esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
## 3 Problem Definition and Features Designation
### Features for Bitcoin Price Fluctuation Prediction
#### 3.2.1 Basic Features  
Features extracted from the large price fluctuation period are as basic features. The details are described as follows.  
The change in price, \(f_{1-1}\) is equal to or greater than \(\eta\). The larger the value of \(f_{1-1}\) is, the stronger the large price fluctuation signal is.  
The speed of change, \(f_{1-2}\) is an important feature. For instance, some time series data needs four intervals to achieve \(\eta\), but some time series data only needs one interval to achieve \(\eta\). The difference shows the degree of the change in price. The smaller the \(f_{1-2}\) is, the stronger the large price fluctuation signal is.  
The change in turnover, \(f_{1-3}\) that was adopted can be explained as follows. On the one hand, if we fix the trading amount, a larger price needs more turnovers to achieve the same price change rate compared with a smaller price. It also indicates that manipulators need more money to achieve this price change rate. On the other hand, if we fix the price change rate, a larger turnover means that the buyers and sellers have some different opinions regarding this price change rate. In addition, the turnover is difficult to compare between a \"bull\" market (i.e., an increment in the market) and a \"bear\" market (a decline in the market). Therefore, we adopt the change in turnover as the feature. Formally, it can be calculated as:  
\[f_{1-3}=\frac{\sum_{t=1}^{T}(P_{t}\times V_{t})-\sum_{t=1}^{T_{\text{long}}}(P _{t}\times V_{t})}{\sum_{t=1}^{T_{\text{long}}}(P_{t}\times V_{t})}, \tag{1}\]  
Figure 2: The demonstration of Bitcoin price fluctuation.  
where \(T_{\text{long}}\) is a previous long period, \(T\) is a current short period \(P_{t}\) and \(V_{t}\) are the price and volume at time \(t\), respectively.  
**The change in volatility**, \(f_{1-4}\) is an important indicator. It is a measure of the uncertainty and is used to reflect the risk level of Bitcoin price. The larger the value of \(f_{1-4}\) is, the stronger the signals are. Formally, the change in volatility between current period (cur) and previous period (pre) is computed as:  
\[f_{1-4}=\frac{\sigma_{\text{cur}}-\sigma_{\text{pre}}}{\sigma_{\text{pre}}}, \tag{2}\]  
where \(\sigma\) is the standard deviation of the rate of return.
## 3 Application of Automatic Optimization System  
### MCM Thermal Model  
To verify the IOS, this paper takes the example of a multi-chip integrated circuit for space telescopes. Focused on the thermal management of the power electronic system heat source layout optimization problem, a simplified thermal model of a multi-chip PCB substrate was established for simulation verification. The model consists of the substrate and 14 chips, forming an MCM model as shown in Figure 4, where the thermal effects of each heat source are simplified as thermal loads acting on the corresponding unit substrate network.  
The High-Temperature Co-fired Ceramic (HTCC) substrate is made by co-firing ceramic sheets with conductive pastes such as W, MO, and Mn at high temperatures. It has higher thermal conductivity (compared to ordinary PCB board), high heat resistance, high insulation, high strength, and a well-matched coefficient of thermal expansion, making it the optimal choice for the substrate of MCM components. Therefore, this paper uses it as the substrate for MCM components. Details of the relevant parameters are listed in Tables 1-3. Additionally, the structure of a single chip and a schematic diagram illustrating heat transfer as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module** & **Unit [mm]** \\ \hline Substrate & \(120\times 120\times 2\) \\ Element & \(20\times 20\times 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: MCM geometric parameter.  
Figure 4: MCM model.  
Figure 3: Automatic optimization module.  
This part primarily introduces the specific structure of the MCM (Multi-Chip Module) along with various materials, dimensions, power consumption, and process parameters. It also provides a detailed environment description of the specific application employed by the automatic system.
## 2 Mycobacteria Diversity and Evolution  
### General Characteristics of the Mycobacterium Genus  
The genus _Mycobacterium_ belongs to the _Mycobacteriaceae_ family included in the _Actinobacteria_ phylum and presently encompasses more than 220 recognized species and subspecies [1].  
The genus was first proposed in 1896 by [PERSON] and [PERSON] to include organisms described to be halfway between fungi and bacteria [2], and the first identified and studied members were _M. tuberculosis_ and _M. leprae_[2].  
Overall, the _Mycobacterium_ genus includes aerobic to microaerophilic, slightly curved or straight rods (0.2-0.6 \(\times\) 1.0-10 \(\upmu\)m), non-motile, and asporogenous organisms [12]. The distinguishing characteristics of this genus include acid-fastness and the presence of mycolic acids. Although these are present in other Actinobacteria-related genera, only _Mycobacterium_ species have long chains of mycolic acids with 60 to 90 carbon atoms [12]. This characteristic cell wall with lipid-rich content enables these bacteria to be differentiated based on staining techniques, once high mycolic acid content makes the organisms resistant to the decolorization with acid alcohol [12]. Therefore, mycobacteria are difficult to stain by [PERSON]'s method, however, they are usually considered Gram-stain positive [12]. This genus includes organisms present in natural and anthropogenic-related environments, with high GC DNA content (ranging from 61 to 71%) and an optimal growth temperature ranging from 25 to 45 \({}^{\circ}\)C, depending on the species [12].  
Mycobacterial genome sizes span a wide range, from 3.2 Mb (_Mycobacterium leprae_) to 8.0 Mb (_Mycobacterium mageritense_) [13]. Considering the growth rate, usually, mycobacteria are divided into two groups: the slow growers, that require more than 7 days to develop visible colonies on solid medium, and fast growers, that require 3-7 days to develop visible colonies. The colonies may be white- to cream-colored; however, some strains produce yellow to red-pigmented colonies with (i.e., photochromogenic) or without light (i.e., scotochromogenic) stimulation [12].
###### Abstract  
The aim of this research project was to analyze the influence of slot size and low-friction on pain and the oral health-related quality of life (OHRQoL) of subjects receiving fixed appliances. A group of 120 patients (61 male, 59 female) were chosen for this randomized clinical trial. Participants were classified into four groups (30 patients in each). We compared conventional (C group) and low-friction (LF group) brackets and 0.018\" and 0.022\" slots. Pain was assessed at 4 (T0), 8 (T1), and 24 (T2) hours, and 2 (T3), 3 (T4), 4 (T5), 5 (T6), 6 (T7), and 7 (T8) days after the start of treatment by using the visual analogue scale (VAS). OHRQoL was assessed at 1 month using the Oral Health Impact Profile (OHIP-14). Data was analyzed using the analysis of variance (ANOVA) test with post-hoc Bonferroni correction. For pain on the visual analogue scale, statistically significant differences (\(p<0.05\)) were found for T0 and T3. For OHRQoL, statistically significant differences (\(p<0.01\)) were found in the domains of physical pain, psychological discomfort, psychological disability, and overall OHIP. The group with 0.022\" low-friction brackets showed a lower pain score and less impact on OHRQoL. The type of bracket system used and bracket slot size influenced patients' perceptions of pain and their OHRQoL.  
pain; oral health-related quality of life; orthodontics; oral health +
Footnote †: journal: Applied Sciences
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 4 Simulation Results  
This section focuses on simulating the interference experienced by an RAS telescope from an NGSO satellite system. To do this, we first generated the CDFs of the NGSO satellite EIRP for all cells within its field of view based on the relevant radiation characteristics and geographic location. Then, we simulated the number of visible satellites for the RAS telescope according to the constellation configurations and obtained the activation probabilities for different beam scheduling strategies. Through these results, we could get the EPFD at the RAS receiver based on the beam activation probability. Finally, we obtained the EPFD distribution in each cell of the RAS telescope through multiple trials with random start times, each lasting for a duration of 2000 s.
## 3 Application and Experiment Test
### Dataset
(**a**) Scatter plot of each independent variable; (**b**) correlation heatmap; (**c**) distribution plots of variables. The dataset comprising 180 blasts at the Tonglushan Copper Mine in China indeed offers valuable insights into the blasting practices and outcomes specific to this mine. Figure 5 illustrates the extensive range of data, particularly concerning parameters like explosive charge per delay (Qmax), Qtotal, D, VD, DT, RMI, and \(\alpha\). Referring to Table 1, which summarizes the blast-induced ground vibration dataset at the Tonglushan Copper Mine, we can observe a diverse distribution of key metrics such as Qmax, Qtotal, D, VD, B, PPR, DT, RMI, f, \(\alpha\), VoD, and PPV. These metrics provide a comprehensive overview of the blasting activities and their resulting ground vibrations, encompassing various aspects of the blasting process and its effects. Considering the breadth and depth of this dataset, it is reasonable to assert that the information generated from these 180 blasts is likely to be representative of the blasting practices and outcomes at the Tonglushan Copper Mine. This dataset can serve as a valuable resource for analyzing and optimizing blasting operations, as well as for developing models and strategies to minimize environmental impacts and enhance safety.
## 5 The Efficiency of the Order Reduction Method  
In this section, we will discuss the efficiency of the order reduction method via the bifurcation analysis of the original and the reduced system.  
On the basis of the POM energy curve (Figure 22), we can obtain the 3-DOFs reduced model when the rotating speed is 1120 rad/s. As is shown in Figure 23, we can see clearly that the reduced system (Figure 23b) preserves the period-doubling bifurcation, the rotating speed of complex motion and the topological structure of the original one (Figure 23a). The comparison of the bifurcation diagrams verifies the efficiency of the TPOD method, we can also use other dynamical behaviors (phase portraits, amplitude-frequency curve, etc.) to discuss the efficiency [49; 50], we don't state one by one here.  
Figure 22: The POM (proper orthogonal mode) energy of the 24-DOFs (degrees of freedom) model with coupling fault.  
**Remark 7**.: _The reduced order reduction model obtained by the TPOD method preserves the dynamical characteristics of the original one well. The TPOD method is proposed by the present authors, it is applied in the rotor models supported by the sliding bearings or ball bearings with looseness. The efficiency of the order reduction method applied in the rotor system with coupling fault further generalizes this method. The Runge-Kutta method is used to calculate the dynamic response, and we can obtain the results in Figures 3-23. It is promising to adapt this method to the uncertainty quantification of faulted rotor systems with parametric variabilities [57; 58; 59; 60]. Fortran Language is used to solve the dynamic equation._
## 4 Discussion
It is important that the data are well understood prior to feature extraction so the specific feature that highlights the diagram type straightaway is heavily present in the dataset. Ideally, each feature would be equally found in the category it belongs to, but some features can be found across multiple diagram types, and then the specific components that belong to only one category are the ones that support the model's ability to generalize. Outlined below are key recommendations derived from the study's findings, aimed at improving the application of feature-based neural networks in UML diagram classification:  
1. Use of Feature-Based Datasets for Small Data Scenarios: feature-based datasets reduce the dimensionality and computational cost, allowing neural networks to learn faster and more effectively. Therefore, for projects where data collection is challenging or expensive (e.g., specialized UML diagrams), it is recommended to adopt a feature-based approach, similarly highlighted in [5]. 2. Use of Feature-Based Datasets for Faster Training and Deployment: as the datasets are inherently smaller, this makes the approach ideal for rapid prototyping and situations where computational resources are limited. Thus, software engineering education  
Figure 8: Non-UML diagram analysis using Copilot. and agile development cycles can benefit from these models to iterate and deploy solutions quickly. 3. Standardizing UML Diagram Feature Extraction for Consistency: To support consistent model performance across diverse UML diagram sources, a standardized feature extraction protocol should be developed and followed. This ensures that the models receive well-defined input features, reducing errors during classification and making model training and deployment more reliable across educational and industrial settings. It is recommended to use the official documentation [10] to ensure all research follows the same reference.
# Design of External Rotor Ferrite-Assisted Synchronous
## 3 Design of Fe-PMaSynRM for High Power Density
### Magnetic Equivalent Circuit-Based Analytical Model  
A lumped-parameter model (LPM) has been developed to analytically calculate the electromagnetic performance of the external rotor Fe-PMaSynRM. The LPM uses the \(d\)- and q-axis equivalent magnetic circuit (EMC) components to calculate d- and q-axis inductances. The EMC components of the external rotor Fe-PMaSynRM are shown in Figure 6. The q-axis EMC is mapped on the simplified rotor architecture of three-flux barrier per pole design in Figure 6. The d- and q-axis EMC for single-flux barrier per pole structure is shown in Figure 7a,b, respectively. Table 1 depicts the EMC parameters, which have been utilized to develop d- and q-axis circuits shown in Figures 6 and 7. It has been shown that the saturation effect needs to be in the d-axis EMC and can be ignored in q-axis EMC [32]. The dimensional parameters in Figure 3 can be used to obtain the reluctances in d- and q-axis EMC [33].  
Figure 5: (**a**) Variation of average torque with the degree of curvature and (**b**) maximum allowable q-axis current vs. degree of curvature.
# A Novel Carbon Fiber Composite Material for the Simulation of Damage Evolution in Thick Aquifers
## 5 Discussion
### Limitation  
In laboratory, similar material simulations, the self-sensing functionality of carbon fibers depends on forming a continuous and uniform conductive network within the material. However, in the actual preparation process, carbon fibers are often difficult to distribute uniformly within the material [39]. Due to their low density, tendency to entangle, and significant influence from electrostatic forces during mixing, carbon fibers tend to agglomerate or float, leading to enhanced conductivity in local areas, while other regions cannot sense.  
This uneven distribution directly weakens the overall self-sensing monitoring performance of the material. Signals may become overly concentrated in certain areas, producing \"short-circuit\" responses, while other areas lack conductive pathways, making it impossible to detect microdamage. Furthermore, the accumulation of carbon fibers in some areas may obscure the development of fine cracks, leading to distorted monitoring results. Toaddress this issue, future work could consider replacing carbon fibers with easily dispersible carbon nanotubes (CNTs) [40] in similar materials. Due to their unique structure and better dispersibility, carbon nanotubes can distribute more evenly within the material, forming a stable conductive network that enhances self-sensing performance. Additionally, superconductive carbon black also exhibits good dispersibility and conductivity, serving as an alternative to carbon fibers. It can reduce dispersion problems while maintaining good conductivity, thus improving the accuracy and reliability of damage monitoring.
## 4 Preliminaries
### Basic Operations  
The MPC-based secret sharing protocols do not need to reconstruct the shares in the calculations. To construct new protocols based on the RSS scheme, this paper will call the sub-protocol of the original work to implement the new protocol on a partial step. In this section, the paper describes the basic operations the algorithms needed.  
**Selectshare**\((\Pi_{\SS}(P_{1},P_{2},P_{3}))\): \(\Pi_{\SS}\) is a critical sub-routine for majority protocols. It takes two shares \([\![a]^{L},[\![b]\!]^{L}\) as input, for which \([\![b]\!]^{2}\) is a select bit. The output \(c\) is either \(a\) or \(0\) depending on \(b\). Set \([\![c]\!]^{L}\leftarrow\Pi_{\SS}\): Input form \(\{[\![a]^{L},[\![b]^{L},[\![c]\!]^{L}\}\), set \(c=a\) if \(b=0\); otherwise, set \(c=0\) if \(b=1\); the algorithm can abstract this formula as \([\![c]\!]^{L}=(1-[\![b]\!]^{2})\)\([\![a]\!]^{L}\).  
**ReLU**\((\Pi_{\text{ReLU}}(P_{1},P_{2},P_{3}))\): ReLU is a necessary component of protocol design and network construction, and it is a significant activation function used in neural networks; \(\Pi_{\text{ReLU}}\) takes one share \([\![a]\!]^{L}\) as input, the output \([\![b]\!]^{L}\) is \([\![a]\!]^{L}\) if \(a>=0\) and \(0\), otherwise. Set \([\![b]\!]^{L}\leftarrow\Pi_{\text{ReLU}}\): set \(b=0\) if \(a<0\) and set \(b=a\) if \(a>=0\)  
**DReLU**\((\Pi_{\text{DReLU}}(P_{1},P_{2},P_{3}))\): DReLU is a derivative of ReLu, for the reason that the paper uses \(\mathbb{Z}_{L}\) to perform fixed point arithmetic. It notes that the number defines the positive and negative by the first bit, so it is known that the positive numbers are the first \(2^{L-1}\) numbers; therefore, the most significant bits of positive numbers are \(0\) and negative numbers otherwise. The relation between DReLU and ReLU can be written as:  
\[\text{DReLU}(\text{a})=\text{a}\cdot\text{ReLU}(\text{a}) \tag{1}\]  
Set \([\![b]\!]^{L}\leftarrow\Pi_{\text{DReLU}}\): Set \(b=1\) if \(a>0\) and Set \(b=0\) if \(a<0\).  
Figure 2: Replicated secret sharing.  
**Pow \((\Pi_{\textsf{Pow}}(P_{1},P_{2},P_{3}))\)**: \(\Pi_{\textsf{Pow}}\) is used for estimating the scaling factor to calculate the initial value of division and the rsqrt algorithm; it can always be combined with the truncation function to determine a suitable initial value of the denominator. It takes one share \([\![a]\!]^{L}\) as an input and the output \([\![b]\!]^{L}\), which is the bounding power of \([\![a]\!]^{L}\). Set \([\![b]\!]^{L}\leftarrow\Pi_{\textsf{Pow}}\): Input \([\![a]\!]^{L}\) in \(\mathbb{Z}_{L}\), then get \([\![b]\!]^{L}\) in clear as output, where \(a\in(2^{b},2^{b+1})\)
# Design and Experiment of Dry-Ice Cleaning Mechanical Arm for Insulators in Substation
## 3 Mechanical System of Electrified Dry-Ice Cleaning Robotic Arm
### The Nozzle Design for Dry-Ice Cleaning Insulator  
Considering that the diameter of the insulator is not uniform, and the lower part of the insulator is a steel frame, the top part is a high-voltage wire, so the end of the dry-ice spray will not be able to adopt the full surrounding structure. At the same time, based on the requirements of high-voltage-insulation, there should be no electrified body or conductor within the safe distance from the top electrified point, so the active actuator should be avoided at the end of the mechanical arm as far as possible. Combined with the characteristics of the constant attitude of the end components of the mechanical arm, considering the factors of insulation, safety, convenience, etc., the operation mode of up and down cross-cleaning with two side nozzles is proposed. As shown in Figure 4a, the end of the mechanical arm is composed of two cleaning circuits A and B by one spray head on both sides; under the action of direction-motion, rotating \(\theta_{A}\) to the left side of the insulator, so the spray head on A-side forms a cleaning space \(S_{A}\) (b); rotating \(\theta_{B}\) to the right side of the insulator, the spray head on B-side forms a cleaning space \(S_{B}\) (Figure 4c); the associative action of the spray heads on both sides forms a closed cleaning operation space, and the enclosed circle \(S_{AB}\) is the maximum insulator cleaning range of the mechanical arm (Figure 4d). Under the action of the lifting movement of the mechanical arm, the end can complete the cleaning operation of the whole insulator pillar.  
Figure 4: Schematic diagram of insulator cleaning nozzle and work. (**a**) Cleaning nozzle; (**b**) right nozzle cleaning area; (**c**) left nozzle cleaning area; (**d**) left and right nozzle cleaning combined area.