esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Contrast-induced acute kidney injury (CIAKI) is a common complication with limited treatments. Intermedin (IMD), a peptide belonging to the calcitonin gene-related peptide family, promotes vasodilation and endothelial stability, but its role in mitigating CIAKI remains unexplored. This study investigates the protective effects of IMD in CIAKI, focusing on its mechanisms, particularly the cAMP/Rac1 signaling pathway. Human umbilical vein endothelial cells (HUVECs) were treated with ionkevol to simulate kidney injury in vitro. The protective effects of IMD were assessed using CCK8 assay, flow cytometry, ELISA, and Western blotting. A CIAKI rat model was utilized to evaluate renal peritubular capillary endothelial cell injury and renal function through histopathology, immunohistochemistry, immunofluorescence, Western blotting, and transmission electron microscopy. In vitro, IMD significantly enhanced HUVEC viability and mitigated ionkevolity by preserving intercellular adhesion junctions and activating the cAMP/Rac1 pathway, with Rac1 inhibition attenuating these protective effects. In vivo, CIAKI caused severe damage to peritubular capillary endothelial cell junctions, impairing renal function. IMD treatment markedly improved renal function, an effect negated by Rac1 inhibition. IMD protects against renal injury in CIAKI by activating the cAMP/Rac1 pathway, preserving peritubular capillary endothelial integrity and alleviating acute renal injury from contrast media. These findings suggest that IMD has therapeutic potential in CIAKI and highlight the cAMP/Rac1 pathway as a promising target for preventing contrast-induced acute kidney injury in at-risk patients, ultimately improving clinical outcomes.  
contrast-induced acute kidney injury; contrast media; intermedin; peritubular capillaries; endothelial barrier; cAMP; Rac1 +
Footnote †: journal: Molecular Sciences
## 3 Results
### Generating the ConceptMap  
The results of the formalization are stored in a ConceptMap. The tuples defined in a ConceptMap are a unidirectional association between source and target. The first code marks the source, in this case a code pertaining to LOINC's _Property_ axis. \"MRat\" is the official abbreviation for a \"Mass Rate\", a measurement of a mass over a time interval (e.g., iron in 24 h urine). This code from the source gets associated with a code in the target. In this case, \"MRat\" is represented by the base unit combination \"s-1.g\" ergo gram per second or, in a more abstract fashion, mass over time. FHIR ConceptMaps generally require there to be an equivalence statement to describe the nature of the mapping. We decided to go with the description \"narrower\" in order to reflect the loss of precision going from _Property_ to base unit representation. The ConceptMap was generated based on a CSV file that can be found in the supplementary materials. The tool Snapper ([[https://ontoserver.csiro.au/site/technical-documentation/snappernap-guide/](https://ontoserver.csiro.au/site/technical-documentation/snappernap-guide/)]([https://ontoserver.csiro.au/site/technical-documentation/snappernap-guide/](https://ontoserver.csiro.au/site/technical-documentation/snappernap-guide/)) (accessed on 29 April 2022)) was used to create the ConceptMap itself. The following is an excerpt of it:  
{  \"code\":\"MRat\",  \"target\":[  {  \"code\":\"s-1.g\",  \"equivalence\":\"narrower\",  \"comment\":\"Propertiescansharethesamebaseunits\"  }  }  } } In our implementation, this ConceptMap accomplishes the important tasks of one, filtering out UCUM recommendations that do not fit the given _Property_; and two, minimizing the effects of invalid example UCUM units in the LOINC data set by being able to detect them.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**LOINC Property**} & \multirow{2}{*}{**Possible UCUM Unit**} & \multicolumn{2}{c}{**UCUM Base Unit**} \\  & & **Representation** \\ \hline Mass & [lb\_av] & g \\ Volume & mL & m3 \\ Volume Content & mL/[lb\_av] & m3-g-1 \\ Time Mass Concentration & ug.h/L & m-3.g.s \\ Frequency & Hz & s-1 \\ Length & m & m \\ Volume/Area & L/m2 & m \\ \hline \hline \end{tabular}
\end{table}
Table 2: This table shows some examples that detail how a LOINC _Property_ might be represented on the instance level (a UCUM unit that can be used for measurements related to that _Property_) and its representation in the underlying model.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 1 Introduction
It is well recognized that power-saving and delays are always a tradeoff. To optimize the tradeoff, several DRX algorithms have been introduced in [48; 49; 50; 51; 52]. On the other hand, PSM and eDRX are two solutions to optimize the device power consumption proposed for EC-GSM, LTE-M, and NB-IoT technologies for delayed insensitive but energy sensitive applications, which have been introduced in 3 GPP Rel.12. In [53], the authors have conducted an energy consumption analysis over an NB-IoT device under different traffic intensities and coverage areas. However, to the best of our knowledge, there is no such algorithm proposed, where both PSM and eDRX have been jointly analyzed to ensure optimum battery lifetime. Therefore, this work is a combined proposal of energy efficient techniques from the perspective of both energy generation and consumption sides. From the generation point of view, we propose PV/BG system architecture for operating a rural Long Term Evolution(LTE) HetNet with a reliable and novel energy sharing policy. To validate the model, a rural area named Saldanga Union Parishad of Debiganj Upazila under the district Panchagarh in Bangladesh has been selected, which is located at \(26^{\circ}7.1^{\prime}0^{\prime\prime}\) North, \(88^{\circ}45.6^{\prime}0^{\prime\prime}\) East. From the energy consumption point of view, a sleep mode-based resource scheduling algorithm has been proposed for both NRT and IoT applications. The key contributions of this study are summarized below:  
* A novel off-grid hybrid solar PV/BG system is proposed for operating an LTE HetNet in a rural area with reliable services. * An energy sharing model through an external low loss resistive power line is proposed for the HetNet cellular environment that ensures the reliability and efficient utilization of RESs. * A resource scheduling algorithm is proposed for the NRT and specifically IoT applications that achieve a large amount of power-savings through permissible delay in comparison with the existing model. * HOMER optimization software is used to obtain optimal system architecture of the proposed framework and to perform the energy yield analysis, cost analysis and carbon footprint analysis. * A Monte-Carlo-based simulation is used to evaluate the model in terms of throughput and energy efficiency under different bandwidths (BWs), allowing the possibility of dynamic traffic demand, inter-cell interference, and shadow fading. The rest of the paper is organized as follows: Section 2 presents the system architecture and mathematical models of the proposed HetNet architecture. Section 3 summarises the system implementation and cost modeling. Section 4 analyses the results with the simulation setup. Finally, Section 5 concludes the paper by summarizing the main outcomes.
## 1 Introduction
In contrast, the CWT-based time-frequency analysis approach provides better resolution both in time and frequency [15]. Meanwhile, a support vector machine (SVM) is a supervised machine learning model that can classify the data. It can be applied to classifying the noises of refrigerant flow by learning the signals identified by the CWT. The SVM tries to obtain a hyperplane for generalization errors to decrease in the learning process. The SVM defines the objective function for learning by using a margin to find the optimal boundary out of a number of hyperplanes. This is to find the hyperplane that maximizes this margin. If it is impossible to classify data linearly, the data can be mapped into a high dimensional space for separation by introducing a slack variable or exploiting a kernel [16; 17]. When the kernel-based SVM is used, an appropriate hyperparameter value should be selected for the kernel function. In general, a grid search method is used to determine the hyperparameter value. This method tries to find optimal parameter values within a fixed range. The classification performance of the SVM can be enhanced once an appropriate kernel hyperparameter is determined by the grid search method and applied to the SVM. Noise classification can be performed using a jury test, but noise can be classified using a hyperplane, without the need for experts, through an SVM. It also has the advantage of being able to consistently evaluate air conditioning refrigerant noise. In addition, if refrigerant noise occurs, it can be identified using CWT, enabling noise control through refrigerant control. To evaluate the performance of SVM with regards to other refrigerant flow noises, noises are modified and compared by jury test. Modified sound sources are produced through amplification/reduction or frequency modulation of the frequency band that characterizes the refrigerant flow noises. This study proposed a method for classifying air conditioner refrigerant flow noise based on the SVM and the CWT. To this end, the first- and second-order moments were used to determine if the flow noises were abnormal signals in Section 3. In Section 4, the occurrence time of refrigerant flow noise were identified by finding the singular points of the CWT coefficient and locating the start and end points by means of listening to sounds. Further, the CWT was used to analyze the time-frequency characteristics in the identified start/end time of refrigerant flow noise. An appropriate kernel hyperparameter was obtained by a grid search method, and the SVM-based classification of refrigerant flow noise was performed by means of 5-fold cross validation in Section 5. In addition, an enhanced method for classification of refrigerant flow noise was presented with the kernel feature vectors of the SVM in the analyzed frequency characteristics. Finally, measured sound sources are produced based on classified refrigerant flow noises to compare the classification accuracy through a jury test and SVM in Section 6.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 7 Conclusions  
In this paper, we proposed a novel multi-hop routing protocol for LPWANs, named FRDR, which aims to reduce transmission disruption probability. FRDR comprehensively considered RFRV, distance to the GW, residual energy, and transmission hops as routing criteria, thereby deriving a low-latency, long-lifetime, and high-success-rate routing decision policy through a DQN-based framework. Simulation results confirmed that, compared with MHR and DQIR, our FRDR significantly reduces transmission disruption probability and extends network lifetime while maintaining a comparable delay. Specifically, when the PDR of MHR and DQIR drops to 0.80, FRDR maintains a higher PDR, achieving a minimum improvement of 14.71% and 18.90%, respectively.  
Our current research focuses on multi-hop routing optimization under standardized scenarios with generalized assumptions. However, real-world deployments introduce non-ideal factors such as edge-positioned GWs, mobile GWs, and asymmetric link conditions, which significantly impact protocol performance. Consequently, future study will focus on addressing challenges arising from these non-ideal factors to enhance the robustness and scalability of the routing protocol in practical deployments. Additionally, field trials will be conducted to evaluate the practical feasibility and performance of the proposed protocol.  
**Author Contributions:** Conceptualization, S.T.; methodology, S.T.; validation, S.T.; formal analysis, S.T.; data curation, S.T.; writing--original draft preparation, S.T.; writing--review and editing, S.T., H.T., J.W. and B.L.; supervision, J.W. and B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to express their sincere gratitude to [PERSON] for her contributions to data curation and valuable discussions throughout the development of this study. We also sincerely thank the reviewers for their critical comments and suggestions for improving this manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 1 Introduction
### Related Works  
The existing literature reviews or surveys on FAS tend to focus on a single topic or lack comprehensiveness. [PERSON] et al. [16] discussed various indicators of face liveness detection (FLD) in detail, providing a reference for devising suitable solutions to face spoofing problems. However, it did not cover DL for FAS. [PERSON] et al. [17] discussed 3D mask face anti-spoofing methods in detail from both software and hardware perspectives. Although DL algorithms were explored, the number of referenced studies was limited. [PERSON] et al. [18] centred on the application of a convolutional neural networks (CNNs) without addressing the generalization of face anti-spoofing. [PERSON] et al. [19] concentrated on DL-based methods and multi-modal fusion face anti-spoofing algorithms, but lacked sufficient a discussion on generalization aspects. [PERSON] et al. [20] mainly paid attention to the bibliometric analysis of PAs based on domain adaptation.  
Recent surveys have provided new insights into FAS. For instance, a review [15] outlined DL techniques but lacked a focus on traditional methods. Similarly, a review on face adversarial attacks [21] analyzed adversarial techniques but overlooked areas like multimodal fusion and domain generalization. The comprehensive review [22] offered an overview of both DL-based and traditional hand-crafted approaches, but did not discuss cross-domain learning in-depth. Furthermore, a review [23] concentrated on liveness detection in smartphone scenarios yet did not address the broader FAS field. Additionally, the review [24] mentioned domain generalization issues in FAS but did not thoroughly analyze the specific implementation challenges or limitations involved.  
Table 1 summarizes the characteristics and limitations of the existing literature reviews on FAS. Overall, current reviews in the FAS field tend to have a narrow focus and lack a holistic and integrative perspective. Key areas, such as the systematic exploration of domain generalization, multimodal fusion, the spatiotemporal efficiency of algorithms, and the unified detection of presentation attacks and deepfakes remain underexplored. In addition, discussions on generalization capability, handling of complex attack scenarios, and emerging approaches such as self-supervised and few-shot learning could be further expanded. Our review aims to provide a more comprehensive and in-depth analysis of the FAS field, offering valuable insights to guide future research directions.
## 3 Results
### Soil Buffering Capacity  
The effects of soil alone, CLB, and B1, B2, and B3 on soil pH buffering capacity are shown in Figure 2 and Table 21. Regardless of treatment, there was a negative linear relationship between soil pH and added mmol H\({}^{+}\). Soil alone showed the lowest soil pH and pH buffering capacity (Figure 2).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Treatment** & \multicolumn{2}{c}{**Leggni Equation**} & \multicolumn{2}{c}{**R\({}^{2}\) Value**} \\ \hline Soil & y = 9.576 + 0.0034x & 0.9758 ** \\ CLB & y = 8.285 + 0.0107x & 0.9991 ** \\ B1 & y = 10.066 + 0.0056x & 0.9976 ** \\ B2 & y = 10.698 + 0.0061x & 0.9965 ** \\ B3 & y = 10.548 + 0.0068x & 0.9979 ** \\ \hline  & \multicolumn{2}{c}{**P**} \\ \hline Soil & y = 0.217 + 0.0037x & 0.9678 ** \\ CLB & y = 19.293 + 0.0058x & 0.9545 ** \\ B1 & y = 1.610 + 0.0036x & 0.9921 ** \\ B2 & y = 2.558 + 0.0042x & 0.9950 ** \\ B3 & y = 4.106 + 0.0043x & 0.9928 ** \\ \hline  & \multicolumn{2}{c}{**K**} \\ \hline Soil & y = 4.100 + 0.0045x & 0.9559 ** \\ CLB & y = 243.808 + 0.0055x & 0.8549 ** \\ B1 & y = 21.183 + 0.0052x & 0.9521 ** \\ B2 & y = 37.163 + 0.0049x & 0.9415 ** \\ B3 & y = 51.267 + 0.0039x & 0.9447 ** \\ \hline \hline \end{tabular} Note: ** significant at P = 0.01, * significant at P = 0.05, ** not significant at P = 0.05.  
\end{table}
Table 20: Regression equations and R\({}^{2}\) values relating added nitrogen, phosphorus, and potassium as well as desorbed nitrogen, phosphorus, and potassium in treatments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Treatment** & \multicolumn{2}{c}{**Initial pH**} & \multicolumn{2}{c}{**pH Buffering Capacity (mmol H\({}^{+}\) kg\({}^{-1}\) pH\({}^{-1}\) Sample)**} \\ \hline Soil & 4.52 & 17.86 \\ CLB & 7.61 & 62.50 \\ B1 & 6.41 & 29.41 \\ B2 & 6.89 & 27.78 \\ B3 & 7.06 & 31.25 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Effects of treatments on initial suspension pH and pH buffering capacity.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Treatment** & \multicolumn{2}{c}{**Langmuir**} & \multicolumn{2}{c}{**Freundlich**} \\ \cline{2-5}  & \multicolumn{2}{c}{**K\({}_{\text{L}}\) (\# g\({}^{-1}\))**} & \multicolumn{2}{c}{**q\({}_{\text{m}}\)**} & \multicolumn{2}{c}{**MBC (\#g g\({}^{-1}\))**} & \multicolumn{2}{c}{**K\({}_{\text{F}}\) (\# g\({}^{-1}\))**} & \multicolumn{2}{c}{**\(\frac{1}{n}\)**} \\ \hline Soil & nd & nd & nd & 23.01 & 0.911 \\ CLB & 0.0010 & 441 & 0.44 & nd & nd \\ B1 & nd & nd & nd & 1.46 & 1.367 \\ B2 & nd & nd & nd & 0.41 & 1.550 \\ B3 & 0.0020 & 1866 & 3.73 & nd & nd \\ \hline \hline \end{tabular} Note: nd: not determined.  
\end{table}
Table 19: Variables from Langmuir and Freundlich isotherms for potassium adsorption of different treatments.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 3 Proposed Architecture
### Satellite Node Selection
#### 3.3.4 Planning Objectives
\[SYNC_{sig}^{\min}\leq\sum_{i=1}^{N}\sum_{j=1}^{N}\left(Sync_{n_{k}}^{S_{ij}} \right)\leq SYNC_{sig}^{\max} \tag{21}\]  
The \(Sync_{n_{k}}^{S_{ij}}\) represents the relationship between the two satellites \(i\) and \(j\) in the time period \(n_{k}\), and \(SYNC_{sig}^{\min}\) and \(SYNC_{sig}^{\max}\), respectively, represent the minimum and maximum time relationship required for signal processing [27]. Generally, only the minimum relationship represented by \(SYNC_{sig}^{\min}\) needs to be considered, but the load balancing rate of resources will be considered at the same time to maintain the resource balance between space-based systems, so it is constrained by \(SYNC_{sig}^{\max}\) to avoid taking up too many satellites.
# Static Liquefaction Assessment Combining Shear Wave Velocity, Peak Strength and Soil Grading
###### Abstract  
A large set of undrained compression triaxial tests was carried out on different types of cohesionless soils, from sands to silty sands and silts. Shear wave velocity measurements were also carried out. These tests exhibit distinct state transitions ranging from flow liquefaction to strain softening or strain hardening. With the purpose of defining a framework to assess soil liquefaction, it was found that the ratio between the shear wave velocity (\(V_{S0}\)) and the peak undrained deviatoric stress (\(q_{\text{peak}}\)), \(V_{S0}/q_{\text{peak}}\), could be accurately used to define a boundary between liquefaction and strain hardening for sands and between strain softening and strain hardening for silty sands and silts. Since this ratio is a function of the tested material, the prediction of these boundaries can be made as a function of soil grading, namely via the coefficient of uniformity, \(C_{U}\). Despite not being regarded as a strong geomechanical parameter, \(C_{U}\) is easily determined from a grain-size distribution test and has an empirically proven correlation with critical state parameters.  
Keywords:liquefaction; shear wave velocity; peak undrained deviatoric stress; soil grading; coefficient of uniformity +
Footnote †: journal: _Sciences_
## 3 Innovative SHM Techniques
### Distributed Sensors
#### 3.1.3 Self-Sensing Materials  
The novel concept of \"smart bricks\" for the strain-based structural health monitoring of masonry structures is currently under investigation by several researchers [139; 140; 141; 142; 143] and has potential for applications in CHS. Smart bricks are modified fired clay bricks made to be electrically conductive and piezoresistive by adding a suitable conductive filler to  
Figure 9: An RFID sensor equipped with a piezoelectric sensor for crack detection ((**a**), from [136]; capacity piezoelectric sensors embedded in mortar joints (**b**), from [137]).  
the clay and embedding or installing electrodes on the surface of the bricks during their manufacturing process (Figure 10a). Smart bricks can be used for strain sensing and damage detection in masonry structures subjected to in-plane compressive loading. Particularly, smart bricks will output changes in their electrical resistance that are proportional to local changes in their stress-strain conditions (assuming the bricks remain in their elastic range of deformation) when a damage or a crack forms within a masonry wall, for instance, due to the activation of a local failure mechanism after an earthquake (Figure 10b). These bricks may be installed when replacing damaged bricks in existing masonry structures following the so-called \"scuci-cuci\" technique for restoring the wall's structural continuity, as mentioned in the previous section.  
[PERSON] et al. [139], for the first time, proposed titanium-doped smart bricks with embedded electrodes made of special steel, experimentally demonstrating their effectiveness in detecting minimal strain variations occurring within a small-scale masonry wall subjected to eccentric compression loads (both in small loading conditions and at the ultimate limit state).  
Analogously, [PERSON] et al. [140] proposed a new formulation for smart bricks using stainless steel microfibers as conductive fillers and a copper plate placed horizontally on the top and bottom of the external brick's surfaces as electrodes. The authors concluded that adopting copper electrodes led to more reliable and repeatable results than did the use of embedded electrodes.  
Currently, the use of smart bricks for SHM purpose has been tested for (i) the monitoring of strain in masonry panels under compression loads [141], (ii) detecting and locating damage caused by earthquakes in masonry buildings [142], and (iii) strain monitoring and early crack detection in masonry structural elements subjected to in-plane shear loading [143]. The outcomes of this research work evidenced that the smart brick technology is mature enough to be tested in real full-scale masonry buildings.