esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Data Analysis
#### 4.2.2 Humidity  
As shown in Figure 13, the average humidity values for the respective months were found to be 81.7%, 84.5%, 89.5%, and 88.1%. Humidity exhibited an increasing trend from June to August, followed by a decrease in September, reaching an observed value of 88.1%. The minimum humidity levels were observed to be 50.2%, 45.8%, 41.2%, and 49.4% for the respective months, with August recording the lowest minimum humidity. On the other hand, the maximum humidity levels were observed to be 99.4%, 99.3%, 99.4%, and 99.5% for the respective months, with the maximum values averaging over 99.0%.
# Technical Interoperability for Machine Connectivity on the Shop Floor
## 5 Discussion
### Communication Standards  
One function block that needed to be developed in this situation was support for OPC UA. While it was straightforward to use existing open source solutions for the actual communication, there were some issues with slight variations in the implementations that had to be considered. Upgrading software in an industrial setting is not always an option as the machine in question is crucial to the manufacturing process. This means that the adaptability of the IoT platform is important.  
Two systems support OPC UA as the communication protocol, the grinding machine onboard computer and the IMX (vibration system), which is a natural choice since it is an open, platform-independent system that supports SOA and can enable other future synergies [17; 49]. A limitation of OPC UA that was evident during this project is the difference in the various implementations, due to their being self-certified.  
With the IO-link enabled sensors it is possible to extract the data directly bypassing the machine computer. That avoids medtling with the machine program, but it can also miss out on the possibility of semantic alignment that adopting the OPC UA standard can enable. In this case, a separate translation software was developed which required some work, but modern software frameworks simplified this task [45]. The solution presented here is a stand-alone REST API supporting the few sensors that were mounted on the machine. Such implementation could be made to support almost all IO-link sensors by connecting it to the common device library service that, at 2017-07-24, had more than 80% of all devices documented [19; 50]. Since the presented application was developed, the company ifm electronic GmbH (Essen, Germany) have released gateways with an IoT connection (see Figure 6), which basically works similarly to the REST API that was developed for this application, with a web service exposing the sensor data as a generic JSON (JavaScript Object Notation) document.  
MQTT is used to send real-time data to the mobile operator support system. The data is sent using the JSON structure, which is the de facto standard for Web services and aligns well with the document database solution. There is little to say about this, other than that it shows the benefit of utilizing well-defined open standards.
## 5 Results, Analysis, and Discussion
### Temperature Fluctuations  
Due to a large number of measured data, temperature fluctuations in selected sections were presented only for a specific period of time--one week. Temperature distributions on the external surface of the components were presented in Figure 6. Nevertheless, all results collected during one year of measurements were comprehensively analyzed and conclusions stated below were justified.  
In Figure 6, temperature fluctuations for two sections: the enhanced with PCM-perlite (P4) and the reference panel (P6), were presented. Based on the results presented in Figure 6 it was observed that temperature on the external surface of panels modified by the PCM-composite was visibly lower during the day and higher during the night-time, compared to the reference panel. As expected, application of PCM reduced daily amplitudes of the external surface temperature. It was also observed that due to the highly intense and sudden occurrence of solar radiation during latent heat accumulation in PCM-composite temperature rose, but did so more slowly than the reference panel. Moreover, when the temperature dropped below the solidification temperature of paraffin, release of accumulated heat caused a very slow decrease of temperature.  
Figure 5: Verification of the external boundary condition.  
Figure 6: Temperature fluctuations during a selected period of time for proposed and reference cases (in—internal temperature T1, ext—external temperature T7). DH: degree hours.  
To compare both cases (PCM and reference) the degree hours (DH) related to the average temperature (\(T_{av}\)) for reference panel were calculated.  
\[\mathrm{DH}_{x}=\frac{1}{12}\sum_{i=1}^{n}\lvert T_{i,x}-T_{av}\rvert \tag{1}\]  
where  
\(n\)--number of readouts during a one-year measurement (\(n\) = 105,120),  
\(1/12\)--coefficient due to 5-min time step of measurement readout,  
\(T_{i,x}\)--surface temperature, \(x\) denotes specific test section (PCM/REF).  
Values of DH are the measure of temperature fluctuations and were calculated as an absolute value of temperature during an analyzed period of time. It means that the periods of heat accumulation and releasing were summed up. Calculated DH are displayed in Figure 6. The values obtained for the reference (REF) case were almost two times higher than for the PCM case. These results confirmed the two times lower dynamics of the wall with a PCM layer in comparison with a traditionally insulated partition.
# A Study on Combustion Characteristics of Insensitive Triple-Base Propellant
###### Abstract  
Research on combustion characteristics can provide basic information and theoretical support for the design of insensitive propellant. This work aims to investigate the combustion characteristics of insensitive triple-base propellant. All propellants were prepared based on same triple-base propellant, but they were desensitized with the same desensitizer in different ways. The high-speed camera, spontaneous luminescence, NO, NH chemiluminescence, and OH-planar laser induction fluorescence (PLIF) methods were employed to capture the combustion flame and derive the distributions of important intermediates. Results show that ignition delay times of insensitive propellants are obviously longer. This indicated that the application of the desensitizer has a partly hindering effect on the early ignition stage. The combustion time of insensitive propellants is mostly similar, which means that the desensitizer has little influence on the intensity of actual combustion. The change in flame height and area of insensitive propellants over time indicated that the combustion progressivity of some insensitive propellants was more prominent, which means that the desensitizer concentration and desensitizing methods all affect the performance of insensitive propellant. The signal intensities of NO and NH show a negative correlation, indicating that a competitive relationship probably exists between the formation of NO and NH radicals during the reaction process. The high concentration of OH mainly locates outside NO, suggesting that there may be a transformation between NO and OH. The maximum signal intensity of NO and NH of different insensitive propellants confirmed that both the concentration of desensitizers and the desensitizing methods exhibit important effect on the reaction process.  
combustion; insensitive propellant; optical diagnosis; NO chemiluminescence; NH chemiluminescence +
Footnote †: journal: Applied Sciences
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 3 LoRaWAN Wireless Technology  
Due to the requirements of a long range and low power consumption, it was decided to focus on LPWAN standards. NB-IoT depends on 3 GPP (4G or 5G) networks already deployed or specific high-cost deployment. On the other hand, SigFox and LoRaWAN allow a more extended reach. Both are similar in technical specifications, but LoRaWAN has the advantage of remaining an open protocol and therefore allowing the deployment of a low-cost proprietary network. For these reasons, LoRaWAN was chosen as the protocol and architecture for this work. A comparison between the mentioned LPWAN technologies is shown in Table 1[19; 20; 21; 22].  
LoRaWAN is a protocol for LPWAN networks that specifies the physical layer, the logical or medium access control (MAC) layer and the application layer. Figure 2 shows the LoRaWAN protocol stack. It also proposes a network architecture indicating how the devices are to be connected, as well as the management and parameterization of the channels. The latest version of the protocol published by the LoRa Alliance is version 1.1[23], which guarantees compatibility with its predecessor 1.0, the first version of the protocol.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LoRaWAN** & **Sigfox** & **NB-IoT** \\ \hline Frequency & 868 MHz (EU) & 868 MHz (EU) & Depends on operator \\ Standard & LoRa Alliance & Owner & 3 GPP Standard \\  & & & (Release 13–16) \\ Coverage & 5 km (Urban) & 10 km (Urban) & 1 km (Urban) \\ (Theoretical) & 20 km (Rural) & 40 km (Rural) & 10 km (Rural) \\ Modulation & LoRa, FSK, GFSK & BPSK, GFSK & QPSK, OFDM \\ Transmission rate & 22 kbps (LoRa) & 100 bps & 10 Mbps \\ (Theoretical) & 100 kbps (GFSK) & & \\ End device cost & EUR 3–5 & EUR \textgreater{} 2 & EUR \textgreater{} 20 \\ Gateway price & 100 € gateway & 4000 € base station & 15,000 € base station \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of LPWAN technologies.  
Figure 2: LoRaWAN protocol stack.  
In the following subsections, the main features of LoRaWAN technology are presented. These subsections explain the logical layer of the technology. For more information related to the physical layer and how it works in this technology, please refer to [24; 25; 26].
## 3 Results and Discussion
### Application to BPA and Bacteriophage MS2 Degradation  
When the optimized PANI/TiO\({}_{2}\) composite was applied to the photocatalytic degradation of 5 mg/L BPA, BPA was degraded by \(80\pm 0.75\%\) in 360 min (Figure 8a). To calculate the degradation rate (\(k_{\rm app}\)), the pseudo-first-order degradation kinetics of BPA by PANI/TiO\({}_{2}\) were plotted (Figure 8b) to solve Equation (1): [27]  
\[\ln(C_{0}/C)=k_{\rm app}t \tag{1}\]  
Figure 7: Removal ratios of MB by 4% PANI/TiO\({}_{2}\) with different catalyst doses under visible-light irradiation (MB = 5 uM).  
BPA had a rate constant of \(0.00375\pm 0.00018\)/min, and the correlation coefficient (R\({}^{2}\)) was 0.99, which indicates that BPA degradation by PANI/TiO\({}_{2}\) follows pseudo-first-order kinetics.  
Bacteriophages are potential indicator organisms for the efficiency of treatment processes because they are characterized by their stability to sunlight, temperature, and chemical/physical treatments [28]. PANI/TiO\({}_{2}\) degraded 96.2% of bacteriophage MS2 in 120 min under visible-light irradiation (Figure 9). The first-order rate constant of bacteriophage MS2 was \(0.02637\pm 0.0055\)/min. These results highlight the applicability of PANI/TiO\({}_{2}\) for the removal of recalcitrant organic compounds and bacteriophages.  
Figure 8: (**a**) Degradation of bisphenol A (BPA) by 4% PANI/TiO\({}_{2}\) under visible-light irradiation, and (**b**) pseudo-first-order kinetics of BPA degradation (BPA = 5 mg/L, catalyst dose: 0.8 g/L).
## 8 Conclusions  
This study proposes a procedure for generating input ground motion time histories for seismic risk assessment using recorded data from a moderate-magnitude earthquake. A simplified PSHA was used to generate a spectrum for seismic risk assessment, and the magnitude and distance of scenario earthquakes were identified by PSHA de-aggregation. A CMS was constructed to reflect the actual ground motion characteristics and the natural period of structure for seed motion selection. A moderate-magnitude earthquake in Korea was utilized in the EGFM and synthesized to represent the scenario earthquake's magnitude. Subsequently, the ground motion time histories were selected and matched to ensure compatibility with a UHS.  
In the process of the finite fault modeling process using the EGFM, multiple candidate seed motions consistent with the scenario earthquake were generated. Among these, it is reasonable to select a seed motion that matches the CMS. This is because it is similar to the spectral characteristics of actual earthquakes and the natural period of structure, compared to the UHS. Additionally, performing spectral matching to the UHS using such a seed motion improves the process by reducing the MSE. These results highlight the advantage of  
Figure 14: Result of the spectral matching with the fourth iteration in Figure 13. The solid black line and the dashed gray line represent the response spectra matched to the UHS by selecting the ground motion time histories based on the UHS and CMS, respectively: (**a**) EW components; (**b**) NS components.  
using the CMS for seed motion selection or synthesis. In conclusion, this approach differs from traditional methods, which commonly suggest that the seed motion selection should focus on the spectrum most similar to the target spectrum, such as UHS. seed motion selection based on the UHS. This procedure also improves the efficiency of ground motion generation for seismic risk assessments in regions of low to moderate seismicity.  
Conceptualization, J.H.K.; analysis, H.Y.J.; writing--original draft preparation, H.Y.J.; writing--review and editing, J.H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. R5-2022-0014482).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Classifier Models
#### 2.5.1 Support Vector Machine (SVM)  
It is a supervised classification technique that constructs a separating hyperplane maximizing the margin between the input data classes that are viewed in an n-dimensional space (n is the number of features used as inputs). Essentially, this involves orienting the separating hyperplane to be perpendicular to the shortest line separating the convex hulls of the training data for each class, and locating it midway along this line.  
In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.6 O-RAN Interfaces  
The O-RAN architecture is designed to promote interoperability, multi-vendor support, and innovation in the RAN. Here are the main interfaces specified in the O-RAN architecture:  
* Open Front Haul (OFH) interface: this interface connects the O-RU to the O-DU and carries the digitized baseband signal and control information between the two units;  
Figure 8: Non-RT RIC architecture service-based view, in which the services are exposed to rApps via the R1 [21].  
Figure 7: Non-RT RIC functional architecture, showing the R1 and external interfaces [21].  
* Fronthaul interface: This interface connects the O-RU to the O-CU in the O-RAN Cloud RAN architecture. The interface carries the digitized baseband signal and control information between the O-RU and the O-CU;
* E1 interface: This interface connects the O-DU to the O-CU and carries control and management information between the two units;
* E2 interface: This interface connects the O-CU to the O-CU-CP and carries control and management information between the two units;
* A1 interface: This interface connects the O-RAN Controller to the O-RAN Element Management System (EMS) and provides management and monitoring capabilities for the O-RAN network;
* O1 interface: This interface connects the O-RAN SMO to the O-RAN Controller and provides service orchestration and management capabilities;
* O2 interface: This interface connects the O-RAN Controller to the O-RAN Radio Resource Management (RRM) and provides resource management and optimization capabilities for the O-RAN network;
* O3 interface: This interface connects the O-RAN Controller to the O-RAN Network Management (NM) and provides network management and monitoring capabilities.
## 5 Discussion  
1. The geological conditions underground in the study area of this paper are exceedingly complex, with varying degrees of joint and fissure development among different ore bodies and disparities in lithological weaknesses. The numerical simulations in this study assume a homogeneous, monolithic ore rock body, providing conclusions of general regularity. However, these conclusions may exhibit distinct characteristics under varying geological conditions. Future research could focus on ore rock bodies where joint and fissure development results in suboptimal blasting effects.
2. Constrained by computational demands, particularly the high processing power required by the FEM-SPH method, this study omits the post-fracture collision scenarios between ore rock and tunnels. Even under simplified boundary conditions, the computation extended beyond 200 h. Accounting for such collisions would render solutions infeasible on standard computational equipment, as the real scenario involves complex interactions where ore rock rebounds and accumulates into a much pile, challenging accurate quantification.
3. The dynamics of ore rock movement are intricately linked to the size of fragments resulting from blasting. Future research could develop fragment size prediction models grounded in big data analytics theory, utilizing more extensive field-measured data to furnish more advanced predictive techniques for blasting operations.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 3 Results
### Across Data  
Figure 7 with MAAPE and NRMS presents the results of evaluating across the complete test sets (Maize_1, Maize_2, Maize_3, Maize_4, Soybean_1, and Soybean_2). In this context, the best prediction performance is achieved through the conventional method (C), which outperforms method A by 56.57% and 42.86% in terms of MAAPE and NRMSE, respectively. Overall, method C significantly surpasses method A in both of the evaluated metrics. Figures MAAP_20 and NRMS_20 contain the metric analysis conducted specifically on the top 20% of all test datasets defined as a single set. In this context, the trend of each individual dataset is followed, where method A displays an advantage in performance over method C, with a reduction of 52.86% in MAAPE_20 when compared with MAAPE and a decrease of 41.05% in NRMSE_20 when compared with NRMSE. These results clearly underline the significant improvement in the performance of method A in this scenario. For a more detailed exploration, refer to Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Metric** & **Average** & **SE** \\ \hline Soybean\_2 & A & MAAPE & 0.1371 & 0.0020 \\ Soybean\_2 & A & MAAPE\_20 & 0.0392 & 0.0006 \\ Soybean\_2 & A & NRMSE & 1.7617 & 0.0018 \\ Soybean\_2 & A & NRMSE\_20 & 1.5405 & 0.0025 \\ Soybean\_2 & C & MAAPE & 0.0631 & 0.0010 \\ Soybean\_2 & C & MAAPE\_20 & 0.0729 & 0.0011 \\ Soybean\_2 & C & NRMSE & 0.8439 & 0.0015 \\ Soybean\_2 & C & NRMSE\_20 & 3.0088 & 0.0202 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prediction accuracy results for dataset Soybean_2 using conventional (C) and augmented (A) methods, with metrics MAAPE and NRMSE for the total testing and MAAPE-20 and NRMSE_20 for the best 20% testing.  
Figure 7: Prediction accuracy performance results for the across dataset using conventional (C) and augmented methods (A) in terms of mean arctangent absolute percentage error (MAAPE), mean arctangent absolute percentage error for the top 20% cultivars (MAAPE_20), normalizing the mean square error (NRMSE), and normalizing the mean square error for the top 20% cultivars (NRMSE_20).
## 6 Conclusions  
In this paper, we present a novel program repair technique called CodeTransFix, which is based on neural machine translation (NMT) technology. Our approach incorporates large language models of code, CodeBERT, with the Transformer architecture and aims to significantly improve the ability to fix buggy code. The primary aims of our approach are as follows: (1) to adopt a modular architecture that separately learns the conversion from bugs to fixes and the context around the buggy code, thereby reducing the noise introduced by the surrounding context during the fixing process; (2) to utilize the enriched parameters learned from the CodeBERT model to improve the comprehension of the strict syntactic features and complex semantic dependencies of the code.  
To validate the ability of CodeTransFix to fix bugs, we conducted experiments on the Defects4j v1.2 dataset and compared it with the current state-of-the-art Automated Program Repair (APR) methods targeting single-block bugs. The experimental results demonstrated that CodeTransFix achieved a 23.3% performance improvement (seven more bugs fixed). In addition, we performed generalization tests on the Defects4j v2.0 benchmark, with the results showing that CodeTransFix outperformed other state-of-the-art APR methods.  
However, challenges remain. First, the current context definition focuses on local code blocks, potentially missing cross-module dependencies. Second, while our evaluation is Java-centric, CodeTransFix's architecture is language-agnostic. Future work will extend it to multi-block bugs and explore cross-language adaptation by leveraging multilingual LLMCs (e.g., CodeBERT) and domain-specific fine-tuning for languages like Python.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L; validation, Y.L.; formal analysis, L.Q.; writing--original draft preparation, Y.L.; writing--review and editing, S.Y.; funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the following two projects: \"Research on the Application of Artificial Intelligence Technology in Software Precision Testing\" (Grant Number 25422208) and \"Research on Comprehensive Safeguard Technology for Complex Equipment of Large Ships Based on V-DT Driving and AI Enablement\" (Grant Number JC2024021).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.