esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The RDT-SSI Method Under Non-Stationary Excitation
### Acquisition of Structural Free-Decay Response Based on the Improved RDT Method  
The traditional stochastic reduction method is aimed at stationary stochastic processes with zero mean, and the \(f(t)\) discrete linear equation of motion under the action of non-stationary zero-mean stochastic excitation is  
\[M\ddot{x}(t)+C\dot{x}(t)+Kx(t)=f(t) \tag{1}\]  
where M, C, and K are the mass, damping, and stiffness matrices of the structure; \(x(t)\), \(\dot{x}(t)\), and \(\ddot{x}(t)\) are the displacement, velocity, and acceleration vectors; \(f(t)\) refers to zero-mean, non-stationary external load vectors.  
According to the basic principles of structural dynamics, the displacement response of the i-th degree of freedom in a linear structural system can be expressed as the superposition of multiple modal displacement responses, namely:  
\[x(t)=\Phi\cdot q(t)=\sum_{r=1}^{n}\varphi_{r}\cdot q_{r}(t) \tag{2}\]  
Figure 1: Flowchart of identification.  
where \(\Phi\) is the complex mode matrix, \(q(t)\) is the modal displacement response vector, \(\varphi_{r}\) is the r-th-order mode shape, \(q_{r}(t)\) is the r-th-order component of the modal displacement response \(q(t)\), and n is the modal order.  
The traditional stochastic reduction method firstly divides the response into N average time periods; each time period has the same time length \(\tau\), and all time periods have the same initial conditions. The stochastic properties can be obtained by averaging the superposition of the N-segment responses, i.e.,  
\[\delta(t)=\frac{1}{N}\sum_{i=1}^{N}q_{r}(t_{i}+\tau) \tag{3}\]  
where \(\delta_{t}\) is the stochastic feature obtained by averaging the N time periods superimposed; \(t_{i}\) is the moment when the stochastic signal passes the threshold condition; and \(q_{r}(t_{i}+\tau)\) is the averaging time period of the response in the stochastic subtraction method.  
\(\delta_{t}\) consists of two parts: free-decay response and forced vibration. It is worth noting that the zero-mean unsteady ambient vibration response signal has a mean of 0, and the variance decreases gradually with the increase in N and tends to 0. Therefore, the forced vibration part is regarded as a noise term.  
The basic idea of using the trigger method of non-stationary excitation is as follows [18]: the value of the trigger level is taken at \(x_{s_{1}}\) equal intervals from the range \(x_{s_{2}}\), and the RDS is triggered by the value of each trigger level, and the amplitude modulation is superimposed and averaged to obtain a new RDS. Specifically, the value of the trigger level \(x_{s_{1}}\) is taken to \(x_{s_{2}}\), with a step size of \(\Delta x_{s}\), forming a total of m groups, where \(m=\frac{x_{s_{1}}-x_{s_{2}}}{\Delta x_{s}}+1\). The RDS corresponding to the value of each trigger level \(x_{s_{1}}\) is adjusted proportionally to the initial value \(\delta^{\prime}\), and the rest is weighted and averaged to obtain a new RDS, and the weighting function is as follows:  
\[\delta=\frac{1}{m}\sum_{i=1}^{m}\left(q_{i}\delta^{\prime}_{i}\right) \tag{4}\]
###### Abstract  
Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVI\({}_{\text{RS}}\) and NDWI\({}_{\text{RS}}\)) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.  
spectrometry; Sentinel-2; pasture quality index; normalized difference vegetation index; normalized difference water index; supplementation; decision making
## 2 Experiment Description
### Terrestrial Experiment  
#### 2.1.1 Measurement Configuration  
The geodetic antenna (JAVAD GrAnt G3T) on top of the IfE van is connected via GNSS splitter to two different receiver types, cf. Figure 1a,b. Two geodetic JAVAD (JVD) Delta TRE G3T receivers (JVD 0081, JVD 0082) together with two high sensitivity u-blox (UBX) NEO MST receivers (UBX 0867, UBX 1771) form a zero-baseline on the roof of the vehicle. One of each receiver type is connected to an external Rubidium frequency standard (Microsemi MAC SA.35m). The signal wave generator serves as a link in-between the atomic clock and the UBX receiver to adapt the frequency [12]. The other two receivers were driven by their respective internal temperature controlled crystal oscillator (TCXO).  
This zero-baseline configuration on a moving platform allows us to characterise the individual properties of geodetic and high sensitivity receivers and their combinations in an urban environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Urban Experiment Details** & **Approximate GPS Time** \\ \hline A & Start of the experiment. Standstill phase. & 11:45:00 \\ \hline A & End of the standstill phase. & 11:53:00 \\ \hline B (B\({}_{\text{Start}}\)) & Repeatedly driven trajectory. Five rounds in total. & 12:26:00 \\ \hline B (B\({}_{\text{End}}\)) & End of the repeatedly driven trajectory. & 13:02:00 \\ \hline C & End of the experiment. & 13:37:00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of marked points on the urban trajectory map.
# Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
## 4 Discussion  
### Principal Findings  
The impact of telemonitoring methods with mobile applications on patients with chronic cardiovascular illnesses was assessed in this systematic review. Using them enhances self-care and blood pressure control ratios. Some effects varied between the RCTs concerning quality of life. The majority of studies raised at least some bias-related issues. For some of the papers analyzed, the analysis of the risk of bias raised discordant opinions among reviewers and was, therefore, subject to further evaluation.  
A significant concern in interpreting these findings is the variability in sample sizes across the studies. Many of the included trials had small sample sizes, with over half (54.5%) of the studies involving fewer than 100 patients per arm. This variability in sample sizes introduces challenges in detecting real differences in outcomes and increases the risk of type II errors, where true effects may not be observed due to insufficient statistical power. For instance, smaller sample sizes may limit the ability to generalize findings and accurately assess the efficacy of telemonitoring interventions. Larger and more consistent sample sizes are crucial for drawing more reliable conclusions and ensuring that observed effects are not merely due to random variation or sampling error.  
Most studies showed that telemonitoring helped CVD patients maintain their blood pressure. These results imply that telemonitoring can be very helpful in controlling blood  
Figure 2: Risk of bias assessment for included studies undertaken using the Risk of Bias 2 (RoB2) tool [29].  
pressure and possibly lowering complications associated with hypertension. Improvements in quality of life were found in four out of nine trials (44.4%), suggesting that telemonitoring approaches could benefit patients' mental and emotional health. In 83.3% of the trials, improved self-care abilities were observed, highlighting the potential of telemonitoring to enable patients to manage their health conditions better.
## 4 Data Processing and Features
### Deep Learning
#### 5.2.2 Recurrent Neural Network (RNN)  
Recurrent Neural Networks (RNNs), FFNNs with a temporal twist, solve this problem. This neural network features links between passes and time. They are a kind of artificial neural network whose nodes form a directed graph along a sequence, enabling information to flow back into previous levels and remain [37; 68; 69].  
**Advantages of using RNN**[85]  
* When it comes to illustrating the temporal connections that exist between the inputs and outputs of the neural network, this tool proves to be beneficial.  
**Disadvantages of using RNN**[85]  
* Difficult to train and instruct properly.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 6 Rockburst Prevention and Control
### Performance of Rockbolts in High-Stress Ground Conditions  
The key difference between deep and shallow rock is the increase in in-situ rock stresses, leading to large deformations in soft rocks and rockbursts in hard rocks in deep excavations [204]. A bolt is an essential component in a support system, but a standard bolt is not effective under high-stress conditions. An energy-absorbing bolt, on the other hand, has a higher bearing and deformation capacity and can absorb more energy generated during dynamic rock collapse [205]. Figure 17a depicts some acceptable energy-absorbing bolts. A good rockburst support system should consist of high-strength energy-absorbing bolts and appropriate surface support components [206]. [PERSON]'s large-scale dynamic testbed study offers guidance on selecting the appropriate bolt type based on the surrounding rock energy demands and deformation capacity [207].  
Rockbolts are categorized into five types based on their load and deformation capacity: stiff, medium yielding, high yielding, very high yielding, and extremely high yielding. The behavior of high-yielding rockbolts is influenced by loading conditions and environmental factors such as loading velocity and installation quality. Figure 17b shows the energy dissipation capacity of different forms of reinforcement. It depicts the energy dissipation and deformation capacity of each category for a specific type of rock reinforcement. In rockburst-prone and squeezing ground conditions, both internal and external support devices should be able to absorb a significant amount of the released strain energy to prevent sudden failure of the support network, rather than only balancing the ground load. Rockbolts with high-loading capacities that absorb energy are beneficial for rock support, especially in high-stress rock masses.  
Figure 17: (**a**) Typical energy-absorbing bolts [184; 208; 209; 210; 211; 212; 213; 214; 215], (**b**). Energy dissipation capacity categories of different types of reinforcement (Modified from [204]).
## 2 Materials and Methods
### Pre-Procedural Imaging  
#### 2.3.1 CT  
Pre-procedural contrast-enhanced CT images were taken using a 64-slice multi-detector CT (Somatom Sensation, Siemens, Forscheim, Germany). After the initial unenhanced images of the abdomen were obtained, Iohexol 1.5 mL/kg (Omnipaque 300 mg/mL; GE, Princeton, NJ, USA) was power injected through a peripheral vein in the forelimb or another peripheral vein when a forelimb vein was inaccessible. This was followed by an injection of 40 mL of saline at a rate of 0.4 mL/s and images were subsequently acquired during both the arterial dominant phase (20 s after injection) and in the portal venous dominant phase (60 s after injection). The following CT parameters were used: 250 mA; 120 kVp; 3-mm collimation; and 2:1 pitch. The pigs were not maintained on anesthetic during the CT procedure as it took less than 5 min to complete the study.
# Hunting Molecules in Complex Matrices with SPME Arrows: A Review
## 4 SPME Arrow Performance  
### Benchmarking  
In the previous sections, several advantages and disadvantages of SPME Arrows when compared to traditional SPME fibers were reviewed. However, those were mostly focused around the physical attributes associated with SPME Arrows. The current section focuses on how the aforementioned physical differences in phase area and volume translate into analytical performance. Several benchmarking applications have been chosen to compare SPME Arrows with traditional SPME fibers. It is important to note that the 1.1 mm SPME Arrow is the focus for the remainder of this section and for most of the manuscript, as this configuration is available in the most popular SPME phases (e.g., polydimethylsiloxane (PDMS) and divinylbenzene (DVB)), as shown in Table 2, whereas the 1.5 mm SPME Arrow is only available in a 250 \(\upmu\)m polydimethylsiloxane (PDMS) configuration, which is anticipated to have less applications. It is important to point out that the 1.5\" mm SPME Arrows commercially launched a few weeks prior to the writing of this manuscript, so there are little data to warrant a discussion beyond what was mentioned previously.  
When comparing a 100 \(\upmu\)m PDMS 1.1 mm SPME Arrow against a 100 \(\upmu\)m PDMS 23 gauge SPME fiber after the headspace (HS) extraction of volatile organic compounds (VOCs) in drinking water [spiked at 2.5 ppb as per International Organization for Standardization (ISO) method 17943] via GC coupled to mass spectrometry, it was found that equilibration times, extraction times, and desorption temperatures were equivalent among the two devices. Figure 5 presents a chromatogram overlaying instrumental response of the SPME Arrow and SPME fiber for the 92 VOCs. The SPME Arrow's response is higher than the traditional SPME fiber. On average, the SPME Arrow demonstrated a \(\sim\)4\(\times\) increase in response over traditional SPME fibers. This observation is consistent with the SPME Arrow's larger phase volume, which correlates to a greater volume/mass of target analyte collected and thereby an increased analytical response. It is important to note that \"on average\" was stated in the previous point, because the increase in response for very volatile compounds like vinyl chloride was \(\sim\)10\(\times\) on the SPME Arrow vs. the traditional SPME fiber. However, a semi-volatile compound like naphthalene only saw \(\sim\)2\(\times\) increase in sensitivity on the SPME Arrow compared to the traditional SPME fiber. Such differences in extraction recoveries are correlated to different vapor pressures (i.e., Henry's law constants) of said analytes and, expectedly, the availability in the headspace being the rate-limiting step. Likewise, it was observed that the SPME Arrow generated linear results(0.998 median R2) over a wide calibration range (0.0025-166 \(\upmu\)g/L); with excellent precision (3.24% median RSD); and low sensitivity (30.0 ng/L median MDL) for all 92 ISO 17943 VOCs [25].
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
## 2 Methods
### Optimization Based on Grayscale Constraint  
After completing the geometric constraint relationship judgment, we can get optimized line segments. Grayscale constraint was performed on the first optimized line segment to realize the second optimization (Figure 6).  
Furthermore, it must be mentioned that direction information of all line segments is traverse after geometric optimization. When any two line segments, \(l_{1}\) and \(l_{2}\), have the same direction (the angle between the two lines \(\theta<1^{\circ}\)) and the distance between them is less than 20 pixels, which is suitable for the current typical megapixel/medium-resolution image in the experiment section of paper, these two line segments are temporarily merged into one virtual line segment.  
In addition to this, grayscale points on the central positions of AB (midpoint of \(l_{1}\)), BC, and CD segments (midpoint of \(l_{2}\)) on virtual line segments are used as center grayscale to open a \(3\times 3\) matching window with NCC similarity judgment. NCC is often used to compare the similarity of two images and realize the matching features. Here, we propose an extended NCC that compares the similarities of the three windows (small patches) opened on one image. If \(NCC\geq\psi\) (where \(\psi\) is grayscale threshold among three matching windows, \(\psi\) = 0.8), the line segment has high grayscale-level similarity that means a virtual line segment is an optimized line segment. In this case, we combine \(l_{1}\) and \(l_{2}\) to get a real line segment. If \(NCC<\psi\), the virtual line segment is not a real merging line segment. In this case, \(l_{1}\) and \(l_{2}\) are retained and the virtual line segment is released. The specific expression of \(NCC\) is given in Equation (9).  
\[NCC=\frac{\sum\limits_{i,j=0}^{N-1}\left(G_{p}(i,j)-\frac{1}{N^{2}}\sum\limits _{i,j=0}^{N-1}G_{p}(i,j)\right)\left(G_{q}(i,j)-\frac{1}{N^{2}}\sum\limits_{i,j=0}^{N-1}G_{q}(i,j)\right)}{\sqrt{\sum\limits_{i,j=0}^{N-1}\left(G_{p}(i,j)- \frac{1}{N^{2}}\sum\limits_{i,j=0}^{N-1}G_{p}(i,j)\right)^{2}}\sum\limits_{i, j=0}^{N-1}\left(G_{q}(i,j)-\frac{1}{N^{2}}\sum\limits_{i,j=0}^{N-1}G_{q}(i,j) \right)^{2}}\left\{\begin{matrix}p=1&q=2,3\\ p=2&q=3\end{matrix},\right. \tag{9}\]  
where \(G_{p}\) and \(G_{q}\) represent grayscale windows at position \(p\) and \(q\) on virtual line segment, respectively, and \(N\) represents the size of the grayscale window, which is 3 here.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup
### Training and Adaptation Protocol
#### 3.2.2 Training Configuration  
Initial training on SC 2 spk employs the NoAM optimizer with 200,000 warm-up steps. The multi-speaker adaptation phase uses the Adam optimizer with a learning rate of \(10^{-5}\) over 100 epochs. For domain adaptation experiments, we maintain the same optimizer configuration while exploring the following parameters and configurations:  
* Dropout rates: 0.1 (baseline), 0.3, 0.5.
* Weighted sampling inversely proportional to speaker count frequency.
* Subsampling factors: 10 (baseline), 5.
* Gaussian smoothing kernel with \(\sigma=2\) and 3-frame window.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{8}{c}{**Number of Speakers**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-7}  & **2** & **3** & **4** & **5** & **6** & **7** & \\ \hline CH1 (Train) & 155 & 61 & 23 & 5 & 3 & 2 & 249 \\ CH2 (Test) & 148 & 74 & 20 & 5 & 3 & 0 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of audio recordings by number of speakers in the CallHome database for training (CH1) and testing (CH2) datasets. The columns represent the number of speakers per recording, while values indicate the total count of audio files in each category.  
Figure 2: Distribution of audio duration in hours by number of speakers for CallHome training (CH1) and testing (CH2) datasets.
## 2 Observations
### The Rate Of TEC from GNSS Observations  
In the study we aim to assess the relation between the irregularities in the ionospheric plasma density and recorded variations in the pulsar observations. The Rate of TEC (ROT) is a well-known and commonly-used measure of the TEC variation in time [48]. ROT-derived Rate Of TEC Index (ROTI) describes the occurrence of the radio wave disrupting irregularities in the ionospheric plasma density and is widely-described, very efficient (due to an excellent data coverage) tool in ionospheric studies.  
Rate of TEC has also a straightforward correspondence with the plasma irregularities in situ measurements provided by the Langmuir probes from missions like SWARM [49].  
ROT can be expressed as:  
\[\text{ROT}=\frac{\text{TEC}_{\text{i+1}}-\text{TEC}_{\text{i}}}{\text{t}_{ \text{i+1}}-\text{t}_{\text{i}}} \tag{3}\]  
where TEC\({}_{\text{i+1}}\) and TEC\({}_{\text{i}}\) are the TEC values in two subsequent epochs within the continuous arc of the single satellite observation and t\({}_{\text{i+1}}\) and t\({}_{\text{i}}\) are the corresponding epochs of observation. ROT is usually expressed as the change of TEC in the interval of 1 min-in this case unit of the ROT is TECU/min. In the case of single receiver, single satellite and continuous observation arc, the absolute value of TEC is not required for ROT calculation. The transmitter and receiver hardware biases-mandatory to mitigate during the absolute TEC estimation-are cancelling each other during the differential ROT calculation. The TEC value along the line of sight (slant TEC-STEC) can be calculated as follows:  
\[\text{STEC}=\left(\frac{\text{L1}}{\text{f}_{1}}-\frac{\text{L2}}{\text{f}_{2 }}\right)*\frac{\text{f}_{2}^{2}*\text{f}_{2}^{2}}{\text{f}_{1}^{2}-\text{f} _{2}^{2}}*\frac{\text{c}}{\text{K}} \tag{4}\]  
where L1 and L2 are carrier phase observations performed on frequencies f\({}_{1}\) and f\({}_{2}\), c is the speed of light in the vacuum, and K is a constant factor equal to 40.3.  
ROTI index is calculated as a standard deviation of the ROT values within the chosen interval. The ROT-based ionospheric irregularities map for the northern hemisphere are provided as an IGS product [50; 51]. ROT and ROTI are successfully used in different ionospheric structures description like plasma bubbles [52] and TIDs [53].  
In this study we focus on the raw ROT, as ROT is derived directly from the GNSS radio signal, and can be treated as a measure of the ionosphere-conducted disruption in the signal. Such an approach meets the idea of the distortion in pulsar signals observations--in both cases we observe irregularities impact on signals from the radio sources.
## 2 Materials and Methods
### LCA and C2C for Product Design
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **LCA** & **C2C** \\ \hline Objective & Eco-efficiency & Eco-effectiveness \\ \hline Strategy & Minimization and cleanness & Maximization and regeneration. \\ \hline Knowledge & Open, developed by the global scientific community. Application by experts in environmental analysis. & Restricted and licensed for use. No specialized training required. Some design guides available [60; 61; 62; 63; 64; 65; 66; 67; 68; 69] \\ \hline Normative and guidelines & Environment: ISO 1404 and ISO 1404 [9; 70] & \\  & Social: UNEP and SETAC & \\  & Guidelines [71] & \\  & Economic: ISO 15686-5 [72] (only building and construction) & \\ \hline Certification & More accepted: some certifications require LCA, for example, ISO 14025: 2006 [52; 25] & Certification in 5 levels: Basic, Bronze, Silver, Gold or Platinum.Private [54] \\ \hline \hline \multirow{2}{*}{Scope} & Assessment. Indirectly, in design and optimization technology. & Design and assessment \\ \hline Assessment dimensions & Environmental and economic criteria, (quantitative). Social criteria (qualitative or semiquatitative); independent procedures. & Environmental and social in a single procedure. Qualitative. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of LCA and C2C (advantages in black; disadvantages in blue).