esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FPHs for Human Functional Benefits
### FPH with Bone and Skin Health Implications  
FPHs have emerged as powerful multifunctional ingredients for skin health, with growing evidence supporting their role in combating photoaging and pigmentation disorders [41; 42]. Sourced from a variety of marine species, FPHs are rich in bioactive peptides that exert a wide range of beneficial effects, including antioxidant, anti-inflammatory, anti-apoptotic, and anti-melanogenic actions. These peptides not only help preserve extracellular matrix integrity and enhance skin hydration but also protect against UV-induced oxidative stress and DNA damage, making them ideal candidates for functional foods, nutraceuticals, and cosmeceuticals [43].  
Recent studies have illuminated the molecular mechanisms behind these effects. [PERSON] et al. [44] demonstrated that gelatine peptides from Pacific cod skin inhibited MMP-1, MMP-3, and MMP-9 by suppressing the MAPK pathway, thereby preserving collagen and enhancing dermal structure in UV-exposed skin. Similarly, [PERSON] et al. [45] found that silver carp scale-derived peptides (SCPs1) inhibited melanogenesis in B16 melanoma cells via downregulation of the cAMP-CREB-MITF axis. These peptides also exhibited strong antioxidant activity by reducing reactive oxygen species (ROS) and boosting intracellular glutathione levels, positioning them as dual-function agents for both skin brightening and oxidative protection.  
Furthering this line of research, [PERSON] et al. [46] investigated peptides from silver carp bones (FBBPs), reporting cytoprotective effects in UVB-irradiated skin cells, suppression of TNF-\(\alpha\) in inflamed macrophages, and significant reductions in melanin production and tyrosinase activity. Complementing these findings, [PERSON] et al. [47] identified three peptides--TCP3, TCP6, and TCP9--from skipjack tuna, which protected keratinocytes from UVB damage by stabilising mitochondrial membrane potential and regulating apoptosis-related markers through activation of the Nrf2-Keap1 pathway.
## 2 Materials and Methods
### Post-Stroke Participants
#### 3.2.2 Glass Grip
\begin{table}
\begin{tabular}{l c c c} \hline
**Participant** & **Condition** & **LH Flex PeakAmp (V)** & **RH Flex PeakAmp (V)** \\ \hline HER-01-001 & Non-actuated & 1.171659 & 1.042512 \\ HER-01-001 & Left Actuation & 0.889562 & 1.801664 \\ HER-01-001 & Right Actuation & 1.796198 & 2.546574 \\ HER-02-001 & Non-actuated & 1.593946 & 1.052788 \\ HER-02-001 & Left Actuation & 0.835917 & 1.644414 \\ HER-02-001 & Right Actuation & 0.682261 & 2.097046 \\ HER-02-002 & Non-actuated & 1.223903 & 1.329954 \\ HER-02-002 & Left Actuation & 1.455255 & 3.152663 \\ HER-02-002 & Right Actuation & 1.651295 & 3.163606 \\ \hline \end{tabular}
\end{table} 
Table 11: Time-domain peak amplitude feature for flex sensors—glass grip. Figure 17: HER-02-001—EMG and accelerometer data FFT spectra—glass grip: (**a**) right hand and (**b**) left hand. Figure 19: Glass grip exercise—PCA time and frequency domain features. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Participant** & **Condition** & \begin{tabular}{c} **LH Accel Dominant** \\ **Freq (Hz)** \\ \end{tabular} & \begin{tabular}{c} **LH Accel Total** \\ **Power (\(V^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **RH Accel Dominant** \\ **Freq (Hz)** \\ \end{tabular} &
\begin{tabular}{c} **RH Accel Total** \\ **Power (\(V^{2}\))** \\ \end{tabular} \\ \hline HER-01-001 & Non-actuated & 0.099562 & 1878.102515 & 0.099562 & 1888.174548 \\ HER-01-001 & Left Actuation & 0.165937 & 1890.988507 & 0.099562 & 1887.806243 \\ HER-01-001 & Right Actuation & 0.099960 & 1830.000000 & 0.099960 & 1830.000000 \\ HER-02-001 & Non-actuated & 0.100180 & 1890.451842 & 0.100180 & 1890.495245 \\ HER-02-001 & Left Actuation & 0.268817 & 1889.718931 & 0.100806 & 1874.682196 \\ HER-02-001 & Right Actuation & 0.298686 & 1890.608011 & 0.099562 & 1832.358876 \\ HER-02-002 & Non-actuated & 0.133574 & 1889.471475 & 0.100180 & 1890.800619 \\ HER-02-002 & Left Actuation & 0.862870 & 1890.057772 & 0.365060 & 1878.191281 \\ HER-02-002 & Right Actuation & 0.333934 & 1890.839838 & 0.233754 & 1889.583496 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Frequency-domain features for accelerometer sensors—glass grip.
## 4 Primary Findings
### Holistic Analysis of Findings
#### 4.1.3 Critical Factors
Many companies suffered from a low level of ICT in-country and poor internet quality (Q14) [68]. Other companies highlighted the problem of disintegration between implemented I4.0 projects (Q15). Lastly, interviewees also discussed **government-related** factors. Firstly, the absence of flexibility in regulations slowed many implementation processes. Legislation, along with generally high bureaucracy, may impede the deployment of ready technologies (Q16). Another interviewee acknowledged governmental support in terms of tax deductions and financial leasing. However, this support is difficult to obtain because of corruption, and the feasibility of such programs is questionable (Q17). There are many roadmaps but no clear tools for their implementation. (Interviewee 13)  
Additionally, the COVID-19 lockdown influenced the identified factors in various ways. Respondents report that human resources and cultural factors have benefited from the pandemic. The remote work conditions forced many employees to become more digitally literate and techno-friendly. Contrarily, technical aspects suffered an unexpectedly heavy extra-load on the ICT infrastructure. Moreover, the ongoing coronavirus crisis amplified the financial issues associated with digital transformation.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 2 Principles of Power Flow in Power Systems
### MT-HVDC Systems Power Flow  
The main aim of MT-HVDC systems PF is to determine the DC voltage magnitude at each converter station and the PF within the DC grid. Considering \(n\) converter stations in MT-HVDC systems, the following items should be considered to derive the PF equations for the \(i\)th converter station of MT-HVDC systems.  
* Interface of the converter station with the AC grid
* AC side of the converter station
* Interface of the converter station AC and DC sides
* DC side of MT-HVDC systems
* Control modes of the converter station
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 5 Discussion
The current study revealed the uncertainties in time factor determination and further study will be conducted taking into account the DInSAR results. Local geological conditions that may result in shifting the location of mining subsidence also have an influence [16; 58]. The standard Knothe model idealises the ground deformation process which result in discrepancies between predicted and observed subsidence. As is described in [28], the uncertainties of the model parameters bring significant influence on the model accuracy. The coefficient of variability (standard deviation) for vertical displacements determined by [59] is equal to \(\pm 6\%\). The determined average standard deviation (difference between maximum values of predicted and observed vertical displacements) is equal to \(\pm 14\%\)[2]. The result is referenced to \(68\%\) of probability, and, for a higher percentage of probability, it will result in higher deviations. The verification with levelling (Figure 20) shows that the trends in the subsidence and the shape of the subsidence trough are correctly represented by DInSAR. Thus, the radar results may be considered as effective for model parameter improvement. Still, the 3D decomposition aspect of the deformation will be further studied.
## 2 Materials and Methods
### Problem Modelisation
#### 2.1.2 Optimization Statement  
Given a Live-Streaming vCDN constituted by the parameters enlisted in Section 2.1.1, we must decide the SFC deployment scheme for each incoming session request \(r\), considering the penalties in the resulting RTT caused by the eventual instantiation of VNF containers, cache MISS events, and over-utilization of network resources. We must also consider that the entity of the vCDN operational costs is derived from our SFC deployments. We must deploy SFCs for every request to maximize the resulting QoS and minimizing Operational Costs.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meaning** \\ \hline \(N_{\text{CP}}\) & The set of content-provider nodes \\ \(N_{H}\) & The set of VNF hosting nodes \\ \(N_{\text{UC}}\) & The set of user clusters \\ \(K\) & The set of VNF types (cache, compressor, transcoder, and streamer) \\ \(t\) & A fixed time-step in our simulation \\ \(R_{t}\) & The set of incoming SFC requests during time-step \(t\) \\ \(T\) & The max tolerable RTT for SFC any Live-Streaming request \\ \(b_{r}\) & The maximum scaled bitrate of \(r\) \\ \(p_{r}\) & The mean scaled session payload of \(r\) \\ \(f^{k}_{r}\) & The \(k\)-type VNF requested by \(r\) \\ \(l_{r}\) & The _channel_ or content provider requested by \(r\) \\ \(s_{r}\) & The _session workload_ of \(r\) \\ \(\gamma_{res,i}\) & The unit cost of _res_ resource in hosting node \(i\) \\ \(d_{i,j}\) & The data propagation delay between nodes \(i\) and \(j\) \\ \(f^{k}_{i}\) & The \(k\)-type VNF container instance in node \(i\) \\ \(a^{l,k}_{m}\) & 1 if \(f^{k}_{n}\) is instantiated at the beginning of \(t\) \\ \(x^{k}_{r,i}\) & 1 if \(f^{k}_{r}\) is assigned to node \(i\), 0 otherwise \\ \(z^{r}_{i,j,k}\) & 1 if the link between \(i\) and \(j\) is used to reach \(\hat{f}^{k}_{r}\), 0 otherwise \\ \(y_{l,j,k}\) & 1 if \(f^{k}_{i}\) is currently ingesting channel \(l\), 0 otherwise \\ \(c^{t}_{res,k,i}\) & The _res_ resource provision in \(f^{k}_{i}\) during \(t\) \\ \(\sigma_{k,r,res}\) & The client _res_ resource demand of \(r\) in any \(k\)-type VNF instance \\ \(v_{k,r,res}\) & The _res_ resource demand for content ingestion in any \(k\)-type VNF instance \\ \(u_{res,k,i}\) & The current _res_ resource usage of \(f^{k}_{i}\) \\ \(\mu_{res,k,i}\) & The current _res_ resource utilization of \(f^{k}_{i}\) \\ \(\rho^{r}_{res,k}\) & The processing time contribution of _res_ resource in \(f^{k}_{i}\) for \(\hat{f}^{k}_{r}\) \\ \(\phi_{p}\) & The normalization exponent for mean payload \\  & in the _session workload_ formula \\ \(\phi_{b}\) & The normalization exponent for maximum bitrate \\  & in the _session workload_ formula \\ \(o_{i,j}\) & The unitary data-transportation cost for the link between nodes \(i\) and \(j\) \\ \(\upsilon_{r}\) & 1 if the SFC assigned to \(r\) respects the maximum tolerable RTT, 0 otherwise \\ \(\rho^{*}_{res,k}\) & The processing-time contribution of _res_ in any \(k\)-type VNF \\  & assuming optimal utilization conditions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key notations for our vCDN SFC Deployment Problem.
## 3 Results and Discussion
### Strength Prediction and Relationship Analysis
#### 3.4.4 Variable Importance  
Sometimes priority setting is an important factor, which can be visualized by variable importance. Overall variable importance results in Table 9 show that replacement percentage and additive type are two major factors for strength prediction. However, in comparison to compressive and flexural strength, which have a similar pattern, tensile strength has an alternative relationship, with the additive type having bigger impact than percentage replacement.  
Figure 9: Interaction profile for ranges of: (**a**) compressive strength; (**b**) tensile strength; (**c**) flexural strength with four variables.
## 4 Application of Cyclodextrin-Based Adsorbents
### Removal of Organic Compounds  
Along with the evolution of civilization, the progress of industrial processes is also taking place, due to the growing demands of people for resources, including resources for medicines and food. During the drug production process in pharmaceutical companies, an inseparable part of it is the generation of huge amounts of wastewater. Due to incomplete treatment, residues in sewage will be released into the environment and then into the human body, causing negative consequences over the years [116].  
It is similar in the case of the food and packaging industry, which uses harmful endocrine-active compounds (EDCs) in the production of food packaging [117]. Due to increased temperature (e.g., when heating food), they enter the body along with food [118]. In addition, cans or plastic packages containing toxic EDCs often get into the water through inappropriate garbage handling. In the waters of the seas, lakes, and oceans, these toxic EDCs degrade into micro- and then nano-plastic, harming many species of animals, plants,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbent**} & \multirow{2}{*}{**Dye**} & **Adsorption** & \multicolumn{2}{c}{**Removal**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**References**} \\  & & **Capacity** & & & & \\  & & **(mg/g)** & & & & \\ \hline \(\beta\) -CDs polymer & Pb(II) & 196.4 & 83.30 & 2 & [111] \\ Zr/CM- \(\beta\)-CD & Cd(II) & 118.3 & - & 7.0 & [112] \\ Fe\({}_{3}\)O\({}_{4}\)-PEI/ \(\beta\)-CD & Pb(II) & 73.1 & 73.40 & 6.0 & [113] \\ CD-Fe\({}_{3}\)S\({}_{4}\) & Pb(II) & 256.0 & 74.47 & 6.0 & [114] \\ \(\beta\) -CDPP & Pb(II) & 576.92 & 95.00 & 2 & [115] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 7: \(\gamma\)-CD MOF-NPC; SEM images (**a,b**), TEM images (**c,d**) and the mapping of C, K and O element(**e-h**) [109].  
and others. Therefore, endocrine-active compounds and drugs remain in, and accumulate in, polluted water, which has gradually attracted the attention of people [119]. Most organic pollutants in this section tend to bio-accumulate, persist in the waters for an extended time and are highly toxic to humans and animals [120]. Since cyclodext can self-adapt their structure to the structure of the adsorbed pollutant by reorganization, they are an ideal example of a material with great potential to remove organic contaminants from the environment. Adsorption of organic pollutants as a means of removal was tested on cyclodextrin-based materials [121]. Previous work has shown that cyclodext help remove endocrine-active compounds, pharmaceuticals, and other organic compounds [122; 123]. Furthermore, removing the aforementioned groups of impurities involves model compounds, such as bisphenol A, nonylphenol, estrogen, carbamazepine, ibuprofen, naproxen and ciprofloxacin [124; 125]. The maximum adsorption capacities and removal efficiencies of various CD-based adsorbents for selected organic compounds are listed in Tables 3 and 4.
## 2 Introduction and Definition of Multi-Modal Fusion
### Multi-Modal Fusion
#### 2.3.2 Feature-Level Fusion: Advantages and Applications  
Building on the previous discussion of multi-modal fusion strategies, this section elaborates on the specific advantages and applications of feature-level fusion. Feature-level fusion is particularly beneficial in scenarios where each modality provides complementary insights into the same underlying phenomenon. By extracting and combining high-level features from each modality, this approach maximizes data utility while minimizing the influence of noise and irrelevant information.  
* Capturing complementary information. Each modality emphasizes different aspects of the data, making feature extraction essential for identifying and leveraging their complementary characteristics. This leads to a more comprehensive understanding of the underlying phenomenon.
* Enhancing model robustness.  
Figure 4: Different types of multi-modal fusion. (**a**) Data-level fusion; (**b**) feature-level fusion; (**c**) decision-level fusion.  
Fusing features from different modalities increases the model's robustness, reducing its sensitivity to deficiencies in any single modality. This ensures that the model remains effective, even under suboptimal conditions.
* Flexibility and scalability.  
Feature-level fusion provides flexibility by allowing independent feature extraction for each modality. This modular approach facilitates the use of different architectures, optimized for each modality, and supports the integration of additional modalities or more advanced feature extraction techniques as needed, enhancing the model's scalability.
## 6 Summary  
### Conclusions  
In summary, this study has investigated and proposed the NCA-RPCA algorithm for wavefield image-based damage detection. The algorithm combines the advantages of non-convex approximation, robust principal component analysis, and alternating optimization techniques. Through extensive experiments and analyses, the algorithm's effectiveness and superiority in handling various scenarios of wavefield images have been demonstrated. The NCA-RPCA algorithm effectively separates wavefield images into low-rank and sparse components, enabling accurate identification and localization of structural damage. It exhibits robustness to noise and outlier interference, making it particularly suitable for real-world applications where data can be complex and noisy.  
By processing damaged images, this algorithm demonstrates the capability to swiftly and accurately detect damaged areas within the images. It effectively identifies anomalous regions and provides reliable diagnostic outcomes. Building upon its success in single defect detection, the algorithm also showcases strong scalability. It remains effective when dealing with multiple defects, efficiently locating and marking multiple areas of damage rather than being limited to a single defect. This emphasizes the algorithm's robustness and adaptability in handling intricate scenarios. Such achievements hold significant practical implications in the field of defect detection. In numerous real-world applications, multiple defects in various locations are common. The success of this algorithm validates its reliability in real-life situations, underscoring its value for practical deployment.  
Furthermore, the algorithm's adaptability to different types, sizes, and positions of defects has been thoroughly examined. It excels in detecting both single and multiple defects, showcasing its versatility and robustness in challenging situations. Compared to existing methods, the NCA-RPCA algorithm exhibits higher efficiency, faster convergence, and improved accuracy in detecting anomalies within wavefield images.  
In summary, this algorithm excels in both single defect detection and multiple defect detection, showcasing remarkable advantages over traditional methods. Its strong detection capability and application potential are evident, signifying its effectiveness. This advancement contributes to the progression of damage detection techniques in structural health monitoring. The strengths and achievements of the NCA-RPCA algorithm provide a solid foundation for its practical implementation across various industries, offering promising prospects for enhancing tasks such as data analysis, anomaly detection, and pattern recognition.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 4 Differential Diagnosis and Disease Phenotyping and Staging in Breath Analysis  
As can be observed on Table 2, exhaled VOCs have been used not only for disease diagnosis in comparison to healthy subjects, but also for the discrimination between different diseases (e.g., GCa, LC, and asthma/COPD [43]) or even for the discrimination between the different stages of a particular disease (e.g., CKD stages [113]). One of the main prerequisites for the development of clinically applicable diagnostic tests is the effective discrimination between different diseases with similar symptoms and biochemical pathways [200]. The uncertainty in the differentiation of patients with distinct diseases comprises one of the main drawbacks for studies distinguishing a specific disease from HC [201]. Thus, the use of breath analysis for differential diagnosis as well as disease staging or phenotyping, using either analytical techniques or sensing devices (Table 4), attracted significant research interest over the last years. As expected, the contribution of nanomaterials is of great importance with many recent publications of nanomaterial-based sensors focusing on disease differentiation, staging or phenotyping, rather than the simple discrimination between patients and HC (Table 4).
## 5 Performance Analysis and Discussion
### Sensitivity of Overheads, VM Performance Variations and Task Failures  
In real cloud environment, unexpected delays, VM provisioning delays, inaccuracies in task runtime estimations and VM performance variations can cause failures. The sensitivity of our mechanism to overheads, delays and performance variations was evaluated. It was modeled using a normal distribution around the real value with 30% provisioning delay and task failure probability. The loss in VM performance was considered based on a normal distribution with mean of 15% and standard deviation of 10%. The relative performance of our method is given in Figure 10. It shows four curves, each representing (i) accurate estimations and no delays and failures; (ii) inaccurate estimations of VM performance variation; (iii) inaccurate estimations of provisioning delays and (iv) task failures  
Figure 8: Normalized Deadline (ND) for each workflow with different methods.  
Figure 9: Valid Schedule Rate (VSR) versus Normalized Deadline (ND).  
respectively. These results demonstrate the algorithm's good sensitivity to inaccurate estimations. It is also found that the proposed mechanism is successful in meeting its deadline constraint in most of the cases. However, task failure has a larger impact on performance due to its direct effect on overall makespan.
## 1 Introduction
As mentioned earlier, it does not require significant engine or power plant modifications and it is largely fuel agnostic (provided that the separation process allows for the varying CO\({}_{2}\) levels in the flue gas stream). For these reasons, it is also very well-suited for retrofitting existing vessels. For maritime applications, the ship's operating environment poses a number of additional challenges such as increased needs for security in handling including hazard protection and increased needs for low toxicity due to the inability of rejecting chemicals into the open sea in times of operation, as well as the limiting demand for spacing in a finite volume. As such, the number of degrees of freedom for the choice of technology is compromised. Considering the above, this paper analyzed the possibilities of on-board application of solvent-based CO\({}_{2}\) capture utilizing modular membrane contactors. It is then the different characteristics and performance behavior of the available solvents together with the specific requirements of the ship in the form of constraints that determine the suitability for use in a maritime environment. It has to be noted that the processing and use of the captured CO\({}_{2}\) is not within the scope of this work. As such, the dimension of CO\({}_{2}\) storage on-board was not accounted for and the study focused on the analysis of solvent properties and their compatibility with the maritime sector's requirements. The paper is structured as follows. Section 2 summarizes work performed so far on maritime CCS technologies and defines appropriate key performance indicators (KPIs) that can be used to assess the suitability of solvents for on-board carbon capture. A brief outline of the key features and requirements of membrane contactor technology for CO\({}_{2}\) separation is presented in Section 3. A comprehensive review of possible solvents for membrane-based carbon capture is performed in Section 4 while Section 5 presents a critical assessment of the most promising solvents for on-board use based on the developed KPIs.