esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of the Concentration of Total Phenols and Flavonoids in the Fir Honeydew Honey  
The concentration of total phenols and flavonoids in the diluted FHH sample was determined using modified methods developed by [PERSON] et al. [29] and [PERSON] et al. [30]. First, 2 g of FHH were diluted in 20 mL of the 1:1 mixture of methanol (Honeywell Fluka, Charlotte, NC, USA) and distilled water [pH was calibrated to 2 with concentrated hydrochloric acid (Honeywell Fluka, Charlotte, NC, USA)]. The solution was further filtered through Whatman filter paper (Sigma Aldrich, Darmstadt, Germany).  
To determine the concentration of total phenols, gallic acid (Sigma Aldrich, Darmstadt, Germany) was used as a standard to collect data for the calibration curve. Gallic acid (0.01 g) was diluted in 4 mL of the mixture of methanol and distilled water, used for the extraction of FHH. The concentrations of gallic acid for the calibration curve ranged from 0.97 to 125 \(\upmu\)g/mL. One hundred \(\upmu\)L of diluted FHH sample or gallic acid in different concentrations were added to 2.5 mL. of distilled water. As a blank, 100 \(\upmu\)L of methanol was used. Furthermore, 100 \(\upmu\)L of undiluted 2 M Folin-Ciocalteu (FC) reagent (Sigma Aldrich, Darmstadt, Germany) were added to each mixture. The mixtures were incubated for 6 min at room temperature in the dark. Then, 500 \(\upmu\)L of 20% sodium carbonate (Sigma Aldrich, Darmstadt, Germany) were added to each mixture, and the incubation was prolonged by 30 min under the same conditions. After incubation, 100 \(\upmu\)L of each sample were transferred to the microtiter plate and the absorbance (760 nm) was measured on a multiplate reader (Varioskan, Thermo Scientific, Waltham, MA, USA). The mean value of three measurements was used and the total phenol content was expressed in mg gallic acid equivalents (mg GAE/100 g honey).  
To determine the concentration of total flavonoids, quercetin (Sigma Aldrich, Darmstadt, Germany) was used as a standard to collect data for the calibration curve. Quercetin (0.0005 g) was diluted in 4 mL of the mixture of 2% aluminium chloride (Sigma Aldrich, Darmstadt, Germany) in methanol. The concentrations of quercetin for the calibration curve ranged from 0.24 to 125 \(\upmu\)g/mL. Five mL of diluted FHH sample was added to 5 mL of the mixture of 2% aluminium chloride in methanol. As a blank, 5 mL of methanol were used. The mixtures were incubated for 10 min at room temperature in the dark. After incubation, 100 \(\upmu\)L of each sample was transferred to the microtiter plate and the absorbance (415 nm) was measured on the multiplate reader. The mean value of three measurements was used, and the total flavonoid content was expressed as mg quercetin equivalents (mg QE/100 g honey).
## 5 Major Equipment and Technologies
### Agricultural Communication  
The timely communication and reporting of information are considered to be the central part of advanced agriculture. The strong connection and participation of many factors are needed to retrieve efficient and significant purposes. Telecom operators play an essential role in disseminating communication reliability in agriculture development. Moreover, expanding the advancement of the IoT and widespread knowledge to improve the advanced agricultural industry needs a highly efficient management system. Many factors, including coverage, energy consumption, reliability, and cost, are significant and necessary before selecting a communication method.
## 3 Improved Algorithm
### Improved Thresholding Function
#### 3.3.2 Deviation Analysis  
When \(\alpha\to 0,\ \lambda_{j}^{k}\to 1\), the improved thresholding function is a hard thresholding function. When \(\alpha\to\infty\), \(\lambda_{j}^{k}\to 0\), the deviation of the improved thresholding function from the hard thresholding function is shown as follows:  
\[\lim_{\omega_{j}^{k}\to+\infty}\overline{\omega}_{j}^{k}-\omega_{j}^{k} =\lim_{\omega_{j}^{k}\to+\infty}\text{sgn}\Big{(}\omega_{j}^{k} \Big{)}\Bigg{(}\left|\omega_{j}^{k}\right|-\frac{T_{j}}{e^{\sqrt{|\omega_{j}^{k2 }-T_{j}^{2}|}}}\Bigg{)}-\omega_{j}^{k} \tag{18}\] \[=\lim_{\omega_{j}^{k}\to+\infty}-\Bigg{(}\frac{T_{j}}{e^{\sqrt{| \omega_{j}^{k2}-T_{j}^{2}|}}}\Bigg{)}=0\]\[\begin{array}{ll}\lim_{\omega_{j}^{k}\rightarrow-\infty}\overline{\omega}_{j}^{k}- \omega_{j}^{k}&=\lim_{\omega_{j}^{k}\rightarrow-\infty}\text{sgn}\Big{(} \omega_{j}^{k}\Big{)}\Bigg{(}\Big{|}\omega_{j}^{k}\Big{|}-\frac{T_{j}}{e^{\sqrt {|\omega_{j}^{k2}-T_{j}^{2}|}}}\Bigg{)}-\omega_{j}^{k}\\ &=\lim_{\omega_{j}^{k}\rightarrow-\infty}\Bigg{(}\frac{T_{j}}{e^{\sqrt{| \omega_{j}^{k2}-T_{j}^{2}|}}}\Bigg{)}=0\\ \end{array} \tag{19}\]  
\(T_{j}/e^{\sqrt{|\omega_{j}^{k2}-T_{j}^{2}|}}\) ensures that the deviation of the improved thresholding function from the hard thresholding function is zero at infinity.  
By choosing the appropriate \(\alpha\), the improved thresholding function can be made to converge quickly to the hard thresholding function. This reduces the reconstruction error caused by compressing wavelet coefficients. \(\alpha\) makes the algorithm more flexible and can be applied to different working environments.  
Figure 4 shows the denoising characteristics of the proposed thresholding function and the conventional thresholding functions. It can be observed that the improved thresholding function approximates the hard thresholding function more and more rapidly as \(\alpha\) decreases. The improved thresholding function overcomes the shortcomings of the conventional thresholding functions. Since the improved thresholding function is continuous at \(\pm T_{j}\) and does not produce additional oscillation like the hard thresholding function, it can enhance the smoothness of the denoised signal and effectively protect the details of the signal. Since it does not compress the wavelet coefficient sequence larger than the threshold as the soft thresholding function, it does not cause signal distortion after reconstruction.
# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
## 2 Audio Datasets
On BUZZ1, for the RF model, feature 5 (spectral entropy) and MFCC features 10-15 were selected with RFE; on BUZZ2, for the KNN model, feature 5 (spectral entropy) and MFCC features 12-15, MFCC feature 18, and MFCC feature 20 were selected with SFS; on BUZZ3, for the KNN model, feature 5 (spectral entropy) and MFCC features 10-14 and MFCC feature 16 were selected with SFS; on BUZZ4, for the SVM model, feature 3 (spectral centroid), feature 4 (second moment of spectrum), feature 5 (spectral entropy), and MFCC features 9, 11-14, and 18-20 were selected with RFE. This investigation corroborated our earlier finding [16] that MFCCs are useful features in standard ML models trained to separate bee buzzing from other audio categories in external audio beehive monitoring.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.7 Resource Management for Integrated Terrestrial and Non-Terrestrial Networks  
Integrating terrestrial and non-terrestrial networks, including satellites, drones, and high-altitude platforms, requires EMF-aware resource management [114; 115]. Research must address cross-network coordination and dynamic EMF optimization to ensure comprehensive performance and safety.
## 2 Study Site and Available Data
### Available Data  
This study was carried out for 4 hydrological years, from 2016-2017 to 2019-2020. The 3 first years were used to understand wet-snow dynamics and their connection to streamflow. The last year was used as an evaluation year to test our findings. Three data sources have been used in this study: meteorological information, remote sensing imagery, and streamflow simulations.
# Biological Importance of Cotton By-Products Relative to Chemical Constituents of the Cotton Plant
## 4 Chemical Compounds in Cotton
### Terpenes
#### 4.1.2 Monoterpenes (C10)  
The monoterepenes (C\({}_{10}\)H\({}_{16}\)) are a class of terpenes that consist of two isoprene units and can be linear (acyclic), monocyclic (containing one ring), or bicyclic (containing two rings) [137]. There are over 1000 monoterepenes known to occur in nature and examples of common monoterepenes in plants include myrcene (acyclic), limonene (monocyclic), and pinene (bicyclic) (Figure 5) [137]. Together with the sesquiterpenes, monoterepenes are major constituents of essential oils extracted from various plant materials [138]. Biochemical modifications of monoterepenes such as oxidation, hydroxylation and rearrangement of atoms result in the formation of monotereponoids such as geraniol and linalool [135; 139]. In the cotton plant, there are some acyclic monoterepenes which belong to the group of constitutive compounds, such as \(\alpha\)-pinene, \(\beta\)-pinene, and limonene amongst others, as well as herbivore-induced monoterepenes [130; 132]. Although monoterepenes found in cotton are distributed in different parts of the plant, including leaves, seeds, flowers, stems, and roots, they are predominantly concentrated within the leaves and flowers (Figure 2) [124; 140; 141].  
Figure 4: Biosynthesis of terpenes from isopentenyl pyrophosphate, a product of the mevalonate and non-mevalonate pathway.
## 4 Discussion
### Discussion of the Results of the Analysis for a Variable Fuel Weight  
The analysis of Table 8 and the parameters presented there reveals the following: data show that satellites of a similar weight with chemical propulsion systems, e.g., monopropellants (those that use hydrazine or AF-M315E), may achieve specific impulses in the range of 190-255 s. For small satellites (CubeSat and NanoSat), a realistic specific impulse for the fuel weight from 0.3 kg to 0.7 kg is in the range of 200-250 s, depending on the propulsion type. Specific impulse (I\({}_{\text{sp}}\)) increases with the increasing weight of fuel, thus optimising its use. The typical values for these types of systems in CubeSat and NanoSat satellites may reach 200-255 s for the specific impulse.  
In order to maximise the operational efficiency of nanosatellites, it may be necessary to consider the types of fuel. For example, hydrazine, and its environmentally friendly substitute LMP-103S, are perfect for precise orbital manoeuvres thanks to their high specific impulse (220-260 s) [31]. High-test peroxide (HTP) works well in green propulsion systems due to reduced toxicity [32]. Krypton and xenon, used in ion thrusters, enable long-lasting manoeuvres at minimum fuel consumption, which is crucial in satellites, such as Starlink or GOCE [33]. The choice of fuel should be adapted to the requirements of the mission, including the precision of manoeuvres, and the duration and safety of the mission.  
Conclusions:  
* Higher values of specific impulse and effective exhaust velocity improve the efficiency of propulsion, enabling a larger increase in velocity at lower fuel consumption. This makes it possible to perform more demanding manoeuvres.
* Larger weight of fuel allows to achieve a larger increase in velocity, which is beneficial when planning more demanding changes of the orbit.
* The duration of the manoeuvre increases significantly with the weight of fuel, which may affect planning satellite operations, e.g., the need to store energy and the availability of the propulsion system.
* Selecting the appropriate weight of fuel and specific impulse is crucial for nanosatellite systems, to ensure the maximum efficiency of the mission while complying with energy limitations.
* There is no universally optimal fuel mass. It should be carefully selected based on specific mission requirements, such as the number of planned manoeuvres, the precision of orbital changes, and time constraints.
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 2 Fundamentals of the Model  
### Maximal Information Coefficient  
The Maximum Information Coefficient (MIC) was first proposed by [PERSON] et al. in science based on the mutual information theory, which is used to measure whether there is a linear or non-constant functional relationship between two variables [27]. The MIC has a strong robustness and stability and is not affected by the outliers. The value of the MIC is between [0 and 1], and the bigger the MIC between the two variables, the more suitable it is for the load forecasting; the stronger the degree of correlation, the more suitable an input sequence is for load forecasting. The decomposition steps are as follows:  
Assuming that \(X=\{x_{i}\}\) and \(Y=\{y_{i}\}\) are two random variables and \(n\) is the sample size, where \(i=1,2\cdots,n  
where \(a\) and \(b\) represent the numbers of dividing grids in the horizontal and vertical directions, and \(B(n)=n^{\alpha}\) means the maximum value of the grid. The constant \(\alpha\) is set empirically, which plays a critical role in determining whether the maximum mutual information coefficient can achieve a superior generalization. [PERSON] et al. suggest that the \(\alpha\) is taken as 0.6.  
Compared with correlation analysis methods, such as Pearson and Spearman, the maximum mutual information coefficient (MIC) more objectively quantifies statistical associations when handling nonlinear data with varying noise levels and functional relationships [28]. With a lower complexity and stronger robustness, this paper employs the Maximal Information Coefficient (MIC) to mine the features between the weather and power load, aiming to filter out more suitable inputs for the prediction model.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### Pioneer
The encoder for this code is shown in Figure 6b. For each input \(b_{n}\), the encoder produced two outputs defined by the polynomials  
Figure 5: A block diagram of the digital telemetry unit and transmitter used for _Pioneer_ 9, 10, and 11. Adapted from [21]. \[ \begin{split} G^{(1)}(D)=1&+D+D^{2}+D^{4}+D^{5}+D^{7}+D^{8}+ D^{9}\\ &+D^{11}+D^{13}+D^{14}+D^{16}+D^{17}+D^{18}+D^{19}+D^{21}\\ &+D^{22}+D^{23}+D^{24}+D^{25}+D^{27}+D^{28}+D^{29}+D^{31}\\ G^{(2)}(D)=1&+D^{2}+D^{4}+D^{5}+D^{7}+D^{8}+D^{9}\\ &+D^{11}+D^{13}+D^{14}+D^{16}+D^{17}+D^{18}+D^{19}+D^{21}\\ &+D^{22}+D^{23}+D^{24}+D^{25}+D^{27}+D^{28}+D^{29}+D^{31}\end{split}  \tag{2}\]  
The two coded bits are multiplexed to produce an output clocked at twice the rate as the input. The generator polynomials differ in only one position: \(G^{(2)}(D)\) is missing the \(D^{1}\) term of \(G^{(1)}(D)\). This one-bit difference endows the non-systematic code with the quick-look-in property [23]. This code achieved a coding gain of approximately 6.9 dB. Decoding for the _Pioneer_ 9, 10, and 11 missions was performed in the ground station. The output of the coherent phase demodulator was applied to the integrate-and-dump detector in the subcarrier PSK demodulator. The output of the integrate-and-dump circuit was quantized to 3 bits [24]. The convolutional decoder performed sequential decoding [22] on the 3-bit quantized values. The decoding algorithm was modified to exploit the presence of known values embedded in the telemetry stream [17]. This modification added an extra layer of error detection.
## 3 Results and Discussions
Compared to similar work carried out in the field of DR classification, our proposed enhanced model was able to classify the different stages of diabetic retinopathy for a Mauritian dataset. The enhanced model was trained using the APTOS augmented dataset, and this model was used to classify the Mauritian dataset images with an overall accuracy of 79%. Furthermore, it can be said that our proposed model can be used for early detection of DR compared to [PERSON] et al. (2014), where the proposed model had a low accuracy for the early stages of DR. Meanwhile, [PERSON] et al. (2012) and [PERSON] et al. (2017) applied transfer learning for a binary classification, namely images having DR or No DR, whereas our model was used to classify all 5 stages of DR both for the APTOS and Mauritian dataset. In this paper, we have reported the use of several parameters to address overfitting of the models compared to the work of [PERSON] et al. (2012) and [PERSON] et al. (2013). Finally, our model outperforms [PERSON] et al. (2012) and [PERSON] et al. (2011) in terms of accuracy.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
###### Abstract  
Ground subsidence primarily occurs due to complex factors, such as damage to underground facilities and excavation work, and its occurrence can result in loss of life and damage to property. Therefore, factors that induce ground subsidence must be investigated to prevent accidents. This study aims to evaluate and predict the ground subsidence risk in urban centers in South Korea. To this end, a machine learning-based ground subsidence risk prediction model was constructed by utilizing data on the underground facility attribute information, permeability coefficient, stratigraphic thickness, and height. The random forest, XGBoost, and LightGBM machine learning algorithms were used to develop the prediction model, and the SMOTE sampling technique was employed to address data imbalance. The reliability of the developed model was verified using the evaluation metrics of F1-score and accuracy. The best-performing model was selected to create a risk map and visualize the areas with ground subsidence risk. The results indicate that the incorporation of additional data improves model performance and reliability. Thus, the machine learning model with various factors developed in this study offers foundational insights for the prevention and risk management of ground subsidence.  
ground subsidence; machine learning; ground subsidence risk prediction model; hazard map +
Footnote †: journal: applied sciences  
1
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# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging
### Recurrent Neural Networks (RNNs)
#### 2.2.2 Architectures and Applications  
While RNNs are less commonly used in medical imaging compared to CNNs, they have shown potential in specific applications that involve sequential data. Some well-known RNN architectures include the basic RNN (shown in Figure 3), long short-term memory (LSTM) [80], and gated recurrent unit (GRU) [81]. These architectures have been employed in various medical imaging tasks [83; 84; 85], such as image captioning, video analysis, and multimodal data fusion [86]. For instance, RNNs have been used in conjunction  
Figure 2: A sample illustration of a CNN architecture for segmentation of MRI-based images. The figure depicts the input layer (medical image), convolutional layers, pooling layers, fully connected layers, and the output layer (classification or segmentation results).  
with CNNs for medical image captioning, where the goal is to generate a descriptive text for a given image [87]. In this context, a CNN is used to extract features from the image, while an RNN is employed to generate a sequence of words based on the extracted features [88]. This approach has been applied to generate radiology reports for chest X-rays and MRI scans [89]. Additionally, RNNs have been utilized for analyzing medical videos, such as endoscopy and laparoscopy videos [90]. In these applications, RNNs can be used to track and analyze temporal changes in the videos, enabling tasks such as surgical tool tracking, tissue segmentation, and surgical workflow analysis [91; 92; 55; 93; 94].
## 2 Analysis of Forces Acting on Strands during Traveling
### Analysis of Force under Different Strand Paths on the Distribution Reel  
In the twisting process, the strands are twisted from the cylinder to the distribution reel, and each strand changes from a triangular distribution to a circumferentially uniform distribution. Each strand shows a different twisting-deviation path at the distribution reel, as shown in Figure 3. \(\theta_{i}\) represents the angle of rotation corresponding to different deflection paths of strands with different numbers at the distribution reel, \(\theta_{i}=\pi-2\theta_{yoz}\). The angle \(\theta_{yoz}\) is the angle between each strand and surface \(yoz\).  
The vector radius of any point A on the strand can be expressed as (8):  
\[\overset{\rightarrow}{\tau}=\frac{d\overset{\rightarrow}{a}}{ds}=\frac{dx}{ ds}\overset{\rightarrow}{i}+\frac{dy}{ds}\overset{\rightarrow}{j}+\frac{dz}{ds} \overset{\rightarrow}{k} \tag{8}\]  
where \(dx\), \(dy\), and \(dz\) are the three co-ordinates of any point A on the strand, and \(i\), \(j\), and \(k\) are the base vectors of the co-ordinate axis. The cosines of the three directions of the tangent vector \(\tau\) with respect to the space co-ordinate axis system are \(dx/dsdy/ds,dz/ds\). The length from the strand to the point A where it is led out of the cylinder is denoted as S. The deviation angle \(\theta_{yoz}\) at any point A can be expressed as:  
\[\theta_{yoz}=\arctan\frac{S\frac{dz}{ds}}{\sqrt{(S\frac{dx}{ds})^{2}+(S\frac{ dy}{ds})^{2}}} \tag{9}\]  
Figure 3: Force analysis of different twisting-deflection angles on the distribution reel.  
Due to the different paths of each strand on the distribution reel, the length of strand \(S\) varies. When different strands undergo a rotational motion along the central axis, the equivalent torsion radius \(R_{i}\) is related to the twisting-deflection angle \(\theta_{i}\) as follows:  
\[\theta_{i}R_{i}=S_{i} \tag{10}\]  
where \(i\) represents the corresponding number of the twisted strand, and \(\theta_{i}\) and \(S_{i}\) correspond to the twisting-deflection angle and the length of the twisted strand with different numbers, respectively.  
We then combine Equations (10) with (5). This can be integrated and simplified within the range of the span to obtain:  
\[T_{i}=\rho\big{(}\frac{S_{i}}{\theta_{i}}\omega_{r}\big{)}^{2} \tag{11}\]  
Under the condition of the rotating twisting and traveling, the inner wall of the guide hole will exert a certain hindering force on the strand, which can be expressed as:  
\[F_{fi}=\mu m(\omega_{r})^{2}\frac{S_{i}}{\theta_{i}}\sin\theta_{i}+(T_{0}+T_{i })\cos\theta_{i} \tag{12}\]