esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Total Volatile Basic Nitrogen (TVB-N) Analysis  
TVB-N measurement includes trimethylamine, dimethylamine, ammonia, and other volatile basic nitrogenous compounds, and is one of the most widely used indicators to determine the degree of fish spoilage caused by bacteria and autolytic enzymes [8; 17; 18]. In the present study, the initial TVB-N value of the fresh fish fillets was 84.3 \(\mu\)g g\({}^{-1}\). The TVB-N value of the fillets treated with tea polyphenols decreased on the first day itself, presumably due to the antioxidative activities of the polyphenols. Subsequently, the TVB-N values increased gradually in all samples during the chilled storage (Figure 2). The TVB-N value in the control group increased to 252.7 \(\pm\) 29.3 \(\mu\)g g\({}^{-1}\) after 8 days, while in samples treated with the tea polyphenol, it increased to 172.6 \(\pm\) 12.3 \(\mu\)g g\({}^{-1}\). Since TVB-N is produced mainly by bacterial decomposition of fish fillets, the higher TVC values observed in the control, as compared to the treated samples after 8 days of the cold storage, could account for the higher TVB-N values. The TVB-N value of the fillets treated with tea polyphenol was 258.4 \(\pm\) 14.6 \(\mu\)g g\({}^{-1}\) after 16 days, which was lower than those of the control during the chilled storage period (Figure 2). Our results showed that the tea polyphenols application significantly reduced the TVB-N values of crisp grass carp during cold storage (\(p<0.05\)); TVB-N results were consistent with the TVC results (Figure 1). Tea polyphenols dip could lead silver carp to either a rapid decrease of the bacterial population or decrease  
Figure 1: Changes in the total viable count (TVC) of crisp grass carp fillets during storage at 4 \({}^{\circ}\)C. CFU, Colony-Forming Units. \(\blacksquare\), distilled water treatments; \(\bullet\), tea polyphenol treatments.  
the ability of bacteria for oxidative deamination non-protein nitrogen compounds, or both [6]. Similarly, dipping crisp grass carp in tea polyphenols could effectively inhibited microbial growth, and TVB-N value level was significantly lower for the treated samples than for the control samples. The inhibitory effects of tea polyphenols on the TVB-N values have also been reported in silver carp [6], crucial carp [8], large yellow croaker [9], and weever [10]. When considering a TVB-N value level of 250 \(\mu\)g g\({}^{-1}\) fresh fish as the acceptable limit proposed by [PERSON] et al., [19], the shelf life of the control and the treated samples were about 8 and nearly 16 days, respectively. Although TVB-N is widely used as an indicator for fish deterioration, various authors have reported different acceptable values of TVB-N for different fish species, different treatments, and different processing conditions [6]. Therefore, the shelf life of the crisp grass carp during the chilled storage was determined from the TVC values observed in this study.
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 3 TET-1 Data Processing Chain  
The processing chain of TET-1 data consist of the steps presented in Figure 1. These steps are described in greater detail in the following subsections. The processing of MODIS and field data is similar, but as MODIS data originates from the MODVOLC database [21, 35], it does not undergo exactly the same processing chain.
## 5 Discussion
### Limitations of STPC
#### 5.1.2 Component Lifetime Concerns  
Key components such as DC link capacitors and cooling fans are subject to degradation over time, particularly under continuous operation or in harsh environments. This may affect the long-term reliability of STPCs compared to passive transformer-based systems [27].
# Handover Management in 5G Vehicular Networks
## 1 Introduction
This process applies the Mobility State Estimation (MSE) technique of the LTE [22], which was improved in [23], in order to facilitate the decision process by taking into consideration that both Macrocells and Femtocells can serve the territory of each vehicle. Thus, the vehicle's velocity is continuously monitored and characterized as Normal, Medium, or High, considering the number of HOs performed by the vehicle during a sliding time window [23]. Furthermore, it ranks the candidate networks by taking into consideration the vehicle's velocity, network characteristic criteria, provider policy criteria, and energy-aware criteria. Specifically, if the vehicle's velocity is Normal, all the networks, including both Macrocells and Femtocells, are considered as network alternatives. On the other hand, if the vehicle's velocity is Medium, some Femtocells along the vehicle's trajectory are skipped, in order for the HO rate to be reduced. Finally, if the vehicle's velocity is High, only the available Macrocells are considered as network alternatives. Moreover, in the predictive HO scenario, each time that the grade of the user satisfaction drops below a predefined threshold, the HO is initiated and the process of the network selection is performed. Correspondingly, in the reactive HO operation, whenever the vehicular user loses the connectivity with his/her serving network, it connects to the available network that obtained the higher ranking during the network selection process. The discussed scheme implements several characteristics, including:  
* The HO management services, namely the velocity and alternative network monitoring, the HO initiation, the network selection, and the HO execution are performed at the Fog and the Cloud infrastructures to reduce the workload at the vehicle;
* Both predictive and reactive HOs modes are supported for the mobility transfer;* The vehicle's velocity is considered in order for unnecessary HOs to short-range cells (such as Femtocells) to be avoided, since a vehicle that moves with high velocity will remain for a short time period inside their coverage area;
* HO initiation is performed by taking into consideration the user satisfaction, which is estimated using both the Signal-to-Noise-plus-Interference (SINR) and the perceived Quality of Service (QoS) parameters, due to the fact that users that perceive a good SINR may not be satisfied by the QoS that they also perceive from their current networks;
* Network selection takes into consideration contradictory criteria for different application types and users' SLAs to satisfy the requirements of demanding services;
* The HO scheme applies an improved version of the MIH framework and the FP-MIPv6 protocol. The remainder of the paper is organized as follows: In Section 2, the related research literature is revised. Section 3 describes the proposed HO scheme, and Section 4 presents the implemented signaling process. Subsequently, Section 5 presents the simulation setup and the experimental results. Finally, Section 6 concludes the discussed work.
## 1 Introduction
In practical engineering, the vibration signals collected by sensors are usually disturbed by a large amount of noise. Combined with the previous research, for the vibration signal with noise, by increasing the number of layers of the network, the network can learn deeper and richer features. However, in the process of error back propagation, with the increase in network level, it is more likely to cause gradient disappearance or gradient explosion, and the training process will be difficult [29]. Secondly, the deeper the network layer is, the easier it is to cause network degradation and increase sample errors in the training process [30; 31]. At the same time, the more training samples, the more signal features the network can learn, but a large number of learning samples will lead to the over-fitting phenomenon of the network. Based on the above problems, this paper proposes a method that combines sparse representation with one-dimensional convolution neural network (SR\(-\)1 DCNN). Through the sparse representation of the original signal, the fault feature information is extracted effectively, and the signal is reconstructed. Then, the reconstructed signal is input into the 1 DCNN, which is used to extract deeper feature information and classification to complete the classification and diagnosis of various fault types of rotating machinery parts. The main contributions of this paper are as follows:  
1. An intelligent fault diagnosis method based on optimized adaptive learning dictionary and convolution neural network is proposed. The original signal is sparsely reconstructed directly by using adaptive learning dictionary and used as the input of 1 DCNN, which reduces the interference of noise to a great extent and fully retains the characteristic information at the same time. 2. Through the use of various techniques such as data enhancement and batch normalization, the robustness and generalization ability of the network model in different noise environments are improved. This method is more suitable for accurate and effective fault classification and diagnosis. 3. By using the bearing dataset of CWRU and the experimental dataset of the worm gear reducer of Shanghai University of Technology, it is proved that this method has better diagnosis accuracy and provides a better solution for the fault diagnosis of rotating machinery. The rest of the paper is organized as follows: Sections 2 and 3 are dedicated to the theories of sparse representation and convolution neural network. Details of the flow chart of the proposed method are presented in Section 4. In Sections 5 and 6, experiments are carried out with two different datasets, and a comprehensive evaluation and method comparisons are carried out. Section 6 makes a discussion on the method, and the conclusion is obtained in Section 7 at last.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
###### Abstract  
A web mapping XYZ Tile Layer Service, such as Google Earth (GE), provides an amazing resource for the visualization of spatial data against aerial and satellite imagery with global coverage, typically at a resolution finer than 5 m. However, the increasing requirement on spatial accuracy in farm information requires a greater appreciation of the issues involved in the use of such services. Position errors can be created in the georeferencing and orthorectification of images, transformation between reference frames (datums) in map projection, e.g., using a spheroid as compared to an ellipsoid earth model, and tectonic shifts. A review is provided of these issues, and a case study is provided of the horizontal positional accuracy of web map imagery for Australian mango orchards. Positional accuracies varied from 1.804 to 6.131 m across four farms using GE 2021 imagery, between 1.556 and 3.365 m in one farm for the most recent imagery available from each of four web map providers, and from 0.806 m (in 2016) to 10.634 m (in 2003) in one farm for the period of 2003 and 2021 using the historical GE imagery resource. A procedure involving the estimation of four transformation parameters was demonstrated for the alignment of GNSS data with GE imagery. However, as the scale factor was unity and the rotational value was near zero, the use of a simple horizontal mean shift vector was recommended. Further recommendations are provided on (i) the use of web mapping services, with a comparison of the use of UAV survey imagery, and (ii) the need for metadata, particularly the date of collection, on collected position data, in the context of use in farm management information systems.  
GDA2020; GDA94; WGS84; tectonic shift; visualization; web imagery +
Footnote †: journal: _agronomy_
# Development of Fire Consequence Prediction Model
## 3 Configuration of the Prediction Model
### Result of Accident Consequence Prediction Model  
The accident-consequence deep learning algorithm had five input parameters (leak hole, PPM1, Time 1, PPM 2, and Time 2), two output parameters (effect distance and heat radiation), 146 training data sets, and 26 test data sets. Five hundred training sessions were  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Constraints** \\ \hline \multirow{4}{*}{Input} & Leak hole & 1.19–89.42 mm \\  & PPM 1 & 54.978–66,216 PPM \\  & Time 1 & 0.04–18.88 s \\  & PPM 2 & 123,536–145,097 PPM \\  & Time 2 & 0.09–36.26 s \\ \multirow{4}{*}{Output} & Effect distance & 50.45–151.51 m \\  & Heat radiation & 12.5–137.78 kW/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input and output data (consequence prediction data).  
Figure 9: Jet fire according to the damage distance (Phast 7.11).  
performed with a learning rate of 0.0001. The nodes of the output layers were configured to finally converge to 2 by setting the number of hidden layers to 3, and the number of neurons was reduced to 10, 10, and 5 for each layer.  
Figure 10 shows the graph of the loss (MSE) of the accident consequence prediction model. No additional training was performed because the MSE values of the training and test sets converged to \(10^{-4}\) or lower.  
Finally, for the mean errors of the training and test sets, the effect distance of the consequence and the thermal radiation at this distance were set as the outputs of the algorithm. Table 4 presents the mean errors for each output set.  
For the effect distance of consequence, the training set and test set errors are 8.62% and 12.46%, respectively. Thus, the mean error rate is to approximately 10%. For the power, which is the thermal radiation, the training-set and test-set errors are 6.25% and 6.95%, respectively, which are lower than 10% and match each other well. Therefore, the mean error rate is lower than 5%. In the case where the mean error rate of the damage distance prediction is higher than 10%, the correlation analysis result for the damage distance shows a low correlation of 0.2 on average for other variables, excluding the hole size. Therefore, it is expected that the accuracy can be further improved if more data sets are secured.
## 4 Results and Discussion
The analysis results indicate that by optimizing the composition and properties of dielectric materials, it is possible to purposefully adjust the \"rate\" and \"mode\" of energy release during the discharge process, thereby influencing crack morphology and rock fragmentation efficiency. For instance, selecting oxide-based dielectrics can extend the energy release duration and enhance the persistence of shock waves, while utilizing high-conductivity solutions can accelerate energy deposition and rapidly create high-intensity shock wave scenarios. Future research will focus on the discharge characteristics of mixed dielectric types, with the potential to develop customized rock fragmentation strategies for engineering applications.
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 1 Introduction
### Contributions and Novelties  
This study's primary challenge was the lack of enough IoT radio noise data already in the environments. To the best of our knowledge, this research is the first attempt to examine the direct impact of IoT radio noise in South African industrial, urban, and suburban environments. At the time of this study, the IoT Industry observed limited deployment, contrary to predictions. As a critical contribution, this paper had to innovate by initiating the design, development, and production of IoT noise generators (ING) to emulate IoT product behaviors in radio transmissions. IoT radio noise surveying is necessary in many parts of the world. Even though the experiments took place in specific locations in Durban, this paper presents replicable routines for noise generation, data collection, and analysis to provide valuable trends for other places worldwide. To handle big data, the studies in [9; 10; 11] introduced parallel and distributed computing tools, such as Hadoop for data storage and MapReduce, Spark, Splunk, and Skytree for data processing. Applying these methods would be contrary to the objective of this paper to provide a simplified solution. This paper documents a strategic approach to data reduction on averaging by data segmentation [12].
# The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors
## 5 Conclusions  
Optical fibers are considered as a prominent sensor for condition monitoring in civil structures. Due to the existence of the adhesive layer, the strain transferred from the host structure to the surface bonded optical fiber is smaller than that in the host structure. Thus, the strain measured by the optical fiber needs to be modified to reflect the influence of the adhesive and bonding length, to consequently improve the accuracy of the measurements. In this work, a theoretical model to characterize the influence of the adhesive and bonding length on the strain transfer is presented to establish the relationship between the strains in the host structure and the surface bonded optical fiber. This relationship will eliminate the need for calibration tests prior to employment of the optical fiber in structural sensing applications. Experimental measurements of the optical fiber strain were conducted using the Mach-Zehnder interferometric technique, and compared with the theoretical predictions. Good agreement was observed between the experimental measurement and theoretical prediction. The percentage of strain in the host material actually transferred to the optical fiber is dependent on the bonding length of the optical fiber and the adhesive. Parametric studies showed that the longer the bonding length and the stiffer the adhesive, the more strain is transferred to the optical fiber. Present work explicitly demonstrates the importance of the adhesive and bonding length on the sensitivity of the optical fiber strain sensor. As a result, the structure strain sensed by the optical fiber is underestimated and thus required to be corrected. The present work can be used to calibrate the sensors and measure the quality of the attachment.  
The authors gratefully acknowledge the financial support provided by National Science Council of Republic of China under grant No. NSC 101-2622-E-155-015-CC3 for this work.  
Figure 7: Coefficient of strain transfer _versus_ bonding length for three different adhesives obtained by experimental measurement Equation (8) and theoretical prediction Equation (9).  
**Author Contributions:** These authors contributed equally to this work. [PERSON] designed the experimental tests. [PERSON] conducted the experimental tests and analyzed the experimental results. [PERSON] wrote the paper.  
**Conflicts of Interest:** There are no conflict of interest.
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Light Regulation of Sulphur Assimilation and Glutathione Metabolism
#### 4.3.2 Light Quality-Associated Regulation of Sulphur Assimilation and Glutathione Metabolism  
Sulphate reduction was induced by supplementary BL and was not affected by FRL, based on the amount of \(APR\) expression and the amount of Cys in wheat (Figure 5, Table A1) [67]. This effect is probably mediated by HY5, which depends on spectral conditions. BL proved to be more efficient than RL in the activation of HY5 at the transcriptional and post-transcriptional levels in _Arabidopsis_[80]. The ratio of the blue and red components in the spectrum regulates the binding of HY5 to the specific elements in the promoter of its target genes. HY5 may be responsible for the coordinated light-dependent transcriptional control of many metabolic pathways, including sulphate, nitrate, and carbon assimilation, which are interconnected by O-acetylserine (Figure 5) [165; 124; 168].  
As described for sulphate reduction, glutathione metabolism was also affected by the light spectrum (Figure 5). Supplementary BL did not affect the amount of GSH, whereas supplementary FRL even decreased it compared to WL in wheat, and both spectral modifications reduced the transcription of the _glutathione synthase_ gene [67]. The transcriptional regulation of the genes of GSH-metabolism by spectrum was also confirmed in maize, in which BL had a stronger effect on their expression compared to RL [169]. GSH, due to its central position in the redox system, may mediate the effect of the light spectrum on many metabolic processes, but the clarification of these regulatory mechanisms needs further investigations.  
Light spectrum-associated changes in the size and the redox state of the glutathione pool may affect the response to various abiotic stresses. This hypothesis was not confirmed in wheat since the reduction in the GSSG/GSH ratio by FRL was not accompanied by changes in the recovery after drought in a drought-tolerant wheat genotype [91]. Similarly, the growth of wheat seedlings in supplementary FRL also affected the redox state of the glutathione pool during cold treatment, but it did not alter the recovery of the plants compared to their cultivation in WL [92]. The change in the redox state of the glutathione pool may not derive from the transcriptional regulation of the genes involved in GSH synthesis as observed in wheat and _Arabidopsis_[170]. However, the enzymes involved in the reduction of GSSG or conjugation of GSH to other molecules may be controlled at the gene expression level. Accordingly, the transcription of the \(GR\) gene was repressed by supplementary FRL in wheat [67]. The expression and activity of GSTs are increased by RL [72; 167]. The involvement of a GST in phytochrome A-mediated signalling was demonstrated through its interaction with the FR insensitive 219 protein in _Arabidopsis_[171]. In addition, an HY5-controlled GST was identified in _Arabidopsis_[123]. GSTs, in turn, can modify the metabolic processes through the glutathionylation of their target proteins.
###### Abstract  
Radio-frequency technologies are widely applied in many fields such as mobile systems, healthcare systems, television and radio broadcasting, and satellite communications. However, one major problem in wireless communication based on radio frequencies is its impact on human health. High frequencies adversely impact human health more than low frequencies if the signal power transgresses the permissible threshold. Therefore, researchers are investigating the use of visible light waves (instead of the radio-frequency band) for data transmission in three major areas: visible light communication, light fidelity, and optical camera communication. In this paper, we propose a scheme that upgrades the camera on-off keying (COOK) scheme by using it with the multiple-input multiple-output (MIMO) scheme; COOK has been recommended by the IEEE 802.15.7-2018 standard. By applying technologies, such as matched filter, region of interest, and MIMO, our proposed scheme promises to improve the performance of the conventional scheme by improving the data rate, communication distance, and bit error rate. By controlling the exposure time, the focal length in a single camera and using channel coding, our proposed scheme can achieve the communication distance of up to 20 m, with a low error rate.  
optical camera communication; camera on-off keying; multiple-input multiple-output camera on-off keying +
Footnote †: journal: Sensors