esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The use of 2D-DIC for Cement and Concrete Composite tensile creep monitoring has been successfully tested. Obtained experimental and computed data have been analysed to verify the compatibility of both conventional and innovative testing methods. The DIC imaging setup included an entry/mid-level DSLR camera that was proven to be suitable for testing long-term deformation properties. Thus, a new approach to Cement and Concrete Composite tensile creep research was verified. Results show that the applied testing method is suitable for studying cement and concrete composite tensile creep in uniaxial tension. Generally, the conventionally measured and computed NMOD and NTOD values for CT specimen displacement trends showed close similarities in their nature (characteristics of graph curves correspond) for both surface-attached and non-contact measuring techniques. The maximum tensile creep deformation of early age cement composite with PVA fibres after 45 days of constant tensile loading reached 0.0083 mm (NMOD) and 0.0055 mm (NTOD).  
Further research needs to be carried out to study the mechanics of non-cracked Cement and Concrete Composite CT specimens. The finite element method may be employed to model behaviour of such Cement and Concrete Composite specimens under sustained load. Furthermore, general knowledge of related mathematical problems needs to be improved, especially regarding creep processes in such materials.  
Figure 9: Displacement values are that obtained from physical measurements, DIC analysis and mathematical calculations.  
**Author Contributions:** Conceptualization, A.S. and T.K.; methodology, A.S.; software, T.K., A.S. and J.V.; validation, T.K., A.S. and R.G.; formal analysis, A.S., T.K. and R.G.; investigation, A.S., T.K. and R.G.; resources, K.K.; data curation, T.K., A.S. and A.C.; writing--original draft preparation, A.S. and T.K.; writing--review and editing, R.G., J.V., K.K. and A.C.; visualization, T.K. and A.S.; supervision, A.S., K.K. and A.C.; project administration, A.S.; funding acquisition, K.K. and A.C. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the European Regional Development Fund within the Activity 1.1.1.2 \"Post-doctoral Research Aid\" with the Specific Aid Objective 1.1.1 \"To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and infrastructure\" of the Operational Programme \"Growth and Employment\" (No.1.1.1.2/VIAA/3/19/401). This work has been financed by the Polish National Agency for Academic Exchange under the International Academic Partnership Programme within the framework of the grant: E-mobility and sustainable materials and technologies EMMAT (PPI/APM/2018/1/00027).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Time Synchronization  
Basically, spectrum information is gathered by cooperative sensing in CR-WSNs. In cooperative sensing, all CR-WS nodes in the network share the same quiet period, and sense the spectrum simultaneously. Time synchronization is indispensable, because a coordinated and simultaneous quiet period is used for spectrum sensing and time frame/slot allocation.  
Actually, reliable signal detection is only possible by fusing sensing results from multiple sensors in the presence of shadow fading [44]. Therefore, all the clocks in a CR-WSN should be synchronized.  
Time differences can cause nodes to sense the spectrum at different times; this may lead to incorrect sensing results, and the presence of primary users may be overlooked.  
Furthermore, time synchronization enables the use of time division multiple access (TDMA) techniques that are generally considered more efficient than contention-based media access control (MAC) layer techniques. Many MAC schemes for CR-WSNs may need time frame/slot structures, and require precise time synchronization for efficient operation. Some applications may also require strict time synchronization, such as tracking. Clustering can provide significant support for perfect synchronization by overcoming any inherent out-of-synchronization deficiency.
# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
## 4 Discussion
### Effectiveness of Fine-Scale Image Time Series in Tropical Crop Type Mapping
In addition, the presence of mixed croplands can exacerbate spectral variation considerably due to the diverse growing stages of individual crops. Obtaining a substantial number of additional field samples posed logistical challenges for achieving precise mapping of the combined classes. When compared with the data sourced from the Office of Agricultural Economics of Thailand [27], our study revealed a blend of consistent trends and distinctive insights. Specifically, our findings confirm the reported increases in durian plantations, with Chanthaburi experiencing a 51% rise, Trat a 102% surge, and Rayong a 41% expansion from 2018 to 2022. This aligns with our observations (see Figure 9), in which we too detected a notable uptick in the spatial coverage of durian trees. Similarly, our study noted the stability of rubber plantation dynamics over time, consistent with the aforementioned report. An intriguing divergence emerges in regions like Chanthaburi and Rayong, where extensive rubber plantations have undergone a substantial transition to durian cultivation--a trend mirrored in both the government's findings and our own. Nevertheless, an inconsistency arises concerning rice cultivation trends. Our analysis identified a 10% decrease in rice cultivation from 2018 to 2022, contradicting the government's portrayal of a relatively stable trend. This disparity could be attributed to uncertainties inherent in field surveys to complete the government report or remote sensing techniques. From the remote sensing perspective, recent drought events [49] have affected crop phenology within paddy fields and/or their surface spectral characteristics, possibly causing modeling errors using remote observations.
## 3 Recent Literature Analysis
### Cyber Security
#### 3.1.4 Datasets and Models  
Dataset design was a recent research area in the novel literature that aimed to provide heterogeneous data for research validation. A need for a dataset that enables intrusion detection was highlighted as a research need in the context of complex cyber threat scenarios where data is sourced from real-world IoT networks [33]. In such cases, an IoT distributed architecture can be utilized to evaluate the security applications of AI, thereby enhancing the security posture.  
The classification of the data generated by IoT devices was also an area of interest in the recent novel literature. One study focused on classifying IoT network traffic through the utilization of machine learning and logistic regression methodologies in particular, in order to differentiate between conventional and IoT devices on home networks [34]. The consequences of such research have the potential to expand into other fields, such as industrial applications.  
Threat modeling from a CPS perspective was also investigated within the recent novel literature. [PERSON] et al. released a study containing a cyber security framework for CPS, focusing on developing a model for analyzing attack scenarios [35]. Whilst explicitly focused on the energy sector, such studies have the potential to influence a wide variety of scenarios, assuming that the model developed has enough breadth to be cross-applicable within different contexts. This is something that may require further investigation and evaluation.
# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 3 Results
### Monthly Variations in Bee Foraging Activity  
The analysis of seasonal hive weight dynamics revealed significant differences between months in both rural (Kruskal-Wallis test: H = 17.72, df = 4, \(p\leq 0.01\)) and urban apiaries (H = 16.35, df = 4, \(p\leq 0.01\)). Post hoc tests with Bonferroni correction confirmed that early spring (April) was significantly less productive than the late spring peak period (May-June) (\(p<0.05\)) (see Figure 3 and Figure S1 in the Supplementary Materials). A similar pattern was observed in late summer (August), which also exhibited significantly lower gains compared to the peak months. A similar trend was observed in urban apiaries, although the nectar foraging peak occurred at a slightly later time point, and the summer maximum was notably higher than in rural locations (see Figure S2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Annual Foraging** & **Type of Location** & **Mean (kg) \({}^{*}\)** & **Range** & **SE \(\pm\)** \\ \hline \multirow{2}{*}{Total weight gain} & Urban & 72.9  & 33.9–94.4 & 11.5 \\ \hline \multirow{2}{*}{Total weight loss \({}^{*}\)} & Urban & 18.5  & 25.2–11.1 & 2.4 \\ \hline \multirow{2}{*}{Number of foraging days} & Urban & 67.4  & 48.5–66.5 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Annual foraging metrics (mean \(\pm\) SE) for urban and rural apiaries: total hive weight gain, total weight loss, and number of foraging days.  
Figure 3: Monthly hive weight gains (kg) from April to August in rural and urban apiaries. Boxplots show the median (line inside the box), mean (white circle), interquartile range (box: Q1–Q3), whiskers (range without outliers), and outliers (black diamond symbols). Different letters in the lines for the same locations (urban or rural) indicate significant differences between means for months based on post hoc comparisons (Bonferroni correction, \(p<0.05\)).
## 5 Simulation Results
### Experiment Considering the Hata Model for Urban Areas  
In this experiment, the Hata model introduced in Section 5 is used. Additionally, the path ambiguity problem has been solved by the methods introduced in Section 4. In order to simulate a large range of SNR, the simulation length is set to 100 s, and the two emitters on the y-axis are located so as to have different distances with respect to the origin.  
Figure 12 presents the trajectory and the positions of the four emitters, and Figure 12 presents the correlation results at the initial point of the trajectory in the Hata model. The four correlation peaks have different amplitudes, depending on the ranges between the emitters and the PD. From this figure, we can see that the amplitude of the correlation peak decreases as the distance between the PD and the emitter increases. Therefore, when the distance increases too much, even if it is less than \(\Delta p_{max}\) (the maximum detectable range for one DVB-T signal), the ranging operations could become critical if the SNR becomes too low.  
By multiplying the estimated delay by the speed of light, we obtain the range:  
\[r_{m}[n]=r_{t}[n]+\epsilon_{r}[n] \tag{24}\]  
where \(n\) is the discrete time normalized by the OFDM symbol duration, \(r_{m}[n]\) is the pseudo-range measurement, \(r_{t}[n]\) is the true pseudo-range and \(\epsilon_{r}[n]\) is the range error due to noise. The range error for emitter E4 is shown in Figure 13, from which it can be easily observed that the error depends on the SNR, which, in turn, depends on the distance between the emitter and the PD.  
In this simulation, the estimated position can be expressed as:  
\[\begin{split} P_{x,m}[n]&=P_{x}[n]+\epsilon_{x}[n]\\ P_{y,m}[n]&=P_{y}[n]+\epsilon_{y}[n]\end{split} \tag{25}\]  
where \(n\) is the estimation time, \(P_{i}[n]\), with \(i\in\{x,y\}\), is the true value of the PD position on the i-axis, \(P_{i,m}[n]\) is the corresponding estimated value and \(\epsilon_{i}[n]\) is the estimation error. The total positioning error can be calculated through the following equation:  
\[\epsilon[n]=\sqrt{\epsilon_{x}^{2}[n]+\epsilon_{y}^{2}[n]} \tag{26}\]  
Figure 12: Correlation results in the Hata model.  
Several different trajectories have been simulated. The true trajectories and the positioning errors, \(\epsilon[n]\), are shown in Figure 14, from which no significant dependence on the SNR can be observed. The reason is that along the path, the power of some received signals decreases, while that of others increases, so giving approximately a stationary performance in terms of positioning error.  
Figure 14: Different trajectories and the positioning errors.  
Figure 13: Pseudo-range error for emitter E4.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Physical System Layer  
Physical systems exchange a control input signal \(u(t)\) and sensor measurement \(y(t)\) through the network. When an attacker intrudes into the network, the attacker can modify these two types of control-related data on the network, resulting in divergence from the physical state, \(x(t)\).  
Figure 3 shows attack locations for cyber-physical attacks in the physical system layer. There are three ways to manipulate control-related data on the network: first, the attacker only manipulates the sensor measurement packets \(y(t)\) transmitted to the computing system; second, the attacker only manipulates control input signal packets \(u(t)\) transmitted to actuators in the physical system; third, the attacker simultaneously manipulates both control input signal \(u(t)\) and sensor measurement \(y(t)\).
# The HD(CP)\({}^{2}\) Data Archive for Atmospheric Measurement Data
## 5 Value-Added Services
### Data Quality Management  
We define the data quality as the degree to which the data fulfills our requirements [25]. The data are carefully analyzed to make sure that they meet our standard in order to archive high quality data. High quality in this context means that the data are reliable and suitable for climate simulations and can be used without investing unreasonable time or cost, which, in essence, means that the data are easily accessible and understandable.  
Figure 2: The HD(CP)\({}^{2}\) data publishing workflow.  
The reliability of data is related to their sources, the acquisition methods and the evaluation and storage procedures (see Section 4). With regard to the data acquisition, researchers who want to be accepted as data producers can register at the HD(CP)2 data archive administration. After verification of their identity, they are required to upload a sample dataset which will be evaluated (see Section 5.1).  
For the evaluation of the observation data and their associated metadata, a software-based control system was established. The software ensures strict compliance with the HD(CP)2 standard and cross-checks the content of the metadata file against the global information stored in the NetCDF file header. Due to the large amount of different instruments and variables, human interaction cannot be omitted yet. We have created a role called the Observation Expert User (OEU), who is responsible for the final acceptance. The OEU checks the data and metadata in the NetCDF file and the editor-generated XML formatted metadata files. Even a formally correct dataset could include incorrect units, false standard names or corrupt data. For example, the decisions of whether a measurement makes sense for a specific instrument (there might be unreliable structures resulting from programming errors) or even whether the units in the dataset itself are chosen properly (there might be incorrect units like Pa/hPa for air pressure, or K/\({}^{\circ}\)C for temperature) still need a human factor. Therefore, a visual check of each sample set is necessary; the NetCDF file is examined with a generally agreed-upon software such as \"Ncview\" and \"Panoply\" to have a look into the time series itself. This guarantees that the data are of high quality and meet our standards.  
The procedure still involves coordination and harmonization, especially when a new variable is introduced, but it is very helpful in supporting researchers to prepare their data for reuse.  
Once a sample set is accepted, the whole dataset can be uploaded. The approved dataset will be transferred to the TDS, cataloged, and can be served almost immediately to a user.  
Figure 3: The metadata editor.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### Error Indicator  
We further investigated the relationship between positioning error and error indicator \(\delta\) (see Equation (4)). The results of this analysis are shown in Table 1. No measurement was found containing a value of \(\delta\) equal to 0. From Table 1 it can be seen that when the value of \(\delta\) increases, positioning error  
Figure 13: The accuracy limit in the proposed method shown as the CDF of the positioning errors.  
also increases. Therefore, \(\delta\) can be used as an indicator, when UE experience high \(\delta\) value it should keep communicating with other devices in order to have better positioning result.
## 2 Materials and Methods
### Experimental Apparatus  
The schematic of the experimental set-up is shown in Figure 1. The test engine is a four-cylinder common-rail direct injection (CRDI) diesel engine equipped with a turbocharger and an exhaust gas recirculation (EGR) system without cooler. The four-cylinder diesel engine has a displacement of 1991 cc and a compression ratio of 17.7. The specification of the test engine is shown in Table 2. An eddy current dynamometer (DY-230 kW, Hwanwoong Mechatronics, Gyeongsangnam-do, Korea) was used for controlling the engine speed and the engine load. The series of tests were carried out using diesel and diesel-ethanol blends. Emission characteristics of diesel and diesel-ethanol blends on engine such as CO, HC, and NOx emissions were recorded using a portable exhaust gas analyzer (MEXA-554 JKNOx, Horiba, Kyoto, Japan) and a multiple gas analyzer (GreenLine MK2, Eurotron (Korea) Ltd., Seoul, Korea). A partial flow collection analyzer (OP-160, Koeng Co., Ltd., Seoul, Korea) was used to measure particle matter. Combustion pressure for combustion analysis of the combustion chamber was obtained using a piezoelectric pressure sensor (KISTLER Type 6056A, Kistler Korea Co., Ltd., Gyeonggi-do, Korea) at the glow plug position. For analysis of the combustion pressure, data were obtained over an average of 200 cycles. Data acquisition was performed and recorded using a DAQ board (PCI 6040e, National Instrument, Austin, TX, USA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties (Units)** & **Diesel** & **Ethanol** \\ \hline Density (kg/m\({}^{3}\) at 15 \({}^{\circ}\)C) & 836.8 & 799.4 \\ Viscosity (mm\({}^{2}\)/s at 40 \({}^{\circ}\)C) & 2.719 & 1.1 \\ Calorific value (MJ/kg) & 43.96 & 28.18 \\ Cetane index & 55.8 & 8 \\ Flash point (\({}^{\circ}\)C) & 55 & 12 \\ Oxygen content (\%) & 0 & 34.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of neat diesel and ethanol.
# The Current Development Status of Agricultural Machinery
###### Abstract  
The scenario adaptability of agricultural machinery chassis in hilly and mountainous regions has become a key area of innovation in modern agricultural equipment development in China. Due to the fragmented nature of farmland, steep terrain (often exceeding 15\({}^{\circ}\)), complex topography, and limited suitability for mechanization, traditional agricultural machinery experiences significantly reduced operational efficiency--typically by 30% to 50%--along with poor mobility. These limitations impose serious constraints on grain yield stability and the advancement of agricultural modernization. Therefore, enhancing the scenario-adaptive performance of chassis systems (e.g., slope adaptability \(\geq\) 25\({}^{\circ}\), lateral tilt stability \(>\) 30\({}^{\circ}\)) is a major research priority for China's agricultural equipment industry. This paper presents a systematic review of the global development status of agricultural machinery chassis tailored for hilly and mountainous environments. It focuses on three core subsystems--power systems, traveling systems, and leveling systems--and analyzes their technical characteristics, working principles, and scenario-specific adaptability. In alignment with China's \"Dual Carbon\" strategy and the unique operational requirements of hilly- mountainous areas (such as high gradients, uneven terrain, and small field sizes), this study proposes three key technological directions for the development of intelligent agricultural machinery chassis: (1) Multi-mode traveling mechanism design: Aimed at improving terrain traversability (ground clearance \(\geq\)400 mm, obstacle-crossing height \(\geq\) 250 mm) and traction stability (slip ratio \(<\) 15%) across diverse landscapes. (2) Coordinated control algorithm optimization: Designed to ensure stable torque output (fluctuation rate \(<\pm\)10%) and maintain gradient operation efficiency (e.g., less than 15% efficiency loss on 25\({}^{\circ}\) slopes) through power-drive synergy while also optimizing energy management strategies. (3) Intelligent perception system integration: Facilitating high-precision adaptive leveling (accuracy \(\pm\) 0.5\({}^{\circ}\), response time \(<\) 3 s) and enabling terrain-adaptive mechanism optimization to enhance platform stability and operational safety. By establishing these performance benchmarks and focusing on critical technical priorities--including terrain-adaptive mechanism upgrades, energy-drive coordination, and precision leveling--this study provides a clear roadmap for the development of modular and intelligent chassis systems specifically designed for China's hilly and mountainous regions, thereby addressing current bottlenecks in agricultural mechanization.  
hilly and mountainous regions; agricultural machinery chassis; core technologies; research advances; development trends 2025
## 3 Results and Discussion
### PyC, Split Time and EC Concentration
Figure 7a shows the relationship between PyC and the split time obtained in this study. A linear regression analysis for Post-Ox split time shows that the slope is 0.039 \(\pm\) 0.007. Figure 7b is the PyC vs. split time adopted from another research group [25]. Linear regression analysis for Post-Ox split time from 673 s to 751 s shows that the slope is 0.007 \(\pm\) 0.0007. It is noted that PyC is mainly positive for Post-Ox split time for both cases. It is difficult to directly compare our results to the other research group's results because different protocols were employed. We used the NIOSH 5040 protocol, while the other group did the EUSARR2. In addition, the ranges of split time and the EC concentration were different from each other. It is certain that PyC is not zero any more at Post-Ox split time. Perhaps further study needs to better understand the rigorous relation between PyC and split time. Figure 6: Relationship between split time, PyC and EC concentrations: (**a**) Split time vs. EC concentration; (**b**) PyC vs. split time; (**c**) PyC vs. EC concentration.