esa-sceva/satcom-chunk-collection huggingface dataset:
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 4 Experimentation and Evaluation Results
### Comparison Results of the Two Algorithms Set on Close-By Tomato Fields  
The FITRA irrigation algorithm prevails over the majority MVA algorithm at the following:  
1. It can save water up to 7%. Comparing the results with the majority algorithm, the FITRA irrigation algorithm manages to keep ground moisture 12.5% wetter with 7% less water consumption.
2. The FITRA algorithm can achieve higher crop yield that existing algorithms by up to 14%.
3. The FITRA algorithm pours water smoother than the majority algorithm, assisted by adaptive fuzzy or NN process and achieving more targeted watering cycles according to the plant's needs. The result of such adaptation is that with the central valve changes it takes fewer times for smaller time intervals and when needed. Moreover, the FITRA watering temp control process prevents the algorithm from extreme temperature condition irrigation.
4. The farmer can weigh the sensors value based on the sensor's distance from the actuator, while in the majority algorithm all sensor measurements have the same weight.
5. As well as ground moisture, the FITRA algorithm monitors the air temperature. It capable of interrupting (WTC process) the irrigation in extreme temperatures, so as to avoid the freezing or the burning of the plants.
6. The FITRA algorithm monitors the sensor values for errors or transition to erroneous states. If a value is out of logical range it is skipped and is not taken into consideration for the next calculation interval.
## 4 Experiments and Results
### Field Testing in Relevant Environment  
The LAnTeRN proof-of-concept system was taken to Exelis' outdoor test range, where it was set up and operated under a variety of conditions. The results of the field tests show that the system is measuring the differential transmission accurately to within about 0.5% relative to the model. The model used LBLRTM with a CO\({}_{2}\) concentration of 380 ppm and the 1976 US standard atmosphere and temperature and pressure were taken from a local weather station. Until an absolute calibration can be performed, this level of performance verification is very good. The data also show the improvement in SNR that can be had by integrating (averaging) for longer periods of time. Figure 8 shows that the averaging performance of the system closely matches the expected performance. Figure 9 shows the ratio averaged at different integration times to show how significantly longer lock-in periods reduce the measurement noise. Note that  
However, we believe the model under predicts the system SNR at low optical signal powers because it potentially underestimates the reduction of equivalent noise bandwidth resulting from the digital lock-in processing. This effect is not seen at higher return powers where atmospheric scintillation becomes the dominant noise term over the system electronic noise. Also note in the table that the model currently overestimates the SNR for the high signal and longer integration case. This is due to a limitation imposed by the transmitter's ability to maintain a fixed ratio of the transmitted beams. The current system and algorithm are only able to maintain that ratio to about one part in 1,000, ultimately setting the maximum SNR that can be achieved through averaging.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 4 Searching the CSS Based on the Wavelet Packet  
In finite element analysis, due to the interpolation characters of the isoparametric elements, \(y_{i}\) (\(x_{i}\)) is not on a smooth curve but in the form of a wave. Each line has only one maximum point, and these points constitute a set of good signal data \(y_{i}\) (\(x_{i}\)). Our task is to use a wavelet packet to smooth these points and locate the CSS.  
The algorithm steps of the proposed method are as follows:  
1. Combined with FEM, the SRM is used to bring the slope into the LES and obtain the FOS of the slope.
2. The coordinates of the maximum equivalent plastic strain point are sought in the plastic zone of the slope. Firstly, a set of parallel lines are arranged along the slope depth direction, then the corresponding search points are deployed on each line, and finally the coordinates of the maximum equivalent plastic strain point are searched by interpolation.
3. The obtained data points are decomposed by wavelet packet. According to the given signal data, a suitable wavelet basis function \(\psi_{a,b}\) and the decomposition level is determined. Although there exist a huge number of possible wavelet bases, this paper is limited to the Daubechies orthogonal wavelet family. After the test, the Daubechies six-coefficient wavelets were used in all work.
4. The wavelet packet coefficients are processed by threshold quantization. According to the distribution of the data and its high-frequency part, each frequency band's wavelet packet decomposition coefficients are processed by a threshold.
5. Wavelet packet reconstruction is performed. The processed wavelet packet decomposition coefficients are reconstructed by wavelet packet, and the location of the CSS is obtained by connecting the corresponding data points.  
The implementation of the proposed method is summarized in the flow chart as in Figure 2.
# Magnonic Metamaterials for Spin-Wave Control with Inhomogeneous Dzyaloshinskii-Moriya Interactions
###### Abstract  
A magnonic metamaterial in the presence of spatially modulated Dzyaloshinskii-Moriya interaction is theoretically proposed and demonstrated by micromagnetic simulations. By analogy to the fields of photonics, we first establish magnonic Snell's law for spin waves passing through an interface between two media with different dispersion relations due to different Dzyaloshinskii-Moriya interactions. Based on magnonic [PERSON]'s law, we find that spin waves can experience total internal reflection. The critical angle of total internal reflection is strongly dependent on the sign and strength of Dzyaloshinskii-Moriya interaction. Furthermore, spin-wave beam fiber and spin-wave lens are designed by utilizing the artificial magnonic metamaterials with inhomogeneous Dzyaloshinskii-Moriya interactions. Our findings open up a rich field of spin waves manipulation for prospective applications in magnonics.  
spin waves; Dzyaloshinskii-Moriya interaction; ferromagnetism; spintronics Article
## 3 Outline of the Simulation Method
### Validation of the Simulation Method  
The validation of the simulation method employed in this study was conducted by comparing its outcomes with the findings presented by [PERSON] and [PERSON] [8]. Figure 2 illustrates the comparison between the results of the normalized bearing capacity obtained through RPFEM and those reported by [PERSON] and [PERSON] [8]. The material properties considered in this analysis include the cohesion \(c\) with a value of 65.6 kPa, the shear resistance angle \(\phi\) set at 13.5 degrees, and the unit weight (\(\gamma\)) equal to 18 kN/m\({}^{3}\). The bearing capacity ratio (BC/BC\({}_{\rm NV}\)), introduced in this study to convey the bearing capacity results, is defined as the ratio of the footing's bearing capacity on soil with a cavity to that on soil without a cavity. While a slight discrepancy is acknowledged, attributed to differences in the constitutive model, a general alignment in the trend of the bearing capacity ratio is evident in both cases, as depicted in Figure 2.  
The comparison yields a noteworthy observation, suggesting a good level of reliability in the simulation method applied in this study. It is important to emphasize that the slight variation in results is primarily linked to distinctions in the constitutive models utilized. Consequently, the overall agreement in the trend of the bearing capacity ratio supports the credibility of the simulation method in this investigation.
## 4 Materials and Methods
### Dataset
#### 4.2.3 Remote Sensing Data  
The remote sensing data used in this study included the GLASS _LAI_ product dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type product dataset (MCD12Q1) from the United States Geological Survey (USGS) acquired between 2003 and 2012. The _LAI_ data were from the characteristic parameter product of GLASS. Based on the _LAI_ products, such as MODIS and VEGETATION, as well as surface measurement data of global _LAI_, this product adopted the time series observation data integrated using a generalized neural network algorithm, making it suitable for long-term monitoring of vegetation growth on a regional scale [39]. The spatial resolution was 1 km, the temporal resolution was 8 d, and the time scale used in the study was from 2003 to 2012.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 3 Results
### Pre-Verification
#### 3.1.2 Frequency Ratio Verification  
The impedance ratios were calculated with multiple combinations of frequencies in order to find out the optimal frequency ratio. It is possible to compare the performance in these subsequent experiments with the neural network-based multifrequency impedance method for root canal length measurement. In addition, frequency ratio verification provided a reference for the following feature selection and evaluation.  
The impedance ratio results, according to the frequency combinations, are illustrated in Figure 10. The tooth type and the file were kept constant.  
Figure 8: The impedance of different teeth varying with the distance between the file and the apical foramen.  
Figure 9: The impedance of the file at different position from apical foramen varying with frequency.  
In Figure 10, the impedance ratios did not change significantly when the file was far from the apical foramen. When it was close to the apical foramen (especially when the distance was less than 1 mm), the impedance ratio dropped rapidly, while the gradient was steepest at the apical foramen. Moreover, the larger the difference between the high and low frequencies, the more significantly the impedance decreased when the file was close to the apical foramen; in contrast, it was not beneficial to determine the position of the file. The data for Figure 10 were obtained under the conditions of a molar tooth type and file #15; the curve trend was similar for other conditions.  
The aforementioned phenomena correspond with feature selection. The impedance ratios selected as features with large variances were the impedance ratios of substantially divergent high and low frequencies.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol  
### Vulnerability to Replay Attack  
The initially specified version of the SRUP protocol provides a good degree of security against a malicious party changing or spoofing the C2 messages that are being sent to the device. By using cryptographic signatures, applied over all of the data contained within the SRUP message, any part of the data that is altered will cause the signature to fail to validate against the content of the message. In the context of software update, further assurance is provided by including the output from a secure hashing function (SHA-256), to validate the integrity of the file to be retrieved.  
There is, however, a potential vulnerability that remains in the implementation: namely potential susceptibility to \"replay attack\". A replay attack is one where a malicious party captures a message and re-transmits it at a future time in order to make the receiving device perform the previous operation again. In the case of the initial implementation of SRUP (which only implemented the update message-set), there is a relatively low risk associated with this: the attacker would only be able to make the device download a valid update. However, with patience, after capturing an initial update message and after detecting a second update, it would in theory be possible to roll the device back to a previous version. It may also be possible to use this mechanism to stage a denial-of-service attack against a specific device by constantly sending it messages forcing it to apply the same update (and potentially taking it off-line whilst it did so). Clearly, as the protocol grows to add different classes of message, the potential damage of a message being replayed to a device is much higher.
# Research and Evaluation Method for Echelon Usage of Double-Life Equipment Considering Calendar Life
## 3 Analysis and Solution
### When the Number of Units Used Each Time Is Less Than Total: \(M<n\)
#### 3.2.1 Maximum Working Life Controllable Range  
Based on the number of units used, \(M<N\), the consumption of each working life is uncertain, but it can be seen that the maximum consumption of a single unit of equipment per month is \(Ty\div M\). No matter how much a single piece of equipment is used, it cannot exceed this amount, so the working life (\(Twl_{i}\)) and calendar life (\(Tcl_{i}\)) should meet the following conditions:  
\[\frac{Twl_{i}}{Tcl_{i}}\leq\frac{Ty}{M},i\in[1,N],M\leq N\]  
Under the condition that the above formula is satisfied, there is still a situation that life waste cannot be avoided through usage and control. As shown in Figure 3, the working life of the diagonal part of equipment No.07 will be wasted.  
Assuming that the working life of the first \(i\) equipment is within the maximum life curve, the optimal decision can be found through usage and control, and the optimal decision can be found. The optimal decision in the first month is \(f(1)\), and the optimal decision in the \(Tcl_{i}\) month is \(f(Tcl_{i})\). After \(Tcl_{i}\) months, the working life of the \(i\) equipment and the previous equipment is consumed at the end of the calendar life, and then the maximum working life of the first \(i\) equipment in \(Tcl_{i}\) months for the task consumption is:  
\[\begin{array}{l}Max(\sum\limits_{j=1}^{i}Twl_{j})=\sum\limits_{j=1}^{Q}Ty+ \sum\limits_{j=P}^{Tcl_{i}}(\frac{Tcl_{i}-j+1}{M}Ty)\\ Q=\text{ReLU}(Tcl_{i}-M)\\ P=\text{ReLU}(Tcl_{i}-M+1)\\ \end{array}\]  
The maximum working life of the equipment No.\(i\) is:  
\[MaxTwl_{i}=Max(\sum\limits_{j=1}^{i}Twl_{j})-\sum\limits_{j=1}^{i-1}Twl_{j}\]  
The controllable range of the equipment No.\(i\)'s working life is:  
\[0\leq Twl_{i}\leq\min(MaxTwl_{i},Tcl_{i}\frac{Ty}{M}) \tag{1}\]  
Figure 3: Echelon usage diagram with \(M<N\).  
The maximum life curve is drawn according to the maximum life value of each equipment item, as shown in Figure 3.  
When the working life of equipment No.\(i-1\) is within the controllable range, and equipment \(i\) meets the above formula at the same time, the working life of the equipment \(i\) is within the controllable range. Using the idea of dynamic programming, the constraint conditions of the controllable range of working life are analyzed and solved. That is to say, to determine the controllable range of the working life of the \(i\) equipment, we should first find out whether the working life of equipment No.\(i-1\) is within the controllable range, and by analogy, we can determine whether the working life of the subsequent equipment is within the controllable range from the working life of the first piece of equipment.
## 2 Materials and Methods
### ProstateX-2 MR Imaging and Image Data  
The ProstateX-2 data was obtained from the Cancer Imaging Archive (TCIA) [22; 23], and included MR images, lesion centroid coordinates, and MR-guided biopsy Gleason scores (GS). The PIRADS scores of these lesions were not included in the dataset. MRI exams were performed on two Siemens 3T MR scanners (Magnetom Trio and Skyra, Siemens, Munich, Germany) without an endorectal coil. Image data included: (a) small field-of-view (FOV) axial T2 (Transverse Relaxation Time) turbo spin-echo sequence with 0.5 mm resolution and 3.6 mm slice thickness; and (b) single-shot echo-planar diffusion-weightedimaging (DWI) with 2 \(\times\) 2 \(\times\) 3.6 mm resolution, and b-values of 50, 400, 800 s/mm\({}^{2}\); the Apparent Diffusion Coefficient (ADC) map was calculated by the scanner software. Additional multiplanar T2 and dynamic contrast-enhanced sequences were included in the dataset but were not used in this study. The three-dimensional centroid Digital Imaging and Communications in Medicine (DICOM) coordinates of the prostate lesions were provided for lesion localization. The axial T2 and ADC map image containing the centroid of the lesion were utilized in this study.
# AI-Enabled Compact and Efficient Dynamic Light Scattering System for Precise Microparticle Sizing
###### Abstract  
A cost-effective and efficient AI-DLS framework integrating dynamic light scattering (DLS) with artificial intelligence (AI) enables precise microparticle size characterization. A compact DLS system was developed using a helium-neon laser, high-frequency photodetectors, and optimized optical components, achieving significant miniaturization--4.5% volume, 16.7% weight, and 25% cost of conventional systems. Advanced signal processing, such as Kalman filtering, improved data quality, while deep learning models (deep neural network (DNN), one-dimensional convolutional neural network (1D-CNN), and 2D-CNN with Gramian angular field transformations) enhanced prediction accuracy. The 2D-CNN model achieved exceptional results, with a mean absolute error of 10 nm and 99% accuracy. The AI-DLS system matched the stability and accuracy of traditional instruments, reducing test time by 80%. This scalable, low-cost solution overcomes traditional DLS limitations, offering broad accessibility for scientific and industrial microparticle characterization.  
dynamic light scattering; particle size measurement; convolutional neural networks; gram angle field +
Footnote †: journal: Applied sciences
## 4 Experimental Evaluation
### Model Comparison Experiments  
#### 4.3.1 Experimental Setup  
To select a suitable model for Shui manuscript character detection, we conducted two sets of experiments using different combinations of Shui manuscript image datasets as input. Because the image size required by the SSD is 300 \(\times\) 300 pixels, we need to crop the large image to this size. Therefore, Experiment 1 and Experiment 2 use different datasets. Table 3 shows the dataset information for the two sets of experiments, and each dataset has a large number of text images, and each image contains many samples of Shui characters. The dataset used in Experiment 1 contains 60,124 Shui manuscript images, all without data enhancement. The images had 81,371 character samples in 114 categories and were all in \"jpg\" format with a size of 300 \(\times\) 300 pixels. The dataset used in Experiment 2 was Shuishu_T introduced in Section 3.1, which contained 10,853 Shui manuscript images. The images had 164,789 character samples in 111 categories and were all in \"jpg\" format with a size of 1002 \(\times\) 1509 pixels. To ensure a consistent data distribution, we used the hold-out method to divide the dataset. We followed the usual practice of dividing the training and test sets in a 7:3 ratio and selecting a small number of samples from the training set to form the validation set.
###### Abstract  
State-of-the-art semantic role labeling (SRL) performance has been achieved using neural network models by incorporating syntactic feature information such as dependency trees. In recent years, breakthroughs achieved using end-to-end neural network models have resulted in a state-of-the-art SRL performance even without syntactic features. With the advent of a language model called bidirectional encoder representations from transformers (BERT), another breakthrough was witnessed. Even though the semantic information of each word constituting a sentence is important in determining the meaning of a word, previous studies regarding the end-to-end neural network method did not utilize semantic information. In this study, we propose a BERT-based SRL model that uses simple semantic information without syntactic feature information. To obtain the latter, we used PropBank, which described the relational information between predicates and arguments. In addition, text-originated feature information obtained from the training text data was utilized. Our proposed model achieved state-of-the-art results on both Korean PropBank and CoNLL-2009 English benchmarks.  
Keywords:Korean semantic role labeling; BERT; semantic information; text-originated feature information
# Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves
day\({}^{-1}\). Though several studies have been attempted to understand the source and sink strengths of the Sundarbans mangroves, there is a huge uncertainty in annual estimates due to variability of CO\({}_{2}\) uptake across days over a year. Since the studies so far are mainly for short time periods, the annual CO\({}_{2}\) sink strength of this complex mangrove system remains unknown. Recent studies by [PERSON] _et al._ focus on estimates of carbon sources and sink strengths in various global forests [22]. A huge data gap for the global carbon sequestration estimate in the Indian subcontinent was evident. The present study will also provide the first continuous and accurate measurements of carbon sequestration from Indian mangroves on an annual basis. This study is an initiative under the Indian Space Research Organisation--Geosphere Biosphere Programme (ISRO-GBP) under the 'National Carbon Project (NCP)'. One of the main objectives of NCP is to establish a network of EC flux towers in different representative forest ecosystems in the Indian subcontinent to understand their CO\({}_{2}\) source and sink strengths [23; 24; 25]. As part of this, an eddy covariance flux tower was established in mangrove forests in the Sundarbans Biosphere Reserve in April 2012 and has been taking measurements since then. The present EC flux tower stands as one of the few towers established in mangrove ecosystems across the world (FLUXNET). The main objective of the project is to continuously measure and understand vegetation-atmosphere exchanges of CO\({}_{2}\) and energy fluxes over Sundarbans (India). The current study is an extension of measuring CO\({}_{2}\) and H\({}_{2}\)O fluxes over the flux tower site and presents the first annual results (April 2012-March 2013) of NEEmeasurements over the Sundarbans mangroves, India. The paper also discusses NEE measurements partitioned into GPP and \(R_{e}\) (ecosystem respiration, both autotrophic and heterotrophic respiration). Further, we discuss the partitioning of net radiation into sensible and latent heat fluxes during the study period.