esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 5 Addition of Fault-Tolerance
### Algorithm to Add Masking Fault-Tolerance  
In this section, we present an algorithm for adding masking fault-tolerance in the presence of unchangeable environment actions. The intuition behind this algorithm is as follows: first, we utilize the ideas from adding stabilization. Intuitively, in Algorithm 1, we constructed the set \(R\) from where recovery to the invariant (\(S\)) was possible. In the case of stabilization, we wanted to ensure that \(R\) includes all states. However, for masking fault-tolerance recovery from all states is not necessary. We also need to ensure that recovery from \(R\) is not prevented by faults. This may require us to prevent the program from reaching some states in \(R\). Hence, this process needs to be repeated to identify a set \(R\) such that recovery to \(S\) is provided and faults do not cause the program to reach a state outside \(R\). In addition, in masking fault-tolerance, like failsafe fault-tolerance, the program must satisfy safety of specification even in presence of faults. Thus, the details of Algorithm 3 are as follows. We start with an invariant equal to the original invariant (i.e., \(S^{\prime}=S\)). Like Algorithm 1, we set \(\delta^{\prime}_{p}\) to \(\delta_{p}|S\) which is the set of all program transitions inside the invariant. Like in the case of Algorithm 2, set \(ms_{1}\) includes all states such that no matter how they are reached, there is a computation that is not desirable. In the case of failsafe fault-tolerance, such computation violates safety. On the other hand, here in case of masking fault-tolerant, such computation either violates safety or never reaches the invariant. \(ms_{2}\) is the set of states such that, if they are reached by a program or fault transition, or starting from them, there exists a computation suffix which either violates safety or never reaches the invariant. Same as Algorithm 2, set \(mt\) always includes all transitions to states in \(ms_{2}\). We initialize sets \(ms_{1}\), \(ms_{2}\) and \(mt\) as done in Algorithm 2 and expand them in the loop on Lines 6-43. In this loop, we use Algorithm 1 and Algorithm 2 with some modification.
## 6 Optimal Features for Earthquake-Induced Structural Displacement Monitoring
Slightly larger RMSEs are observed for the concentric circular pattern (Type 4) compared to square checkerboard patterns with the same geometric dimension and color (Type 1, 2, and 3). Furthermore, additional oscillations are observed in tracking results from the concentric circular pattern. Figure 17 presents the comparison of the displacement results between the multiple reference targets at the northeast corner of the roof level (region 2) from the 67% MCE\({}_{\text{R}}\) Ferndale earthquake test (MID 12). From the overlay of the displacement time series, there are good visual alignments between the ground truth measurements and UAV vision-based tracking results from all the reference targets in the region. However, additional oscillations can be clearly observed in the tracking results of the concentric circular pattern from the zoom-in view. These additional oscillations occur within low-amplitude regions of the displacement time series (e.g., \(t\) = 7-8.5 s in the X-direction and \(t\) = 4-5.5 s in the Y-direction for MID 12). These oscillations are not observed in the tracking results from the square checkerboard patterns. This difference is caused by the different algorithms applied in the detection and tracking of the two different types of features (i.e., orthogonal features and circular features). Orthogonal features (corner points) in the square checkerboard patterns are detected first in the reference frame. Then, sub-pixel coordinates of the feature points in the previous video frame are used as the input of the Kanade-Lucas-Tomasi (KLT) feature tracker in the following video frame for displacement tracking. However, the KLT algorithm cannot be applied to circular features since there are no explicit feature points. A Hough Transform is applied to each video frame with a coarsely updated ROI for circular features. The potential error in the ROI updating can be propagated in the Hough Transform in the subsequent video frame. Therefore, compared to concentric circular patterns, orthogonal-shaped patterns with explicit feature points that can be tracked by the Kanade-Lucas-Tomasi (KLT) algorithm are observed to reduce error propagation during the video sequence and reduce the displacement measurement noise.
# Analysis of Characteristics of Roof Fall Collapse of Coal Mine in Qinghai Province, China
## 1 Introduction  
Although the clean energies, such as wind, solar, and water, are booming in the modern age, coal is still the largest source of energy for generating electricity in the world. With the rapid development of megacities, the demand for coal resources is increasing. Nevertheless, underground coal mines suffer many hidden hazards during the coal mining process, such as roof fall, gas explosion, surface subsidence, and so on [1; 2; 3]. As can be seen from Table 1, roof fall is the most frequent and serious hazard, causing injuries and fatalities, and is a great concern for underground coal miners [4]. Some authors [1; 5] reported that roof falls threaten the life of miners directly and have the potential to result in the loss of coal mine companies' property, which includes damage to underground equipment, downtimes, etc. Given the serious destruction of roof falls, many researchers [6; 7; 8] made great efforts on the occurrences of roof falls associated with underground coal mines. Some scholars [9; 10; 11] summarized the main contribution factors of the development of roof fall hazards, and these factors include geological uncertainty, stress change, mining condition, the surrounding environment, and enterprise safety management.  
Through appropriate mine design, the stress condition and mining condition can be estimated during the mining process. However, geological uncertainty is the nature of underground space and is very difficult to estimate. The uncertainty in geology would change the stress condition and increase the risk of roof falls. The risk assessment is a necessary task during the whole mining process. Some researchers [20; 21; 22] realized the importance of risk assessment for engineering projects and performed lots of interesting studies in different fields, such as water resources [21; 23], underground constructions [20; 24; 25; 26], infrastructures [27; 28; 29; 30; 31], etc. Additionally, the surrounding environment of the mine is complicated, and the seepage of water is one significant impact on the stability of a mine's roof. The seepage of water in the rock fissure may weaken the strength of the roof and trigger the collapse of the roof [32; 33; 34]. It is necessary to have clear knowledge on the development of a roof fall during the mining process. The geological survey and numerical simulation [9; 35; 36] are essential. The combination of digital models and an appropriate monitoring system is a reasonable choice to assess the possible risks associated with coal mining [37; 38; 39].  
This study investigates a roof fall, and the following rescue is reported as well. The potential reasons for this roof fall disaster were discussed, and some reflections were presented. Based on this roof fall, some suggestions were put forward for future coal mining control.
# Solanesol Biosynthesis in Plants
## 4 Key Enzymes in Solanesol Biosynthesis
### 1-Deoxy-D-xylulose 5-Phosphate Reductoisomerase (DXR)  
DXR catalyzes the molecular rearrangement and reduction of DXP to form MEP (Figure 2) [3; 6]. The _DXR_ gene has been identified in _Herba menthae_[29], _S. lycopenetium_[30], _Zea mays_[31], _Hevea brasiliensis_[32], and _N. tabacum_[33]. Overexpression of _DXR_ genes leads to increases in thechlorophyll and carotenoid contents of _A. thaliana_ leaves [34]. Overexpression of _DXR_ genes in _H. menthae_ increased the production of mint essential oils by 50% [35]. [PERSON] et al. [36] cloned the _DXR_ genes of _Salvia militorrhiza_ and found that their expression increased under high osmotic pressure and fungal elicitor treatment, with expression being positively correlated with the tanshinone content. [PERSON] et al. [33] identified two _DXR_ genes in _N. tabacum_, _NtDXR1_ and _NtDXR2_; southern blotting and genotyping indicated that _NtDXR1_ and _NtDXR2_ originate from _Nicotiana tometosiformis_ and _Nicotiana sylvestris_, respectively. Overexpression of the tobacco _DXR_ genes in chloroplasts significantly increased the content of terpenoids, including solanesol [37]. Transient expression of potato _DXR_ genes in _N. benthamiana_ significantly increased the solanesol content [1]. Hence, _DXR_ genes are key to solanesol biosynthesis, and their overexpression promotes the accumulation of downstream metabolites such as solanesol.
## 7 Results and Discussion
### Impact of Device Heterogeneity on FL Performance
#### 7.1.3 Impact on Privacy Across Datasets  
As illustrated in Figures 10-13, the privacy preservation behavior of FL systems is highly dependent on the type of participating devices. Across all datasets, it is evident that the smartphone-only configuration maintains the highest privacy scores throughout the training process. This confirms that smartphones, which are simulated with higher privacy sensitivity, are either selected less frequently or incur lower privacy loss per round, making them the most robust in terms of privacy protection. Conversely, IoT-only clients experience the most rapid privacy degradation. These devices are modeled with low privacy sensitivity, leading to frequent selection and quick exhaustion of their privacy budgets, especially noticeable in the EMNIST and CIFAR-10 datasets, where the score falls below 0.85 within the first 100 rounds. Mixed-device configurations, such as (edge + smartphone) and (IoT + smartphone), exhibit moderate and more stable privacy trends. The All Devices setting reflects a balanced selection pattern, benefiting from the presence of higher-privacy smartphones while still engaging less-privacy-sensitive IoT nodes. This validates the potential of heterogeneous client pools to achieve privacy-aware training without relying solely on any single device category. Furthermore, we observe that dataset complexity impacts the rate of privacy decline. In simpler datasets like MNIST and Fashion-MNIST, privacy scores stabilize earlier, whereas more complex datasets like EMNIST and CIFAR-10 require more communication and client involvement, resulting in greater cumulative privacy loss. These results confirm that device heterogeneity plays a crucial role in privacy dynamics, and that strategic client selection especially with RBPS can preserve privacy more effectively than uniform or homogenous selection strategies.  
Figure 10: Impact of device heterogeneity on privacy over 300 rounds (MNIST).  
Figure 9: Impact of device heterogeneity on battery over 300 rounds (CIFAR-10).
## 6 Conclusions  
A new data-driven approach to construct the _HI_ is presented. This _HI_ represents every moment conditions of the bearing and can be considered as a digital twin of the bearing during its failure stage. Furthermore, this _HI_ can be used for RUL estimation. First, the Pauta criterion was employed to determine the failure threshold and a normal dataset. Since the CWT is suitable for analyzing the non-stationary signals, it was used to convert raw vibrational signals into two-dimensional feature images. The wavelet power spectrum image clearly revealed the degradation process of the bearing and included information in both time and frequency domains. Subsequently, the CAE model was used for dimensionality reduction through the feature extraction, and it was trained by the normal operation dataset. The values of the bottleneck nodes of the trained CAE represented the conditions of every moment of the ball bearing life; they were used to construct _HI_. Finally, the wavelet power spectrum image of the failure stage was fed to the trained CAE. The distance between the values of bottleneck nodes in normal and failure stages was measured by _MD_ formula, and then the _HI_ was constructed. To improve the _HI_ curve monotonicity, we used an exponential function to remove random fluctuations in the _HI_ curve. Experiments were conducted on the run-to-failure IMS dataset to verify the performance of the proposed method.  
The results indicate that the constructed _HI_ was capable of representing the health status of the bearing and tracking the evolution of degradation over the whole lifetime of the bearing. Moreover, constructing the _HI_ with the proposed method required no prior knowledge or failure history data. Therefore, it is suitable for industrial applications. Furthermore, to prove the effectiveness of the proposed method, we compared this method with several other methods, such as RMS, EMD-SVD-K-medoids, skewness, kurtosis, and ApEn, showing a considerable superiority. The method, at the current state, is limited to gradual degradation and excludes any sudden failure.  
Future research directions are to use the proposed method for other mechanical components such as ball screws, gears, and cutting tools.  
[PERSON] proposed the idea, set up the network, and conducted the calculation. [PERSON] and [PERSON] contributed to the supervision of the work, proposed some valuable suggestions, and performed the revisions. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Leading House South Asia and Iran seed money grant at ZHAW Zurich University of Applied Science.  
The authors declare no conflict of interest.
## 1 Introduction
### Problem Definition  
According to research [24], user preferences are not fixed and indicate constant fluctuations in their preferences for items. Hence, it is not easy to find a similarity method that matches all user behaviors. The user similarity measure based on the item genre is not widely explored in RSs. Furthermore, traditional similarity measures do not make accurate recommendations for new users or items, and they are less effective with sparse datasets [16; 25]. Initially, a precise similarity measure can be configured between users depending on a user's preferences for an item's genre. Basing on movie lens [26] and Yahoo Music datasets, our research experiments looked at user preference for movie/music genres. Some movies have multiple genres (see Figure 1); two users, u1 and u2, are highly similar in terms of their choices and ratings of action, fantasy, adventure, and horror. At the same time, they are different in their choices in terms of comedy and drama movie genres. This is, consequently, related to their personalities.  
Traditional similarity algorithms hold all u1 and u2 ratings collectively. Calculating their similarity shows that their relationship is medium because of their remarkable similarity for action, fantasy, adventure, horror movie genres, and the weak similarity between comedy and drama. Hence, the results fail to show that their preferences for many movie genres are notably significant and are reasonably soft for the other genres. Thus,the traditional similarity algorithms are incapable of representing the different choices of multiple-interest users. Furthermore, a similar rating might not mean similarity in terms of actual preferences. Therefore, relying solely on user ratings may produce incorrect similarity between users or items. A user can frequently imagine the story and spirit of a movie according to that movie's genre(s); thus, a movie's genre helps to attract a user's attention to decide whether or not to see that movie [9]. Accordingly, extracting critical choices and contextual information from user profiles is essential to establish vital similarity metrics among users. Therefore, we aimed to solve this problem.
## 3 Results and Discussions
### Empirical CDFs for Acceleration Peaks and RMS (g) Data  
Figures 7 and 8 show the CDFs (Cumulative Distribution Function) of the acceleration peak and RMS data, it represents the percentage of the events that are below a given acceleration peak value or RMS level. The CDFs were calculated and plotted from data of each road condition, laboratory test and stacked levels, respectively.  
The acceleration data (Figure 7) shows that the highest acceleration values were recorded on urban roads in the field, and on the van's floor 90% of the total events on the floor were below 0.85 g and in the 3 rd level were below 1.18g, which are about 2 times higher than the measured values on the motorway in layers 1-3, and 1.8 times higher than it was on the floor. In the case of the laboratory test the data shows that 50% and 90% of the values on the floor were below 1.49 g and 1.84 g, which is 2.2 and 4.35 times higher than in the case of urban roads, but these significant differences do not appear in stacked layers data. Here these values were 1.2 times (for 1 st layer), 1.28 times (2 nd layer), and 1.33 times (3 rd layer) higher. Comparing the laboratory test and summary data for field, at 90% occurrence, it can be seen that laboratory events were 2.73 times higher on the floor, 1.43, 1.58, and 1.59 times higher upwards in the layers. It is a very interesting observation that in the laboratory test the floor curve crossed the 3 rd and 2 nd layer's curve at the point  
Figure 8: Empirical CDFs of RMS (g) values, (**a**) Motorway, (**b**) Arterial road, (**c**) Country road, (**d**) Urban roads, (**e**) Summary, (**f**) Laboratory.  
of 96% and 98%, which can mean that some vibration events generated by the vibration controller produce higher acceleration events on stacked unit layers. This phenomenon can also be observed in the CDFs curve for RMS (g) data.  
In terms of RMS data (Figure 8) it can be clearly visible that the functions of stacked layers almost coincide below 50% probability. The highest RMS value at this point was calculated in the 3 rd layer on urban roads, it was 1.25 RMS (g), and on the floor the highest was 1.11 RMS (g). Comparing these values with the data obtained from the motorway these were approximately 1.9 times higher in each layer at the 90% occurrence level. As laboratory test results show 90% of the RMS values were below 0.51 RMS (g) on the floor, and 0.45, 0.47 and 0.49 RMS (g) from the 1 st to 3 rd layers and these values were 2.6, 1.8, 1.9, and 1.7 times higher than the values of the field summary data from floor to 3 rd layer, where those were 0.20, 0.25, 0.26 and 0.28 RMS (g), respectively. Appendices A and B contains specifications for CDSs of each measurement that were conducted for this study.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 3 Equivalent Model of the Induction Motor
### Electro-Mechanical Characteristics Calculation  
The electrical model of the motor (Figure 6) and Equation (12) allow us to write the following:  
\[I_{2}=\frac{V}{\sqrt{r^{2}+l^{2}\cdot\omega^{2}}}. \tag{15}\]  
Then,  
\[P_{tr}=\frac{3\cdot V^{2}\cdot r}{r^{2}+(l\omega)^{2}}=\frac{3\cdot V^{2}}{ l\omega}\cdot\frac{1}{\frac{r}{l\omega}+\frac{l\omega}{r}}. \tag{16}\]  
The electromagnetic torque of the motor is given as a function of the transmitted power by the following formula:  
\[T_{em}=\frac{P_{tr}}{\Omega_{S}}=\frac{3\cdot V^{2}}{l\omega\Omega_{S}}\cdot \frac{1}{\frac{R}{sl\omega}+\frac{sl\omega}{R}}. \tag{17}\]That is, by replacing \(\omega_{S}\) with \(\omega/p\), we have  
\[T_{em}=\frac{3p}{l}\cdot\bigg{(}\frac{V}{\omega}\bigg{)}^{2}\cdot\frac{1}{\frac{R }{sl\omega}+\frac{sl\omega}{R}}. \tag{18}\]  
This equation is mathematically maximal for \(s_{max}=\frac{R}{l\omega}\), thus giving the value  
\[T_{max}=\frac{3p}{2l}\cdot\bigg{(}\frac{V}{\omega}\bigg{)}^{2}. \tag{19}\]  
Then,  
\[T_{em}=2T_{max}\cdot\frac{1}{\frac{R}{sl\omega}+\frac{sl\omega}{R}}. \tag{20}\]  
The maximal value of the electromagnetic torque is then independent of \(R\), as well as of the rotor resistance \(R_{R}\).  
The curve representing the expression of the torque as a function of the slip has a symmetry with respect to the origin, as shown in Figure 7. This symmetry indicates that when the rotor is moved by another means than the electromagnetic one at a rotational speed higher than its nominal speed, the stator supply acts as an electromagnetic brake and imposes a resistant torque to the axis load, which is visible in the negative values of \(s\).
## 4 Parametric Study
### Effect of Backfill Slope Angle \(i\)  
In Figure 12a, \(K_{\rm a}\) increases significantly as \(i\) increases from \(0^{\circ}\) to \(15^{\circ}\). The magnitude of the increase with different \(i\) values is increased as \(k_{\rm h}\) increases. When \(k_{\rm h}=0.2\) and \(i=0^{\circ}\), \(8^{\circ}\), and \(15^{\circ}\), the corresponding \(K_{\rm a}\) increases sequentially, reaching values of 0.699, 0.857, and 1.142, respectively. As shown in Figure 12b, the existence of cohesion (\(c/\gamma H=0.05\)) reduces \(K_{\rm a}\).  
In Figure 12c, \(\alpha_{\rm a}\) is decreased as \(i\) changes from \(0^{\circ}\) to \(15^{\circ}\). When the normalized cohesion \(c/\gamma H\) changes from 0 to 0.05, \(\alpha_{\rm a}\) tends to increase with same \(i\). This trend becomes more and more obvious with further increases in \(k_{\rm h}\).  
Figure 12d shows the typical normalized dynamic active earth pressure distribution for different values of \(i\) with \(k_{\rm h}\) = 0.2 and \(k_{\rm v}\) = 0. When \(i\) increases from 0\({}^{\circ}\) to 15\({}^{\circ}\), in the case of \(c/\gamma H\) = 0, the dynamic active earth pressure at the bottom of the wall increases from 0.670 to 1.039. When \(i\) is 0\({}^{\circ}\), 8\({}^{\circ}\), and 15\({}^{\circ}\), in the case of \(c/\gamma H\) = 0.05, the corresponding normalized critical depth \(z_{\rm c}/H\) is 0.105, 0.116 and 0.140, respectively.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 3 Communication
### Coding Experiment
#### 3.1.2 Design of Acquisition and Demodulation Module  
To ensure the precise transmission of information, we devised the acquisition and decoding algorithm illustrated in Figure 8. For modulation, each code element was assigned a duration of 1s. On the acquisition side, the MMWR operated at a sampling rate of 1 kHz, enabling 1000 samples per code element. The demodulation algorithm was constructed based on the FMCW principle, with multichirp values being averaged. Subsequently, a one-dimensional FFT was applied, yielding a time-domain image.  
Figure 8: Schematic diagram of demodulation algorithm of water-to-air cross-medium communication system.  
Figure 7: Comparison before and after filtering. (**a**) Before filtering. (**b**) After filtering.  
Following the filtering procedure, the time-domain waveform of the water surface wave generated by acoustic excitation was obtained. Subsequently, the filtered signal underwent a sliding window FFT with a window size of 256. The sliding window FFT is well-suited for real-time signal processing and spectral analysis. This approach allowed for the elimination of switching noise interference caused by speaker frequency switching, the precise detection and correction of intersymbol errors, and the real-time output of the surface wave spectrum. From the spectrum, the peak frequency component was extracted and output. A high output corresponds to 243 Hz, while a low output corresponds to 146 Hz. This process not only satisfies the frequency resolution of a single FFT but also enables the accurate differentiation of individual code elements.