esa-sceva/satcom-chunk-collection huggingface dataset:
# A Methodology Linking Tamping Processes and Railway Track Behaviour
## 2 Basics of Track Geometry
### Track Behaviour  
The behaviour of railway tracks over time (or cumulated track load) can be analysed on a global level or on a detailed level. Global analyses consider the entire service life, from the installation of the track to its dismantlement or renewal [21]. Detailed analyses of the track behaviour investigate individual deterioration periods, beginning and ending with a major maintenance activity. It is common practice to confine deterioration periods of ballasted tracks by tamping operations [11; 18; 22].  
[PERSON] et al. [22] describe the theoretical track behaviour of an individual cross section, based on the longitudinal level, as presented in Figure 3. Immediately after tamping, a rapid deterioration describes initial track settlements, which eventually abate. Subsequently, the deterioration period transitions into its main stage, characterised by a steadily worsening track geometry. In the final phase--which is rarely reached in practice [9; 22]--the already poor track quality is subjected to an accelerating rate of geometry deterioration. If the longitudinal level signal itself is analysed rather than a derived track quality index, exponential defect growth can be observed at isolated defects [23].  
Numerous models have been established by a variety of researchers to best describe and predict the track behaviour. [PERSON] [9] analysed linear, logarithmic, and exponential functions and concluded that all three do occur in practice: The logarithmic function describes initial settlements, the exponential function describes the theoretical end-of-life behaviour, and a linear function is best suited to represent the main phase of the deterioration period. Besides these rather simple functions, many more deterioration and prediction models have been investigated by a large number of researchers. A comprehensive overview of published models is provided by [PERSON] et al. [24] or [PERSON] et al. [25; 26].
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 3 Results
### Experiment Results on NYU Dataset  
In this subsection, we test our proposed method on an RGB-D loop closure detection dataset NYU. Firstly, Siamese CNN is utilized to extract descriptors in both RGB stream and depth stream; and then, we investigate the performance of three fusion strategies for the RGB and depth stream at different locations of the SCNN
## 3 Regularization
### \(L_{2}\) Regularization  
\(L_{2}\) regularization, also known as _weight decay_, is a regularization technique that adds parameter norm penalty  
\[penalty=\frac{1}{2}\left\|\theta\right\|_{2}^{2}=\frac{1}{2}\sum_{i}\theta_{i} ^{2} \tag{39}\]  
to the cost function \(J\). New, regularized cost function \(J\) used for training is given by  
\[\tilde{J}(\theta;\mathcal{D}) =J(\theta;\mathcal{D})+\frac{\lambda}{2}\left\|\theta\right\|_{2} ^{2} \tag{40}\] \[=J(\theta;\mathcal{D})+\frac{\lambda}{2}\theta^{T}\theta, \tag{41}\]  
where \(\lambda\in[0,\infty)\) is regularization parameter that controls the strength of regularization and \(\mathcal{D}\) mini-batch of training data. During the training process, the minimization of \(\tilde{J}\) results in a decreased original cost \(J\) and \(\left\|\cdot\right\|_{2}\) of the model parameters. The step in each iteration is now made based on  
\[\
abla_{\theta}\tilde{J}(\theta;\mathcal{D})=\
abla_{\theta}J(\theta;\mathcal{ D})+\lambda\theta. \tag{42}\]  
Gradient descent update in iteration \(t\)  
\[\theta_{t}=\theta_{t-1}-\eta\
abla_{\theta}\tilde{J}(\theta_{t-1};\mathcal{D}) \tag{43}\]  
after substituting (42) becomes  
\[\theta_{t}=\underbrace{(1-\eta\lambda)\theta_{t-1}}_{\begin{subarray}{c}\text {decay parameters}\\ \text{by constant factor}\\ \text{proportionally}\\ \text{to their size}\end{subarray}}\underbrace{-\eta\
abla_{\theta}J(\theta_{t-1}; \mathcal{D})}_{\text{make update}}. \tag{44}\]  
Penalizing parameters proportionally to their size results in a model with smaller, more dispersed parameters. In this way, the model is encouraged to use all input values _a little bit_ instead of focusing only on the some with large corresponding weights. The other intuition behind \(L_{2}\) regularization is that the penalty imposes prior to the complexity of the model. By penalizing large parameters, we obtain a less complex model that will reduce overfitting due to its inability to memorize all training data.
## 2 Background Literature
### Combination of Both ML and MM/SM for Groundwater Level Change Prediction  
Other researchers have used a combination of ML and MM/SM to forecast changes in groundwater levels. Thus, ref. [58] employed a mixture of statistical and machine learning models, such as entropy-SVM-SG, entropy-SVM-RBF, and entropy-SVM-LN, but did not conduct a systematic review. In another related study, ref. [23] created a sensitivity map to the incidence of land subsidence using statistical and machine learning methods, but this work did not conduct a comprehensive review. Furthermore, ref. [22] employed ANN and MM to estimate groundwater level, although the work was not conducted in a systematic manner. A study by [59] employed extreme learning machine (ELM), modular finite-difference groundwater flow (MODFLOW), and wavelet-extreme learning machine (WA-ELM) methodologies to model groundwater level; again, they failed to perform a systematic review. According to our study, modeling groundwater level fluctuations using a combination of ML and MM methodologies is rare, and new techniques in this domain are needed.
## 4 Solution for MCRC Problem
### VM Workload Prediction
#### 4.1.2 An Attention-based RNN Prediction Method  
The time series data denoised by the CAE based filter are used to predict the resource usage of the VM in the future time period. The proposed prediction method is based on attention mechanism, named as the ARP algorithm,and is based on our previous work [50]. The ARP algorithm predicts the usage of four resources by VMs at the same time, without separate processing.  
The ARP algorithm consists of two parts: the encoder and the decoder. In the encoder, an attention module is used to adaptively select the relevant series. Then in the decoder, the relevant encoder hidden states is selected via another attention module [50]. We demonstrate the neural network structures of the encoder and the decoders in Figures 3 and 4, respectively.  
Figure 1: The neural network structure of the CAE based filter.  
Figure 2: Example of CAE based filter result.  
Figure 3: The neural network structure of the encoder of ARP.  
Figure 4: The neural network structure of the decoder of ARP.  
Let \(S_{j,l}=\{s^{cpu}_{j,l},s^{mem}_{j,l},s^{disk}_{j,l},\}^{\top}\), \(s^{cpu}_{j,l}=\{s^{cpu}_{j,1},s^{cpu}_{j,2},\cdots,s^{cpu}_{j,L}\}^{\top}\) (\(L\) is size of the window), \(s^{mem}_{j,l}=\{s^{mem}_{j,1},s^{mem}_{j,2},\cdots,s^{mem}_{j,L}\}^{\top}\), \(s^{disk}_{j,l}=\{s^{disk}_{j,1},s^{disk}_{j,2},\cdots,s^{disk}_{j,L}\}^{\top}\), \(s^{net}_{j,l}=\{s^{net}_{j,1},s^{net}_{j,2},\cdots,s^{net}_{j,L}\}^{\top}\). The attention mechanism in the encoder calculates the encoder attention weight \(a^{q}_{j}\) based on the previous \(l-1\) hidden state, and then get \(S^{\prime}_{j,l}=\{a^{1}_{l}\times s^{cpu}_{j,l},a^{2}_{l}\times s^{mem}_{j,l},a^{3}_{l}\times s^{disk}_{j,l},a^{4}_{l}\times s^{net}_{j,l}\}\). Then \(S^{\prime}_{j,l}\) is fed to the LSTM unit in the encoder. The attention mechanism in the decoder calculates the attention weight \(\beta^{l}_{l}\) based on the previous \(l-1\) hidden state. At time \(l\), for \(L\)\(S^{\prime}_{j,l}\), their attention weights are \(\beta^{1}_{l}\) to \(\beta^{L}_{l}\) at time \(l\), respectively. The input information is represented as a weighted sum of the encoder hidden states across all the time steps, and then is fed to the LSTM unit of the decoder. The output result of LSTM is the prediction of the next time period. For the specific explanation and mathematical description of the model, such as the calculation method of the attention weights, please refer to our previous work [50].  
As the examples, Figures 5-7 demonstrate, respectively, the results of the CPU part after using the ARP algorithm (\(L=10\)) to predict the resource usages of three different VMs in the Alibaba CDC [49].  
After accurately predicting the resources usage of each VM in the next period, we can perform host workload detection and VM selection.
## 3 Results and Discussion
### Properties in Fresh Condition
#### 3.2.1 Consistency  
The consistency of cement mortars is an indirect measure of workability. This property indicates the ability to flow. The mix test was carried out with the reference sand and the cements with the proposed substitutions, spreading the mass on the shaking table. The results on the cement mortars consistency are found in Table 1. The mean values corresponding to three determinations of consistency are provided. In addition, the loss of fluidity of cements with the addition is detailed, in relation to the standard cement (OPC).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mortar** & **Medium Consistency (mm)** & **Loss of Fluidity (\%)** \\ \hline \hline \(\mathrm{COP}\)—10\% P & 142.6 & 18.47 \\ \(\mathrm{COP}\)—20\% P & 136.8 & 35.17 \\ \(\mathrm{COP}\)—10\% PR1 & 140.1 & 20.11 \\ \(\mathrm{COP}\)—20\% PR1 & 137.2 & 36.22 \\ \(\mathrm{COP}\)—10\% PR2 & 140.4 & 19.15 \\ \(\mathrm{COP}\)—20\% PR2 & 138.4 & 35.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Consistency of mortars made with different waste additions. (\(\mathrm{COP}\) = Cement Portland Ordinary).  
A decrease is detected in the measurement of the diameters of the extended mass (medium consistency), when the percentage of substitution increases from 10% to 20%. The loss of fluidity has a linear trend as the percentage of addition waste increases (from 10% to 20%). The consistencies are very similar for all samples, as well as the loss of fluidity as seen in Figure 1.  
Studies on admixture cements using metakaolin [38] show that the resulting mixtures increase the water demand necessary to maintain their workability. The replacement of metakaolin by low cement content (5% by mass) practically does not affect the fluidity of the paste. However, with higher percentages of substitution (greater than 10%), reducing or superplasticizing products in the addition of water are usually necessary to guarantee workability.
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
## 3 Results and Discussion
### Fat Thickness Effects  
The thickness of the fat layer significantly affects medical imaging and physiological monitoring, especially in simulating abdominal signal propagation [35; 36]. Fat layer thickness can influence the propagation characteristics of bowel sound signals. To investigate its specific effects, we varied the fat layer thickness in the propagation model. This approach enabled a detailed analysis of how different thicknesses affect signal transmission, providing deeper insights into the factors influencing bowel sound acquisition and analysis. Figure 5 illustrates the changes in the IWC waveform on the surface of the fat layer when the thickness ranges from 5 to 20 mm. The waveform exhibits a clear trend: as the thickness of the fat layer increases, the amplitude of the IWC waveform decreases. This reduction in amplitude indicates a positive correlation between fat layer thickness and signal attenuation. These findings underscore the importance of considering fat layer thickness in simulations of bowel sound propagation.  
Figure 6 illustrates the relationship between attenuation values and fat layer thickness. Signal attenuation was calculated using the root-mean-square (RMS) ratios of the initial and final signals, with the results expressed in decibels (dB), which represent the logarithmic change in signal intensity [37]. As the thickness of the fat layer increases, the signal attenuation also increases, but the relationship is non-linear. Initially, the attenuation grows rapidly with small increases in thickness, especially between 4 mm and 10 mm, due to stronger scattering and absorption interactions. However, as the thickness continues to increase, from 10 mm to 20 mm, the rate of attenuation growth slows down, eventually reaching a saturation point where further increases in thickness have minimal additional effect on signal attenuation. This non-linear trend suggests that the primary contributors to attenuation, such as scattering and absorption within the fat, have diminishing returns at higher thicknesses [38].  
Comprehending the impact of fat layer thickness on signal attenuation is essential for the clinical interpretation of bowel sounds and for accounting for individual differences during diagnostic assessments. The fat layer's thickness directly influences the amplitude of the detected signal, as shown in the observed decrease in waveform amplitude with increasing thickness. Beyond a critical thickness, further increases in fat layer thickness result in smaller changes in attenuation, which highlights the need to account for individual variations in fat distribution during diagnostic assessments. Understanding this relationship helps to refine non-invasive diagnostic techniques and enhances the interpretation of bowel sounds.  
Figure 5: IWC on fat layer surface at varying thickness.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 3 Results
### vsiRNA Sequencing Analysis  
A library of sRNA from plants 28 dpi was constructed and sequenced on the Illumina NextSeq-500 platform. Of 16 million reads, 7,499,184 corresponded to 18-24 nt sequences. From this total, 68% belonged to the _N. benthamiana_ genome, and 32% belonged to the VIGS vector genome (Figure 2d). The VIGS vector genome vsiRNAs were distributed equally between DNA-A (including Krt18) and DNA-B. The distribution of the vsiRNAs in the DNA-A genes was as follows: AV1 gene remainder (0.275%); Krt18 (6.165%); AC3 (8.11%); AC2 (17.40%); AC1 (18.03%); and in the DNA-B genes: BV1 (6.11%); and BC1 (43.44%) (Figure 2e).  
Sequencing analysis showed that 21, 22, and 24 nt RNAs were the most abundant (83%) in the VIGS vector genome (Figure 2f). The vsiRNA sequences were differentially accumulated throughout the VIGS vector genome, as shown in the heatmap in Figure 2g. The red color indicates that in that region of the VIGS vector, there was a high accumulation of vsiRNA (hotspot), the gold color, an intermediate accumulation, and the green color, a low accumulation (non hotspot). When analyzing hotspots, we found that the average GC content is higher than nonhotspots, as shown in Figure 2h.
###### Abstract  
Objective: This study aimed to evaluate the relationship between the Prognostic Nutritional Index (PNI) and demographic characteristics, presenting complaints, clinical diagnoses, and patient outcomes in patients admitted to the emergency department for gastrointestinal (GI) emergencies. The predictive value of PNI for the clinical course of patients with GI emergencies was investigated. Method: This retrospective cross-sectional study included 583 patients with a diagnosis of GI emergencies in the emergency department of a tertiary university hospital between January 2021 and December 2024. Data such as age, sex, presenting complaints, final diagnosis, and emergency department outcomes (discharge, ward admission, and transfer to intensive care unit) were collected. The PNI value was calculated using serum albumin (g/dL) and total lymphocyte count (/mm\({}^{3}\)) with the formula PNI = 10 \(\times\) albumin + 0.005 \(\times\) lymphocyte. The PNI was calculated based on serum albumin levels and peripheral lymphocyte counts. Results: The mean age of the study group was 63.4 \(\pm\) 17.4 years, and 52.1% of the patients were female. The number of patients with a PNI value \(<\) 38 was significantly higher in the intensive care unit (\(p\) < 0.001). PNI values were considerably lower, especially in patients diagnosed with malignancy, cirrhosis, and GI hemorrhage (X\({}^{2}\) = 71.387; \(p\) < 0.001). The PNI was an independent predictor of outcomes in patients with GI emergencies. The mean PNI was significantly higher in discharged patients but significantly lower in patients admitted to the intensive care unit (\(p\) < 0.002). The cut-off score for PNI was calculated using the median value, and the cut-off score for PNI was \(<\)38. Conclusion: PNI is a powerful biomarker for predicting the clinical severity and prognosis of patients with GI emergencies. Since it can be easily calculated from routine biochemical tests, it can be used as a practical and effective risk stratification tool. The evaluation of PNI, especially for the early detection of critically ill patients at high risk of malnutrition, may contribute to the reduction of morbidity and mortality through the timely initiation of appropriate supportive therapies.  
P ognostic Nutritional Index; gastrointestinal emergency; emergency department; malnutrition; intensive care 2025, 15, 8269  
Faculty of Medicine, Balikesir University, Balikesir 10145, Tarikye; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS] Tel.: +90-542-600-53-02
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 4 Existing Shortcomings  
In summary, we find that each non-destructive detection method of WRs has its own advantages, but there are still many shortcomings [9; 77; 78], as shown in Table 2.  
In view of the problems existing in electromagnetic detection, optical detection, ultrasonic guided wave method, acoustic emission method, and other detection methods, the following are specified in detail:  
1. In the field of WR electromagnetic detection, the existing theory and technology have developed in depth. However, according to the current research situation, the problems in the electromagnetic non-destructive detection of WR mainly include the influence of different WR structures on the detection signal, the quantitative analysis of LF type defects, the formulation of testing standards, and the relationship between LMA, LF defects and the strength of WRs (details can be found in reference [78]). Only by solving the quantitative detection of LF type defects and the relationship between defects and the strength of WRs, can the service life of WRs be truly and accurately predicted by non-destructive testing.
2. In the field of WR optical detection, the existing research has a small amount of data, namely no big data sets, and the algorithm is with shallow structure. It has some problems such as limited  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
exavation ability, low computational efficiency and poor robustness. It is difficult to carry out high-speed detection, high efficiency and reliable identification, therefore can not be directly applied to real-time state health monitoring of WR. By introducing the deep learning [79; 80; 81; 82] method into the WRs damage detection/monitoring, it is expected to achieve real-time, reliable and accurate damage identification and location, thus realizing the detection and monitoring of WR surface damage [83].
3. The ultrasonic guided wave method, acoustic emission method and other detection methods are still in the stage of theoretical research and laboratory, and there is still a certain distance away from practical application, due to the limitation of technical level and the method itself. UGW and AE method are difficult to be used in actual dynamic detection, and the signal-to-noise ratio needs to be further improved; Eddy current method is less studied and affected by skin effect. Its reliability needs further study; Ray Method's development prospects are relatively limited compared with other methods because of the expensive instruments and the potential impact of radiation on the human body. Only by further deepening theoretical and experimental research and solving more of the above difficult problems can we promote the progress of these detection methods.
## 4 Results and Discussion
### Rotordynamic Analysis  
Figure 9 shows a Campbell diagram and the damping ratio calculated from the rotordynamic analysis. In Figure 9a, the mode shapes corresponding to the damped natural frequencies are also shown. All modes shown in Figure 9a were rigid body mode. The conical mode was found at the lowest natural frequency and the translational mode was found at the higher frequency. However, these values were predicted to be located at higher frequencies than the rated speed, and thus it is  
Figure 8: Stiffness and damping versus rotor speed. (**a**) Stiffness; (**b**) Damping.  
Figure 7: Minimum film thickness versus applied load.  
expected that there are no critical speeds below the rated speed of 21,000 rpm. Moreover, the analysis results showed that the first critical speed was at 27,000 rpm, and thus the separation margin between the rated speed and the first critical speed is more than 20%. These results indicate that a large vibration will not occur at the rated speed. In addition, as shown in Figure 9b, the damping ratios of all rigid body modes are larger than 0.1, and thus unstable vibrations will not occur. This phenomenon results from the designed hydrostatic bearing's large stiffness of \(\sim\)1 \(\times\) 10\({}^{8}\) N/m and its large damping of \(\sim\)2 \(\times\) 10\({}^{4}\) Ns/m, as shown in Figure 8. Therefore, it is expected that the designed pump-drive turbine can be stably operated at any speed below the rated speed, indicating a large operation speed range. This rotordynamic characteristic is favorable for the pump-drive turbine operation because a large operation range is possible depending on the cycle condition.  
Figure 10 shows the predicted unbalance response of the pump-drive turbine rotor. In the figure, the shaft vibrations at the two radial bearings are presented and the vibration amplitudes in the vertical and horizontal direction (x and y directions in Figure 2) are shown. For the prediction, the unbalance of the ISO G2.5 was applied at the node of turbine and pump wheels, as shown in Figure 3. The vibration amplitude in the vertical direction is the same as that in the horizontal direction at both bearing locations because of the symmetric stiffness and damping of the bearing. In addition, the vibration amplitude increases to 25,000 rpm and no critical speed is present at up to 25,000 rpm, as predicted in the Campbell diagram. Vibration amplitude at the pump side bearing is larger than that at the turbine side at all speeds; the maximum amplitude at the rated speed is predicted to be 3 \(\upmu\)m at the pump side bearing.  
Figure 10: Predicted unbalance response at the two bearings (ISO G2.5).  
Figure 9: Campbell diagram and damping ratio. (**a**) Damped natural frequency; (**b**) Damping ratio.
## 3 Results
### Satellite-Based Re estimations

Table 3: Satellite-based ecosystem respiration (Re) estimations and comparison with eddy-covariance (EC) tower-based Re observation from 2009 to 2011.