esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations
### Numerical Example
The simulations were carried out in a measurement noise environment with a mean of 0 and a variance of \(5\times 10^{-3}\), and the sampling time was set to \(t_{s}=0.01\) s for practicality. Moreover, the initial values of the observer state and that of the system state were set to be different from each other, \(\hat{d}_{1}(0)=z_{2}(0)=0.5\), \(x_{1}(0)=1\). The result obtained is shown in Figure 6, which strongly agrees with the statement noted in Remark 1. Specifically, it is evident from Figure 6 that \((L=1)\) yields the longest convergence time, yet the smallest peaking phenomenon and is least susceptible to noise. In contrast, \((L=5)\) takes the shortest amount of time for the estimation error to converge to zero but, at the same time, produces the largest peaking phenomenon and is most susceptible to noise. The most appropriate selection is \((L=3)\), which represents a tradeoff between convergence time and sensitivity to noise and different initial values. Thus, it is concluded here that the greater Lipschitz constant \(L\) leads to the faster convergence of the disturbance observer, yet more sensitivity to noise and larger peaking phenomenon in the transition period, especially in case where the initial values of the disturbance observer and the system are different from each other.
# Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
## 2 Materials and Methods
### Instrument Design
#### 2.1.4 Control Handle  
The control handle of the instrument is a crucial part of the instrument, serving as the interface between the user and the distal tip. To ensure ease of use and familiarity for medical professionals, the handle was designed with similarities to regular endoscopy devices. A star knob concept, similar to that of an endoscope, is used for steering the distal tip of the instrument, while a push-pull slider was designed for opening and closing the grasper jaw. An exploded view of the control handle, including its elements, is shown in Figure 2.
## 5 Simulation Results
It is clear that there is no significant performance difference between two echo channels in contrast to other methods, as illustrated in Figures 3 and 4. This phenomenon is attributed to the fact that the WLS approach is able to mitigate the effect of frequency selectivity on the different subcarriers. Regarding the complexity of both methods, the flops of the proposed WLSE scheme when \(N_{b}=8\) and \(16\) are reduced by \(56.3\%\) and \(65.7\%\) when compared to that of the existing WLSE method, respectively. Note that increasing \(N_{b}\) eases the computational requirement of the proposed scheme at the sacrifice of estimation range. Figure 4: Performance of the frequency-offset estimation methods in Echo Channel 2: (**a**) \(N_{b}=8\); (**b**) \(N_{b}=16\). Figure 3: Performance of the frequency-offset estimation methods in Echo Channel 1: (**a**) \(N_{b}=8\); (**b**) \(N_{b}=16\).
A promising technique for creating mutants with altered gene expression is the use of Cas9 nuclease, which does not edit DNA but carries some additional functional elements. Cas9 proteins in which both nuclease domains are inactivated (deadCas9, dCas9) bind to DNA but cannot make cuts. Depending on where the dCas9:sgRNA binding site is located, transcription of the corresponding gene stops at either the initiation or elongation stage. By targeting gRNA to DNA regions where transcriptional repressors bind, the opposite effect can be achieved, i.e., activating gene expression. Another method of transcription regulation involves creating chimeric proteins by fusing dCas9 with eukaryotic transcription factors or the \(\omega\) subunit of bacterial RNA polymerase. This provides an alternative approach for creating overexpressing lines with minimal introduction of foreign genes into the plant genome [12]. The present review describes the biosynthesis pathways of isoprenoid antioxidants, flavonoids, ascorbic acid, and glutathione. The mechanisms of the antioxidant activity of these compounds in relation to various ROS are also described. This review also emphasizes the success in developing a wide range of plants of different species with increased content of non-enzymatic antioxidants with deep analysis of the molecular genetic approaches used. Given the indispensable role of the CRISPR/Cas9 genome editing system in advancing functional genomics, we pay special attention to CRISPR/Cas9-based editing of plants. The most promising strategies for creation of crops with the efficient antioxidant properties and other valuable characteristics are summarized. The further development of these strategies may represent a universal approach for comprehensive qualitative improvement of plants under various environmental stresses. Functioning of Non-Enzymatic Antioxidants in Higher Plant Cells and the Ways of Boosting Their Biosynthesis
## 3 Performance Analysis of iPyroxy Block  
Iron pyrite, often referred to as fool's gold (FeS\({}_{2}\)), is a mineral that may be extracted from sedimentary and metamorphic rock formations. Upon extraction, it is subjected to a crushing process to provide a fine powder with a particle size of around 300 microns. The crushing increases the surface area of the pyrite particles and facilitates a more uniform dispersion of the material when combined with a polymer matrix. Powdered iron pyrite may be efficiently integrated with commercially available epoxy resin for use in composite products. The epoxy resin acts as a resilient adhesive binder, creating a solid composite that leverages the distinctive characteristics of iron pyrite.  
iPyroxy blocks are formed by combining iron pyrite with epoxy resin in a ratio of 3:2, consisting of three parts iron pyrite to two parts epoxy resin. The liquid is thereafter put into a standard mold measuring 5 cm \(\times\) 5 cm \(\times\) 5 cm. After molding, the produced block undergoes a curing step lasting 24 h. The epoxy resin undergoes polymerization throughout this curing period, creating a robust matrix encapsulating the iron pyrite particles. The curing process is crucial for the iPyroxy block to attain optimal mechanical strength, durability, and structural integrity. The resulting composite material demonstrates mechanical properties derived from the cured epoxy resin matrix in conjunction with the electrical conductivity of iron pyrite. The 24 h curing period guarantees that the iPyroxy block is fully solidified, making it appropriate for use in various industries, including construction and electronics, or any other area that needs a composite material with specific mechanical and electrical properties (Figure 2).  
iPyroxy's remarkable dielectric value of 9.9 indicates good insulating qualities. This number indicates the material's resistance to electrical current flow, making it useful as a dielectric in various settings. The higher dielectric constant of iPyroxy results from the 3:1 ratio of epoxy resin to iron pyrite (metallic brilliance). Because of its high dielectric  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Element**} & **Net** & \multirow{2}{*}{**Weight \%**} & \multirow{2}{*}{**Atom \%**} & **Atom \%** & \multirow{2}{*}{**Formula**} \\  & **Counts** & & & & **Error** & \\ \hline C & 131 & 8.21 & 16.10 & \(\pm\)1.11 & C \\ O & 449 & 33.63 & 49.48 & \(\pm\)2.87 & O \\ F & 17 & 1.49 & 1.84 & \(\pm\)2.49 & F \\ Mg & 58 & 1.28 & 1.24 & \(\pm\)0.23 & Mg \\ Al & 260 & 4.24 & 3.70 & \(\pm\)0.24 & Al \\ Si & 495 & 7.15 & 5.99 & \(\pm\)0.38 & Si \\ S & 93 & 1.10 & 0.81 & \(\pm\)0.12 & S \\ Ca & 1079 & 16.62 & 9.76 & \(\pm\)0.29 & Ca \\ Fe & 713 & 26.28 & 11.08 & \(\pm\)0.71 & Fe \\ Total & & 100.00 & 100.00 & & \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition in iPyroxy.  
Figure 2: (a) Particular mold; (b) casting of 6 iPyroxy block; (c) iPyroxy block.  
value, iPyroxy may be used in processes that need electrical resistance and insulation, such as making capacitors, electronic components, or insulating materials.
## 6 Field Tests
### Test Results and Analysis  
Based on the analysis of Table 6, it can be observed that the traditional S75 wireline coring tool has an average core recovery rate of only 52% in loose and fractured cover layers, with the lowest recovery rate dropping to 39%. This recovery rate is far below the engineering drilling requirements. However, after switching to a hydraulic closed coring system, the average core recovery rate increased to 97.83%. The new technique has shown a core growth rate of 88.13% compared to traditional rope core drilling techniques. Additionally, unlike traditional techniques, the new technique does not require any additional operations during the drill collar pull, thereby effectively increasing the proportion of pure drilling time in each cycle, significantly improving drilling efficiency. Notably, during the 23 rd test, the core recovery rate was nearly 100%. This improvement can be attributed to the design of the water-insulated core bit, featuring internal openings and external grooves, which significantly minimizes the erosion impact of the drilling fluid on the core during the drilling process. Additionally, the core claw plays a significant protective role during the extraction of the core barrel. Field experiments indicate that the hydraulic closed coring system can meet the engineering requirements for wireline coring projects in loose and fractured formations. This innovative design greatly enhances core recovery efficiency, which is of significant value to the improvement of engineering quality. It also presents new ideas for similar wireline coring exploration operations in loose and fractured geological layers.
## 2 Model Description
### Atmospheric Stability Classification
#### 2.2.1 Richardson Gradient (RG) Method  
The Richardson number, \(Ri\), synthesizes the effects of thermodynamic and kinetic factors caused by the turbulence, reflecting more turbulent information [25]. Therefore, the RG method can distinguish the atmospheric stability more accurately. The \(Ri\) in the surface layer can be expressed as [26]  
\[Ri=\frac{gz}{T}\frac{\Delta T}{\Delta u^{2}}\ln\frac{z_{2}}{z_{1}} \tag{4}\]  
where \(\Delta T\) is the difference of temperatures at \(z_{2}\) and \(z_{1}\). \(\Delta u\) is the wind speed difference. \(T\) is the average atmospheric temperature, \(g\) is the acceleration of gravity, and \(z\) is the average geometric height calculated as \(z=\sqrt{z_{1}z_{2}}\). Table 1 shows the classification standard of \(Ri\) in different terrains [27].  
Atmospheric stability depends on the net heat flux to the ground, which is equal to the sum of incident radiation, radiation emitted, latent heat, and sensible heat exchange between the atmosphere and underlying surface. When the radiation incident on the ground is dominant, the air parcels at the lower part rise, leading to a vertical air motion, and making the atmosphere unstable. Therefore, the thermal effect aggravates the air movement and prevents the wind speed from changing dramatically in the vertical direction. In this case, the Richardson number is negative. When the ground cools down,the temperature increases with increasing the height, which weakens the vertical air movement. The situation is recognized as a stable state and a positive Richardson number is obtained. The neutral stability corresponds to the case where the thermal effect is not significant. This situation occurs when the cloud is dense, and the Richardson number is zero.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 3 Implementation and Results
### Dataset and Topographical Features  
This dataset contains 5007 RSSI entries collected over 13 square km of Glasgow City urban areas. Data is collected from three different base stations (BS1, BS2, BS3) communicating to a handheld mobile station (MS) that is equipped with a LoRa SX1272 module [36] and a global positioning system (GPS). In addition, topographical features are extracted at each measurement location using image processing techniques [10] and paired with the corresponding RSSI value. Details of these features are as follows:  
* SF: LoRa's spreading factor that varies from 7 to 12;
* Height difference: altitude difference between the transmitter and receiver;
* Lat, Lon, Alt: latitude, longitude, and altitude of the MS, acquired from GPS;
* FSPL: free-space path loss (FSPL) calculated \(L_{0}=20\log(\lambda/4\pi d)\) using the distance \(d\) between the BS and the MS, transmission wavelength \(\lambda=346.55\) m, frequency 865.20 MHz;
* Clutter: calculated from the number of buildings on the Rx-Tx direct path;
* Road width: relative width of the street;
* AoA: acute angle of the LoS and the road axis;
* LoS ratio: clear the LoS ratio relative to \(d\); this is a non-zero value when part of LoS is over the river;
* BS ID: base station identification BS-ID (BS1, BS2, BS3);
* Area Type: terrain type, (dense-urban = 1, urban = 2, open-urban = 3)  
After encoding the BS ID, using one-hot-encoding, there are a total of 13 features in this dataset. Some of the parameters, such as the distance between MS and BS and radio frequency, are not explicitly present, and instead, they are calculated as the FSPL. Figure 1 provides a detailed representation of the BSs location, illustrating their respective distances, the designated measurement area, and the various area types. Area types 1 and 2 are distinctly color-coded. Any region within the measurement area that is not color-coded should be interpreted as area type 3, offering a clear visual distinction between the different zones.  
Figure 1: Area of experiment with location of base stations and area types.
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Product/Services
#### Lending and Borrowing  
Lending and borrowing in the DeFi ecosystem involve providing monetary assets, either fiat or digital currency, to another party in exchange for a steady income stream. DeFi lending platforms enable cryptocurrency holders to lend significant funds quickly and anonymously to borrowers. To access this service, borrowers must provide sufficient collateral deposited in a smart contract and commit to repaying the loan within a pre-determined timeframe. DeFi borrowing and lending introduce efficiency, accessibility, and transparency innovations compared to TradFi and participation in borrowing and lending is open to everyone, thereby democratizing financial service access. Enabled by smart contracts, the decentralized framework of DeFi lending and borrowing ensures a transparent and equitable blockchain-based system, granting users traditional financial tools like interest and liquidity while incorporating the advantages of decentralization, thereby fostering the continued expansion and development of the DeFi ecosystem [6; 46].  
One example of a lending protocol is a pool-based system, where users provide liquidity or funds to a pool that others can borrow. In return, those who provide liquidity earn interest-like income. P2P lending is another option, allowing borrowers to obtain loans directly from individual lenders. Decentralized lending protocols facilitate borrowing with minimal obstacles. Prominent DeFi lending platforms like _Aave_, _Maker_, and _Compound_ utilize smart contracts to enable lending and borrowing of cryptocurrency without intermediaries, thereby reducing the risks associated with traditional loans [6; 46].
## 2 Overview of Supplementary Damping Systems
### Principles of Energy Dissipation Mechanisms
#### 2.2.2 Viscoelastic Damping  
Viscoelastic damping utilizes materials that exhibit both viscous and elastic properties. The energy dissipation occurs due to shear deformation of the viscoelastic material. The complex modulus of a viscoelastic material is expressed as:  
\[\text{G}^{*}=\text{G}^{{}^{\prime}}+\text{i}\cdot\text{G}^{{}^{\prime\prime}} \tag{2}\]  
where G' is the storage modulus (elastic component) and G\" is the loss modulus (viscous component). Key characteristics of viscoelastic damping include:  
Frequency-dependent behavior.  
Temperature sensitivity.  
Combined stiffness and damping properties.
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 3 Results and Discussion
### Dynamic Response Analysis of the Three-Directional Vibration Isolator
#### 3.3.3 Vibration Isolation Performance and Transmissibility Analysis
\tag{30}\]  
Within the considered range of small excitation force amplitudes, the peak dynamic displacements and peak force transmissibility of both the three-directional isolator and the linear isolator are illustrated in Figure 10. As the excitation force amplitude increases, the peak dynamic displacement of the three-directional isolator also increases, while its peak force transmissibility fluctuates, exhibiting an overall increasing trend. Compared to the linear isolator, the three-directional isolator demonstrates a larger peak dynamic displacement but lower force transmissibility, indicating improved vibration isolation efficiency. According to Equation (14), the performance of the three-directional isolator is primarily influenced by the initial inclination angle \(\theta_{0}\). Considering practical engineering constraints, the initial inclination angle is selected within the range of 35.76\({}^{\circ}\) to 39.26\({}^{\circ}\), as larger angles are impractical for real-world applications. Figure 10: Influence of excitation force on peak dynamic displacements and peak force transmissibility of the isolators (\(\theta_{0}=37.6^{\circ}\), \(\xi=0.05\)). As shown in Figure 11, both the peak dynamic displacement and peak force transmissibility of the three-directional isolator increase with a larger initial inclination angle. Again, in comparison with the linear isolator, the three-directional isolator exhibits a larger peak dynamic displacement but lower force transmissibility, reinforcing its superior vibration isolation capability.
## 3 Methodology and Models
### Feature Engineering  
#### 3.2.1 Feature Scaling  
To ensure that no single feature dominated the model due to differences in scale, specific scaling techniques to the datasets were applied. The input features (X), represented in the histogram in Figure 3, were scaled using the StandardScaler, which transforms the data so as to have a mean of 0 and a standard deviation of 1. The rescaling resulted in data within an approximate range of [\(-\)2.5, 2.5], as visualized in the histogram, helping to improve the model's training and convergence. For the target variable (y), shown in Figure 4, the MinMaxScaler has been applied, which scaled the energy production values to a range of [0, 1]. The histogram of the scaled target values highlights this transformation, ensuring that the model's output was consistent with the scaled input features.  
The histogram in Figure 3 showcases the distribution of the input features after applying the StandardScaler. As seen in the figure, the majority of the data points are clustered around 0, with a significant concentration within the range of \(-\)2.5 to 2.5. This tight clustering around the mean of 0 indicates that the standardization process was successful in centering the data. A small number of outliers are visible beyond this range, suggesting that, while most of the features fall within a normalized range, there are some values that exhibit more variance. This distribution highlights how the StandardScaler equalizes the spread of features, reducing the risk of any single feature disproportionately influencing the model's learning process. The multiple colors in the input features histogram correspond  
Figure 3: Histogram of the input features dataset. (Different colors correspond to different features).  
with the various features in the dataset, explaining the presence of different distributions within the same figure.  
The histogram in Figure 4 represents the distribution of the target variable (energy production) after being scaled using the MinMaxScaler. As expected from this scaling method, the data are rescaled to fall within the range of [0, 1]. The distribution shows that the majority of the target values are evenly spread across this range, with a slight concentration towards the middle (around 0.5 to 0.8), indicating that energy production values are neither too low nor too high in most instances. This transformation ensures that the target values were on a comparable scale with the input features, facilitating smoother learning and improved model performance.  
In general, the x axes in the two histograms depict the normalized values of the input X and target y subsequent to the implementation of the corresponding scaling methods. The y axes represent the frequency of occurrences for each scaled value in the dataset, illustrating the distribution of the data post-scaling.
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 5 System Evaluation with Air Pollutant Prediction Models
### Analysis Based on Different Deep Learning Network  
In this test, we fix the features and choose another model, ConvLSTM. Only the PM\({}_{2.5}\) and PM\({}_{10}\) features are selected as input features of the ConvLSTM, and the time lag is set to 6 h for the training. The MAPE of ConvLSTM with only PM\({}_{2.5}\) and PM\({}_{10}\), and with 6 h of time lag is 34.4%, which is lower than those of the GRU and LSTM networks. We can refer to Figure 1b to see why the predictive performance is better when using a model reflecting the spatial information. In (b), [PERSON]'s I for PM\({}_{2.5}\) is 0.538, which shows a relatively significant spatial correlation. Since PM\({}_{2.5}\) has high spatial autocorrelation, we expect that the predictive performance is better when considering spatial information.
## 3 Methodology  
In this section, we provide a detailed description of the RiQ-KGC model, which is shown in Figure 1. RiQ-KGC is composed of three functional modules, namely relation instantiation, quaternion space rotation, and geometric decoding. The relation instantiationmodule is utilized to localize the relations and make them entity-specific representations. The quaternion space rotation module rotates the entity in quaternion space through the instantiated relation to obtain geometric information. The geometric decoding module analyzes the information obtained from the above two modules. Its output compares the similarity with each entity in the quaternion space, and the entity with the highest score is considered the target result for the triple. Starting with the problem definition of link prediction, we will describe the working mechanism of each module of RiQ-KGC. For ease of comprehension, the primary notations relevant to this paper are enumerated in Table 1.