esa-sceva/satcom-chunk-collection huggingface dataset:
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
###### Abstract  
Gear modification, which involves the removal of material from the theoretical surface to improve the contact characteristics of the gear face, is widely applied in gear vibration reduction and noise optimization design. This paper establishes a dynamic model of the straight bevel gear (SBG) transmission system to accurately and efficiently evaluate the effects of different modification strategies on the vibrational characteristics of SBGs. Initially, the time-varying meshing stiffness (TVMS) of standard SBGs was calculated, and methods such as the slicing method and deformation coordination equations were used to calculate the TVMS under tooth profile modification (TPM), Lead crown relief (LCR), and comprehensive modification (CM), which were then validated against finite element method (FEM) calculations. Subsequently, taking into account the impact of time-varying meshing point vectors and the degree of contact overlap, a finite element node dynamic model of the SBG transmission system was established. Finally, by comparing the dynamic characteristics under different modification conditions, the study further elucidates that selecting the appropriate modification method and amount according to different service scenarios is an effective means to suppress gear transmission vibration. This research provides a theoretical basis for the design of gear modification and vibration control for SBGs.  
straight bevel gears; vibration; dynamic; modification; time-varying meshing stiffness +
Footnote †: journal: applied sciences
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 3 True Random Number Generators Design and Evaluation on FPGA Technology
### Candidate Solutions
#### 3.2.3 FiRO and GaRO  
Several feedback polynomials can be selected in case of FiRO and GaRO. One of the possible choices is the one presented in [20] where all the FiRO and GaRO feedback networks are individually tested into Intel FPGA Cyclone V technology for oscillators lengths respectively equal to 10 and 11. Polynomials identified as best-in-class in [20] have been chosen for the implementation of the entropy source, and state sampling support has also been added, to sample not just the final inverter but all the internal states of the TRNG. Figures 11 and 12 show the structure of FiRO GaRO, both with state sampling.  
Figure 11: Fibonacci Ring Oscillator with state sampling. The output bus is XORed together to form a 1-bit output in the higher level.  
Figure 12: Galois Ring Oscillator with state sampling. The output bus is XORed together to form a 1-bit output in the higher level.  
Figure 10: Output values histograms for eight parallel Meta-RO elements.  
Figure 9: Sampling strategy for Meta-RO assessment.  
The first iteration of the test consisted of instantiating four FiRO and four GaRO with the feedback configurations reported below, and reading their 8-bit parallel output 131,072 times. Please note that \(F\) stands for FiRO and \(G\) for GaRO.  
\[p_{0}^{F}(x)=1+x+x^{2}+x^{3}+x^{5}+x^{6}+x^{7}+x^{8}+x^{9}+x^{10} \tag{9}\]  
\[p_{1}^{F}(x)=1+x+x^{2}+x^{3}+x^{4}+x^{6}+x^{7}+x^{10} \tag{10}\]  
\[p_{2}^{F}(x)=1+x+x^{2}+x^{3}+x^{4}+x^{5}+x^{6}+x^{10} \tag{11}\]  
\[p_{3}^{F}(x)=1+x+x^{2}+x^{3}+x^{4}+x^{5}+x^{6}+x^{7}+x^{9}+x^{10} \tag{12}\]  
\[p_{0}^{G}(x)=1+x^{2}+x^{3}+x^{5}+x^{6}+x^{7}+x^{9}+x^{11} \tag{13}\]  
\[p_{1}^{G}(x)=1+x^{2}+x^{3}+x^{5}+x^{6}+x^{7}+x^{8}+x^{11} \tag{14}\]  
\[p_{2}^{G}(x)=1+x^{2}+x^{3}+x^{4}+x^{5}+x^{7}+x^{9}+x^{11} \tag{15}\]  
\[p_{3}^{G}(x)=1+x^{2}+x^{3}+x^{4}+x^{5}+x^{6}+x^{9}+x^{11} \tag{16}\]  
No particular constraints are given for the sampling frequency and the placing and routing. Comparing the acquisition run depicted in Table 2 with the ones acquired for Meta-RO, it is evident the reduced bias confirmed by the ones and zeros count.
# Theoretical Study of Large Uncoupling Coefficient Loading for Surface Blasting
###### Abstract  
Smooth surface blasting control technology is aimed at blasting the rock body until it is left with a smooth surface and to protect it from damage; the current air spaced axial uncoupled charge and air spaced radial uncoupled continuous charge are effective charging structures for smooth surface blasting. Reserved air spacing can effectively reduce the blast wave and the peak pressure of the explosive gas, improving the quasi-static pressure of the explosive gas under the action of rock surface blasting with fracture seam quality. In order to ensure the effect of surface blasting, small-diameter light surface holes are more often used; with the development of drilling machinery, the use of large-diameter light blast holes with an oversized uncoupled coefficient of loading structure effectively improves the efficiency of the construction and at the same time achieves better blasting results. However, according to the bursting assumption of obtaining the theory of light surface blasting in the application of large uncoupling coefficient loading, light surface blasting has certain limitations. In this regard, the bursting theory explores the air spacing uncoupling charge in line with the multi-faceted exponential expansion of the critical uncoupling coefficient and is in accordance with the following: the requirements of light surface blasting and the field loading structure; the derivation of the quasi-static pressure on the wall of the gunhole under the action of large uncoupling, uncoupling coefficient, and the parameters of the spacing between the gunholes; the establishment of the axial uncoupling coefficient and the radial uncoupling coefficient-equivalent relationship between the uncoupling coefficient and the theoretical relationship between the selection of the spacing between the holes; the uncoupling coefficient and the selection of the theoretical relationship between the spacing between the holes. This study reveals the mechanism by which different parameters of surface blasting can achieve good results in engineering practices. A slope in Guizhou is an example of sample calculations and the application of two different charging structures applied to field loading, which have achieved good surface blasting results.  
smooth blasting; air spacing; uncoupling factor; charging structure; theoretical research Article
# Handover Management for D2D Communication in 5G Networks
###### Abstract  
This study addresses the handover management issue for Device-to-Device communication in fifth-generation (5G) networks. The Third Generation Partnership Project (3 GPP) drafted a standard for proximity services (ProSe), also named device-to-device (D2D) communication, which is a promising technology in offering higher throughput and lower latency services to end users. Handover is an essential issue in wireless mobile networks due to the mobility of user equipment (UE). Specifically, we need to transfer an ongoing connection from an old E-UTRAN Node B (eNB) to a new one, so that the UE can retain its connectivity. In the data plane, both parties of a D2D pair can communicate directly with each other without the involvement of the base station. However, in the control plane, devices must be connected to the eNB for tasks such as power control and resource allocation. In the current standard of handover scheme, the number of unnecessary handovers would be increased by the effect of shadowing fading on two devices. More important, the handover mechanism for D2D pairs is not standardized yet. LTE-A only considers the handover procedure of a single user. Therefore, when a D2D pair moves across cell boundaries, the control channels of the two UEs may connect to different base stations and result in increased latency due to the exchange of D2D related control messages. Hence, we propose a handover management scheme for D2D communication to let both parties of a D2D pair handover to the same destination eNB at the same time. By doing so, the number of unnecessary handovers, as well as the handover latency, can be reduced. In the proposed method, we predict the destination eNB of D2D users based on their movements and the received signal characteristics. Subsequently, we make a handover decision for each D2D pair by jointly factoring in the signal quality and connection stability. Expected improvement can be attained, as revealed in the simulation. Unnecessary handover can be avoided. Consequently, both UEs of a D2D pair reside in the same cell and, therefore, result in increased throughput and decreased delay.  
long term evolution advanced; Device-to-Device; handover; control signal; quality of service +
Footnote †: journal: Applied Soft
## 3 Methodology
### Data Privacy  
Data privacy concerns are increasingly relevant in the use of LLMs, primarily due to the risk of unintentionally retaining or exposing sensitive data from training datasets. While LLMs do not store information in a conventional memory structure, the AI community has concerns that LLM providers may collect user prompts and responses for model refinement. This practice can integrate sensitive information into the model's knowledge, making it vulnerable to exposure in later interactions.  
The primary interface between a user or application and an LLM is the prompt, making any prompt that contains sensitive information a potential privacy risk. Traditional privacy-preserving techniques, such as Differential Privacy (DP), often fall short in the context of LLMs. [PERSON] et al. [31] highlight that standard DP methods treat all data points uniformly, which can degrade model performance. As an alternative, prompt obfuscation methods have emerged in research as a simple yet effective approach to enhance privacy without significantly impacting utility.  
Prompt obfuscation involves transforming the original text or a part of it to obscure its meaning, significantly reducing its readability and recognizability while preserving the ability to recover the original content accurately. This balance ensures both privacy protection and data integrity. Several methods can be used for obfuscation, each with varying levels of complexity and effectiveness. Base64 encoding converts text into an ASCII representation of binary data, making it less readable to humans. ROT13 applies a simple letter substitution by rotating each character 13 positions in the alphabet. Hex encoding represents each character as a two-digit hexadecimal number, while URL encoding replaces special characters with percent-encoded equivalents. Finally, reversing the string provides a basic yet sometimes effective obfuscation by simply inverting the order of characters. The choice of algorithm depends on the desired trade-off between simplicity, obfuscation strength, and ease of reversibility.  
This application involves processing customer and component information, some of which is considered sensitive. Applying heavy obfuscation to the entire prompt would degrade the performance of the language model due to the added complexity. To balance privacy and model efficiency, this work utilizes ROT13 to obfuscate only the sensitive words identified prior to prompt construction. ROT13 is a simple Caesar cipher variant that shifts each letter by 13 positions (e.g., 'A' becomes 'N,' 'Z' becomes 'M'). Although not designed for strong encryption, ROT13 is effective for lightweight text scrambling, making it well-suited for scenarios where obfuscation, not security, is the primary goal [32]. An example of ROT13 obfuscation is shown in Figure 5.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 1 Introduction
### Motivations and Contributions
This method can analyze the characteristics of the current channel environment and intelligently select the most appropriate parameter configuration to optimize the performance of the CCB model;
* To demonstrate the superiority of our proposed scheme, we conducted extensive simulation experiments and compared our results with those of other schemes. The simulation results indicate that, compared to the existing fixed-structure SemCom system, CCB significantly enhances the image transmission performance of the satellite-ground channels and shows stronger adaptability and stability. The remainder of this paper is organized as follows. In Section 2, we first introduce the system model, including the network model, channel model, and the proposed CCB architecture. Then, in Section 3, we introduce the CCB MappingNet, including the design principle and training algorithm. Section 4 is dedicated to the details of the denoiser and parameter selection mechanism. Section 5 provides the experiment results and a direct comparison of several methods to quantify the performance gain of the proposed method. Finally, Section 6 concludes this paper.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 4 Traffic Performance Analysis
### Bidirectional Communication
#### 4.2.2 In eEFA  
The number of network frames generated in the network by the first sent frame, denoted by \(nf_{eEFA}^{1,send}\), is calculated as:  
\[nf_{eEFA}^{1,send}=n_{d}+\sum_{i\in QR}n_{i} \tag{18}\]  
When the destination receives the first sent frame, it sends the first replied frame back to the source. The number of network frames generated in the network by the first replied frame, denoted by \(nf_{eEFA}^{1,reply}\), is calculated as:  
\[nf_{eEFA}^{1,reply}=n_{d}+\sum_{i\in AQR}n_{i} \tag{19}\]  
The number of network frames generated in the network by sending the \(k\)th frame (\(k\geq 2\)), denoted by \(nf_{eEFA}^{k,\ send}\), is calculated as:  
\[nf_{eEFA}^{k,\ send}=n_{S}+\sum_{i\in AQR}n_{i}+n_{d} \tag{20}\]  
The number of network frames generated in the network by replying to the \(k\)th frame (\(k\geq 2\)), denoted by \(nf_{eEFA}^{k,\ reply}\), is calculated as:  
\[nf_{eEFA}^{k,\ reply}=n_{d}+\sum_{i\in AR}n_{i}+n_{s} \tag{21}\]  
Generally, the number of network frames generated in the network when the source sends \(N\) unicast frames to the destination in bidirectional communication eEFA, denoted by \(nf_{eEFA}^{bi}\), is calculated as:  
\[nf_{eEFA}^{bi}=nt_{eEFA}^{1,send}+nt_{eEFA}^{1,reply}+(N-1)\Big{(}nt_{eEFA}^{k,\ send}+nt_{eEFA}^{k,\ reply}\Big{)} \tag{22}\]  
Therefore, \(nf_{eEFA}^{bi}\) is calculated as:  
\[nf_{eEFA}^{bi}=\left(\sum_{i\in QR}n_{i}+2n_{d}+\sum_{i\in AQR}n_{i}\right)+2 (N-1)\Bigg{(}n_{s}+n_{d}+\sum_{i\in AQR}n_{i}\Bigg{)} \tag{23}\]
## 1 Introduction
They allow for the efficient handling of large data volumes and accelerate computations, which is critical for our objectives. This study aims to bridge the gap by leveraging parallel machine learning algorithms to enhance traditional reservoir simulation frameworks, specifically focusing on the black oil model dataset. This research focuses on exploring specific parallel machine learning algorithms, including random forest (RF), artificial neural networks (NNs), polynomial regression (PR), gradient boosting (GB), and decision trees (DTs) to predict oil recovery factors. By exploring the capabilities and advantages of parallel machine learning algorithms, this paper seeks to make significant contributions to computational sciences and reservoir engineering. It is anticipated that the findings will accelerate and optimize the decision-making processes within the industry by offering faster, more accurate predictive tools, ultimately facilitating more efficient reservoir management.
## 8 Results
### Quantitative Results
#### 8.2.4 Perceived Satisfaction of Multi-Modalities  
The instrument used was adapted from the Post-Study Usability Questionnaire. The PSSUQ is a 16-item survey that measures users' perceived satisfaction with a product or system [44]. Using this scale, students' perceived satisfaction with multi-modalities was reported. The majority of the participants were in agreement and strong agreement with the 20 items in this scale, with slight differences. Over 190 respondents agreed with 35% of the statements. The item \"The information shown in multi-modalities was effective in helping me complete tasks and scenarios\" scored highest in agreement (66%, number of people: 198) and had the highest mean score of 4.07 (S*D = 0.64), while the item \"I find that all the existing modalities in the current game-based applications can satisfy most of my needs to assist my learning performance\" had the lowest agreement (56.7%, number of people: 170) and the lowest mean score of 3.87 (S*D = 0.79) of the 20 statements. Thirty percent of the statements had a mean score over 4.0, and the total mean score on this scale was 3.98 with a standard deviation score of 0.72. The corresponding results in the table indicated that multi-modalities were effective in helping university learners to complete tasks and scenarios, and all the existing modalities in the current applications could satisfy most of the users' needs to assist their learning performance, except for game-based learning applications. Most learners agreed that multi-modalities in the learning applications can improve their learning performance with well-organized content and a pleasant interface. Therefore, a high satisfaction with multi-modalities was expressed.  
Figure 8: Percentage statistics for perspectives on writing modality in different types of learning applications.
## 2 Theoretical Derivation of PIC Model
### Contact Pressure Caused by Wire Twisting  
The primary source of contact pressure between wires is the extrusion force caused by wire twisting. When calculating the contact pressure caused by the twisting of wires, the primary consideration lies in the axial tension exerted on the outer wire, giving rise to the radial contact pressure on the inner wire. As Figure 3 shows, in the formation of the cable, the wires undergo slight twisting, each winding in a helical form around the inner layer of wires. At this point, the tangential tension in \((i,j)\) wire is denoted as \(T_{(i,j)}\), the twisting angle is represented by \(\alpha_{(i,j)}\), and the twisting force per unit length is given by \(q_{(i,j)}^{\text{tr}}\)[29; 34]:  
\[q_{(i,j)}^{\text{tr}}=\frac{T_{(i,j)}}{r\Big{(}1+\tan^{2}\alpha_{(i,j)}\Big{)} }=\frac{T_{(i,j)}}{r}\cdot\cos^{2}\alpha_{(i,j)} \tag{1}\]  
Figure 3: Outer wire twisting: (**a**) the outer cable is strained and twisted, (**b**) wire helix angle, (**c**) wire cross-section.  
Figure 2: Cross-section of cable and contact point labeling: (**a**) wire positional labeling, (**b**) wire contact point labeling.  
The twisting force \(q^{tw}_{(i,j)}\) is directed towards the center of the cable cross-section. Each wire not only bears the extrusion contact pressure generated by the twisting of the upper layer but also transmits this contact pressure, along with its own twisting force, to the lower layer of wires. Assuming complete transmission of this contact force, the contact pressure at different contact points can be obtained by superimposing the twisting forces of different layers of wires.  
When calculating the contact pressure at a specific point, it is necessary to consider the specific location of that point on the cable cross-section. Contact points can be classified into two types based on their positions: diagonal and non-diagonal.
## 3 Results
### Writing Ability
#### 3.2.3 Muscle Activity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Muscle Activity (\%)**} & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{2}{c}{\begin{tabular}{c} **[PERSON]–[PERSON]** \\ \(p\) **Value** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **[PERSON]–[PERSON]** \\ \(p\) **Value** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **[PERSON]** \\ \(p\) **Value** \\ \end{tabular} } \\ \hline \multirow{4}{*}{FCR} & \multirow{4}{*}{Consonants} & \multirow{4}{*}{AG} & PS & \(18.6\pm 3.5\) & 0.044 & 0.019 & \multirow{4}{*}{0.930} \\ \cline{3-4}  & & NPS & \(19.0\pm 7.0\) & 0.006 & 0.937 & & \\ \cline{3-4}  & & \multirow{2}{*}{SW} & PS & \(23.2\pm 4.7\) & 0.046 & 0.019 & & \\ \cline{3-4}  & & NPS & \(19.6\pm 9.7\) & 0.001 & 0.937 & & \\ \cline{3-4}  & & \multirow{2}{*}{Vowels} & AG & PS & \(19.8\pm 5.7\) & 0.049 & 0.023 & & \\ \cline{3-4}  & & NPS & \(19.8\pm 7.6\) & 0.005 & 0.388 & & \\ \cline{3-4}  & & \multirow{2}{*}{SW} & PS & \(25.7\pm 4.3\) & 0.037 & 0.023 & & \\ \cline{3-4}  & & & NPS & \(20.1\pm 10.6\) & 0.001 & 0.388 & & \\ \hline \multirow{4}{*}{FCRL} & \multirow{4}{*}{Consonants} & \multirow{4}{*}{AG} & PS & \(31.4\pm 9.1\) & 0.016 & 0.638 & & \\ \cline{3-4}  & & NPS & \(40.8\pm 11.5\) & 0.002 & 0.182 & & \\ \cline{3-4}  & & \multirow{2}{*}{SW} & PS & \(34.3\pm 13.3\) & 0.018 & 0.638 & & \\ \cline{3-4}  & & & NPS & \(36.2\pm 10.4\) & 0.042 & 0.182 & & \\ \cline{3-4}  & & \multirow{2}{*}{Vowels} & \multirow{2}{*}{AG} & PS & \(26.3\pm 5.8\) & 0.031 & 0.814 & & \\ \cline{3-4}  & & & NPS & \(30.6\pm 10.0\) & 0.002 & 0.754 & & \\ \cline{3-4}  & & \multirow{2}{*}{SW} & PS & \(26.3\pm 7.2\) & 0.038 & 0.814 & & \\ \cline{3-4}  & & & NPS & \(31.3\pm 11.9\) & 0.002 & 0.754 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Differences in muscle activity between the paralyzed side (PS) and nonparalyzed side (NPS) when writing Korean consonants and vowels using Angel Grip (AG) and Steady Write (SW).
# Intelligent Target Design Based on Complex Target Simulation
## 2 Design Principle
### Luneburg Ball Reflector  
A Luneburg ball reflector (Luneburg lens) is a passive reflector made by coating a layer of metal reflector on the surface of a Luneburg ball, based on the principle of reflection and refraction of light in an uneven medium. The ideal Luneburg ball reflector is a mirror-symmetric spherical structure whose permittivity gradually changes along the ball radius. The permittivity gradually changes from 2 in the center to 1 on the surface of the Luneburg ball. The relative permittivity of the outermost medium of the reflector is the same or close to that of air [17].  
The Luneburg ball refractive index \(n\) is a function of distance \(r\) from the center of the ball to a point on the surface of the ball. It can be written as follows:  
\[n(r)=\sqrt{2-\left(\frac{r}{R}\right)^{2}} \tag{3}\]  
where \(r\) represents the distance from the center of the ball to a point on the outer surface of the dielectric ball, and \(R\) represents the radius of the Luneburg ball.  
The dielectric constant of each layer of the dielectric ball is  
\[\varepsilon(r)=n(r)^{2}=2-\left(\frac{r}{R}\right)^{2} \tag{4}\]  
However, it is not possible to actually find materials that have a continuous dielectric constant gradient. Due to the unavailability of required materials, several layers of materials with different dielectric constants are usually used in actual production to simulate the effect of a dielectric constant with a continuous gradient, which not only reduces the processing difficulty but can also achieve the basic performance of a Luneburg Ball.