esa-sceva/satcom-chunk-collection huggingface dataset:
# Promising Novel Biomarkers in Cardiovascular Diseases
## 1 Introduction  
Cardiovascular diseases (CVD) still account for the majority of global morbidity and mortality and, therefore, represent a significant economic burden not only in economically sovereign countries [1, 2]. Thus, increased attention has been paid to biomarkers to better assess the individual risk of developing cardiovascular disease, along with associated complications as well as disease severity. Already established biomarkers, most importantly, brain-type natriuretic peptides (BNPs) and C-reactive protein (CRP), possess well-researched prognostic and diagnostic value. However, they are not universally applicable [3, 4, 5] and the influence of concomitant factors (age, renal function, gender etc.) on \"traditional\" cardiac markers is yet to be fully understood. Recently, the importance of chronic inflammatory processes in cardiovascular disease has been highlighted and consequently focus has been set to cardiac biomarkers indicating inflammation.  
The goal in evaluating the use of novel cardiovascular biomarkers is to find solutions for clinical problems, such as early recognition of cardiovascular disease, monitoring of chronic heart failure (HF), prognosis after myocardial infarction or obtaining information about the response to specific therapies. An array of novel cardiac markers are potential candidates for answering these questions; however, whether a single, \"lone-ranger\"-cardiovascular biomarker is preferable, or a \"multi-marker\" approach, where the strengths of different entities are combined to provide an optimal solution in a diverse set of requirements, is not yet known [6].  
In this review, we sought to gather and summarize the current state of scientific knowledge on the strengths and weaknesses of four different, novel cardiovascular biomarkers (sST2, H-FABP, GDF-15 and suPAR) and their role in identifying inflammatory processes in CVD.
# Quantum Computing for Healthcare: A Review
## 3 Applications of Quantum Computing for Healthcare
### Pricing of Diagnosis (Risk Analysis)  
Creating pricing strategies is considered one of the key challenges that contribute to the complexities of the healthcare ecosystem [67]. In pricing analysis, quantum computing helps in risk analysis by predicting the current health of patients and predicting whether the patient is prone to a particular disease [68]. This is useful for optimizing insurance premiums and pricing [1]. A population-level analysis of disease risks, and mapping that to the quantum-based risk models, could help in computing financial risks and pricing models at a finer level. One of the key areas which could support pricing decisions is the detection of fraud; healthcare frauds cost billions of dollars in revenue [69]. In this regard, traditional data mining techniques offer insights into detecting and reducing healthcare fraud. Quantum computing could provide higher classification and pattern detection performance, thus uncovering malicious behavior attempting fraudulent medical claims. This could in turn help in better management of pricing models and lowering the costs associated with frauds [70]. Quantum computing can substantially accelerate pricing computations as well, resulting in not only lowering the premiums but also in developing customized plans [71].
## 1 Introduction
There are publicly available population grid datasets, such as the Gridded Population of the World (GPW) [35], Global Rural Urban Mapping Project (GRUMP) [36], LandScan [37], WorldPop [38], etc. These population datasets are mainly created using areal weighting method, intelligent interpolation method, random forest algorithm, etc. Since these datasets simulate population distribution on the global scope, the modeling conditions vary significantly in different regions, making it difficult to ensure model accuracy in areas with complex environments [39]. [PERSON] et al. [40] found that LandScan performs well in modeling urban population distribution but has lower reliability in rural areas. [PERSON] et al. [41] compared the differences between LandScan and GPWv3 datasets in many regions and found that LandScan has a better ability to represent the heterogeneity of population spatialization. [PERSON] et al. [42] compared and analyzed the errors of GPWv3, GRUMPv1, WorldPop, and China Specific Population Grid (CnPop) at the township scale. The results show that the WorldPop dataset has the highest accuracy, but it also has large errors in hilly areas such as the Hengduan Mountains. In summary, the random forest algorithm can integrate multi-source geographic information data, effectively modeling the complex relationships between population data and spatial distribution indicators, and perform population grid predictions at various resolutions. However, the work of domestic and international scholars primarily focuses on aspects such as multi-source data fusion during the random forest modeling process, remote sensing data processing, and the generation of population datasets with different resolutions. There has been limited exploration into the impact of different parameter optimization methods within the random forest modeling process on population spatialization. Furthermore, the publicly available datasets still require further improvements in accuracy in certain regions. Therefore, this paper will further enrich data sources, improve data quality, and conduct population spatialization modeling research by combining multi-source remote sensing data and multi-source remote sensing data. sensing geographic information data and utilizing the currently prevalent random forest algorithm. The primary focus of the article lies in the meticulous exploration of methodologies for refining and adjusting the parameters of the random forest model. Special attention is given to scrutinizing the impact of various parameter optimization techniques on the model's accuracy. Subsequently, by combining cross-validation methods, the optimal model parameters will be selected to enhance the model's structure and improve predictive accuracy. Finally, the population spatialization model is constructed based on optimal parameters, and a spatial population distribution dataset of Sichuan Province at the 1 km resolution is generated. At the same time, the dataset developed in this study is compared with public datasets such as GPW, LandScan, and WorldPop for verification.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Availability
#### 3.3.3. Fault Management  
According to [41], Fault Management is \"the management of the abnormal operation of a computer system\" and \"the purpose of fault management is to return a computer system to normal operation\". Fault Management includes all the necessary steps from the detection of a fault to its removal from the system.  
Figure 8: Hierarchical graph of Availability property and relevant patterns.  
Authors in [42] present a series of such steps as part of the so-called \"flow of fault management\" in networks: (i) collect alarms, (ii) maintain customer satisfaction through immediate action, (iii) filter and correlate the alarms, (iv) diagnose faults through analysis and testing, (v) determine a plan for correction and implement it, (vi) verify the fault is eliminated, and (vii) record data and determine the effectiveness of the current fault management function.  
Finally, widespread fault-tolerant techniques for distributed networks, along with more efficient methods, are available in the literature [43]. Such techniques include Simple Network Management Protocol (SNMP) and TCP/IP, clustering SNMP agents, and replication of crashed agents by peer cluster [44], application of k-nearest neighbor algorithm for poison message failure [45], usage of MultiRouter Traffic Graphing for the creation of a network monitoring tool [46], a policy-based application for fault identification and action decision [47], and an eXtensible Markup Language (XML) notation for the specification of dependencies between managed resources [38].
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 3 Results
### Trend Component Displacement Prediction  
The training progress is shown in Figure 12, where RMSE and loss decrease and stabilize with increasing iterations. Figure 13 displays the prediction results, with the evaluation metrics shown in Table 6. RMSE = 0.94794, MAE = 0.46536, and R\({}^{2}\) = 0.99755 validate the effectiveness of the GRU network in trend component prediction. During the training phase, the predicted and true values align closely, with nearly overlapping curves, indicating strong fitting capability on existing data. In the testing phase, despite using unseen data, the prediction curves closely match the true values with accurate overall trends, demonstrating robust generalization. Even in phases with rapid changes, GRU maintains stable trend tracking with minimal errors.  
Figure 11: Relationship between periodic displacement and rainfall.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rainfall** & **GRD** \\ \hline Current Rainfall & 0.7008 \\ Previous two-day rainfall & 0.5770 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GRD results.  
Figure 12: Training progress diagram.  
Figure 13: Prediction results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **RMSE** & **R\({}^{2}\)** & **MAE** \\ \hline Training Set & 0.14544 & 0.99984 & 0.11474 \\ Test Set & 2.0976 & 0.89838 & 1.8645 \\ Overall Data & 0.94794 & 0.99755 & 0.46536 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation metrics of the GRU model.  
consistently adheres to the true value curve throughout the prediction period, particularly excelling in rapid growth phases with superior trend capture capability. Quantitative metrics (Table 7) show that GRU's RMSE (0.94794) is significantly lower than the RMSE values of the other models, indicating minimal prediction error. Its R\({}^{2}\) (0.99755) reflects near-perfect data variation explanation, while its MAE (0.46536) confirms the smallest average deviation from observed values. All models perform similarly in the early stages, but during rapid late-stage growth, ARIMA exhibits clear underfitting due to limited linear modeling for complex nonlinear sequences. The LSTM predictions are systematically low with delayed responses at trend inflection points, suggesting potential overfitting or insufficient learning. TCN fails to capture rapid displacement increases, revealing inadequate mid-to-long-term trend modeling.
## 3 Methodology  
### Study Scope  
In this research, the scope was focused on building energy consumption during the operational phase, which accounts for approximately 70%-80% of the whole building energy consumption during its life cycle (Figure 6). According to the design standard for the energy efficiency of residential buildings in Severe Cold and Cold regions (JGJ 26-2010), energy consumption for lighting, heating, cooling, ventilation, and appliances is simulated using the commercial software IES (Integrated Environmental Solution)-VE. During the simulation process, the following factors were investigated as keynotes.  
1. Building materials. The initial building constructed in RC was simulated as a reference, and the building material was then replaced by the two bio-based building materials, namely CLT and straw bales, in the following simulation process. The energy efficiency of the three building materials was studied and compared.
2. Heating load. According to the existing research, heating energy consumption is dominant in the Cold and Severe Cold regions [62]. Reduction of the heating energy consumption in the building is effective in lowering the overall energy consumption in these regions. The thermal performance of the three materials during heating was studied.
3. Building orientation and height. In the Cold and Severe Cold regions, building orientation is frequently planned to take maximum advantage of daily and seasonal variations of sunlight. In the simulation process, the main orientation of the studied residential building was changed from the east to the west in steps of 10 degrees. The influence of building orientation on building energy consumption and the differences in energy efficiency for each floor were also examined.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**RC**} & **CLT** & **Straw Bale** \\ \hline \multirow{4}{*}{Rooof} & \multirow{4}{*}{\begin{tabular}{c} Cement (plastic/3 cm) \\ Connect radiated energy(40 mm) \\ Pressset1.(plastic/plastic/30 mm) \\ Vermiculative aggregate(60 mm) \\ EPS installation (10 mm) \\ Redivered correct(100 mm) \\ Pissset1.(plastic/righetti(10 mm) \\ Pissset1.(plastic/righetti(10 mm) \\ \end{tabular})} & \
\begin{tabular}{c} Cement phase(35 mm) \\ - Connect radiated energy(40 mm) \\ Pissset1.(plastic/righetti(10 mm) \\ \end{tabular})} &
\begin{tabular}{c} Cement phase(35 mm) \\ - Connect radiated energy(40 mm) \\ Pissset1.(plastic/righetti(30 mm) \\ \end{tabular}) \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Details of roof, external wall, and ground designs in Beijing.
# Highly Linear Loaded-Line Phase Shifter Utilizing Impedance Transformer and PIN Diode
## 5 Discussion  
A comparison of the key parameters of the proposed design with existing state-of-the-art solutions is summarized in Table 2. The results clearly demonstrate that the proposed phase shifter achieves superior linearity performance.  
Beyond the comparison, this work highlights important design trade-offs inherent in optimizing phase shifters for shared high-power Tx/Rx systems. Additionally, small parasitic variations among PIN diode samples were found to have a magnified impact under reduced impedance, emphasizing the value of precise diode characterization. The proposed methodology, though demonstrated here around 2 GHz for cellular phased arrays, is directly scalable to higher-frequency systems such as C-band radar or 5G mm wave arrays with appropriate coupler scaling and diode selection. Future work could explore integration with multi-bit phase controls or advanced switching elements like GaN PINs to further enhance linearity and power handling. Scaling to multi-bit implementations can be achieved by cascading additional loaded-line sections along the main transmission line, each independently biased to enable discrete phase control. Careful DC network isolation and impedance tuning are required to preserve linearity and minimize insertion loss.  
The measured IIP3 exceeded +100 dBm, constrained by the test setup linearity. This practical limitation suggests that the actual device linearity may be even higher. Such extremely high IIP3 ensures ample margin for integration into phased array systems, where typical system-level requirements are significantly lower.  
Future investigations could extend this work by characterizing the design under temperature variations, which affect PIN diode carrier dynamics and can impact both insertion loss and linearity. Similarly, adapting the design for wideband applications would involve optimizing the impedance transformers and accounting for frequency-dependent parasitic effects through broadband modeling and simulation.
## 3 Optimization Function
### Optimization Matrix and Search Space  
The optimization matrix comprises the variables to be optimized. Its dimension is \(n\) x \(d\), where \(n\) represents the maximum number of agents selected for optimization, i.e., nest for CS, particles for PSO and individuals or population for DE algorithm, and \(d\) refers to the total of variables (i.e., size of the dimensional problem). The search space (SS) refers to the domain of the function to be optimized.  
The optimization matrix for PSO and DE algorithms is defined in (15). It includes the FLC parameters, such as MFs of inputs (\(P_{E}^{3H*}\) and \(SOC\)) and output (\(P_{FLC}\)), the output universe of discourse \(x_{k(-)(+)}\), and the rule base (\(RB\)).  
\[x=\left[\begin{array}{c}x_{1 PE},x_{1 SOC},x_{1 PFLC},x_{1k(-)(+)},x_{1 RB}\\ \vdots\\ x_{nPE},x_{nSOC},x_{nPFLC},x_{nk(-)(+)},x_{nRB}\end{array}\right] \tag{15}\]  
Table 2 presents the number of variables to be optimized for each parameter, which gives a total of 72 (i.e., d = 72). In short, both inputs comprise five fuzzy subsets and 11 variables (i.e., three variables for MFs NS, ZE, and PS, and one variable for MFs NB and PB). The FLC output comprises 9 fuzzy subsets and 23 variables (i.e., three variables for MFs NM, NS, NSS, ZE, PSS, PS, and PM, and one variable for MFs NB and PB). The FLC rule base comprises 25 rules, thus, 25 variables. Finally, this study considers two variables to define the universe of discourse of the FLC output. Note that this study assumes a fixed universe of discourse for input variables.  
Finally, the SS limits are defined based on previous studies [40; 41]. The SS of each FLC parameter is presented below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Number of Variables** \\ \hline Membership functions (MF) for the input \(P_{E}^{3H*}\) & 11 \\ MFs for the input \(SOC\) & 11 \\ MFs for the output \(P_{FLC}\) & 23 \\ \(x_{k(-)(+)}\) & 2 \\ Rule base & 25 \\ Total (\(d\)) & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of variables for each fuzzy logic control (FLC) parameter.  
\[x_{P_{E}}=\left\{x_{P_{E}}\in S{S}_{x_{P_{E}}}\mid-6\leq x_{P_{E}}\leq 6\right\} \tag{16}\]  
\[x_{SOC}=\left\{x_{SOC}\in S{S}_{x_{SOC}}\mid 50\leq x_{SOC}\leq 100\right\} \tag{17}\]  
\[x_{k_{(-)(+)}}=\left\{x_{k_{(-)(+)}}\in S{S}_{x_{k_{(-)(+)}}}\mid 0\leq x_{k_{(-)(+)} }\leq 10\right\} \tag{18}\]  
\[x_{P_{FLC}}=\left\{x_{P_{FLC}}\in S{S}_{x_{P_{FLC}}}\mid k_{(-)}\leq x_{P_{ FLC}}\leq k_{(+)}\right\} \tag{19}\]  
\[x_{RB}=\left\{x_{RB}\in S{S}_{x_{RB}}\mid 1\leq x_{RB}\leq 9\right\} \tag{20}\]
# Wearable Plasma Pads for Biomedical Applications
## 3 Results and Discussion
### Exogenous Nitric Oxide and Capillary Tube Formation in Hdmvecs Induced by the Plasma Pad  
Plasma is known to catalyze and regulate biochemical activities and cellular responses such as proliferation, differentiation, and cell death or apoptosis [36]. The responses may be due to the ROS and RNS generated by non-thermal plasma [37]. Among the various reactive species, NO can modulate angiogenesis, resulting in the promotion of wound healing. Therefore, we analyzed exogenous \(\text{NO}_{2}^{-}\) generated from plasma by using Griess reagents. As shown in Figure 5a, plasma treatment significantly increased exogenous \(\text{NO}_{2}^{-}\) in both DPBS and cell culture media in a time-dependent manner, and the amount of generated \(\text{NO}_{2}^{-}\) reached a maximum 60 mM with 10 min of plasma treatment. This indirectly indicates that the NO generated from plasma plays a key role in vascularization and wound healing.  
Generally, vascularization or angiogenesis plays a key role in both wound healing and tissue recovery. Non-thermal plasma recently emerged as a useful method in biomedical applications due to the ROS and RNS produced. With exogenous NO data, angiogenesis activity was also confirmed  
Figure 4: In vitro and in vivo biocompatibility of the plasma pad. (**a**) Quantitative analysis of in vitro cytotoxicity of the plasma pad on GFP-transfected human keratinocytes (HaCaT cells) and mouse fibroblasts (L-929 cells). (\(n=4\), * \(P<0.05\) compared to control); (**b**) Images of plasma-treated GFP-transfected HaCaT cells and L-929 cells. The images are magnified from those inserted in each picture taken with a 4\(\times\) optical lens. Scale bars are 100 μm; (**c**) Schematic illustration for evaluating the in vivo skin irritation test using the plasma pad; (**d**) Photographs and histological images of mouse skin at day 2 post-plasma treatment for 10 min. Scale bars represent 200 μm.  
with plasma treatment. Although angiogenesis is a very complex process, capillary tube formation by endothelial cells is a key step [38; 39].  
To evaluate the potential for angiogenesis with plasma treatment, a capillary tube formation assay was performed using HDMVECs on Matrigel for 8 h. As shown in Figure 5b, the HDMVECs treated with plasma formed well-defined capillary tubes with enhanced alignment and organization in a time-dependent manner; moreover, the total length of the capillary tubes also increased with plasma treatment, while the negative control HDMVECs formed randomly short and disconnected capillary tubes. This result demonstrates that plasma treatment of HDMVECs may have potential for enhancement of vascularization in the field of tissue engineering.
## 5 Applications
### Food  
Besides wood, also food products, and in particular fruits, have been investigated using optical techniques (mainly NIR spectroscopy) [153; 2; 154], and in particular broadband TD-DOS. Figure 21 displays the absorption and reduced scattering spectra of different fruits and vegetables [155]. The absorption peak of chlorophyll around 670 nm and of water around 980 nm are clearly visible. In the case of kiwifruit, chlorophyll content is so high that only the falling edge of the 670 nm chlorophyll peak is measurable. That initial broadband spectroscopy of food products by TD-DOS permitted to identify the best operating wavelengths for compact off-the-field systems applied to the non-destructive quality assessment of food [156].  
Differently from standard NIR spectroscopy, TD-DOS is largely independent of the optical contact or surface pigmentation. Figure 22 clearly shows that the absorption (left) and reduced scattering (right) spectra of different varieties of apples are not affected by skin removal [157]. This property is due to the physics of TD-DOS, since the photon temporal distribution is determined by the combined effect of scattering (creating a spreading of photon paths) and absorption (ruling the survival probability of each random path), as discussed and shown in Section 2. Therefore, thin superficial layers or simply source or contact instabilities will simply change the signal amplitude without affecting the temporal shape, therefore with no impact whatsoever on the retrieved optical properties.  
Figure 20: Quantification of monomer uptake in softwood samples. The clear increase in the absorption spectrum (**a**) of the natural wood around 1100–1200 nm following soaking in the monomer bath (treated wood) is clearly reflecting the absorption spectrum of the monomers (MIX) [151]. The concentration of the monomer at different positions along the wood sample (**b**) demonstrates a precision in monomer assessment better than 1% in mass [152].  
Figure 21: (a) Absorption and (b) reduced scattering spectra of different intact fruits and vegetables [155].  
Figure 22: Absorption (left pane) and scattering (right pane) spectra of a Golden Delicious (top row), a Granny Smith (middle row), and a Starking Delicious apple (bottom row) before (filled diamonds), and after (empty diamonds) skin removal [157].  
Another field of food quality control and food safety is in the area of dairy products. Several studies have been conducted in this area using photon time-of-flight NIR spectroscopy, enabling independent measurements of the NIR absorption and scattering spectra [158; 159; 160; 161].