esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation
### Experimental Results
#### 3.2.3 Correlation Analysis of Stability and RMS-VAR  
The vibration time-history curves of the bedrock and unstable rock mass, acquired by MEMS sensors, can be processed using Equation (8) to derive the dimensionless RMS-VAR. To correlate this ratio with rock mass stability, the three-axis acceleration data collected from the unstable rock mass were analyzed through their spatial geometric relationships, enabling the calculation of angular deviations toward the potential tipping direction. The computational framework is defined as follows:  
\[\gamma_{x}=\arctan(a_{x}/\sqrt{(a_{y}^{2}+a_{z}^{2})})\times 180/\pi \tag{10}\]  
where \(\gamma_{x}\) is the MEMS sensing device \(x\) direction (tipping direction) relative to the ground horizontal tilt angle.  
As shown in Figure 10, with the reduction in the relative bonding length in the structural plane of the unstable rock mass, the RMS-VAR exhibits a progressive increase, with its magnitude of change significantly exceeding that of the tilt angle. When the relative bonding length decreases from 1.0 to 0.8, the RMS-VAR from 1.02 to 1.46, while the tilt angle remains stable within this range. Upon further reduction to a relative bonding length of 0.5, the tilt angle undergoes an abrupt surge, and the RMS-VAR surpasses three times its initial amplitude. These results demonstrate that the RMS-VAR effectively captures variations in the bonding length of the dominant structural plane and provides earlier indications of stability degradation compared to detectable deformations.  
Figure 9: Relation between NF and safety factor of unstable rock.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
###### Abstract  
**Background:** Hemodialysis (HD) patients have significantly higher mortality rates compared to the general population, primarily due to complex comorbidities. This systematic review and meta-analysis aimed to evaluate and compare the performance of various machine learning (ML) models in predicting mortality among HD patients.  
**Methods:** The analysis followed PRISMA guidelines, including studies that assessed the predictive capabilities of ML models for mortality in HD patients. Review Manager software version 5.4.1. was used for meta-analysis, and the performance of ML models was compared, including logistic regression, XGBoost, and Random Forest models. **Results:** The meta-analysis indicated that the logistic regression model predicted a true positive mortality rate of 8.23%, close to the actual rate of 10.53%. In contrast, the XGBoost and Random Forest models predicted rates of 9.93% and 8.94%, respectively, compared to the actual mortality rate of 13.73%. The highest area under the curve (AUC) was reported for the Random Forest model at a 3-year follow-up (AUC = 0.89). No significant difference was found between the performance of logistic regression and Random Forest models (\(p\) = 0.82). **Conclusions:** ML models, particularly Random Forest and logistic regression, demonstrated effective predictive capabilities for mortality in HD patients. These models can help identify high-risk patients early, facilitating personalized treatment strategies and potentially improving long-term outcomes. However, the observed heterogeneity among studies indicates a need for further research to refine model performance and standardize predictive features.  
machine learning; Random Forest; logistic regression; ESKD; mortality
# SpaFLEX: Field Campaign for Calibration and Validation of FLEX-S3 Mission Products +
## 2 Methodology
### Seasonal Campaign  
Eddy covariance systems are installed in both areas, providing semi-hourly data on the energy and carbon balance of the ecosystem. Next to the towers are two FLOX systems that continuously measure fluorescence levels on various characteristic holm oak trees in the area. These facilities allow the ecosystem to be monitored for temporal analysis.
## 3 Results
### Swarm Satellite Magnetic Field Data  
Figure 2c demonstrates that both the first-time derivative and the residual (the difference between the observed data and the fitted curve) effectively capture ionospheric disturbances. The cubic spline fitting, applied at 60 s intervals, corresponds to approximately 4\({}^{\circ}\) latitude along the Swarm satellite's path. Therefore, spatial variations exceeding this scale were removed from the residuals. In addition to the ionospheric density data, magnetic field data analysis was performed. The wavelet transformation applied to the residual magnetic field data further highlights these disturbances. Figure 3a-c illustrates the residual magnetic field components as white lines superimposed on the wavelet transformation. Power intensity is represented by the coloured bar shown above the data panels. The right and left y-axes represent the amplitude in nanoteslas and the periodic time of oscillations in seconds, respectively. The maximum power spectrum of these oscillations, ranging from 30 s to 60 s, corresponded to the applied fitting interval. Wavelet transformation serves as an auxiliary tool to highlight the locations of significant magnetic perturbations in both hemispheres.  
It is worth noting that we investigated ULF pulsations of the Pc3 (20-45 s) and Pc4 (45-150 s) types during this event using two different datasets. Specifically, we analysed the ULF Level-2 data product from the Swarm-B satellite, as well as ground-based mag  
Figure 3: The residual magnetic field variations in the (**a**) Bx, (**b**) By, and (**c**) Bx components retrieved from Swarm-B satellites shown in white solid lines are superimposed on their corresponding wavelet transformations with respect to geographic latitude. Swarm-B magnetic field data retrieved between 18:52 and 19:25 UT on 4 August 2020.  
netic data recorded by a station near Beirut port and its conjugate point, as shown in Figures S4 and S5 in the Supplementary Materials. This step was necessary to confirm that the ionospheric disturbances observed by Swarm B and the other satellites discussed in the following sections were not related to ULF pulsations. Data from both the Swarm-B ULF product and ground-based observatories PEG and HBK clearly demonstrate that the universal times (UTs) of the detected pulsations are well separated from the ionospheric disturbances recorded by the satellites.
###### Abstract  
Thousands of devices are connected to the Internet as part of the Internet of Things (IoT) ecosystems. The next generation of IoT networks is expected to support this growing number of Intelligent IoT devices and tactile Internet solutions to provide real-time applications. In view of this, IoT networks require innovative network architectures that offer scalability, security, and adaptability. The Recursive InterNetwork Architecture (RINA) is a clean slate network architecture that provides a scalable, secure, and flexible framework for interconnecting computers. SDN technology is becoming a de facto solution to overcome network requirements, making RINA adoption difficult. This paper presents an architecture for integrating RINA with SDN technologies to lower the barriers of adopting RINA in IoT environments. The architecture relies on a RINA-based distributed application facility (DAF), a RINA southbound driver (SBI), and the RINA L2 VPN. The RINA-based DAF manages RINA nodes along the edge-fog-cloud continuum. The SBI driver SDN enables the hybrid centralized management of SDN switches and RINA nodes. Meanwhile, the RINA L2 VPN allows seamless communication between edge nodes and the cloud to facilitate the data exchange between network functions (NFs). Such integration has enabled a progressive deployment of RINA in current IoT networks without affecting their operations and performance.  
edge computing; IoT; future networks; L2 VPN; NFV; SDN; RINA
## 4 Results
### Results on Class-Imbalanced Datasets
The top three scoring transforms are Rot, SP, and FCS, with SP and FCS being new to this paper; the Sca and Jit transforms often find it difficult to outperform other augmentation methods because they add some perturbation to the original signals. Figure 8: Score for individual transformations. Figure 7: Training curves of LSTM2 with SigAugment-R on the 2016A-50 dataset. Based on the average recognition accuracy of the models, we conducted a comparative analysis of the models and methods. Figure 9 shows the recognition accuracy of various modulation types at various SNRs on the 2016A-50 dataset. We analyze the model with the highest median average accuracy across the three experiments. The data augmentation methods proposed in this paper can effectively improve the performance of the model on class-imbalanced datasets. In particular, for the minority classes (PAM4, QAM16, QAM64, QPSK, and WBFM), training LSTM2 with SigAugment-R improved the average recognition accuracy on these five classes from 3.23% to 51.86%. When the SNR is above 0 dB, LSTM2 with SigAugment-R increased the average recognition accuracy of all classes from 49.94% to 83.38%. Figure 10 shows the results for another model, the SE-MSFN. Comparing with Figure 9, we see that the WBFM signal is a hard sample for both models. The LSTM2 identifies QAM16 better than the SE-MFN. The SE-MFN has better results for QAM64. Both models show significant recognition gains for minority class samples and high SNR cases after using our data augmentation method. These results show that the proposed SigAugment is able to effectively mitigate the effects of class imbalance and low-data regime. Figure 10: Recognition accuracy (%) of the (**a**) SE-MSFN and (**b**) SE-MSFN with SigAugment-R on the 2016A-50 dataset for each modulation class, with the values in parentheses in the legend representing the average recognition accuracy for each class across all SNRs. Figure 9: Recognition accuracy (%) of the (**a**) LSTM2 and (**b**) LSTM2 with SigAugment-R on the 2016A-50 dataset for each modulation class, with the values in parentheses in the legend representing the average recognition accuracy for each class across all SNRs.
## 1 Introduction
### Middleware and Distributed Systems  
A major challenge for cooperation among distributed or cyber-physical systems (CPSs) with different kinds of hardware is the interoperability of the deployed devices. As far as underwater and maritime robotics are concerned, this means that AUVs, Autonomous Surface Vehicles (ASVs), Unmanned Surface Vessels (USVs) or Remotely Operated underwater Vehicles (ROVs) will have to find ways to integrate their heterogeneous capabilities to be able to participate in a mission with a common objective. In this manuscript, autonomous vehicles will always fall within one of the different four categories: AUV, ASV, USV or ROV. AUVs and other types of vehicles belong to different vendors and are designed and manufactured differently, based on either proprietary or open source solutions. Thus, in most cases these vehicles lack the ability to communicate, and hence collaborate with each other, due to different information formats and features that they may use.  
Fortunately, many of these issues can be solved by means of a software layer commonly referred to as middleware. Middleware is the software layer used in distributed systems to hide the underlying complexity and heterogeneity of hardware so that a homogeneous-looking set of facilities, commonly provided as an API, will be provided to the higher, more application-based layers. Although its first apparition and usage was documented back in 1968 [4], it later became increasingly popular as a way to guarantee interoperability between legacy and new systems, and it is nowadays one of the most common solutions to address interoperability regarding the Internet of Things (IoT) [5]. Its importance has been recognized by a myriad of different entities for many different purposes, like application implementations or building an environment for cooperative developments. As an example of this latter aspect, the European-driven middleware platform known as FIWARE aims to create a platform where applications can be developed and integrated in a large distributed system by means of the Application Programming Interface that is offered [6]. Middleware can also be enhanced with semantic capabilities which will enrich the information provided by the devices present in a deployment and aid in their integration by offering a common way to define concepts [7].  
The proposed middleware architecture will work as in any other development classified as a distributed system or a CPS. With regards to this application domain, the need to enable interoperability in heterogeneous robots used in underwater or open sea missions will be dealt with by middleware. Data will also be accessed through the higher levels in the architecture, e.g., by a human operator through a Graphical User Interface (GUI). Note that the middleware among AUVs will hide any issue regarding hardware distribution and will give to the human operator the feeling of working with a local system, as shown in Figure 1.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 4 Results
### Monthly Deformation
In the case of the modelled velocity, the abrupt transition in October relates to the switch of maximum values between the two longwalls (Figure 13) and the difference in the time span for the month because of one missing ascending image in the beginning of October. Nevertheless, the main trend is of linear sinking through the whole area of mining activities during the phase of main subsidence. Calculated according to the Equation (6), the acceleration was frequently close to zero for the modelled point and also varies around zero for DInSAR. Two distinct features are visible: Negative acceleration (\(-0.10\) mm/day) for DInSAR in September 2017 and positive acceleration (0.20 mm/day) for the model in October 2017. Figure 11:0 Amount of maximal subsidence by months detected by DInSAR (blue) and predicted from the model (red), compared with the estimated material extraction from coal seam 503. The black axis values on the right and bottom represent the subsidence and corresponding dates, respectively. The grey axis values on the left and above the graph relate to the amount of the extracted material and the time for these events.
# Influence of Uneven Subgrade Frost Heave on Deformation and Damage of CRTSIII Slab Track
## 3 Computational Model
### Description of Subgrade Frost Heave  
There are many ways to express uneven subgrade frost heave deformation. The research of many scholars has shown that the harmonic function analysis method is very practical [31; 32; 33]. However, considering the restrictions on the use area of CRTSIII slab track, in order to get closer to the actual deformation in the frozen soil region, in this study, the selected input is the frost heave deformation formula obtained by fitting field measured data in the literature [2]. As per the experimental data from [2], the cosine curve can be used to simulate uneven subgrade frost heave. Therefore, the cosine function considered for simulating uneven subgrade frost heave, is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multirow{2}{*}{**Size/m**} & **Density** & **Elastic Modulus** & **Poisson’s** \\  & & **(kg/m\({}^{3}\))** & **(MPa)** & **Ratio** \\ \hline \multirow{6}{*}{CRTSIII slab track structure} & Rail & — & 7830 & 205,900 & 0.3 \\ \cline{2-5}  & Precast slab & 5.6 \(\times\) 2.5 \(\times\) 0.2 & 2440 & 36,000 & 0.2 \\ \cline{2-5}  & Self-compacting concrete (lung boss limit) & 5.6 \(\times\) 2.5 \(\times\) 0.09 (0.7 \(\times\) 1.0 \(\times\) 0.1) & 2400 & 32,500 & 0.2 \\ \cline{2-5}  & Concrete base (groove limit) & 16.99 \(\times\) 3.1 \(\times\) 0.3 (0.7 \(\times\) 1.0 \(\times\) 0.1) & 2400 & 32,500 & 0.2 \\ \cline{2-5}  & Subgrade surface & 0.4 (thickness) & 2100 & 350 & 0.25 \\ \cline{2-5}  & Subgrade underlying & 2.3 (thickness) & 2000 & 260 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation model parameters.  
Figure 5: CRTSIII slab track structure with damage plasticity and reinforcement.  
The displacement load \(U(z)\) can be described as:  
\[U(z)=\frac{A}{2}[1-\cos(\frac{2\pi z}{\lambda})] \tag{22}\]  
Here, \(A\) is the frost heaving wave amplitude, \(\lambda\) is the frost heaving wavelength, and \(z\) is the position coordinates of uneven frost heave.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Adsorption Data
#### 4.2.3 pH Effect  
The cadmium and methylene blue uptake by PHSs, CHS-500 and CHS-700 was evaluated at pH 2, 4, 6, 8 and 10 (Figure 5a,b). PHS, CHS-500 and CHS-700 sorption capacities were greater in a basic solution at pH 8 than in an acidic medium between pH 2 and 6. All the adsorbents recorded a maximum adsorption capacity at pH 8. These results were ascribed to the adsorbents' surface deprotonation, which resulted in enhanced electrostatic interaction and higher adsorption. At lower pH values, cadmium and methylene blue uptake was minimal due to weak electrostatic interactions at acidic pH conditions because  
Figure 4: Contact time effect on (**a**) Cd(II) and (**b**) MB [adsorption conditions: adsorbent mass = 20 mg, volume of solution = 50 mL, solution concentration = 100 mg/L, pH = 8 and temperature = 288 K].  
Figure 3: Initial concentration effect on (**a**) Cd(II) and (**b**) MB [adsorption conditions: adsorbent mass = 20 mg, the volume of solution = 50 mL, duration = 720 min, pH = 8 and temperature = 288 K].  
the functional groups on the surface of the adsorbents were protonated and acquired a positive charge [43].
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 4 Model
### Local Model  
According to [PERSON] and [PERSON] (2018), this paper takes the local model as an attention model based on entity embedding. For an entity mention \(m\), if a word in the context is strongly related to at least one candidate entity, the word is considered important.  
In the candidate generation stage, we can obtain the candidate entity set \(C_{i}=(e_{i1},\dots,e_{il_{i}})\). Then we calculate the score of each candidate entity \(e\in C_{i}\) according to the \(P\)-word window local context \(c=\{w_{1},...,w_{p}\}\) around \(m\). First, we calculate the unnormalized support score of each word in the context; that is, the weight of each word  
\[u(w)=\max_{e\in C_{i}}\mathbf{e}^{T}\mathbf{A}\mathbf{w} \tag{4}\]  
where \(\mathbf{A}\) is a parameterized diagonal matrix, \(\mathbf{w}\) is the word embedding (we use the pre-trained word2 vec word embedding), and \(\mathbf{e}\) is the candidate entity embedding, which is trained based on the co-occurrence counts of the word-entity in Wikipedia (2018). If the word is strongly related to at least one candidate entity, its weight score is relatively high. In addition, it is observed that some words with insufficient information will introduce noise to the local model, so the hard pruning method is used to select \(Q\leq P\) words with the highest weight scores:  
\[\overline{c}=\{w\in c|u\{w\}\in topQ\{\mathbf{u}\}\} \tag{5}\]  
Figure 3: The architecture of the proposed KGEL model. It contains three parts: _Local model_, _WikiEmbs Global model_, and _KGEmbs Global model_. Specifically, in the _Local model_, the similarity calculated by context embedding and entity embedding is used as the local score. In the Global model, the scores between the candidate entities of all mentions in the document are taken as the global score. Among them, in the _WikiEmbs Global model_, entity embedding is obtained through word-entity co-occurrence counts, which consider the semantic information. In the _KGEmbs Global model_, entity embedding is obtained through triples, considering the structural information of the knowledge graph.  
Therefore, the final attention weight is:  
\[\beta(w)=\left\{\begin{array}{cc}\frac{\exp[i[w]]}{\sum\limits_{w\in\bar{c}}\exp[ i[w]]}&\quad\text{if }w\in\bar{c}\\ 0&\quad\text{otherwise}\end{array}\right. \tag{6}\]  
Finally, we can obtain the local scores of the candidate entities:  
\[\Psi(e)=\sum\limits_{w\in\bar{c}}\beta(w)\mathbf{e}^{T}\mathbf{Bw} \tag{7}\]  
where \(\mathbf{B}\) is another diagonal matrix that can be trained.
## 4 Evaluation
### Experimental Results
As shown in Table 6, the performances of Bi-GRU-CRFs-NE [20] and attention-CRFs [8] are inferior to those of others because these models did not use any dictionary look-up features. This reveals that the dictionary look-up features have a significant effect on the improvement of NER performances in Korean. As shown in Table 6, MANE outperformed all of the comparison models, although it did not use correctly POS-tagged sentences as inputs. In the five repeated evaluations of NER, the performance variations of MANE were \(\pm 0.0012\) in F1-scores. As a result, MANE always showed higher F1-scores than all of the previous NER models. The \(p\)-values of F1-scores between MANE and the comparison models were from \(1.67\times 10^{-8}\) to \(0.007594\). This implies that the performance differences are statistically significant at the \(0.05\) level. In particular, MANE performed better than MorpheNE. Moreover, MANE greatly outperformed MorpheNE in memory consumption and prediction time, as shown in Table 7.  
This indicates that the LAN of MANE is more effective and efficient than the CRF of MorpheNE in alleviating error propagation problems. In addition, MANE demonstrated higher performances than MANE-NE. This reveals that the proposed architecture may be a good solution to the error propagation problem. The last experiment demonstrated the effectiveness of pretraining the MA layer using different training data sizes, as shown in Table 8.  
In Table 8, \"static\" means that the parameters in the MA layer were frozen after pretraining using the morpheme dataset, and \"fine-tuned\" means that the parameters in the MA layer were fine-tuned during the second training phase, in which the MA and NER layers were trained using the NE dataset. As shown in Table 8, the more training data learned in the MA layer, the better the performance was in the NE layer. In addition, Table 7 shows that the second training phase affected the improvement in the NER performances.
# A Wideband Reflectarray Antenna for Satellite Application with Low Cross-Polarization
## 2 Unit Cell Structure and Characterization
Figure 3: Step-by-step evaluation of the unit cell. Furthermore, using Irt3 in the fourth case(iv), a reflection phase of 800\({}^{\circ}\) is achieved with insignificant variations in the slope after optimization. Finally, the middle layer in case (iv) is removed, which results in a total phase variation of 400\({}^{\circ}\) in case (v). From the above analysis, case (iv) provides the best performance with parameters given in Figure 1.