esa-sceva/satcom-chunk-collection huggingface dataset:
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 2 Biological Actions of Curcumin
### Medicinal Activity in Humans
#### 2.1.1 Curcumin against Inflammation and Oxidative Stress  
Curcumin acts on inflammation at various levels of the arachidonic acid inflammatory cascade, inhibiting the enzymes phospholipase, cyclooxygenase II, and lipo-oxygenase, and having also effects on cytokines [44; 45]. Curcumin was shown to reduce post-operative inflammation in patients having had surgical repair of inguinal hernia and/or hydrocele [46], to ameliorate symptoms of chronic inflammation pathologies such as arthritis [47; 48], psoriasis [49], and bowel conditions (IBS, Crown'sdisease, and ulcerative colitis) [31; 50; 51; 52; 53] and to treat eye inflammations such as the \"idiopathic orbital inflammatory syndrome\" [54] and uveitis [55].  
The antioxidant activity of curcumin is also the result of a multiplicity of actions. Not only does curcumin stabilize superoxide and hydroxyl free radicals due to the electron-donating properties of its phenolic groups [20; 56; 57; 58], but it also induces the expression of antioxidant enzymes. In vitro tests with beta cells of human pancreas islets incubated with curcuminoids have shown increased levels of heme oxygenase 1, gamma-glutamyl-cysteine ligase, and NAD(P)H:quinone oxidoreductaseues and a consequent increase in glutathione levels [59]. Curcumin protects against oxidative stress caused by advanced glycation end products in patients with diabetes, being under evaluation as a new anti-diabetes drug candidate in a series of clinical and pre-clinical studies [60]. It should also be highlighted that the antioxidant benefits of curcumin do not cease with its metabolization, as many of the metabolites present significant antioxidant properties [61].
# Effects of Freeze-Thaw and Wet-Dry Cycles on Tension Stiffening Behavior of Reinforced RAC Elements
## 2 Materials and Methods
### Methods
#### 2.2.2 Freeze-Thaw and Wet-Dry Aging Procedures  
The aging procedures were started after 28 days of curing. Initially, the specimens were kept immersed in water at a temperature of 20 \({}^{\circ}\)C for 48 h. Then, three prismatic specimens of each mixture were subjected to each type of degradation test as explained:  
Figure 2: Production of reinforced elements: (**a**) Preparation; (**b**) Casting; (**c**) Curing.  
* Freeze-thaw cycles (FTC): the samples were submitted to 0, 150 and 300 accelerated freezing and thawing cycles, based on ASTM C666 ([PERSON] et al., 2018), in a climatic chamber with an automatic temperature control system. The total time of one cycle was 5 h, with a temperature reduction from 4 \({}^{\circ}\)C to \(-18\)\({}^{\circ}\)C in 2.5 h and then reheating for 2.5 h until reaching 4 \({}^{\circ}\)C again;
* Wet-dry cycles (WDC): the samples were submitted to 0, 25 and 50 accelerated wetting and drying cycles, based on NBR 13554 ([PERSON] et al., 2018) and ASTM D559 ([PERSON] et al., 2018), and it was performed manually with the use of a water tank and a laboratory oven. The total time of one cycle was 24 h, which consisted of immersion in water at 20 \({}^{\circ}\)C for 3 h, superficial drying at 21 \({}^{\circ}\)C for 1 h and oven-drying at 60 \({}^{\circ}\)C for 20 h.
* In addition, non-degraded samples were kept in a wet chamber and tested as reference at the same age as the degraded samples.
## 3 System Model and Problem Formulations
### Problems Formulations
Objective 2: achieve the minimum waiting time for users of adjacent Sat-Beams:  
\[\text{MIN}[average(Tra(b_{ik})/BW_{i})] \tag{8}\]  
* Conditions: Condition 1: The PDF variances of DoU in the direction of SSPs moving in a Sat-Beam \(B_{i}\) and serving time \(T_{i}\) are observed. \[\Delta p_{g}(B_{i}\,T_{i}\,t)=\frac{\partial p_{g}(B_{i},T_{i},t)}{ \partial t}\,t\in[T_{i-1}\,T_{i}]\] (9)  
Conditions 2: The PDF variances of DoU in the direction of SSPs moving between two adjacent Sat-Beams in one serving time \(T_{i}\) are observed. The DoU of two adjacent F-Sat-Beams is different. \[\Delta p_{g}\left(B_{i}\,B_{j}\,T_{i}\,t\right)=\frac{\partial p_{g}(B_{i}\,T_{i}\,t)}{ \partial t}-\frac{\partial p_{g}\left(B_{j}\,T_{i}\,t\right)}{ \partial t}\,t\in[T_{i-1}\,T_{i}] \tag{10}\]  
The probability of judgment coefficient function can be varied with density variances, which are,  
\[\ln\frac{P(\lambda(b_{m}\lambda B_{i})=1)}{P\big{(}\lambda\left(b_{m}\lambda B _{j}\right)=1\big{)}}=\frac{\varepsilon}{\Delta p_{g}\left(B_{i}\,B_{j}\,T_{i}\,t \right)} \tag{11}\]  
where \(\Delta p_{g}\left(B_{i}\,B_{j}\,T_{i}\,t\right)>0\), indicating that the density variances of \(B_{i}\) increase more than those of \(B_{j}\). Thus, the probability of accessing \(B_{i}\) decreases, achieving load balancing. Condition 3: The density variances of DoU in the direction of SSPs moving between two adjacent Sat-Beams in two serving times \(T_{i}\) are observed. The trajectories of SSPs are different. * Constraints: Constraint 1: The total traffic of users newly accessing the adjacent Sat-Beams is less than the traffic capacity of the Sat-Beams: \[Tra(B_{i})\leq Tra_{\max}(B_{i})\] (12) \[Tra\left(B_{j}\right)\leq Tra_{\max}\left(B_{j}\right)\] (13)  
Load Balancing Scheme Based on the Modeling of Spatial-Temporal DoU and Advanced Genetic Algorithms
## 3 Initial Results
### Blockchain Operations That Address Ethical Issues in AI
#### 3.3.1 Federated Machine Learning and Blockchain for Privacy-Preserving and Censorship Resistance

Table 4: Ethical issues in AI are addressed through blockchain concepts. federated machine learning can address some important ethical issues in AI, such as Regulation on (legal) jurisdictions, Accuracy (false positives), Censorship, and trustability. Using the federated learning approach, the sensitive data, such as healthcare records of sexual exploitation victims, used in training the models, does not leave the organizations (and legal jurisdictions) where they are generated. This ensures that data is not unnecessarily moved across legal jurisdictions and addresses _regulatory issues_ in moving sensitive data during AI processes. Federated machine learning, being a form of ensemble approach, generally has higher performance than single model approaches, thereby increasing the _accuracy_ of AI systems used in sexual exploitation use-cases. Furthermore, since the entities involved in the federated machine learning are independent organizations, a decentralized autonomous organization (DAO) can be established to govern this collaboration, thereby preventing _censorship_. The decentralized governance can determine the minimum data requirements and model performance for an organization to participate in federated learning. This ensures that no single organization can easily undermine or limit access to AI applications that address important societal problems like sextortion. Smart contract verification ensures that only models that meet the requirements specified by the DAO governance are used (ensembled) in sensitive AI applications, hence, improving the trustability of the system.
## 3 Results and Discussion
### Promotion of Apoptosis by Ru(III) Complexes in LoVo-Treated Cells
Effects of drug treatments on apoptosis induction in the LoVo human colon adenocarcinoma cell line were evaluated by Annexin-V/FITC and PI double-staining assay. Levels of apoptotic events were assessed by flow cytometry acquisition using a FACScan cytometer and analysis with WinMDI2.9 and Kaluza softwares. Data are presented as flow cytometry histograms. Annexin V belongs to a family of calcium-dependent phospholipid-binding proteins that bind to phosphatidylcholine-serine (PS) and allow identifying the apoptotic cells found in early and late stages, the last process preceding necrosis; propidium iodide (PI) is a dye used for assessing cell viability and the exclusion of non-viable cells that binds to double-stranded DNA but is excluded from cells with intact plasma membranes. It is used in combination with Annexin V/FITC to assess the cell viability and discriminate between apoptotic and necrotic cells; live cells do not bind Annexin or PI. Use of 24 h treatments with 10 uM of all Ru(III) complexes with quinolones induced higher levels of apoptosis compared to control cells with over 32% of apoptosis being found in the treated cells (Figure 4). Ru-cipro, Ru-pip, Ru-enoxa, and Ru-enro induced levels of apoptosis of 57.84%, 53.57%, 51%, and 54.23%, respectively, higher than the apoptotic events induced by 10 uM CisPt treatment (50.27%). The lowest apoptosis was induced by Ru-levo (32.97%), while Ru-oflo and Ru-nf induced more than 40% of apoptosis, 42.06% and 46.55%, respectively. When drug doses of cell treatments were increased to 50 uM, stronger effects were observed both for Ru(III) complexes and CisPt treatments (Figure 4). Treatments with Ru-cipro, Ru-pip, and Ru enro induced levels of apoptosis comparable with those induced by CisPt treatment (63.76%, 64.33%, and 64.03%, respectively, as compared to 63.89%) (Figure 4). The strongest effect of 73.34% apoptosis was induced by Ru-enoxa treatment. However, a high increase in the total apoptosis was also induced by Ru-nf (51.77%), Ru-levo (42.55%), and Ru-oflo (45.80%) (Figure 4). Induction of apoptosis was also achieved by cancer cell treatments with several other ruthenium (II) and (III) compounds, but levels of apoptotic events depended not only on ligands but also on the doses used and the sensitivity of the cancer cell lines tested [16; 22].
## 1 Introduction
In this line, the Erasmus+ programme--one of the largest student and academic mobility activities worldwide--faces a challenge due to the significant size it has achieved. The number of HEIs that have the Erasmus Charter for Higher Education has increased from a few hundred at the beginning of the 2000s to more than 5200 as of July 2022, from 33 countries (the 27 EU members plus Iceland, Liechtenstein, Norway, North Macedonia, Serbia, and Turkey). Accordingly, the number of students and staff benefiting from the Erasmus+ programme has been growing by the day, entailing a plethora of challenges in management and coordination. To overcome these challenges, the European Commission (EC) has decisively invested in digitisation, launching the Erasmus Without Paper (EWP) project [11] to define common interfaces, document types, and management procedures. On such foundations, it is possible to develop an ecosystem of digital applications that will facilitate management and participation. Integration of these developed digital platforms was declared among the priority areas by the EC, which made it compulsory to complete the digitalisation process for all HEIs by 2025. This communication reports on an experiment conducted in the SUDTE project (\"Supporting Universities in the Digital Transformation in Erasmus+\") [12] to numerically reveal the benefits of the digital transformation process by comparing traditional methods and digital tools used in the execution of Erasmus+ mobility processes between HEIs. Knowing that many processes are affected by institutional or national regulations and their peculiarities and circumstances, the project's partner institutions have surveyed such details as paper wastage, workload, and time invested in managing mobility processes in their respective countries (namely Spain, Italy, and Turkey) using different digital applications:three from different commercial providers and one built in-house. Thereupon, a statistical analysis was conducted to characterise the savings that may derive from digitalisation, providing the first quantitative evidence of savings in terms of paper wastage and administrative time, which may translate into lower staff effort, increased productivity, and greater diligence from the point of view of the beneficiaries of the mobilities: fewer errors, shorter or no delays, etc. This study focuses on the savings in workload, paper, and time savings gained by the digital transformation process that higher education institutions are going through within the scope of the Erasmus+ programme for international offices. The structure of this paper is as follows. The academic literature related to the study is discussed in the background section after the introduction. Next, the methodology section presents information and justifications of the processes used in the study. In the results section, the data obtained from the intervening staff. The computed results are given for later analysis in the discussion section. In the conclusion, a roadmap is drawn for new studies to be carried out in the future.
## 7 Conclusions
** \begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(A\) & Set of origin nodes \\ \(B\) & Set of destination nodes \\ \(D^{ab}\) & Set of permissible solutions \\ \(E\) & Set of O-D pairs \\ \(F\) & Set of structure graph node and (or) link functions \\ \(F_{L}\) & Set of functions defined on a set of links \(L\) \\ \(F_{W}\) & Set of functions defined on a set of nodes \(W\) \\ \(G\) & System structure graph \\ \(L\) & Set of graph links \(G\) \\ \(O\) & Transport system traffic organization \\ \(P\) & Traffic flow in MST \\ \(p^{ab}\) & Set of routes for \((a,b)\) O-D pair \\ \(S\) & System structure graphs \\ \(T\) & Time characteristic matrix \\ \(T_{c}\) & Total traffic flow movement time \\ \(V\) & Set of intermediate nodes \\ \(W\) & Set of graph nodes \(G\) \\ \(\overline{W}\) & Population of the set \(W\) \\ \(X\) & Traffic flow distribution matrix \\ \(X^{*}\) & Traffic flow optimal distribution matrix \\ \(\alpha^{ab}\) & Minimum travel time within a O-D pair \((a,b)\) \\ \(a_{p,ab}^{i}\) & 1 if route \(p\)\(\in\)\(\underline{p^{ab}}\) contains link \((i,j)\)\(\in\)\(L\), 0 otherwise \\ \(\beta^{\prime}\),ab & Flow rate of O-D pair \((a,b)\)\(\in\)\(E\) routed on path \(p\)\(\in\)\(\underline{p^{ab}}\) \\ \(l_{\overline{i}}\) & Link between \(w_{i}\) and \(w_{j}\) nodes \\ \(m_{i}(x_{i})\) & Marginal travel time by link \((i,j)\) \\ \(m^{\prime}\),ab & Marginal travel time by route \(p\) within a O-D pair \((a,b)\) \\ \(p\) & Route \\ \(tr^{ab}\) & Average travel time by route \(p\) within a O-D pair \((a,b)\) \\ \(x_{\overline{i}}\) & Traffic flow volume on link \((i,j)\) \\ \(x^{\prime}\),ab & Traffic flow volume on road \(p\) within a O-D pair \((a,b)\) \\ \hline \hline \end{tabular}   
**Appendix B**  
Code of the Nash equilibrium (scenario #1) for LINGO 18.0  
t14=5+0.05*x14+0.025*(x14^2);  
t25=10+0.04*x25+0.065*(x25^2);  
t36=6+0.03*x36+0.025*(x36^2);  
t47=4+0.04*x47+0.04*(x47^2);t48=5+0.08*x48+0.125*(x48^2); t54=4+0.035*x54+0.02*(x54^2); t56=5+0.035*x56+0.04*(x56^2); t67=1+0.035*x67+0.03*(x67^2); t78=2+0.025*x78+0.035*(x78^2); t1=t14+t48; t2=t14+t47+t78; t3=t25+t54+t48; t4=t25+t54+t47+t78; t5=t25+t56+t67+t78; t6=t36+t67+t78; x14>=0; x25>=0; x36>=0; x47>=0; x48>=0; x54>=0; x56>=0; x67>=0; x78>=0; x1=4; x2=5; x3=7; x18=4; x28=5; x38=7; x18=x118+x218; x28=x328*x428+x528; x38=x638; x1=x14; x2=x25; x3=x36; x14+x54=x47+x48; x25=x54+x56; x36+x56=x67; x47+x67=x78; x48+x78=x14+x25+x36; x8=x48+x78; x14=x118+x218; x25=x328*x428+x528; x36+x638; x47=x218+x428; x48=x118+x328; x54=x328*x428; x56=x528; x67=x528*x638; x78=x218+x428+x528+x638; t1=t2; t3=t4; t4=t5;
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 5 Discussion  
In this study, we proposed VidBlock, a decentralized blockchain-based architecture for streaming, and examined its performance in handling relay node failures across different geographical regions. Our findings demonstrate that VidBlock's architecture, leveraging blockchain and P2P protocols, creates a resilient and adaptable streaming environment, addressing several limitations inherent in traditional, centralized content delivery systems.
## 3 Geophysical Data and Methodology
### Seismic Data Acquisition  
The seismic reflection acquisition experiment included four dense transects across different sections of the fault system. The seismic recording scheme was a 240-channel system made up of 10 GEODE recording units with 24 channels each. The seismic source was a 200 kg accelerated weight-drop provided by the University of Lisbon's Instituto Tecnico Superior (Lisbon, Portugal). The acquisition geometry was designed with a 6 m shot spacing and a 2 m receiver interval. The sample rate was 1 ms, and the total recording time was 4 s. The data was collected using single vertical component exploration geophones with a natural frequency of 5 Hz (Table 2). The seismic data acquired were used in both MASW and P-wave tomography methods described in the next sections.  
\begin{table}
\begin{tabular}{l l l} \hline
**Software Package** & **Processing Step** & **Rationale** \\ \hline Prosys II (Iris Instruments) & Step 1 & Data point inspection \\ \hline \multirow{6}{*}{Resix2 Dinv} & Step 2 & Negative values removal \\  & & (filtering) \\ \cline{2-3}  & Step 3 & Insertion of the topography \\ \cline{2-3}  & Step 4 & Adjust the X,Z points along the GPS track \\ \cline{2-3}  & Step 5 & Edition of bad data points \\ \cline{2-3}  & Step 6 & Selection of inversion parameters \\ \cline{2-3}  & Step 7 & Display of model \\ \cline{2-3}  & Step 7 & parameterization and selection of a finer mesh (Figure 4a) \\ \cline{2-3}  & Step 8 & With the new dataset, return to \\ \cline{2-3}  & Step 6 for a new iteration process \\ \cline{2-3}  & Step 4b & Measured apparent restivity (ohm.m) \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \end{tabular}
\end{table}
Table 1: Processing workflow used to obtain the ERT models in La Torrecilla and La Salud North profiles.  
Figure 4: (**a**) Error distribution after 9 iterations (absolute error = 11.53) between calculated and measured resistivities for the La Torrecilla ERT survey in first inversion. (**b**) Error distribution after five iterations (absolute error = 5.8) between calculated and measured resistivities in second inversion, once the points that exceeded 25% misfit (red crosses) were removed.
# The DREAM Endstation at the Linac Coherent Light Source
## 4 Scientific Application
### Ultrafast Molecular Dynamics in Chiral Systems  
With the advent of LCLS II along with novel operation modalities such as tunable polarization, attosecond X-ray pulses, multibunch modes, MHz bunch repetition rate, and the new DREAM endstation, SLAC has enabled the opportunity to gain unprecedented detailed insights into chirality. Finally, through the innovative FEL capabilities, it will be possible to observe and understand how chiral systems form, restructure, functionalize, and, ultimately, can be controlled. Investigating the origins of chirality with TMO instruments, the knowledge bridge between the macro-biological level, chemical dynamics, and the fundamental spin properties of matter can be made possible [8; 37]. Previously, investigation of the chiral ethane derivative (CHClBrCF\({}_{3}\)) demonstrated that the handedness could also be determined for a more complex species [37]. The fragmentation into constituent fragments of CH+, Cl+, Br+, and CF+, followed by momentum recontruction, showed a clear signal for the two enantiomers from a racemic mixture of CHClBrCF\({}_{3}\). This facilitates the direct comparison of experimental measurements with high-level molecular dynamic simulations of ultrafast photochemical processes. In addition to transient diffractive imaging techniques, time-resolved momentum imaging of ionic fragments that result from molecular breakup following rapid multiple ionization provides a powerful experimental tool for mapping transient molecular configurations and reaction intermediates in light-driven processes.
## 2 Materials and Methods
### Accuracy Assessment  
Accuracy assessment was conducted to evaluate the performance of regression models in predicting grass water content indicators. The derived RF models were assessed for accuracy based on the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and root mean square error percentage (RMSE%). The R\({}^{2}\) is an accuracy assessment parameter that is used to measure the magnitude of variation between observed and predicted samples [107]. Its values range from 0 to 1, and values closer to 1 indicate better goodness of fit of a model [108]. In this study, the R\({}^{2}\) was used to measure the variation between measured and predicted GWC indicators. The RMSE is the square root of the mean square of all residuals [109]. It is a metric that ranges from the optimal value of 0 to infinite [110]. Low RMSE values (closer to 0) indicate accurate model performance, whereas high values denote the failure of a model to fit a dataset [110]. Specifically, the RMSE was used to assess the prediction error between the actual field measurements and the modelled grass water content variables. The RMSE% was used to estimate the magnitude of error from the measured values, expressed as a percentage. To compute the RMSE%, the RMSEs from each model were normalized using the mean of each field-measured variable and then expressed as a percentage [111]. The R\({}^{2}\), RMSE, and RMSE% were calculated as follows:  
\[\mathrm{R}^{2}=1-\frac{\sum\left(\mathrm{Y_{measured}}\right.-\left.\mathrm{Y_ {predicted}}\right)^{2}}{\sum\left(\mathrm{Y_{measured}}\right.-\left.\mathrm{Y_ {predicted}}\right)^{2}} \tag{4}\]  
\[\mathrm{RMSE}=\frac{\sqrt{\sum_{i-1}^{\mathrm{N}}\left(\mathrm{Y_{measured}} -\mathrm{Y_{predicted}}\right)^{2}}}{\mathrm{N}} \tag{5}\]  
\[\mathrm{RMSE\%}=\frac{\sqrt{\frac{1}{n}\sum_{1=\mathrm{N}}^{\mathrm{N}} \left(\mathrm{Y_{measured}}-\mathrm{Y_{predicted}}\right)^{2}}}{\widetilde{Y }}\times 100 \tag{6}\]  
where Y\({}_{\mathrm{measured}}\) is the field-observed GWC, Y\({}_{\mathrm{predicted}}\) is the estimated GWC, N is the number of test samples, i is the included predictor variable, and \(\widetilde{Y}\) is the mean of the observed GWC. These formulas were adopted from [112; 113]  
Based on the testing datasets, the model with the lowest RMSE and RMSE% across all models was selected as the ideal model for explaining more significant variables in predicting the GWC elements. These models were used to generate maps illustrating the spatial distribution of the estimated GWC elements within the study site. Figure 2 summarizes the research methodology adopted in this study.