esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to estimate the effects of cold plasma (CP) and electromagnetic field (EMF) treatment of red clover (_Trifolium pratense_) seeds with different coat colors on germination kinetics, the content of seed phytohormes, and the growth of seedlings. Seeds of red clover cultivate 'Arimaicai' were treated with radio-frequency EMF or capacitively coupled low-pressure CP for different durations. There were no differences in germination kinetics between yellow, brown, and dark purple seeds in control, but the germination rate of seeds treated with CP and EMF depended on seed color: The germination of yellow seeds was stimulated stronger compared to dark purple and brown seeds, and EMF did not stimulate germination in brown seeds. The content of phytohormes in control seeds and the shift in their amount induced by seed treatments were also strongly dependent on seed color. No relationship was found between the effect on germination kinetics and changes in phytohorme levels. In the control, seedlings growing from the yellow seeds were heavier, and the number of root nodules was 12.5 times larger compared to seedlings of dark purple seeds. Seed treatments with CP and EMF significantly increased the number of root nodules, and this effect was stronger in seedlings from dark purple seeds compared to those from yellow seeds.  
cold plasma; electromagnetic field; germination; phytohormes; red clover; root nodulation; seed color; seedling growth +
Footnote †: journal: Applied Sciences  
The Effects of Red Clover Seed Treatment with Cold Plasma and Electromagnetic Field on Germination and Seeding Growth Are Dependent on Seed Color
## 2 Materials and Experimental Characterizations
### Thermal Characterization Method
#### 2.7.1 Thermal Conductivity of Composite Samples  
The thermal conductivity of bricks was determined in steady-state of the box method. The test sample was placed between an electrically heated box at temperature, \(\mathrm{T_{h}}\) and a cold box that was maintained at a cold temperature, \(\mathrm{T_{c}}\), equipped with a cryostat-controlled heat exchanger. The lateral sample faces were insulated with extruded polystyrene in order to avoid heat loss and to impose a unidirectional heat transfer. The device was equipped with four temperature sensors connected to an acquisition card allowing to measure and record the hot temperature, the cold temperature, the ambient temperature of the room, \(\mathrm{T_{a}}\), and the temperature of the box, \(\mathrm{T_{b}}\). When the steady state was established, the temperature gradient between the two faces of the sample was measured. The effective thermal conductivity of the sample was calculated from the [PERSON]'s relation (Equation (6)):  
\[k=\frac{qe}{S\Delta T}=\frac{e}{S\Delta T}\bigg{(}\frac{U^{2}}{R}-C\Delta T^{ \prime}\bigg{)} \tag{6}\]  
Figure 5: 3D-micro computed tomography (\(\mu\)CT) images of composites: (**a**) Reference mortar (Resolution 1 voxel = 22 \(\mu\)m); (**b**) pores in the reference mortar; (**c**) composite with 5 wt% of fibers ((Resolution 1 voxel = 7 \(\mu\)m); (**d**) composite with 10 wt% of fibers (resolution 1 voxel = 7 \(\mu\)m).  
Where:  
\[\Delta T=T_{h}-T_{c} \tag{7}\]  
\[\Delta T^{\prime}=T_{b}-T_{a} \tag{8}\]
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.3 Decision Tree Algorithms  
The six primary processes involved are data preparation, feature generation, classifier training, classifier testing, performance analysis, and validation using specific unseen cases and data. Unsatisfactory performance is often attributed to issues such as inaccurate data preparation, imprecise feature generation, or a lack of diverse traces across classes. To address these challenges, the Decision Tree algorithm suggests redesigning the process stages to enhance overall performance [25; 26; 27; 28; 29; 30; 31; 32; 33]. The process begins with data preparation, where raw data are cleaned, standardized, and properly formatted for use. This step is critical, as poor-quality data can lead to inaccurate results. Next comes feature generation, which involves extracting meaningful features from the raw data to highlight key patterns. If this step is not performed accurately, the model's learning capability may be compromised. During the classifier training phase, the Decision Tree algorithm is trained on the prepareddata, learning to categorize information based on the selected features. Once trained, the model undergoes classifier testing with a separate dataset to assess its generalization ability, ensuring it can make accurate predictions on unseen data. Performance analysis follows, evaluating the model using metrics such as accuracy, precision, and recall identifying areas for improvement. The validation with unseen cases tests the model in real-world scenarios, confirming its reliability and ability to generalize effectively. In Figure 1, the diagram illustrates the suggested process for designing and optimizing a Decision Tree (DT) algorithm. The process highlights key stages such as data preparation, feature generation, and ensuring adequate variability in traces across classes, all of which are essential for achieving optimal model performance.  
Figure 1 outlines the key stages in developing and optimizing a Decision Tree model for transformer fault detection. The process emphasizes iterative improvements, from data preparation and feature extraction to classifier training and performance evaluation. Feedback loops are incorporated to refine the model based on evaluation results, ensuring robust fault classification.  
Unsatisfactory performance can often result from issues at any of these stages, such as inaccurate data preparation, imprecise feature generation, or insufficient variability in traces across classes. In such cases, the Decision Tree algorithm recommends redesigning or adjusting the process stages, such as refining the features, revisiting the data preprocessing steps, or introducing more varied training cases to improve the model's ability to generalize and perform well.  
Figure 1: Process for creating and optimizing the Decision Tree (DT) algorithm.
## 4 Innovation in Surface Disinfection Method
### Plasma-Mediated Virus Inactivation  
Plasma generation is attributed to the creation of anodized gas which contains reactive chemical elements such as electrons, ions, and charged species [106]. Despite that it was discovered in 1927 as the fourth fundamental state of the matter, the microbicidal properties of plasma were really explored only in the last 15 years [107]. Several studies have investigated the effect of plasma treatment on the inactivation of both enveloped and nonenveloped viruses, bacteria, and fungi [108; 109; 110]. It was reported that the applicability of plasma technology in surface disinfection is potentially wide-ranging. However, nitrogen-gas plasma and UV radiation have the most effective impact on viruses and about a six-log reduction/inactivation performance for bacteria. Other techniques used innovative methods by radiofrequency plasma treatment using an Ar/O2 gas mixture [111]. They concluded that plasma can not only disinfect surfaces but also degrade toxins. The mechanisms of degradation are directly  
Figure 4: Mechanisms of antimicrobial surface activation [94].  
Figure 3: (**a**–**c**) Three categories of self-disinfection surfaces [93].  
related to the used gases as well as the method used to generate plasma and the target microorganism. However, the exact mode of the plasma functionality remains largely unexplored. More innovative devices were developed for the disinfection of fruits and vegetables using atmospheric pressure plasma, others for the sterilization of medical instruments using gamma-ray treatment [112]. However, induced changes in the properties of the materials were noticed. A protocol of evaluation of each sterilization method should be developed specifically to the corresponding application with respect to the relative norms. Despite that plasma-mediated virus inactivation is a relatively young field of research, encouraging results were reported in the treatment of the viral pandemic COVID-19 using cold plasma treatment is known as a reactive oxygen and/or nitrogen species [113; 114].
# Changes in Soil Moisture Improved by High-Performance Ester Materials under Dry-Wet Cycles
###### Abstract  
The mechanism for improving soil with high-performance ester material is not yet clear, especially changes in soil moisture under dry-wet cycling conditions. Soil moisture is closely related to its ecological performance, which also leads to a lack of scientific bases for the application range, climate conditions, and long-term performance evolution evaluation of high-performance ester materials in ecological restoration. Our research revealed an optimal ratio for the amount of high-performance ester materials that can improve soil moisture and ecological performance under multiple dry-wet cycling conditions. Dry-wet cycling experiments and planting tests were conducted to study the soil moisture improvement mechanism and the changes caused by high-performance ester materials. Changes in the soil's moisture, EC, and ecological performance were explored. The results indicate that ester materials can effectively improve soil moisture and EC. Even after multiple dry-wet cycles, ester materials can still play an effective role in improving soil moisture and ecological performance. An optimal ratio for the amount of high-performance ester materials was also found. This research reveals the improvement mechanism for ester materials on soil moisture and ecological performance under dry-wet cycling conditions. Our results provide new data and technical guidance for the improvement of soil moisture and ecological restoration by high-performance ester materials.  
soil improvement; ester materials; water retention capacity; dry-wet cycle; ecological restoration +
Footnote †: journal: Applied sciences
## 3 Results
### Daylight Autonomy (DA)  
The analysis of the daylight autonomy (\(DA_{300}\) and \(DA_{500}\)) results across various configurations of window-to-wall ratios (WWR), orientations, locations (Seville and Barcelona), and interior reflectances (bright and dark coatings), as shown in Figures 7-14, reveals distinct patterns, as follows:
for bright interior coatings range from 80% to 87%, showing consistent levels throughout the room's depth, while dark coatings display a wider dispersion of \(DA_{500}\) values, depending on the type of opening.  
For a 15% WWR, the \(DA_{500}\) values in south orientations are significantly lower than those for other window sizes, averaging around 15%. For north orientations, these values increase, reaching up to 30% towards the back of the rooms. A notable ceiling effect causes a substantial decrease in \(DA_{500}\) values, with a nearly 30% difference between centered and upper window configurations. Additionally, for a 15% WWR, centered windows facing north in both locations show \(DA_{500}\) values of 55%, increasing to 64% between 0.50 and 0.75 m but dropping below 0.05 beyond 2.00 m.
## 3 Theoretical Background
### Discrete Wavelet Transform (DWT)  
The wavelet transform DWT decomposes a signal into wavelets, which are discretely sampled. DWT operates on both frequency and time location, providing an advantage over the Fourier transform [25]. This transformation is commonly used in image compression, signal processing, noise removal, and other applications [26].  
DWT transforms the host image into four sub-bands: low-low (LL), low-high (LH), high-low (HL), and high-high (HH). The LL sub-band contains the approximation part, while the other three sub-bands contain detailed information such as the edges and textures of the image. Each sub-band can be further decomposed into four sub-bands, resulting in a multi-level decomposition. The magnitude of DWT coefficients is usually larger in the LL sub-band compared to the other sub-bands. In our proposed method, we have decomposed the host image using a four-level DWT on the LL sub-band. Figure 2 shows the four-level DWT on the LL sub-band.
# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
###### Abstract
[29] used Transformer and a bidirectional gated recurrent unit as probability predictors, and they compared them with two traditional lossless compression methods, arithmetic coding and dictionary-based LZW, to reduce the compression ratio. In addition to dictionary and entropy coding, floating-point encoding has also been widely applied in data compression. [PERSON] et al. [30] proposed an efficient lossless compression algorithm for time-series floating-point data based on existing compression methods, and they significantly improved the compression efficiency of time-series floating-point numbers through three optimization strategies. After optimization, the compression ratio was increased by an average of 12.25%, achieving a significant performance improvement. This paper proposes a block lossless data compression method based on bidirectional long short-term memory networks and multi-head self-attention combined with a multilayer perceptron (BiLSTM-MHSA-MLP). The preliminary probability prediction model (BiLSTM-MHSA) integrates bidirectional LSTM and multi-head self-attention layers. BiLSTM, as an extension of LSTM, enhances the understanding and capture of temporal relationships within sequences by processing input sequences bidirectionally. The MHSA processes the outputs of BiLSTM in parallel, focusing on different parts to obtain richer representations, reduce information loss, and capture more details and semantic information. Additionally, a multilayer perceptron (MLP) and a block length parameter (block_len) are introduced on top of the trained preliminary probability prediction model, enabling block compression during training. This approach dynamically updates the model and optimizes the symbol probability distributions, providing more accurate predictions. Finally, the compression algorithm is successfully ported to the Jetson nano development board, achieving lossless data compression on both the PC and the Jetson nano platforms.
# Design of Triple-Band (DSRC, 5G, 6G) Antenna for Autonomous Vehicle Telematics
## 4 Antenna Fabrication and Measured Results
The designed antenna is the first to cover DSRC, 5G, and even 6G, realizing advanced V2X communications. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Work**} & **Frequency Bands(s)** & **Maximum Peak Gain(s)** & **Isolation between** \\  & **(GHz)** & **(dBi)** & **Ports (dB)** \\ \hline
[7,8] & 5.84–5.98 & & 7.3, 13.6 & \textgreater{}30 dB \\  & 27.5–28.5 & & \\  & 2–2.74 & & \\  & 4.062–8 & & \\ \end{tabular}
\end{table} 
Table 2: Comparison of antenna performance among multi-band antennas. Figure 18: Simulated and measured radiation patterns at (a) 5.9, (b) 28, and (c) 145.5 GHz.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
###### Abstract  
With the acceleration of urbanization, the development and utilization of urban underground spaces are becoming increasingly frequent, and the potential risks in the operation of urban dense cable passage may pose a major threat to the security of the urban power supply. Therefore, a risk assessment method for urban dense cable passage operation based on a fuzzy-analytic hierarchy process is proposed. Firstly, the operation risk parameters of urban dense cable channels are analyzed in detail. Secondly, the weight of each index is calculated based on the analytic hierarchy process to determine the importance of each index in the risk assessment. Then, according to the membership degree of each index, the fuzzy relationship matrix is constructed to describe the relationship between each index and the risk level, and the comprehensive risk level of the cable channel is obtained through the matrix operation. Finally, taking the actual operation of an urban dense cable channel in a certain area of the China Southern Power Grid as the research object, the safety of its operation is comprehensively evaluated, and the effectiveness of the fuzzy-analytic hierarchy process in the operation risk assessment of the urban dense cable channel is verified. Corresponding risk control measures and suggestions are proposed according to the evaluation results.  
urban dense cable passage; risk assessment method; fuzzy-analytic hierarchy process; risk parameters; risk level +
Footnote †: journal: applied sciences
## 3 Results
### Mechanical Properties  
The mechanical properties of CB/CNT/PDA/TPU were tested through uniaxial tensile testing, and compared with the mechanical properties of TPU, PDA/TPU, and CB/CNT/TPU. Each sample was tested five times under the same conditions, and the average value was taken. As shown in Figure 4a, the thickness of the CB/CNT/PDA/TPU nanofiber membrane is relatively thin, approximately 220 \(\upmu\)m. Figure 4b,c highlight the excellent flexibility and bendability of the CCPT nanofiber membrane, demonstrating superior mechanical performance. Uniaxial tensile tests were performed on TPU, PDA/TPU, CB/CNT/TPU, and CB/CNT/PDA/TPU nanofiber membranes. Figure 4d,e present the stress-strain curves along with bar charts for elongation at break and tensile strength. The tensile strengths of TPU and PDA/TPU nanofiber membranes were measured to be 5.4 MPa and 5.7 MPa, respectively. For CB/CNT/TPU and CB/CNT/PDA/TPU nanofiber membranes, the tensile strengths increased significantly to 8.1 MPa and 8.2 MPa, representing an improvement of 50% and 44%, respectively, compared to TPU and PDA/TPU nanofiber membranes. The improved tensile strength is attributed to the excellent mechanical properties of CB and CNT. Upon introducing CB and CNT, they are uniformly dispersed within the TPU fiber membrane, and the CB + CNTs reinforcement phase structure serves as a support, effectively distributing the stress borne by the matrix. The elongation at break for TPU and PDA/TPU nanofiber membranes was 342% and 347%, respectively. This improvement can be attributed to the enhanced interfacial interactions resulting from the PDA layer coating the TPU fibers, which in turn improves their tensile performance. In contrast, the elongation at break for CB/CNT/TPU and CB/CNT/PDA/TPU nanofiber membranes was 275% and 300%, respectively, showing reductions of 67% and 47% compared to TPU and PDA/TPU. This decrease is due to the entanglement and aggregation of the conductive materials, CB and CNT, which limit the deformation capability and reduce the elongation at break. However, CB/CNT/PDA/TPU nanofiber membranes exhibited better elongation at break and tensile performance compared to CB/CNT/TPU membranes, demonstrating the enhanced mechanical properties provided by the PDA modification.
## 2 Materials and Methods
### Classification Model  
To evaluate the performance of a classification model, we use several metrics that are based on the counts of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN):  
* **True Positive (TP)**: The number of correctly predicted positive cases.
* **True Negative (TN)**: The number of correctly predicted negative cases.
* **False Positive (FP)**: The number of incorrectly predicted positive cases (Type I error).
* **False Negative (FN)**: The number of incorrectly predicted negative cases (Type II error).  
Using these fundamental components, we can calculate various performance metrics to understand different aspects of the model's performance:  
Precision measures the proportion of true positive predictions (TP) out of the total positive predictions (true positives plus false positives). It indicates how many of the predicted positives are actually positive.  
Precision = \(\frac{\text{TP}}{\text{TP}+\text{FP}}\)  
Recall measures the proportion of true positive predictions (TP) out of the total actual positives (true positives plus false negatives). It indicates how many of the actual positives are correctly identified by the model.  
Recall = \(\frac{\text{TP}}{\text{TP}+\text{FN}}\)  
The F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both the precision and recall of the model.  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\]  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Description**} \\ \hline Heel strike angle (deg) & The maximum pitch angle at heel strike measured in degrees. \\ \hline Toe-off angle (deg) & The maximum pitch angle at toe off measured in degrees. \\ \hline Stride Length (m) & Distance from one stance position to the next stance of the same foot. \\ \hline Step Speed (m/s) & The forward speed of the foot only during the swing phase. \\ \hline
2D Path (m) & The path length of the foot in the horizontal plane with respect to the stride length, which includes the lateral deviations of the strides. \\ \hline
3D Path (m) & The path length of the foot in 3D space during a step with respect to the stride length, which includes the lateral deviations of strides and the foot elevation. \\ \hline Clearance(m) & The clearance or maximum height of the foot with respect to the ground during the swing phase. \\ \hline \end{tabular} Note: (m) = meters; (s) = seconds; GCT = gait cycle time; (deg) = degrees; (steps/min) = steps per minute.  
\end{table}
Table 1: Cont.  
Specificity measures the proportion of true negative predictions (TN) out of the total actual negatives (true negatives plus false positives). It indicates how many of the actual negatives are correctly identified by the model.  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]
## 4 Concept
### Planning and Installation
#### 4.1.4 Realization of Data Acquisition  
Finally, measurement systems, data acquisition, and IT integration are installed, commissioned, and tested at the task \"Realization of data acquisition\". For this purpose, the specifications of the test plan from \"Conception of the ML application\" and the instructions from \"Design of measurement system and its integration\" are used. During the tests, data is already generated that contains all relevant information and characteristics for the ML application to map the effects to be observed in the CPPS. During commissioning, the first digital fingerprint of the real CPPS is also generated, mapping the behavior at the beginning of its life. The operability of the IT infrastructure is also established so that the database can be generated without interference. During this task, any planning errors are detected and eliminated.  
From a data quality perspective, the following results are achieved with the implemented data collection. Data acquisition functions without errors deliver data from sensors and other data sources up to the data store. This proves the initial functionality and quality of the data acquisition. On one hand, the initial fingerprint provides a data set with which the ML algorithms can be developed--at least in the Data Understanding phase. The algorithms for data preprocessing can also be developed with the data set. On the other hand, it is a basis of comparison for state changes in the CPPS to be monitored, especially regarding the faults and aging or wear effects. In addition, the developed measurement and acquisition methods must be described transparently, for example in metadata, to be able to support the future interpretation of the data.