esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 6 Discussion
### Interpretation of Key Results  
The study demonstrates that all measured and predicted PPV values, including 1.424 cm/s for the diaphragm wall, 1.482 cm/s for the central pillar, and 1.537 cm/s for the roof, remain below the safety threshold of 2.0-2.5 cm/s. The agreement between simulation and field results indicates that the FLAC3D model is reliable for assessing structural responses in metro projects. These findings validate the controlled blasting strategy employed and confirm that the construction is safe for nearby structures.  
A significant observation is that the central pillar experienced the highest PPV among all elements, suggesting that load-bearing components in the core structure are more susceptible to blast-induced stress. This aligns with the theory that central load paths typically absorb greater dynamic loads, making them critical targets for structural monitoring.
## 4 Numerical Simulations
### Operating Efficiency Analysis of the Proposed CLC Control Strategy
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators**Control Strategies & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} \\ \cline{2-5}  & **CLC** & **CAC\_Sine** & **CLC** & **CAC\_Sine** \\ \hline AvgL\_M (m/s\({}^{3}\)) & 0.0052 & 0.0133 & 0.0133 & 0.0133 \\ StdL\_M (m/s\({}^{3}\)) & 0.0558 & 0.2304 & 0.0907 & 0.2573 \\ MaxL\_M (m/s\({}^{3}\)) & 0.3458 & 3.2520 & 0.5256 & 3.7890 \\ AvgL\_A (m/s\({}^{3}\)) & \(-\)0.0067 & \(-\)0.0067 & 0.0007 & \(-\)0.0020 \\ StdL\_A (m/s\({}^{3}\)) & 0.1284 & 0.4651 & 0.1009 & 0.4087 \\ MaxL\_A (m/s\({}^{3}\)) & 0.3676 & 5.1104 & 0.4252 & 5.1577 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Indicator comparison for longitudinal jerks of two control strategies. is large in a few simulation cycles, but the overall memory consumption in the whole simulation cycle can be bounded within a reasonable range. Therefore, the proposed CLC control strategy can satisfy the basic requirements for implantation into embedded application devices. In summary, both the execution time and memory usage of the proposed control strategy in each execution cycle are in a reasonable and acceptable range, which indicates the proposed CLC control strategy has good operating efficiency and can meet the basic requirements of the embedded system implantation algorithm.
## 3 Methodology
### Life Cycle Inventory Analysis  
#### 3.2.1 Lentil Cultivation  
A summary of the cultivation conditions for growing the ICARDA lentil line 6002/ ILWL118/1-1 at the Marchouch experimental station and the Onano lentil variety at the Cerqueto Agricultural Farm (Acquapendente, Italy) is presented in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Lentil** & **ML** & **OL** & **Unit** \\ \hline Overall cultivation area & \(1\) & \(1\) & ha \\ Fallow area & \(0\) & \(0\) & ha \\ Town & Marchouch & Acquapendente & - \\ Latitude & (Morocco) & (Italy) & - \\ Latitude & 33.567 & 42.736616 & DD \\ Longitude & \(-6.684\) & 11.891832 & DD \\ Altitude & 398 & 438 & asl m \\ Minimum grain yield & 0.40 & 0.93 & mg/ha \\ Maximum grain yield & 1.27 & 1.67 & mg/ha \\ Average grain yield & 0.84 & 1.26 & mg/ha \\ Above- and below-ground biomass use & 100\% left in the field after shredding & \% \\ Seed density & 70 & 100 & kg/ha \\
8-30-20 NPK fertilizer & 100 & - & kg/ha \\
20-20-20 NPK fertilizer & - & 20 & kg/ha \\
4-4-4 poultry manure & - & 200 & kg/ha \\ Herbicide (Fusilade max) & 0.50 & - & L/ha \\ Herbicide (Feinzin) & - & 0.125 & kg/ha \\ Overall diesel fuel consumption & 35 & 90–120 & L/ha \\ Lubricant oil consumption & 5 & 1 & L/ha \\ Storage & & in loco & \\ Transportation mode & Tractor with a 10 mg trailer & \\ Field-to-harvester distance & 5 & km \\ Moisture content after solar drying & 0.12 & g/g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cultural conditions used for growing the ML and OL grains examined here.  
Table S1 in the Supplementary Materials presents the input and output data for the conventional production of dry lentils on a nominal 1 ha farm. These data were used to estimate GHG emissions from managed soils using the recently updated IPCC Guidelines [47] with the Tier 1 approach. Specifically, NO emissions into the air and P emissions into water were estimated along with the emission factors reported in the EPD(r) Product Category Rules [48], as detailed in Table S1. According to these EPD(r) rules, the NPK fertilizer was assumed to consist of 50% ammonium nitrate and 50% calcium ammonium nitrate, while poultry manure was classified as animal manure. Additionally, P emissions were estimated as 5% of the applied P from both mineral and organic fertilizers [48].  
As an example, Table S2 shows the inventory related to the conventional production of Morocco lentils on the aforementioned 1-ha farm.
## 1 Introduction
The TVMS and crack propagation behavior in spur gear tooth were, respectively, studied using the extended FE method by [PERSON] et al. [25] and [PERSON] et al. [26]. Even though these studies concern the premise of traditional rotating machinery, the FE method has been proven to be an efficient tool for fault feature analysis of gears. However, it also has a problem with accurate modeling, especially in the case of handling multiple coupled excitation and complex boundary conditions, such as a vibrating screen. At present, there are two main ways to determine gear fault diagnosis. One is model-based gear dynamic analysis, which was discussed in the above paragraph, and fault features are revealed. The other way is signal-based gear vibration diagnosis. In order to enhance the fear fault diagnosis, some methods have been proposed, such as an artificial bee colony (ABC) algorithm for the optimal parameters selection of SVM's kernel function [27], a new semi-supervised method based upon the deep convolutional generative adversarial network (DCGAN) [28] and genetic mutation particle swarm optimization VMD and probabilistic neural network (GMPSO-VMD-PNN) [29]. However, the former method can better reveal the interaction rules of different influencing factors and more easily discover hidden fault features for diagnosis. Fatigue fracture of gear teeth is one of the most dangerous failure forms of gears. Existing research on gear tooth breakage mainly focuses on the influence of fault on gear parameters [23] and system dynamic characteristics [24; 25; 26]. [PERSON] et al. [23] put forward the calculation method of broken tooth gear stiffness and obtained the influence of a broken tooth on gear meshing stiffness. [PERSON] and [PERSON] [24] considered the influence of slight imbalance caused by broken teeth on dynamic gear characteristics. However, there is no research on the dynamic characteristics of the gear transmission fault of box-type vibration exciters with gear tooth damage. In summary, there are no special model for box-type vibration exciters of vibrating screens to reflect the combined action of the gear pair under internal and external excitation and fault excitation. The dynamic meshing characteristics and complex vibration response have not been studied. In view of this, this paper mainly focuses on solving the above problems by making use of virtual prototype technology [30; 31; 32]. The rest of this paper is summarized as follows. Section 2 establishes the dynamic model of the vibrating screen excitation system and verifies the correctness of the model. Sections 3 and 4 discuss the dynamic characteristics of normal gear pair and faulty gear pair from three aspects: the trajectory change of gear rotation center, normal meshing force of gears and the time-frequency domain characteristics of vibration acceleration in vertical, horizontal and axial directions (i.e., x, y and z directions, unless otherwise specified, keeping the same definitions throughout the paper). The conclusion is presented in Section 5.
## 1 Introduction
The electromagnetic behavior of the antenna is mathematically modeled using weighted eigenvalue (\(\lambda_{n}\)), which depicts the number of significant modes that contribute to the resonance and provides information on the orthogonal electric field and surface current distribution of these modes [18]. It also provides information about the modes impedance characteristics, be they purely resistive, inductive, or capacitive. This information aids in tuning the antenna close to the ideal radiating conditions. The solution to \(\lambda_{n}\) conceptualizes the radiator's resonance, inductive, or capacitive mode [19], which can be altered by modifying the antenna structure. The antenna performance was studied for SW applications by placing it inside a watch case with the material properties of stainless steel. The top of the watch case was covered with a reinforced glass of PTFE material. The proposed antenna with stainless steel at the bottom resulted in the lowest SAR of 0.063 and 0.0206 W/kg at respective bands. The SW and mobile phone link budget analysis was performed at 0.5 m for different watch angles. This showed satisfactory results with data supports of up to 1 Gbps. Therefore, the major contributions of this study are as follows:  
1. the design and development of a compact dual-band antenna with the aid of characteristic mode theory to comprehend the resonant modes and their resultant radiation pattern;
2. an analysis of the performance of the proposed antenna for smartwatch applications by embedding it inside the watch case;
3. a study of the RF energy exposure to the human body caused by the proposed antenna with a watch case;
4. an estimation of the data handling capacity of the proposed antenna for a smartwatch at various angles in three-dimensional space.
## 5 Applications and Services
### RIS Technologies
#### 5.3.1 Intelligent Omni Surface  
Different from the RIS which only reflects signals to the same side of the incident signal, the intelligent omni-surface (IOS) can also reflect and refract the signals to both sides of the surface [158], thereby providing full-dimensional rate enhancement [159; 160]. The joint UAV trajectory and phase shift of IOS has been optimized in [96; 105] by maximizing the achievable rate in the context of MEC.
# Application of Virtual Reality in Learning Quantum Mechanics
## 2 Virtual Teaching Modules
### Electron Diffraction Experiment  
An electron diffraction experiment is a classical physics experiment used to study the wave properties of electrons or other small particles. This experiment is intended to confirm the wave-particle duality of matter proposed by [PERSON]. In the electron diffraction experiment, a beam of high-speed electrons is directed through a thin layer of metal, e.g., gold foil, to observe the diffraction pattern produced on a luminescent screen. The lattice structure of the crystal causes electrons to diffract across the grid, forming a diffraction pattern of concentric circles. The pattern of concentric circles depends on the crystal's lattice structure as well as the wavelength of the incoming electrons. A series of alternating bright and dark diffraction spots can be observed, which is an interference effect caused by the wave nature of the scattered electrons.  
The virtual electron diffraction experiment designed in this study allows learners to emit electrons from the electron gun by clicking the eject button and then observe that the electrons passing through the gold foil will produce diffraction patterns on the photographic film behind (Figure 6). Learners can switch the viewing angle to observe the electron beam projected onto the gold foil, where the crystal lattice acts as a diffraction grating to scatter the electrons in a predictable manner and result in a diffraction pattern.
## 3 Compression Algorithm Design  
In this section, the proposed, and implemented, lossless entropy compression algorithm is presented, focusing on low computational power. Its main characteristic is the exploitation of the observed data's frequencies in the time series in order to offer an efficient dynamically adaptive Huffman table for the encoding process. The main novelty is based on extending the approaches derived from previously presented LEC and ALEC algorithms (i.e., low complexity, low code footprint, low compression delay). In this way, the proposed algorithm retains the very good processing delay performance characteristic. However, at the same time it significantly enhances achieved compression rate by increasing adaptability to the data characteristics. Consequently, compression rate exhibited by the proposed extension is highly competitive compared to respective solutions published in relative literature, while it also offers optimum compression rate vs compression processing delay trade-off. It is noted that a detailed analysis of the characteristics of this compression approach and evaluation at software level based on Matlab implementations can be found in [12].
## 5 Discussion
### Experimental Results  
In Section 4, we extensively experimented with three prominent classification models, \(k\)NN, LR, and RF, by varying their respective parameters and underlying datasets. Table 7 summarizes the parameters under which each model performs the best across all twelve datasets (Table 2).  
Next, we compare the accuracy results from using each of the twelve datasets from Table 2 in order to determine which dataset performs the best overall. The winner dataset will be used in our last experiment later in this section. We apply the best-performing \(k\)NN, LR, and RF models to each of the twelve datasets, then we take the average accuracy across the three models.  
Figure 10: Model accuracies after PCA with 18 principal components explaining 95% of the variance. (a) \(k\)NN; (b) Logistic Regression.  
Figure 11 plots the average performance of the three best models against each of the twelve datasets. The average testing and LOOCV accuracies resulting from each dataset are reported. Comparing all twelve datasets, _more_than_50_compounds_ results in the highest accuracy. It is also worth noting that, in general, the average LOOCV accuracy across the different datasets is higher than the average testing accuracy. This observation has been consistent in all the Logistic Regression and Random Forest experiments depicted in Figures 5-8.  
To determine which of the three models performs best, we apply the best \(k\)NN, LR, and RF models from Table 7 on the best dataset determined in Figure 11, i.e., _more_than_50_compounds_. Figure 12 depicts this comparison. The Logistic Regression and Random Forest models having the parameters listed in Table 7 result in the highest accuracy of nearly 81% on the LOOCV metric, while \(k\)NN leads on the testing metric with an accuracy of nearly 82%. This conclusion corroborates our observations from the literature [62].  
Due to the large number of features in our original dataset (see Section 3.1), one key aspect of this study is dimensionality reduction. Feature selection was conducted following our proposed method described in Section 3.2, and using Principal Component Analysis (PCA) [60; 61]. Both dimensionality reduction methods resulted in similar model performances, achieving around 81% accuracy. Figure 12 also depicts the best result achieved by preprocessing the dataset using PCA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Model Parameters** & **Best Performing Values** \\ \hline \multirow{2}{*}{\(k\)NN} & \(k\) & 3 \\ \cline{2-3}  & _weights_ & _distance_ \\ \hline \multirow{4}{*}{Logistic Regression} & _penalty_ & _L2_ \\ \cline{2-3}  & _class\_weight_ & _balanced_ \\ \cline{2-3}  & _solver_ & _liblinear_ \\ \cline{2-3}  & \(C\) & 0.1 \\ \hline \multirow{4}{*}{Random Forest} & _criterion_ & _log\_loss_ \\ \cline{2-3}  & _n\_estimators_ & 12 \\ \cline{2-3}  & _max\_depth_ & 4 \\ \cline{2-3}  & _ccp\_alphas_ & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The specifications of the best \(k\)NN, LR, and RF models.  
Figure 11: Average performance of the three best algorithms across the different datasets.
# Non-Linear Dynamic Analysis of Drill String System with Fluid-Structure Interaction
## 3 Results and Discussion
### The Effect of Mass Ratio \(\beta\) on Dynamic Characteristics  
Because of the different fluid masses, mass ratio \(\beta\) is a key parameter in evaluating the system vibration characteristics. The drill string mass ratio is taken as the control parameter to carry out a detailed study. Figure 8a displays the bifurcation diagram in the interval [0.2, 0.8] at \(k_{\text{a}}\) = 2.1 \(\times\) 10\({}^{6}\) and \(k_{\text{b}}\) = 8.6 \(\times\) 10\({}^{4}\). The system is in periodic motion at [0.2, 0.262], as shown in Figure 9(a\({}_{1}\)-a\({}_{3}\)) at \(\beta\) = 0.218, the system presents periodic-2 motion by the waveform, phase diagram and Poincare map. With the increase in the mass ratio from 0.262 to 0.39, the response of the system is chaotic motion, and the chaotic motion region gradually increases. Further increase in the mass ratio, the motion becomes periodic in the interval [0.39, 0.422]. For the mass ratio from 0.422 to 0.523, the system alternates between exhibiting quasi-periodic motion and chaotic motion. Figure 9(b\({}_{1}\)-b\({}_{3}\)) shows the vibration response of the system at the mass ratio \(\beta\) = 0.521, the system is quasi-periodic motion. As the mass ratio \(\beta\) continues to increase, the system exhibits chaotic motion at [0.523, 0.8], and the chaotic motion area significantly increases. The dynamic responses at \(\beta\) = 0.752 are presented in Figure 9(c\({}_{1}\)-c\({}_{3}\)), and the system exhibits chaotic motion.  
Figure 8: Bifurcation diagram of system response with \(\beta\): (**a**) \(k_{\text{a}}\) = 2.1 \(\times\) 10\({}^{6}\), \(k_{\text{b}}\) = 8.6 \(\times\) 10\({}^{4}\); (**b**) \(k_{\text{a}}\) = 9.2 \(\times\) 10\({}^{5}\), \(k_{\text{b}}\) = 8.6 \(\times\) 10\({}^{5}\) (A: periodic motion; B: quasi-periodic motion; C: chaotic motion).  
Figure 8b displays the bifurcation diagram at \(k_{\text{a}}\) = 9.2 \(\times\)\(10^{5}\) and \(k_{\text{b}}\) = 8.6 \(\times\)\(10^{5}\), with the mass ratio \(\beta\) as the control parameter in the range [0.2, 0.8]. When the mass ratio increases, the system presents chaotic characteristics at [0.2, 0.373], after periodic motion from 0.373 to 0.406, at which point the system returns to chaotic motion. At [0.572, 0.682], periodic-6 motion is observed. By further increasing \(\beta\), the system response consists of periodic, quasi-periodic and chaotic behaviors. Compared with Figure 8a, the chaotic area is reduced, the intensity of chaotic motion decreases, and the area of periodic motion increases.
## 5 Simulation Analysis  
The method proposed in this paper is implemented by C++ and runs under the Ubuntu 16.04, ROS kinetic. The computer hardware configuration is Intel i7-9750H 2.60 GHz, 32 GB RAM. In this paper, a grid map is used to represent the whole environment and the environment is an area of 4 m \(*\) 4 m. The grid size is set to 0.25 m. The center of the area is set as the coordinate origin. Right is the positive direction of the \(x\)-axis and upward is the positive direction of the \(y\)-axis. The time interval is 0.2 s. The probability threshold \(\delta_{o}\) and \(\delta_{r}\) are both 5%. The safety distance is 0.15m. The maximum velocity \(v_{max}\) of each robot is 1 m/s, and the maximum angular velocity \(w_{max}\) is 1 rad/s. The maximum acceleration \(a_{max}\) of each robot is 2 m/s\({}^{2}\), and the maximum angular acceleration \(a_{max}\) is 1 rad/s\({}^{2}\). The prediction horizon length of model predictive priority contouring control is 8. Batch size \(S\) is 3.
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 3 Experiments
### Results and Analyze
#### 3.4.1 Results of Emotion Style Control Experiment  
Table 4 presents the MOS for speech generated by different models. The first row, \"GT\" represents the original audio of the selected English text, which serves as a reference for evaluating synthesized speech quality.  
As shown in Table 4, ECE-TTS achieves a naturalness score consistent with the EmoSphere++ model, both scoring 3.98, while surpassing GenerSpeech. Furthermore, its SMOS of 3.93 is higher than both comparison models, indicating improved speaker similarity and identity consistency in the generated speech. Notably, ECE-TTS achieves the highest EMOS of 3.94, validating that EASV enable precise emotion style control while maintaining natural prosody. These results indicate that the proposed model offers superior emotion style and speech quality control, enabling the generation of high-quality synthetic speech that is more similar to real human speech.  
Beyond subjective evaluations, ECE-TTS also excels in objective metrics, as presented in Table 5. ECE-TTS outperforms GenerSpeech and EmoSphere++ in both aspects. It achieves a WER of 13.91, significantly lower than GenerSpeech (16.34) and EmoSphere++ (15.08), confirming its superior speech intelligibility and semantic accuracy. Moreover, ECE-TTS achieves the highest score on the Aro-Val-Domin SIM metric, with a value of 0.679, compared to 0.627 for GenerSpeech and 0.656 for EmoSphere++. This result indicates its stronger alignment with real speech in terms of valence, arousal, and dominance sequences, further validating its precision in emotion style control. Additionally, ECE-TTS achieves the best performance on the Emo SIM metric (0.594), outperforming GenerSpeech (0.563) and EmoSphere++ (0.578). This demonstrates ECE-TTS's improved consistency with target emotional categories, further validating the effectiveness of our emotion style control strategy. In summary, ECE-TTS excels in both text intelligibility and emotional consistency, establishing its advantage in high-quality emotional speech synthesis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SMOS** & **NMOS** & **EMOS** \\ \hline GT & 4.02 \(\pm\) 0.06 & 4.07 \(\pm\) 0.06 & 4.10 \(\pm\) 0.06 \\ Generspeech [13] & 3.84 \(\pm\) 0.07 & 3.96 \(\pm\) 0.07 & 3.85 \(\pm\) 0.08 \\ EmoSphere++ [35] & 3.91 \(\pm\) 0.08 & **3.98 \(\pm\) 0.07** & **3.86 \(\pm\) 0.07** \\ ECE-TTS & **3.93 \(\pm\) 0.07** & **3.98 \(\pm\) 0.07** & **3.94 \(\pm\) 0.06** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Subjective evaluation results for speech naturalness, speaker similarity, and emotional expressiveness across different models. Bold values indicate the best scores among all methods for each evaluation metric.
###### Abstract  
Networked microgrids (NMG) are gaining popularity as an example of smart grids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware, such as Cisco 4331 Integrated Services Routers (ISR). We evaluate QoD in NMG control data by measuring RTT under varying relative network congestion levels. The results reveal that high-variance background traffic leads to greater RTTs, surpassing the industrial communication response time requirement specified by the European Telecommunications Standards Institute (ETSI) by over 25 times.  
networked microgrids; communication technologies; industrial communication requirements; quality of delivery; round trip time 2025  
28 February 2025