esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents the ship-to-infrastructure and ship-to-ship wireless channel measurements over the coastal waters. Based on the measurement data, the channel characteristics, including power delay profile, root means square delay spread, and propagation path loss, are extracted and analyzed. The results demonstrate that the main influencing factor on the root means square delay spread is the distance between Tx and Rx parts for the wireless channel over the sea without other obstacles. Additionally, for the propagation path loss, effective reflection, divergence and shadowing from water surface are the critical influencing factors. Meanwhile, the diffraction loss caused by earth curvature should also not be neglected for the long-distance wireless communication over the sea. Based on the measurement data, we also make a preliminary analysis on the effective power coverage and the channel capacity. The predicting outcomes show that the channel has a good performance with an average channel capacity of 1.17 Gb/s when the Tx-Rx distance is within 2500 m. Our research result can give a reference for the construction of the offshore 5G mobile communication network. However, the channel measurements in this paper only support single-input single-output (SISO) due to the limitation of the channel measurement equipment. In the future, we plan to explore the application of massive MIMO technology to maritime wireless communications.  
**Author Contributions:** C.L.: Methodology, Software, Validation, Formal analysis, Writing--original draft. J.Y.: Measurment, Writing--review & editing. J.X.: Resources, Conceptualization, Funding acquisition, Writing--review & editing. W.C.: Resources, Conceptualization, Funding acquisition, Writing--review & editing. S.W.: Writing--review & editing. K.Y.: Funding acquisition, Equipment. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This document is the results of the research project funded in part by the MAMIME project (no. 256309) by the Norwegian Research Council, in part by the National Natural Science Foundation of China (no. 61701356), in part by the Fundamental Research Funds for the Central Universities (no. 40120638), and in part by the China Scholarship Council (no. 201706950088).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated and analyzed during this study are available from the corresponding author upon request.  
**Acknowledgments:** We thank Wuhan University of Technology for their support.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Discussion
### Utility of Including Sentinel-1-Derived Data  
Since radar signals are sensitive to soil surface conditions, especially soil moisture, soil surface roughness, and vegetation [73; 120; 121], we included indices derived from the VV and VH polarizations of Sentinel-1 radar signals. Although these indices appeared to be sensitive to vegetation dynamics [74; 75], they increased the prediction performance of DNN models during calibration but with high variability among the three iterations, which is consistent with results of [PERSON] et al. [51] and [PERSON] et al. [27]. The latter found that including radar-derived data with multispectral data in SOC prediction models increased prediction performance. We also included Sentinel-1-derived soil moisture because we found in a previous study [73] that soil moisture had more influence than soil surface roughness in the Naizin watershed on the Sentinel-1 radar signal because of relatively high soil moisture. Consequently, we assumed that including Sentinel-1-derived soil moisture could increase prediction performance by considering the variability in soil moisture among dates and sampling points, especially since our study area is located on both sides of a stream. This approach resulted in the most stable improvement during model calibration and the closest validation performance to that of models calibrated using only S2 bands, which showed the lowest RMSEP (RMSEP = 2.41 (\(\pm\)0.08) and 2.33 (\(\pm\)0.12), respectively), supporting the idea that soil moisture strongly influences the performance of SOC prediction models. [PERSON] et al. [50] found that using Sentinel-2 dates with low soil moisture increased performance of SOC prediction models, but that including soil moisture did not, which was the case for the RF model in our study. Thus, the algorithm used influenced the performance of models calibrated with Sentinel-2 data.  
By using all of the complementary information simultaneously, the DNN algorithm was able to extract the most appropriate information for predicting SOC content; thus, this approach provides models with more features, which increases their learning capacity. [PERSON] et al. [27] tested different combinations of Landsat TM- and PALSAR-derived data and found that using six TM bands, six TM-derived indices, and four PALSAR polarizations yielded the best model. In addition, [PERSON] et al. [51] found that combining Sentinel-1 C-band dual-polarimetric SAR data and Sentinel-2 optical datasets increased prediction performance.
## 6 Conclusions and Future Development Paths
### Limitations and Future Research  
While this study focused on teaching assignments, future research could extend the framework to other administrative processes (e.g., student enrollment, research grant management). Additionally, longitudinal studies are needed to assess the long-term cultural and organizational impacts of BPR in HEIs. Further exploration of AI-driven process automation and blockchain for transparency could address emerging challenges in resource allocation and compliance. Finally, comparative analyses across diverse regulatory environments (e.g., EU vs. non-EU HEIs) would deepen understanding of contextual factors influencing BPR success.  
In conclusion, this case study positions BPR as a vital strategy for HEIs seeking to harmonize bureaucratic accountability with digital transformation. By fostering agility, transparency, and stakeholder centricity, the proposed framework offers a pathway for public universities to thrive in an era of heightened fiscal and quality assurance scrutiny.  
Conceptualization, P.R. and C.C.; simulations and numerical analysis, P.R.; P.R. and C.C. wrote the paper, reviewed it, and edited it. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Advantages of Smart Grid Technology  
Incorporating smart grid technology in the IoT domain brings a variety of advantages that boost the Industrial IoT domain, especially with the quick shift from Industry 4.0 to Industry 5.0 [12; 13; 14; 15]. Some of these advantages are listed in Table 2. These advantages are categorized into different classes, each representing a distinct aspect of smart grid improvement. The descriptions outline the key features and benefits associated with each class, including aspects such as energy management, asset management, reliability, efficiency, renewable energy integration, grid planning, safety and security, electric vehicle (EV) integration, modernized technologies, real-time detection and isolation, network performance, and energy distribution. This classification helps organize and understand the various ways smart grid technology can be enhanced to improve energy management, grid reliability, and overall performance. Next, the well-known types of smart grid-related communication are presented.  
\begin{table}
\begin{tabular}{c c} \hline
**Enhancement** & **Description** \\ \hline \multirow{6}{*}{Effective Energy Management (EEM)} & Achieves more effective energy supply and demand, smart grid energy storage systems, and more efficient grid management by gathering and evaluating vast amounts of data on energy use and grid performance. \\  & Utilizes DERs, Demand Response (DR) technologies, such as Smart Meters (SM), and Energy Management Systems (EMSs) to optimize energy usage and minimize waste. Expected to be replaced by Intelligent Energy Management (IEM) using machine learning and data analytics. \\ \hline \multirow{6}{*}{Enhanced Asset Management} & Efficiently manages and monitors grid assets to lower maintenance and repair costs while enabling users to track their energy use in real time and make intelligent decisions about energy consumption. Controls energy demand response to minimize grid burden and reduce operational costs. \\ \cline{1-1}  & \\ \cline{1-1}  & \\ \cline{1-1}  & \\ \cline{1-1}  & \\ \cline{1-1}  & \\ \end{tabular}
\end{table}
Table 2: Advantages achieved by employing smart grids.  
Figure 2: Visual representation of the IoT-based multi-domain integration within smart grids.
## 4 Results and Discussion
### Case Study 2
#### 4.3.1 Mathematical Modelling of the Problem Formulation  
For this case, the total number of constraints is 12. Seven imperatives arise as a result of the points of confinement of the relay action, and five prerequisites arise due to the coordination condition. The MOT of each relay is 0.1 s. The CTI is considered as 0.2 s. The TMS values of all the relays is \(x_{1}\)-\(x_{7}\). For different fault regions, the current seen by relays and the \(a_{\rho}\) constant are given in Table 11.  
The problem for optimization can be demonstrated as:  
\[minz=10.124x_{1}+43.776x_{2}+16.725x_{3}+13.97x_{4}+24.703x_{5}+22.165x_{6}+4. 145x_{7} \tag{53}\]  
The constraints owing to the MOT of the relays are:  
\[3.252x_{1}\geq 0.1 \tag{54}\]  
\[43.776x_{2}\geq 0.1 \tag{55}\]  
\[4.044x_{3}\geq 0.1 \tag{56}\]  
\[5.811x_{4}\geq 0.1 \tag{57}\]  
\[4.881x_{5}\geq 0.1 \tag{58}\]  
\[22.165x_{6}\geq 0.1 \tag{59}\]  
\[4.145x_{7}\geq 0.1 \tag{60}\]  
Hence, the constraints mentioned in Equations (55)-(60) violate the constraints of the minimum value of the TMS. Hence, these constraints are reconstructed as:  
\[x_{2}\geq 0.025 \tag{61}\]  
\[x_{3}\geq 0.025 \tag{62}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Point** & **Primary Relay** & **Backup Relay** & **T. Fault Current (A)** \\ \hline A & 1 & - & 6579 \\  & 4 & 2 & 939 \\  & 3 & 1 & 2193 \\  & 4 & 5 & 1315.5 \\  & 5 & - & 3289.5 \\  & 6 & 3 & 1096.5 \\  & 7 & 3 & 1315.8 through relays 3 and 5 \\  & - & 5 & 2631.6 through relay 7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Primary and backup relationships of the relays and maximum fault current for Case 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fault Point**} & \multicolumn{8}{c}{**Relay**} \\ \cline{2-9}  & \multicolumn{2}{c}{**1**} & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline A & \(I_{\text{relay}}\) & 8.223 & 1.1737 & - & 2.347 & - & - & - \\  & \(a_{\rho}\) & 3.252 & 43.776 & - & 8.  
\[x_{4} \geq 0.025 \tag{63}\] \[x_{5} \geq 0.025\] (64) \[x_{6} \geq 0.025\] (65) \[x_{7} \geq 0.025 \tag{66}\]  
The constraints owing to the coordination of relays with the CTI taken as 0.2 are:  
\[43.77x_{2}-8.159x_{4} \geq 0.2 \tag{67}\] \[6.872x_{1}-4.044x_{3} \geq 0.2\] (68) \[14.01x_{5}-5.811x_{4} \geq 0.2\] (69) \[22.165x_{3}-6.872x_{6} \geq 0.2\] (70) \[5.809x_{3}-4.145x_{7} \geq 0.2 \tag{71}\]
## 3 Industrial Co-Processing Options
### Co-Processing Other \"Biomass\" Oils  
In addition to the vegetable oil and fat, or animal fat type feedstocks, there is considerable interest in co-processing other \"biomass\" oils. As noted, \"bio-oils\" refer to biomass oils made via pyrolysis, whilst \"bio-crudes\" refer to biomass oils made via HTL. However, it is important to note that in general these \"biomass\" oils are far more challenging to produce, store, and co-process than vegetable oil/animal fat-type feedstocks. Some researchers note the relatively limited availability of fast pyrolysis oil likely makes co-processing possible only at low blend levels [32], although higher co-processing levels might be possible depending on the batch size chosen. Additionally, [PERSON] et al. [12] noted that most co-processing studies focused on fast pyrolysis bio-oil. While some general conclusions can be made, the results of these studies cannot be automatically extrapolated to all bio-oils/bio-crudes.  
Unlike vegetable oils which have a relatively uniform nature, the composition and stability of biomass oils can vary considerably (although using a single-type or uniform feedstock may reduce variability). This variation can be a result of the type of process used to convert the original biomass into an oil (e.g., via fast or slow pyrolysis, catalytic or hydro-pyrolysis, or via HTL) and the type of biomass feedstock used (for, e.g., wood, bark, agricultural residues, sludge; wet or dry, large or small dimensions).  
Regarding their stability, biomass oils and especially bio-oils (i.e., made by pyrolysis) are both unstable and corrosive, and easily degrade (polymerise) to form solids even when being stored. This degradation is accelerated when the oil is heated in an oil refinery. In addition, biomass oils contain water and oxygenated compounds making them immiscible with fossil feeds unless additives are used (raising another difficulty to be overcome for co-processing). Bio-crudes from HTL have an oxygen content of around 10% whereas fast pyrolysis bio-oils have an oxygen content of around 40%. Upgrading these biomass oils to fuels involves removing oxygen as well as cracking the molecules to reduce the chain length. Upgrading fast pyrolysis oils enables their co-processing at higher shares, to up to 20-30%. As a result of the variations in the properties of these biomass oils, there will be large variations in the hydrogen demand during co-processing.  
In summary, the variable nature of bio-oils and bio-crudes makes it virtually impossible to predict an average yield of bio-fractions in products, unless there is a consistent and tightly specified feedstock, which is used in a process which itself is consistent. In addition, bio-crudes and bio-oils will likely be processed in an FCC, although bio-oils would probably need to be partially deoxygenated (in a dedicated hydrotreatment unit) beforehand to increase the bio-oil share being co-processed and thus the renewable content in the finished fuel [12].
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 4 Conclusions  
A DBD plasma source operating with argon and helium was investigated for the direct treatment of winter wheat seeds.  
The germination speed of wheat seeds could be improved by treating for 3 min with both discharges. Germination effects were stable for treatments up to 30 min. Argon and helium plasmas generated in the shaking DBD reactor produced RONS in different ratios; the argon discharge presented higher excited oxygen emission than nitrogen. Acceleration in the germination of wheat seeds was correlated with the presence of ROS in the discharge, which probably led to the incorporation of oxygen radicals at the seed coat, enhancing water absorption. Thus, the argon discharge treatment led to more pronounced acceleration in germination for long treatment times when compared to control and helium plasma treatment. Similar results were obtained for a shorter treatment time of 4 min by increasing the voltage duty cycle. Thus, the treatment time can be easily reduced by simply adjusting the electrical parameters.  
The presented DBD reactor allows accurate control of the discharge power and therefore the gas temperature. The shaking movement allows ambient air to be admixed in the discharge, leading to the generation of RONS. Using the shaking reactor with a set of optimized parameters (e.g., argon with a duty cycle of 50%) led to an improved germination speed that might positively be reflected in later stages of the plant development. The presented DBD reactor can be used to treat plant seeds of variable sizes and shapes, and it can be easily scaled up for agricultural usage and the treatment of a large amount of seeds.  
Conceptualization, T.M.C.N. and H.B.; methodology, T.M.C.N. and H.B.; validation, T.M.C.N., S.H. and N.W.; formal analysis, T.M.C.N., N.W. and H.B.; investigation, T.M.C.N. and S.H.; writing--original draft preparation, T.M.C.N. and H.B.; writing--review and editing, T.M.C.N., N.W., K.-D.W. and H.B.; project administration, H.B.; funding acquisition, K.-D.W., N.W. and H.B. All authors have read and agreed to the published version of the manuscript.  
This research was in parts funded by the Federal Ministry of Education and Research of Germany within the framework of the project \"Physics for Food\" (BMBF FKZ 03 WIR 2801 B).  
We are grateful to [PERSON] and [PERSON] for technical assistance during seed germination experiments.  
The authors declare no conflict of interest.
## 2 Refined Numerical Modelling Approach
### Performance Degradation Simulation Approach
#### 2.5.4 Prestressing  
The spatial prestress was applied in ABAQUS, and the arrangement of prestress tendons in different directions can be seen in Figure 8. From the prestress reinforcement layout, the cracks that occur in Figure 7 may be attributable to the inadequate positive bending moment bearing capacity resulting from the limited prestressed length of the bottom plate, as observed from the perspective of the prestressed reinforcement layout. Internal prestress can be applied via the cooling method, and can be achieved by setting specified predefined fields. The relationship between the prestress value \(\sigma_{p}\) and the applied temperature field is as follows:  
\[\sigma_{p}=\alpha E\Delta T \tag{7}\]  
where \(\Delta T\) is the temperature difference with the unit \({}^{\circ}\)C; \(E\) represents the elastic modulus of prestress tendons (MPa); and \(\alpha\) is the linear expansion coefficient.  
In order to grasp the spatial prestress state, the horizontal, vertical, and longitudinal prestress tendons at different positions of Bridge A were field tested, and three field test methods were used, the transverse tension method, stress release method and the X-ray diffraction method, as can be seen in Figure 9. The first two methods have been widely employed in the field of bridge detection due to their well-established theoretical foundation and convenient operational procedures [30; 31; 32; 33]. The X-ray diffraction method was first proposed in 1987 [34] and is widely recognized as one of the most powerful nondestructive tools for measuring residual stresses in polycrystalline materials, and it has been extensively employed both in service and laboratory research, where it yields satisfactory predicted results [35; 36]. In this paper, the X-ray diffraction method was first used in the prestress testing field, and the X-ray testing machine was self-developed as well. During the test, the instrument was required to align the center of the test point, the center of the X-ray spot, and the center of rotation of the goniometer. The instrument examined changes in lattice spacing within prestressed tendons by utilizing the X-ray diffraction approach and converted them into actual stress values using Bragg's equation.  
Figure 8: Embedded spatial prestress tendons in FE software: (**a**) longitude prestress; (**b**) partial amplification of main-span prestressed tendons on the bottom plate of a box beam; (**c**) horizontal and vertical prestress.  
The on-site test results are listed in Table 4. The loss rate mentioned in Table 4 refers to the effective prestress value tested in the field divided by the design stress value of the prestress (1395 MPa). For all the analyses presented below, unless specified otherwise, the average loss rate in three directions has been adopted within the analysis.
# Influence of Gamification on the Commitment of the Students of a Programming Course: A Case Study
## 1 Introduction
### Hypothesis  
The integration of gamification in educational settings, particularly in programming courses, has been a topic of growing interest in recent years [14]. The potential benefits of gamification, such as increased student engagement, motivation, and improved learning outcomes, have been highlighted in various studies [15; 16; 17; 18; 19]. However, the specific influence of gamification on student commitment in programming courses remains an area that warrants further exploration. In this context, the present research aims to investigate the impact of a web-based gamified tool on the commitment of students to a programming course.  
The central hypothesis of this study is grounded in the belief that the introduction of gamification elements into a programming course can significantly enhance students' commitment and overall learning experience. By incorporating game-like mechanics and dynamics, such as rewards, challenges, and leaderboards, it is anticipated that students will exhibit increased motivation, engagement, and persistence in their coursework. Furthermore, the study seeks to understand students' perceptions and experiences with the gamified tool, particularly in terms of its usability, effectiveness, and overall satisfaction.  
To provide a comprehensive understanding of the influence of gamification on student commitment in a programming course, the following research questions have been formulated:  
1. What is the tool's influence on the student's commitment to the subject?
2. What are the students' perceptions and experiences regarding the usability and the effectiveness of the web-based gamified tool?
3. What are the students' perceptions and experiences regarding their satisfaction with the web-based gamified tool?  
The subsequent sections of this paper are organized in the following manner. Section 2 provides a summary of the web-based gamification tool and its integration into the programming course. This section also outlines the quantitative evaluation methodology employed, the participant demographics, and the survey conducted to gauge students' experiences and perceptions. Section 3 presents a comprehensive analysis of the results derived from the study. Finally, Section 4 discusses the implications of the findings, compares the results with prior studies, and addresses the study's hypotheses and limitations. The conclusions drawn from the research are presented, along with suggestions for potential avenues for future work.