esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multi-Scale Mathematical Morphology  
Multi-scale mathematical morphology can better filter acoustic signals. Suppose \(\varepsilon(\varepsilon=1,2,\ldots\lambda)\) is the scale of the structural element s, then SE can be expressed as:  
\[\varepsilon s=\underbrace{s\oplus s\oplus\cdots\oplus s}_{\lambda- lifetimes} \tag{8}\]  
The multi-scale dilation operator and erosion operator are defined as follows:  
\[x\oplus\varepsilon s=x\oplus\underbrace{\left(s\oplus s\oplus\cdots\oplus s \right)}_{\lambda- lifetimes} \tag{9}\]  
\[x\Theta\varepsilon s=x\Theta\underbrace{\left(s\Theta s\Theta\cdots\Theta s \right)}_{\lambda- lifetimes} \tag{10}\]  
The multiscale opening and closing operator of signal xi can then be further defined as:  
\[x\circ\varepsilon s=\left(x\Theta\varepsilon s\right)\oplus\varepsilon s \tag{11}\]  
\[x\circ\varepsilon s=\left(x\oplus\varepsilon s\right)\Theta\varepsilon s \tag{12}\]  
Ultimately, the multi-scale EAVGH can be defined as:  
\[EAVGH(x_{\text{gg}})=x-\frac{\left(x\bullet\varepsilon s\right)\oplus \varepsilon s+\left(x\circ\varepsilon s\right)\Theta\varepsilon s}{2} \tag{13}\]  
In the process of multi-scale mathematical morphological filtering, the selection of structural element (SE) scales significantly influences the filtering results. Choosing smaller SE scales can preserve more fault feature information but may compromise noise reduction performance. Conversely, selecting larger SE scales can enhance noise reduction performance at the expense of reducing fault information [29; 30; 31; 32]. This further demonstrates that scale selection of structural elements can yield different filtering performances. Therefore, this paper employs multi-scale mathematical morphology as a network and utilizes backpropagation to adaptively determine the SE scale for EAVGH.
## 3 The Design of the Adaptive Fuzzy Sliding-Mode Controller
Substituting the functions (18), (22), (23) and (25) into the formula, the fractional differential form is deformed as the following formula:  
\[c\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}s_{i}(t) =\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}e_{i}+m_{i}e_{i}-{}^{C}D_{t}^{ \alpha}h_{i}(t)\] \[=\sum_{i=1}^{n}f_{i}(x)+g_{i}(x)+d_{i}(t)+u_{i}(t)-{}^{C}D_{t}^{ \alpha}x_{id}+m_{i}e_{i}-{}^{C}D_{t}^{\alpha}h_{i}(t)\] \[=\sum_{i=1}^{n}f_{i}(x)+g_{i}(x)+d_{i}(t)+(-f_{i}(x,t)-\hat{g}_{i }(x|\theta_{ig})+{}^{C}D_{t}^{\alpha}x_{id}-m_{i}e_{i}\] \[-\hat{u}_{isw}(s_{i}|\theta_{iu})+{}^{C}D_{t}^{\alpha}h_{i}(t)- \hat{\delta}_{i})-{}^{C}D_{t}^{\alpha}x_{id}+m_{i}e_{i}-{}^{C}D_{t}^{\alpha}h _{i}(t) \tag{28}\] \[=\sum_{i=1}^{n}g_{i}(x)-\hat{g}_{i}(x|\theta_{ig})-\hat{u}_{isw}( s_{i}|\theta_{iu})+d_{i}(t)-\hat{\delta}_{i}\] \[=\sum_{i=1}^{n}w_{i}+\hat{g}_{i}(x|\theta_{ig}^{*})-\hat{g}_{i}(x |\theta_{ig})-\hat{u}_{isw}(s_{i}|\theta_{iu})+d_{i}(t)-\hat{\delta}_{i}\] \[\leq\sum_{i=1}^{n}\tilde{\delta}_{i}+\hat{g}_{i}(x|\theta_{ig}^{* })-\hat{g}_{i}(x|\theta_{ig})-\hat{u}_{isw}(s_{i}|\theta_{iu})\]  
From Formula (28), the fractional differential form of the Formula (27) is  
\[{}^{C}D_{t}^{\alpha}V_{2}(t) \leq\sum_{i=1}^{n}\left(s_{i}(t){}^{C}D_{t}^{\alpha}s_{i}(t)+ \frac{1}{r_{i}}\bar{\theta}_{ig}^{T}{}^{C}D_{t}^{\alpha}\bar{\theta}_{ig}+ \frac{1}{l_{i}}\bar{\theta}_{iu}^{T}{}^{C}D_{t}^{\alpha}\bar{\theta}_{iu}+ \frac{1}{\gamma_{i}}\bar{\delta}_{i}^{C}D_{t}^{\alpha}\bar{\delta}_{i}\right)\] \[\leq\sum_{i=1}^{n}\left(s_{i}(t)(\tilde{\delta}_{i}+\hat{g}_{i}(x |\theta_{ig}^{*})-\hat{g}_{i}(x|\theta_{ig})-\hat{u}_{isw}(s_{i}|\theta_{iu}) \right)+\frac{1}{r_{i}}\bar{\theta}_{ig}^{T}{}^{C}D_{t}^{\alpha}\theta_{ig}\] \[c\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}s_{i}(t) =\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}e_{i}+m_{i}e_{i}-{}^{C}D_{t}^{ \alpha}h_{i}(t)\]0 \[c\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}s_{i}(t) =\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}e_{i}+m_{i}e_{i}-{}^{C}D_{t}^{ \alpha}h_{i}(t)\]1 \[c\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}s_{i}(t) =\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}e_{i}+m_{i}e_{i}-{}^{C}D_{t}^{ \alpha}h_{i}(t)\]2 \[c\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}s_{i}(t) =\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}e_{i}+m_{i}e_{i}-{}^{C}D_{t}^{ \alpha}h_{i}(t)\]3 \[c\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}s_{i}(t) =\sum_{i=1}^{n}{}^{C}D_{t}^{\alpha}e_{i}+m_{i}e_{i}-{}^{C}D_{t}^{ \alpha}h_{i}(t)\]4According to the adaptive control laws (19)-(21), the fractional differential form of the Formula (29) can be approximated as  
\[ \begin{split} C D_{t}^{\alpha}V_{2}(t)&\leq\sum_{i=1}^ {n}-s_{i}i_{i}\dot{a}_{isw}(s_{i}|\theta_{iu}^{\alpha})\\ &\leq\sum_{i=1}^{n}-\eta_{i}|s_{i}|\end{split}  \tag{30}\]  
To make \({}^{C}D_{t}^{\alpha}V_{2}(t)<0\), the parameters \(\eta_{i}\) must satisfy that \(\eta_{i}>0\). From (27) and (30), the AFSMC satisfies the Lyapunov theory, and to verify the correctness and feasibility of the designed method, simulation experiments and the results are given in the next chapter. From the mathematical analysis, the improved AFSMC works well in solving the complex challenges associated with control systems affected by various external factors.
## 3 System Model and Problem Formulation
### Calculation Model
#### 3.3.2 Edge Computing Model  
If an M-IoT device offloads part of its computational tasks to a UAV, the resulting delay consists of three parts: the communication time to transmit the task to the UAV, the delay in executing the task on the UAV, and the time required to transmit the computation results back to the M-IoT device. The computational cost is determined by the energy consumption and task execution delay of the M-IoT device. Due to the relatively small size of the data being returned, the delay and energy consumption in terms of transmitting the results are generally negligible [30].  
Let \(a_{ij}\) represent the connectivity indicator variable between the \(i\)-th M-IoT device and the \(j\)-th UAV, indicating whether it satisfies the following:  
\[a_{ij}\in\{0,1\},\forall i\in\mathcal{N},j\in\mathcal{M} \tag{8}\]  
Here, \(a_{ij}=1\) indicates that the \(i\)-th M-IoT device decides to offload its task to the \(j\)-th UAV; otherwise, \(a_{ij}=0\).  
It is worth noting that each M-IoT device can connect to at most one UAV, leading to the following constraint:  
\[\sum\limits_{j=1}^{M}a_{ij}\leqslant 1 \tag{9}\]  
For the \(i\)-th M-IoT device, the processing delay on the UAV consists of the time for task offloading transmission and the task processing time. Therefore, it can be calculated as:  
\[T_{i}^{u}=\sum\limits_{j=1}^{M}a_{ij}\Bigg{(}\frac{D_{i}^{u}}{R_{ij}}+\frac{D_ {i}^{u}C_{i}}{f_{ij}^{u}}\Bigg{)} \tag{10}\]  
Here, \(f_{ij}^{u}\) represents the computational resources allocated to the \(i\)-th M-IoT device by the \(j\)-th UAV, which satisfies \(\sum\limits_{i=1}^{N}f_{ij}^{u}\leq F_{\text{max}}^{u}\), where \(F_{\text{max}}^{u}\) denotes the maximum CPU computing frequency of each UAV.  
The energy consumption generated by offloading the task of the \(i\)-th M-IoT device to the \(j\)-th UAV is defined as:  
\[E_{i}^{u}=\sum\limits_{j=1}^{M}a_{ij}\frac{P_{ij}^{u}D_{i}^{u}}{R_{ij}} \tag{11}\]
## 3 Materials and Methods
### Model Architecture Design  
The multi-modal Transformers for _Rchb. f_. quality grading was developed, which includes 1 input embedding layer, 1 UNet, 1 ViT, N Transformer Encoder layers, 1 subsequent pooling layer, 1 linear layer, and 1 Softmax layer. The input embedding layer transformed each feature indices into a dense vector representation. This enabled the model to capture the semantics of features within a continuous numerical vector space, while also placing the features in a learnable embedding space, providing more accurate inputs for subsequent operations. Subsequently, the dense vectors containing feature indices information are element-wise multiplied with the feature values through padding and dot product operations. Since missing feature values have been replaced with padding value 0 in feature values array, the padding value canceled out the corresponding missing feature indices by dot product operation. Through this novel design, the model can precisely learn both the existence of input features and the feature values.  
UNet was used to perform semantic segmentation on _Rchb. f_. images. Dot product operation combined the average RGB value of UNet-segmented penal images with the input embedded feature indices and the corresponding feature values, so that the model understood color information. ViT acted as the primary feature extractor for UNet-segmented penal images, producing a rich feature vector that encapsulates the global visual information of orchid penal. The vector concatenated with the output from the previous dot  
Figure 1: (**a**) Construction of Transformer Encoder and its self-attention. (**b**) UNet construction for metal segmentation.  
product operation of the embedded feature indices and corresponding values. Then, the concatenated features were input into Transformer Encoder and processed through self-attention. The Transformer Encoder's layer needs to be deepened to improve the network's ability to learn from the training set, tentatively to N layers. A pooling operation averaged the output from Transformer Encoder, reducing the data's dimensionality while retaining critical information. The pooled output was then fed into a linear layer and mapped to a dimension consistent with the number of orchid levels. A Softmax layer converts these linear outputs into probabilities, each reflecting the likelihood that an orchid belongs to a particular quality grade. Finally, the architecture of multi-modal Transformers for _Rchb. f_. quality grading was constructed, as shown in Figure 2.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 7 Conclusions  
In this paper, a methodology to design, configure, and deploy a reliable ultra-low power WSN was proposed. It can yield a better energy efficiency, latency, and reliability. Therefore, a comprehensive energy model of the sensor node, along with a modeling framework, were presented and validated through measurements. In addition, a realistic PL model was presented for both urban and suburban areas and based on field measurements. Then, after measuring the noise, the achievable data rate was determined in order to ensure energy efficiency and short latency. Furthermore, in order to mitigate wireless transmission errors, three error correcting techniques were studied and compared in terms of energy consumption, latency, and reliability.  
Figure 27: (**a**) Energy consumption per measurement and (**b**) system latency for a 99.999% reliability.  
Figure 26: Meeting 99.999% reliability target by using BR and 3-Rep-ACK retransmissions.  
Based on the model and analysis presented, a methodology which logically links the comprehensive design and deployment steps of an ultra-low power and reliable WSN was also presented and detailed.  
By using estimations and measurements, it was shown that, following the proposed methodology, the designer can thoroughly explore the design space, make the most favorable decisions when selecting SN components, and efficiently configure and deploy a WSN while taking into account the energy-reliability-latency trade-off of different error correction techniques. Through case studies, it was demonstrated how energy, latency, and reliability are interrelated and traded-off against each other, notably with respect to successful packet transmission probability metrics.  
Therefore, the outcomes of this paper can have a significant impact on the design of WSN in a wide range of energy and latency conscious applications. Whether in smart city, precision agriculture, or other monitoring and control applications, the proposed models and methodology can lead to a substantial improvement of a network's lifetime while recognizing and meeting QoS requirements.  
[PERSON] conceived and designed the experiments, performed the experiments, analyzed the data, and wrote the manuscript; [PERSON] and [PERSON] contributed to the ideas behind the manuscript, supervised the work, and edited the manuscript.  
This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).  
The authors declare no conflict of interest.
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
#### 4.3.2 FRM4 VEG Best Practices  
The Fiducial Reference Measurements for Vegetation (FRM4 VEG) project was initiated by ESA in 2018 [152] with the objective of establishing the protocols required for traceable in-situ measurements of vegetation-related parameters. In this respect, FRM4 VEG actively contributes to CEOS-LPV endeavour in defining best practices for Cal/Val in the land domain with a reinforced focus on the need of traceability and uncertainty estimate, following FRM generic principles. The main operational goal of the FRM4 VEG project was to support the validation of ESA/Copernicus land products generated from the Sentinel-2, Sentinel-3 and Proba-V missions, namely the SR products and the S3 OTCI and OGVI products.  
The first phase of FRM4 VEG project was concluded at the end of 2019 with the elaboration of initial best practices for in situ measurements and methodologies for validation of satellite-derived SR, fAPAR and Chlorophyll content products. This first phase also included the preparation and execution of two field campaigns over two vegetated sites in Europe in the Wytham Woods forest site (UK) and the Barrax agricultural site (Spain). These sites are compliant to FRM generic principles (FRM sites) since they are well characterised and the field measurements within these sites are traceable and follow well-established best practices. A detailed uncertainty budget could therefore be reliably estimated for the validation of satellite-based geophysical products over these FRM sites. An example of such approach was demonstrated in a recent paper, focusing on the validation of S2 and Proba-V SR products over the Barrax cropland site [153]. This paper demonstrated that the adoption of rigorous metrological practices for uncertainty characterisation allows for evaluating the statistical consistency of satellite and in situ data and to assess the quality of the stated uncertainties.  
The second phase of FRM4 VEG started in February 2021 for two years with a planned new field campaign, and with the overall objective of evolving Cal/Val best practices, notably for SR. In the frame of this new phase, a CEOS-endorsed initiative called the Surface Reflectance Intercomparison Exercise (SRIX4 VEG) will be carried out during Summer 2022 [154]. The SRIX4 VEG will be a field inter-comparison campaign calling for the contribution of different Cal/Val teams around the world with the objective of working towards the definition of community-agreed guidelines for drone-based surface reflectance product validation.
## 5 Results and Discussion
### Comparison of Model Results
#### 5.3.1 OLS Regression Analysis  
After the spatial autocorrelation test, multicollinearity analysis, and standardization of the data of independent variables and dependent variables, IBM SPSS Statistics 27 software[48; 49] was used for multiple linear regression modeling analysis. The results of OLS model regression analysis were obtained, as shown in Table 5 below.  
As analyzed in Table 5, the \(p\) value of the t-test of regression coefficients of the five independent variables is less than 0.05, indicating that there is a significant correlation between these independent variables on the dependent variable. Meanwhile, the VIF values of all independent variables are lower than 7.5, indicating that there is no multicollinearity among the five independent variables. The R\({}^{2}\) value of the model is 0.476, which indicates that the model has some correlation and the model fit is average. The t-value of \(X_{5}\) is 6.66, and the regression coefficient B is 0.601, both of which are the highest, indicating that the government and the corporate offices' high significance influences the passenger flow of the railroad transportation. The regression coefficient of \(X_{2}\) has a negative value (B = \(-\)0.163), which indicates that the average house price is globally negatively correlated to the passenger flow.
## 3 Results  
### Synthesis  
A total of 157 records were initially identified from the database searches, after applying the inclusion and exclusion criteria and removing duplicate entries. Screening of the titles and abstracts resulted in 50 studies being chosen for full-text evaluation. A total of 30 publications fulfilled the eligibility requirements and were incorporated into this scoping review. The entire selection process and its results are illustrated in the PRISMA flow diagram (Figure 1).  
The details of the data abstraction, including an overview of the study design, population size, sample characteristics, methodology, primary parameters, observed outcomes, and key findings for all of the studies included in the review, are summarized in Table 1.  
The studies reviewed here were conducted in various countries, including Australia [25; 26; 27; 28], New Zealand [29; 30; 31], China [32; 33; 34; 35; 36; 37], South Korea [38], USA [39; 40; 41; 42; 43], United Kingdom [44], Indonesia [45], and Russia [46; 47].  
From the 30 studies included, 3 were epidemiological studies [27; 48; 49], 24 were interventional clinical trials [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53], and 3 were not dietary intervention studies [22; 46; 47].  
In terms of the population studied, 12 studies involved children and/or adolescent population [22; 25; 27; 34; 35; 37; 38; 45; 47; 53; 54; 55; 38]. The rest of the trials involved adults.  
Human studies investigating the potential effects of \(\beta\)CM-7 are limited and challenging to conduct. Most dietary-intervention studies have focused on evaluating the association between milk consumption and gastrointestinal symptoms (or related serum or urinary
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 4 Simulation Results and Performance Analysis
### Channel Model  
In this paper, the Jakes channel model [35] based on the sine wave superposition method and the additive white Gaussian noise (AWGN) channel model are adopted. The sine wave superposition method relies on deterministic processes to simulate random processes and generates Rayleigh fading channels with a given Doppler spectrum through sine wave synthesis. The simulation parameters are shown in Table 1.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Performance Metrics  
To evaluate model effectiveness, four key performance metrics were used [47]:  
* Accuracy: Calculates the overall correctness of the model in classifying hate and non-hate speech.
* Precision: Evaluates how many instances labeled as hate speech were correctly classified, reducing false positives.
* F-score: The harmonic mean of precision and recall, which balances false positives against false negatives.  
These metrics ensured that the model not only achieved high accuracy but also maintained robust performance across different hate speech contexts.  
This methodology outlines the end-to-end process of hate speech detection in Roman Urdu, covering data gathering, preprocessing, feature extraction, ML and DL models, hybrid approaches, and performance evaluation. By combining traditional ML and modern DL models, we improved the classification accuracy in Roman Urdu hate detection, addressing the linguistic challenges of this low-resource language.
## 3 Results
### Total Phenolic, Flavonoids Content, and Antioxidant Activity (DPPH and ABTS)  
According to the results presented in Table 4, it can be seen that the extraction method employed for sample preparation significantly influences the levels of total phenols, flavonoids, and antioxidant potential. Across all conducted tests, the outcomes consistently indicated markedly higher values for grapevine bud extracts when utilizing the micellar extraction method compared to solvent extraction.  
Micellar extraction from buds resulted in higher yields of total phenolic and flavonoid content compared to solvent extraction. Specifically, SurfE extracts exhibited more than a 30% increase in phenolic content (722 \(\pm\) 13 mgGAE/L) compared to SolvE extracts (486 \(\pm\) 10 mgGAE/L). Similarly, for flavonoid (TF) content, SurfE demonstrated a 40% higher concentration (57.6 \(\pm\) 1.1 mgQE/L) compared to SolvE (32.6 \(\pm\) 0.7 mgQE/L). The antioxidant activity test results underscore the crucial role of the extraction method in obtaining bioactive compounds. Micellar extraction of grapevine buds produced an extract with approximately 10% stronger antioxidant activity compared to solvent extraction. Specifically, the SurfE bud extract exhibited significantly higher antioxidant activity (DPPH: 69.8 \(\pm\) 1.7%, ABTS: 42.4 \(\pm\) 1.9%) than the SolvE bud extract (DPPH: 60.1 \(\pm\) 2.5%, ABTS: 34.6 \(\pm\) 1.4%).
## 4 Experimental Result
### Quantitative Analysis  
#### 4.3.1 Sentence-Level Evaluation  
MedNLI: The MedNLI dataset comprises sentence pairs extracted from the MIMIC-III database [16]. For each pair, one sentence serves as a premise and the other as a hypothesis. The task for the models is to predict whether the relationship between the two sentences is contradictory, neutral, or entailment. Accuracy was the chosen evaluation metric for assessing model performance on this task.  
BIOSSES: BIOSSES is a corpus containing sentence pairs from the Biomedical Summarization Track Training Dataset, focusing on the biomedical domain [24]. The task involves determining the similarity between two sentences. The Pearson correlation coefficient was employed as the evaluation metric to measure the model's ability to capture the degree of similarity between sentences.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### AI Algorithms in Aquaculture
#### 3.2.2 Deep Learning (DL)
Autoencoders perform by compressing large datasets into low-dimensional representations and then rebuilding them, enabling them to identify subtle deviations that might go unnoticed in traditional monitoring systems. A different category of deep learning models, known as multilayer perceptrons (MLPs), is composed of fully connected layers and is especially effective for tasks involving numerical prediction and classification. To categorise water quality conditions, MLPs can evaluate various environmental factors, including salinity, temperature, and dissolved oxygen levels. This information is crucial for enhancing fish health and improving the productivity of aquaculture operations [96; 97]. Deep Learning offers various architectures that cater to aquaculture's diverse needs, from image-based applications to sequential data analysis. Its ability to process complex datasets with high accuracy and efficiency makes DL an essential tool in modern aquaculture management, contributing to improved monitoring, predictive analytics, and sustainable farming practices.