esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modal Based Damage Detection: Development & Challenges
### Development of Modal-Based Damage Sensitive Features
#### 2.1.7 Summation of Modal-Based Damage Sensitive Features  
The present section has detailed the development and varied performance results of numerous modal-based DSFs used in a bridge-related SHM. Many comparative studies have been conducted to assess the aforementioned modal parameters against each other, such as [PERSON] et al. [71], who applied COMAC, DIM, MCM, and MFM to a FE model of a cable-stayed bridge subjected to varying degrees of excitation and noise. It was found that only large damage events were detected, as noise contamination reduced damage sensitivity. [PERSON] et al. [72] performed laboratory tests on RC beams using frequencies, MAC, COMAC, MFM, and DIM. They found that COMAC and DIMperformed well in regard to damage localisation, although damage development was not incrementally detected correctly, meaning that the quantification of damage was not possible.  
[PERSON] & [PERSON] [73] also investigated mode shapes, MCM, & MFM using a simulated model of a composite bridge and real vibration data from a progressive damage test. For the simulated model, all assessed parameters detected and located damage when no noise was present. When noise was added, damage detection and localisation performance diminished considerably for all DSFs. For the real data, it was noted that clear changes were evident in higher modes when damage was introduced; however, lower modes did not exhibit much variation despite the presence of severe damage. It was noted that MCM and MFM demonstrated the greatest detection and localisation capabilities. [PERSON] & [PERSON] [74] echoed many of these findings in their assessment of frequencies, mode shapes, MCM, and DIM, with superior damage sensitivity observed for higher modes, particularly for modal curvature-based parameters, and a diminished performance overall when subjected to noise. They concluded that many of the assessed damage features were not reliable for damage detection and that many spatial parameters produced only rough estimations of damage location in the absence of advanced signal processing techniques.  
In summation, the modal-based DSFs evaluated within have been shown to be susceptible to noise contamination and perform inconsistently when comparatively assessed in groups. Also, the assumption of linear stationary structural vibration response made by most frequency domain transforms used to obtain modal-based DSFs may not always be suited to real-World applications, as vehicle-induced excitations on damaged bridges can be highly non-stationary in nature. Having said this, natural frequencies can provide a simple high-level assessment of structures of regular geometry, while mode shapes and their derivatives can deliver useful spatial information when provided with sufficient sensor numbers and low noise levels.
## 3 Implementation of the Model
### Computational Model for Back-Analysis  
In Figure 3 the FLAC numerical model for SBY CFRD is shown with the zoning and the control points selected for comparison with the experimental data. The discretization comprises 15,791 zones and has been restrained by fixing the vertical and horizontal velocities at the base of the dam. The construction phases and the impounding phases have been simulated following the data available in the literature [35; 36; 37; 38].
# Experimental Study of Vegetative Properties in
## 1 Introduction
[22] pointed out that the application of ZL-BC improved soil by increasing micro- and mesopores, and the soil water content was increased by 174% and 303%. The specific surface area and mean weight diameter in soil increased by 171% and 197%, respectively. Additionally, [PERSON] et al. [23] noted in their research that ZL-BC, as a composite modifier, effectively remediates Cd, Pb, As, and W pollution in soil. Although there has been increasing depth in research on ZL-BC composites for pollution treatment, demonstrating their adsorption and purification capabilities in water bodies, there are relatively few studies on the improvement of ecological substrates, and even fewer reports on the vegetative effects of improved substrates. The synergistic effect of ZL-BC-modified substrates on vegetative properties is still unclear. However, the vegetative effect of riparian revent substrates plays a crucial role in maintaining regional water resource security and biodiversity [24]. Hence, optimizing the ZL to BC ratio for maximizing benefits in riparian vegetative restoration has emerged as a critical issue that requires attention. In view of this, ZL and BC were chosen as substrate modifiers in this research. Using Cynodon dactylon (C.d. ), a pioneering herb for reventent in central China, as a case study, various ZL-BC ratios were compared to analyze their effects on seed germination, plant height, and coverage of C.d. The study elucidated the synergistic effect of ZL-BC on vegetative properties and determined the optimal ratio of improved substrate for plant growth. This research aims to offer a theoretical foundation for selecting substrate schemes in ecological bank protection engineering.
## 2 Carrier Tracking Loop and the Phase Error
### Thermal Noise  
Thermal noise exists in every system. It is a random signal with a constant power spectral density. Its effect on a system mainly depends on the PSD and the system bandwidth. However, most of the actual systems aren't ideal, which means its amplitude response isn't an ideal rectangle. In this case, the noise equivalent bandwidth is usually used to analyze the noise effect, which is defined by [12]:  
\[B_{L}=\frac{1}{2\pi|H_{ideal}(\omega)|_{G}^{2}}\int_{0}^{\infty}|H(\omega)|^{2 }d\omega \tag{4}\]  
where \(H(\omega)\) is the transfer function and \(|H_{ideal}(\omega)|_{G}\) is the system gain of the ideal filter. Assuming the PSD of the thermal noise is \(N_{0}/2\), the output power of such a system can be obtained:  
Figure 1: PLL Tracking Loop Structure.  
\[P=\frac{N_{0}}{2}\cdot 2B_{L}|H_{ideal}(\omega)|_{G}^{2}=\frac{N_{0}}{2\pi}\int_{0}^{ \infty}|H(\omega)|^{2}d\omega \tag{5}\]  
The thermal noise phase effect for a PLL is determined by the carrier noise ratio and the loop bandwidth. In a GNSS receiver, the noise enters the PLL through the discriminator after pre-detection integration. The most frequently used \(\mathrm{atan}(\cdot)\) discriminator has the error variance as follows [13]:  
\[\theta_{e}^{2}=\frac{1}{2T_{coh}C/N_{0}}(1+\frac{1}{2T_{coh}C/N_{0}}) \tag{6}\]  
where \(T_{coh}\) is the coherent integration time and \(C/N_{0}\) is the carrier noise ratio. According to the definition of the equivalent noise bandwidth, the coherent integration could be seen as a low pass filter, whose two-sided bandwidth is 1/\(T_{coh}\), and system gain is 1. Thus, the noise Power Spectral Density can be obtained according the equation above:  
\[\frac{N_{0}}{2}=\frac{\theta_{e}^{2}}{1/T_{coh}}=\frac{1}{2C/N_{0}}(1+\frac{1} {2T_{coh}C/N_{0}}) \tag{7}\]  
For a PLL noise equivalent bandwidth \(B_{L}\), the thermal noise phase jitter can be derived as follows under the assumption that the system gain is 1:  
\[\sigma_{e}^{2}=\frac{N_{0}}{2}\cdot 2B_{L}\cdot|H_{ideal}(\omega)|_{G}^{2}= \frac{B_{L}}{C/N_{0}}\Big{(}1+\frac{1}{2T_{coh}C/N_{0}}\Big{)} \tag{8}\]  
From the equation above, it can be seen that the thermal noise phase jitter is a function of the coherent integration time \(T_{coh}\) and the one-sided PLL loop bandwidth \(B_{L}\). Thus, in order to reduce the thermal noise phase jitter, the main methods are reducing the loop bandwidth and increasing the coherent integration time. However, the coherent time is limited by navigation bit, whose rate is 50 Hz for GPS, and the \(B_{L}\) is limited by the signal dynamics due to satellite/receiver motion and clock frequency variations.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 2 Materials and Methods  
The field surveyed, covering three plots of 7.5 ha each, is located in southern Portugal at Herdade da Comenda, Elvas, geographic coordinates 38\({}^{\circ}\)53\({}^{\prime}\)39\({}^{\prime\prime}\) N, 7\({}^{\circ}\)03\({}^{\prime}\)03\({}^{\prime\prime}\) W. The predominant soil of the field is, according to the Food and Agriculture Organization (FAO) classification, a Luvisol matching to Pag and Sr Mediterranean soil types [19], and climate, according to Koppen-Geiger classification, is Csa [20].
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
###### Abstract  
The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.  
Keywords:data-driven method; remaining useful life prognostics; fault signal analysis; grey system; differential hydrological grey method; wheel hub bearings +
Footnote †: journal: Applied Sciences
## 3 A Partial Multiplicative Dimensional Reduction-Based Reliability Estimation Method
### Numerical Demonstration and Validation
#### 3.2.1 A Mathematical Problem of Two Variables  
The following performance function is considered:  
\[G=m-b_{1}p_{1}-b_{2}p_{2} \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case Number**} & \multicolumn{2}{c}{**Non-Probabilistic**} & \multicolumn{2}{c}{**Probabilistic**} & \multirow{2}{*}{**System’s Safety State**} \\  & **Reliability Index \(\boldsymbol{\eta}\)** & & **Reliability Index \(\boldsymbol{\beta_{\mathbf{X}}}\)** & \\ \hline
1 & \(\eta>1\) & \(\beta_{\mathbf{X}}>\alpha\) & Safe \\
2 & \(\eta>1\) & \(\beta_{\mathbf{X}}<\alpha\) & Unsafe \\
3 & \(\eta<1\) & - & Unsafe \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimation of structural reliability.  
Figure 3: The flow chart of reliability estimation.  
where \(b_{1}=2.0\), \(b_{2}=5.0\), \(m\) is a random variable that follows normal distribution, and \(p_{1}\) and \(p_{2}\) are interval variables. Herein, \(m\) follows a normal distribution with a mean value that equals \(\mu\) and a standard deviation that equals (1 (\(m\)- \(N\) (\(\mu\),1)), \(p_{1}\)1 = [4.4,5.6], and \(p_{2}\)1 = [1.7,2.3]).  
The approximate function according to the proposed method is obtained as  
\[G\approx(m-20)\times(\mu-2p_{1}-5p_{2})\div(\mu-20) \tag{22}\]  
Case 1: \(\alpha=3.7\) and \(m\)-\(N\) (21, 1). In this case, according to the proposed method, \(\eta=0.37<1\), and the structural system is determined to be unsafe. The failure probability computed from the corresponding Monte Carlo Simulations (MCSs) is \(P_{f}=15.9\%\), which is in line with the proposed method's expectation.  
Case 2: \(\alpha=3.7\) and \(m\sim N\) (23, 1). In this case, from the proposed method, \(\eta=1.11>1\), \(\beta=3.0<\alpha\), the reliability is 99.87%, and the failure probability is 0.13%. Therefore, the system is also unsafe. On the other hand, from the MCSs, it is obtained that \(P_{f}=0.13\%\).  
Case 3: \(\alpha=3.7\) and \(m\sim N\) (24, 1). In this case, from the proposed method, the non-probabilistic reliability index \(\eta=1.48>1\), \(\beta=4.0>\alpha\), the reliability is 99.997%, and the failure probability is 0.003%. Therefore, the system is safe, and the failure probability obtained by the MCSs is \(P_{f}=0.003\%\).
###### Abstract
However, they are limited by their size, weight, and price; therefore, further solutions are required for clinical applications. Various developments are being conducted [16; 17; 18; 19; 20; 21; 22]. The ICT laparoscopy system must integrate several key components to address the challenges above. A camera module inserted into the abdominal cavity and features camera actuating mechanics driven by motion tracking is essential. Additionally, a camera control module that receives wireless motion-tracking signals from a sensor module must be positioned outside the abdomen. This module regulates the actuation and displays the camera images on the display module. The system must incorporate a motion sensor module that wirelessly captures users' motion information and controls the camera actuator. A display module is necessary to receive and display images on a monitor. A dedicated trocar is required to affix the camera and control modules to the abdomen securely. Lastly, a cleaning module is imperative for clearing the camera lens without removing the camera from the abdomen. The essential functions and implementation details for each module are as follows. First, the camera module must have a diameter smaller than the trocars affixed to the abdomen. A wire-driving structure is needed to facilitate motion transmission from the external camera actuator's power mechanism. Second, the controller module requires wireless communication capabilities to receive motion-tracking information, a power generator to operate the camera actuator, a control circuit for actuation management, video transmission functionalities, and a fixation mechanism to secure the main body against abdominal movements. Third, the motion-sensing module should include real-time motion-sensing capabilities and a foot pedal switch to turn autonomous motion tracking on or off. Fourth, the display module should receive and present video signals, either wirelessly or via wired connections, to the monitor. Fifth, the dedicated trocar must facilitate the camera module's passage while featuring fixation capabilities. The camera module should have a designatedattachment area for coupling with the cleaning module. Finally, the cleaning module should be designed for easy detachment from the camera module (Figure 1). In this feasibility study, a prototype of the ICT laparoscopy system incorporating all the modules was developed and tested to evaluate its functionality and usability.
## 3 Methods
### Feature Extraction Module  
This study effectively extracts the multi-level semantic representation from the input legal text by utilizing the Sentence-BERT model [10] to obtain the initial sentence vector \(E\) from the input text \(\{w_{1},w_{2},\cdots,w_{N}\}\). Sentence-BERT is a supervised sentence embedding model that extends the BERT architecture by incorporating a mean pooling layer, enabling the extraction of fixed-length sentence embeddings. By leveraging a Siamese network architecture, it compares semantic embeddings of input text with manually annotated reference samples, optimizing model parameters using a contrastive learning strategy. This process enhances the semantic representation capacity of sentence vectors.  
Multi-head attention mechanisms are utilized to extract hierarchical semantic information at different levels to align with the multi-level label structure [27]. Each multi-head attention layer captures semantic features at increasing depths, thereby enabling progressive abstraction of textual representations. Given a three-level hierarchical label structure, the multi-level sentence vector representation is computed as follows:  
\[\left\{\begin{array}{l}H_{1}=Multihead(E)\\ H_{2}=Multihead(H_{1})\\ H_{3}=Multihead(H_{2})\end{array}\right. \tag{5}\]  
where \(H_{1}\), \(H_{2}\), and \(H_{3}\) denote sentence vector representations at three different hierarchical depths while \(Multihead(\cdot)\) represents the transformation function applied at each level, implemented using multi-head attention mechanisms.
## 2 Related Works
### Evaluation of Speech Changes
#### 2.2.3 Prosody  
In addition to articulation disorders, people with PD also have disorders of prosody of emotions, problems with understanding metaphors, naming, and defining [67,68]. However, the available parametric data are scarce. This is most often due to the lack of sufficient audio material necessary to carry out this type of analysis, as well as inconclusive results, as described in [69]. The prosodic base features are calculated mainly based on the whole expression. These kinds of changes mainly focus on paralinguistic features such as pitch variation, syllable rate analysis, or the manifestation of emotions in the speech signal. Prosodic information enables listeners to distinguish between different sentence modalities and to make different inferences about the meaning based on the speaker's tone. Just altering the prosody can transform a declarative sentence into an interrogative statement. Prosodic perception deficits have also been reported in individuals with Parkinson's disease (PD) [70; 71]. Some of these studies have examined linguistic prosody [72; 73] while others have focused on emotional prosody [70; 74].  
Every language possesses unique prosodic features that manifest themselves through tone structures, speech melody, and various accents. We observe that the use of only prosody-related features in automatic detectors is uncommon in contrast to other aspects such as the phonatory and articulatory features. Furthermore, despite the clear evidence for the influence of PD on prosody, most of the proposed objective biomarkers or measures are not used in clinical practice to detect PD, as a high percentage of the studies in the literature only show trends and do not establish normative data for PD detection. Moreover, to the best of our knowledge, prosody perception in people with PD has been studied in only five languages [70; 73; 74; 75; 76]. There is no available information on prosody perception in individuals with PD who speak Polish.
## 8 RAN Decomposition Scheme
### RAN Functional Split
#### 8.2.1 Option 8 Split  
This option offers an optimal solution regarding efficient traffic coordination and flexible hardware resources allocation. It presents no restrictions concerning the form of centralized processing that can be effectively implemented. Moreover, it aids in the BS design simplification since there is no required local processing except for the related MFH protocol and digital filtering. Apart from the fact that this might significantly help in simplifying the DU, it will also make the cell sites more efficient [8,421]. However, based on the fact that power control, frequency, and time synchronization implementations are centralized, stringent latency requirements are imposed on the MFH as explained in Section 8.1[426]. Consequently, the option ensures high-layer processing functions centralization to the detriment of the bandwidth requirements and most stringent MFH latency. For instance, the UL transmission entails down-conversion of the received signals to baseband and subsequent conversion to the digital domain. As this necessitates a complete baseband signal with the cyclic prefix (CP) to be forwarded, the demanded MFH bit rate is constant for a given network configuration [427]. Besides, the rate scales linearly in accordance with the bit resolution of the DAC/ADC. A typical value of 15 b/symbol is normally employed due to the associated high peak-to-average power ratio of the time domain signal, as well as for guaranteed precise channel measurements [426]. The required bandwidth for the Option 8 Split considering multiple antenna configurations and multiple sectors is as defined in Equation (13) [220,425,428].  
Moreover, the required CBR as expressed in Section 8.1 and a high degree of independence on the real user traffic are limiting factors of the option. Consequently, to alleviate the latency and subsequently enhance the QoS, significant research efforts have been on means of pushing certain latency-sensitive functionalities to the network edge/intermediate aggregation points that are considerably closer to the edge [426].  
Note that the 3 GPP and other industry groups, such as eCPRI, xRAN, IEEE 1914, and IEEE 802.1 CM among others, have been working relentlessly on different potential FSOns and definitions between the CU and DU for the 5G networks considering different trade-offs [424]. In general, the FSOns can be grouped into a low layer split (LLS) and high layer split (HLS) [424]; however, medium layer options are also mentioned in the literature [8,424]. For the HLS, 3 GPP Rel 15 work item is focusing on Option 2 (Packet Data Convergence Protocol (PDCP)/high Radio Link Control (RLC) split) standardization, while for the LLS point, the promising candidates that are under consideration are Option 6 (MAC/PHY split) and Option 7 (intra-PHY split). The latter has different potential variants such as 7-1, 7-2, and 7-3. We compared the potential options in our initial study in [47]. In the following subsections, we focus and expatiate on a number of options that are under the 3 GPP consideration.
# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 1 Introduction
### Main Contents of Research  
The main effort of this work is related to defect detection and the design of classification algorithms, including the image segmentation algorithm and the feature selection algorithm. The proper selection of a classifier is also important for defect classification, so we focused this work on image segmentation, feature selection, and classification algorithms.  
(1) To improve the image segmentation accuracy of the machine vision system, we investigated a local multi-neural network image segmentation algorithm with the wavelet transform as the classification feature. After combining all the target pixels, some post-processing was performed, and the segmentation result was obtained.  
(2) We targeted the weak link of classification feature selection in existing machine-vision-based bearing surface defect identification, and studied how to effectively implement feature selection, improve the accuracy of defect classification, and avoid large-scale calculations.