a crucial role in recognizing minority class samples.  
Despite achieving significant results in this study, further exploration of parameter optimization methods is necessary. This study focused solely on understanding and studying a single CNN model and did not explore other models. Considering the relatively scarce research on deep learning methods in the context of imbalanced and large datasets, further exploration will significantly advance deep learning methods in big data analytics. Future research on deep learning will likely continue in this challenging direction and attract continued attention from those in academia and industry. For more details, please refer to our GitHub project: [[https://github.com/2221335/Ao](https://github.com/2221335/Ao)]([https://github.com/2221335/Ao](https://github.com/2221335/Ao)) (accessed on 27 September 2024).  
Conceptualization, N.W.; methodology, N.W.; software, R.Z.; validation, R.Z. and N.W.; formal analysis, R.Z.; investigation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, N.W.; visualization, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of \"Double First-Class\" Disciplines in Heilongjiang Province, grant number IJGXCG2022-057.  
Not applicable.  
The datasets used for this study are publicly available.  
The authors declare no conflicts of interest.
# New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
## 6 Conclusions  
Based on data from 66 sets of in situ measurements from three large hydropower stations in China and the BQ values at each test point, this study established a formula (Formula (3)) for predicting the deformation modulus. The formula shows a good fit to the values suggested by the Chinese Code for engineering geological investigation of water resources and hydropower (GB50487-2008) and is thus suitable for making estimations in rock projects where there is a large load. For general rock engineering, Formula (3) predicts relatively low values, so indirect estimation methods based on the relationship between BQ and other classification schemes were developed. Among those, Formulas (8), (10), and (11) are recommended, due to their good fits to measured data.  
Based on the above, the formula for estimating the deformation modulus of rock masses based on the BQ system established in this paper is not only applicable to hydropower engineering, but also to other engineering projects.  
[PERSON] and [PERSON] led the writing of the original draft. [PERSON] proposed the concept and framework of the paper, and participated in the revision of the paper. [PERSON] was responsible for data collection and processing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the correspondence authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Northwest Engineering Corporation Limited. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Results
### Climatic Controls on Carbon Fluxes
#### 3.3.2 PAR vs. GPP  
We employed a simple, rectangular hyperbolae (Michaelis-Menten) model [41] to analyze the relationship between daily PAR and GPP by each month, whereas the data points of GPP were averaged in 50 \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\) PAR bins. This relationship was described by the equation:  
\[\rm GPP\ =\ \frac{A_{max}\times PAR}{K+PAR}+R_{d} \tag{2}\]  
where GPP is the gross primary production, PAR is photosynthetically active radiation, \(\rm A_{max}\) is a maximum rate of photosynthesis, K is the Michaelis-Menten constant, and \(\rm R_{d}\) is a respiratory term. The coefficient of variation (\(\rm R^{2}\)) values were very high during May to September, ranging from 0.84 to 0.95.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(T_{\rm a}\) & **VPD** & **PAR** & **GWD** & \(E_{p}\) \\ \hline NEP & 0.598 ** & 0.333 ** & 0.395 ** & 0.525 ** & 0.268 ** \\ GPP & 0.663 ** & 0.524 ** & 0.426 ** & 0.390 ** & 0.458 ** \\ Re & 0.457 ** & 0.538 ** & 0.100 & 0.079 & 0.504 ** \\ \hline \end{tabular}
**Correlation is significant at the 0.01 level (2-tailed).**  
\end{table}
Table 2: [PERSON]’s correlations between daily net ecosystem production (NEP, g C m\({}^{-2}\) d\({}^{-1}\)), gross primary production (GPP, g C m\({}^{-2}\) d\({}^{-1}\)) and ecosystem respiration (Re, g C m\({}^{-2}\) d\({}^{-1}\)), and mean air temperature (\(T_{\rm a}\), \({}^{\circ}\)C), vapor pressure deficit (VPD, kPa), photosynthetically active radiation (PAR, \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\)), groundwater depth (GWD, m) and pan evaporation (\(E_{p}\), mm) during the growing season (from May to October) across four years, 2013–2016, at the study site.  
Figure 7: The relationship between raw half-hourly nighttime NEE (\(\rm Re_{\rm night}\)) and soil temperature (\(T_{\rm s}\)) data at the study site. Data of \(\rm Re_{\rm night}\) used in curve fitting were averaged in 0.2 \({}^{\circ}\)C \(T_{\rm s}\) bins.  
From the period May to August, GPP increased linearly with an increase in PAR at the beginning of the period and then, it stabilized gradually when the PAR values reached around 800 \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\) for the month of May and around 1000-1200 \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\) for the months of June-August. Then the GPP showed a low and quick increase with PAR, and the maximum GPP was found to be approximately 8.5 \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\) in May. In the period of September and October, the maximum GPP values were found to be approximately 12 \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\) and 5 \(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\), respectively. It is evident that the saturating response of GPP to PAR was different between the leaf emergence (in May), the vibrant growing period (from June to August, that was similar among those months) and leaf senescence period (from September to October) (Figure 8).
# Bottom-Up Model of Random Daily Electrical Load Curve for Office Building
## 2 Modeling Approach
### Appliance Model
#### 2.3.1. Appliance Classification  
Fundamentally speaking, electricity consumption is a random process, affected by the following factors: (i) the physical properties of the building, (ii) the load parameters of the appliances used, and (iii) the specific usage of the appliances [27]. So far, factor (iii) is the most complicated estimation factor because it is related to weather dynamics and indoor occupants' behavior [32]. The room-occupancy rate is the driving factor of energy use [33], and it plays an important role in the change of energy consumption, so the modeling method of electrical appliances is based on the presence of indoor personnel. According to the driving of electrical appliances by indoor personnel, electrical appliances can be divided into the following four categories:  
1. Base Appliances: the behavior of indoor personnel has nothing to do with the power value, and the rated power is always maintained, such as security monitoring, or the power value changes periodically, such as water dispenser.
2. Single-Use Appliances: appliances owned by every person in the room, such as computer.
3. Communal Appliances: appliances shared by at least two or more people, such as printer, projector, paper shredder, etc.
4. Other Appliances: in addition to the above types of electrical appliances, other electrical appliances related to work and rest time and the number of indoor personnel, such as elevators and office lighting.  
The following will introduce the power simulation models corresponding to different types of electrical appliances.
## 3 Results and Discussion
### Physico-Chemical Parameters of the Worts Produced by Different Mashing Profiles
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & **Beer (Modified Mashing)** & & & **Beer (Congress Mashing)** & \\ \hline
**Compound** & \({}^{1}\)**LRI** & & & & & & & & \\ \hline
1-butanol & 650 & 0.0 & 0.0 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\
3-methyl-1-butanol & 716 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \\
2-methyl-1-butanol & 724 & 0.5 & 0.5 & 1.0 & 1.0 & 0.8 & 1.0 & 0.8 \\
1-pentamol, 4-methyl- & 852 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\
1-hexanol & 889 & 0.8 & 0.8 & 0.8 & 1.0 & 0.5 & 0.8 & 0.5 \\
2-phenylethanol & 1091 & 0.8 & 0.8 & 0.8 & 0.8 & 1.0 & 0.8 & 1.0 \\
2-3-butanediol & 1492 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ Ethly acetate & 598 & 0.5 & 0.5 & 0.5 & 0.5 & 1.0 & 0.8 & 0.5 \\ Ethly propionate & 695 & 0.5 & 0.5 & 1.0 & 1.0 & 0.5 & 1.0 & 1.0 \\ Isobutyl acetate & 758 & 0.5 & 0.5 & 0.5 & 0.5 & 0.8 & 0.5 & 0.5 \\ Ethly butyrate & 784 & 0.5 & 0.5 & 0.8 & 1.0 & 0.5 & 0.8 & 0.8 \\
1-butanol 3-methyl-, acetate & 860 & 0.5 & 1.0 & 1.0 & 1.0 & 1.0 & 0.5 & 0.5 \\ Ethly valerate & 883 & 0.5 & 0.5 & 0.8 & 0.8 & 0.5 & 0.8 & 0.5 \\ Ethly hexaonate & 980 & 0.5 & 1.0 & 1.0 & 1.0 & 0.5 & 1.0 & 0.5 & 1.0 \\ Ethly octanoate & 1179 & 0.5 & 0.5 & 1.0 & 0.8 & 0.8 & 1.0 & 0.8 & 1.0 \\ Ethly laurate & 1577 & 0.5 & 1.0 & 0.5 & 0.5 & 0.5 & 1.0 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Heatmp of odour-active compound intensities detected by GC-O in the low-alcohol beers produced using different masking methods, with or without the addition of hops and with different yeast strains for fermentation. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & & & **Beer (Modified Mashing)** & & & **Beer (Congress Mashing)** & \\ \hline \(\beta\)-pinene & 969 & 0.0 & 10 & 0.0 & 0.5 & 0.0 & 0.5 & 0.0 & 0.5 \\ \(\beta\)-myrence & 961 & 0.0 & 1.0 & 0.0 & 1.0 & 0.0 & 0.5 & 0.0 & 0.5 \\ \hline Lemene & 1025 & 0.0 & 0.5 & 0.0 & 0.5 & 0.0 & 0.5 & 0.0 & 0.5 \\ Trans-linalool oxide & 1077 & 0.0 & 1.0 & 0.0 & 1.0 & 0.0 & 0.8 & 0.0 & 0.8 \\ \hline Linalool & 1089 & 0.0 & 0.8 & 0.0 & 1.0 & 0.0 & 0.5 & 0.0 & 0.5 \\ Chromello & 1205 & 0.0 & 0.8 & 0.0 & 1.0 & 0.0 & 0.5 & 0.0 & 0.5 \\ \hline Geramid & 1232 & 0.0 & 0.8 & 0.0 & 1.0 & 0.0 & 0.5 & 0.0 & 0.5 \\ \hline \(\alpha\)-Teripened & 1179 & 0.0 & 0.5 & 0.0 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline Acetophenone & 1036 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline Decanal & 1182 & 0.5 & 1.0 & 0.5 & 0.5 & 0.5 & 10 & 0.5 & 0.8 \\ \hline Benzothiazole & 1196 & 0.8 & 0.8 & 0.8 & 0.8 & 1.0 & 0.5 & 0.8 \\ \hline \end{tabular} \({}^{1}\) LB–linear attention index. The lowest intensity of neurons in these columns is in the darkest red, and the highest intensity is in the darkest green. SD + 5%.  
\end{table} 
Table 6: Cont. Figure 2: Sensory analysis (QDA) of the low-alcohol beers produced using modified masking (**a**) or Congress making (**b**), with or without the addition of hops and with different yeast strains for fermentation. **n** = 5,STD \(<\) 5%.
## 1 Introduction
However, few long-term studies with the necessary dense time-series measurements over varying ecosystems exist, and some of these rely on satellite-based data with low temporal resolutions that do not allow the separation of facultative and constitutive effects. Perhaps for this reason, some comparisons of satellite-based PRI and LUE across ecosystems have resulted in contrasting PRI-LUE relationships for different ecosystems [40; 41]. The lack of accessible and cost-effective data, available at meaningful temporal or spatial scales, has limited the testing of PRI functionality at ecosystem levels. For example, a 16-day MODIS composite lacks the temporal resolution to resolve short-term xanthophyll cycle changes. This has contributed to slow integration and comparison to micrometeorological data and ecosystem monitoring networks [42]. However, the recent availability of inexpensive PRI specific narrowband radiometers [43; 44; 45] has opened the possibility of attaining continuous PRI measurements at low cost. The high temporal resolution of automated optical sensor systems would allow improved accounting of physical and physiological variables affecting diurnal and seasonal PRI measurements and help interpret LUE changes over a phenological cycle. Additionally, continuous PRI measurements allow for their integration with eddy covariance flux measurements, which in turn provides additional ecophysiological validation for PRI as an LUE proxy at ecosystem scales. For this study, we utilized newly commercially developed automated PRI spectral reflectance (SRS-PRI) radiometers and eddy covariance data to explore the LUE dynamics in a deciduous boreal forest. As such, the objectives of this paper are to (1) determine if automated SRS-PRI values can be used as a proxy of canopy light use efficiency and apparent canopy quantum yield over an aspen forest and (2) to explore how continuous PRI measurements can be leveraged to better inform ecosystem scale LUE models.
## 4 Evaluation
### Hardware Devices and Experimental Setup  
This study's experimental system was developed with the Mixed Reality Toolkit (MRTK) in Unity for Microsoft HoloLens2 (field of view 43\({}^{\circ}\)\(\times\) 29\({}^{\circ}\)), which supports hand and eye tracking (viewing angle accuracy of 1.5\({}^{\circ}\)) [13]. Example views of the experimental system in our study are shown in Figure 4.  
Participants were seated in a spacious and quiet room (with a noise level smaller than 60 dB) for the duration of the experiment. The eye tracker was calibrated for each study participant at the start.  
Since the experimental materials were initially in Chinese, we employed ChatGPT to correct and optimize the English translation during the preparation of this work [34]. This usage of ChatGPT ensured that the translation retained the technical accuracy and contextual relevance of the original materials [35].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Touch-Slider** & **Keyboard** & **Pinch-Slider** & **Bimanual Scaling** & **Gesture and Voice** \\ \hline Confirm trigger & Touch & Touch & Pinch & Pinch & Pinch \\ \hline Data input method & Slide & Touch & Slide & Scale & Voice \\ \hline Modalities & Hand & Hand & Hand & Gaze and hand & Gaze, hand, and voice \\ \hline Interaction metaphor & Slider & Keyboard & Slider & Scaling & Voice input \\ \hline Gesture type & \begin{tabular}{c} Motion (slide) + \\ symbolic (touch) \\ \end{tabular} & Symbolic (touch) & \
\begin{tabular}{c} Motion (slide) + \\ symbolic (pinch) \\ \end{tabular} &
\begin{tabular}{c} Motion (scale) + \\ symbolic (pinch) \\ \end{tabular} & Symbolic (pinch) \\ \hline \end{tabular}
\end{table}
Table 1: Summary of similarities and differences between the techniques we studied.
###### Abstract  
In this paper, a solution is proposed to the problem of the unequal phase imbalance of output voltage caused by a three-phase, four-wire, split capacitor inverter when the load is unbalanced. First, the triple-loop control strategy was used to solve the unequal amplitude problem. This method used the feedforward + feedback composite control strategy on the inductor current inner-loop and voltage mid-loop to decrease the disturbance of the power and load. And the Root Mean Square (RMS) of voltage on the outer-loop completed the control of amplitude for the three-phase voltage. Second, to solve the imbalanced phase problem, the imbalance operation mechanism of the three-phase four-wire inverter was analyzed. It is known from the analysis that the phase imbalance is related to the DC-side splitting capacitance. The function relations between the DC-side capacitance and phase angle between each phase was simulated by MATLAB. But, it was too complicated to calculate the magnitude of the capacitance value through the functional relationship. In order to simplify the design of the DC-side splitting capacitor, the relations among the imbalanced current, the voltage fluctuations of the DC-side capacitor and the harmonics of load voltage were analyzed. In addition, by following the requirement of the national standard about the harmonics of load voltage, a DC-side capacitor design was mentioned to decrease the influence of imbalanced phase. Finally, simulation and experimental results show that the three-phase load voltage is stable, the THD value is less than 3%, and three-phase voltage unbalance is less than 2%, thus verifying the effectiveness of the proposed DC-side split capacitor design and control strategy.  
DC-side capacitor design; three-closed-loop control; three-phase four-wire inverter; unbalanced loads; function fitting +
Footnote †: journal: Applied sciences
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 4 Communication Range and Data Rate for TVWS-Based LoRa
### Uplink Delivery Rate  
To further examine the proposed LoRaT-PLM model, evaluation based on uplink delivery rate (UDR), defined as the percentage of packets correctly received by the gateway, is carried out. LoRaWANSim, a LoRaWAN simulator [27], is used to simulate LoRa transmissions for an outdoor urban scenario with one gateway. The uplink delivery rate is computed by varying the distance between the end device and the gateway and considering three path-loss models: the proposed LoRaT-PLM, Okumura-Hata (OH), and the path-loss model proposed for LoRa in [19]. LoRaT-PLM and OH path-loss models are used in LoRa transmissions using the 470 MHz TVWS frequency, whereas the model in [19] uses 868 MHz ISM band frequency for LoRa.  
Figure 9 depicts the UDR as a function of the logarithmic distance between the end device and the gateway. It is seen that UDR decreases as the distance between the end device and the gateway increases, which is as expected. In addition, the proposed LoRaT-PLM model gives better UDR than the conventional OH and the model proposed in [19]. Thus, although the path loss predicted by the LoRaT-PLM model is comparatively more, the model benefits from better UDR, as seen in Figure 9.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 7 Applications and Future Directions
### Study Limitations  
Several limitations should be acknowledged in this work:  
Bluetooth signal characterization: While audio SNR was comprehensively evaluated across different acoustic environments, Bluetooth signal SNR analysis was limited. This study did not include dedicated RF signal strength measurements or detailed characterization of wireless channel conditions across varying distances and environmental factors. The observed increase in command latency with distance provides indirect evidence of signal degradation, but comprehensive wireless communication analysis remains for future work.  
Limited comparative evaluation: Direct quantitative comparison with existing mobile acoustic sensing methods was not conducted, primarily because most of the existing literature addresses audio capture and mobility control as separate problems. While our integrated approach represents a novel contribution, future work should include systematic comparison with alternative architectures where applicable.  
Subjective quality assessment: The audio quality evaluation was limited to three independent evaluators using a five-point scale. This provides initial validation but lacks the statistical power of larger-scale studies. Future work should incorporate standardized objective metrics (PESQ and STOI) and larger sample sizes for more robust quality assessments.  
Environmental testing scope: Testing was conducted in three representative acoustic environments (40 dB, 65 dB, and 85 dB), but broader environmental characterization including outdoor conditions, electromagnetic interference scenarios, and extreme temperatures would strengthen future evaluations.  
These future directions represent natural extensions of the current framework, building upon its strengths while addressing the limitations identified through experimental evaluation. The modular architecture of both hardware and software components facilitates such evolution, allowing incremental improvements without requiring wholesale redesign of the system.