esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Carbon Emission Calculation of the Case Study
#### 3.1.1 Building Materials Production
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Building Materials**} & \multicolumn{1}{c}{**Carbon Emission Intensity**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**Quantity**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**Carbon Emissions (kgCO\({}_{\rm 2e}\))**} \\ \hline \multirow{8}{*}{Mortar} & Dry mixed plastering & \multirow{2}{*}{466.35} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Quantity**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**\begin{tabular}{c} **\begin{tabular}{c} **\begin{tabular}{c} **\
\begin{tabular}{c} **Carbon Emissions** \\ **(kgCO\({}_{\rm 2e}\))** \\ \end{tabular} \\ \end{tabular} } \\  & mortar DP M15.0 & \multirow{2}{*}{298.73} & \multirow{2}{*}{\(\rm kgCO_{\rm 2e}/m^{3}\)} & \multirow{2}{*}{14.17} & \multirow{2}{*}{\(\rm m^{3}\)} & \multirow{2}{*}{4234.47} \\  & Dry mixed plastering & \multirow{2}{*}{466.35} & \multirow{2}{*}{\(\rm kgCO_{\rm 2e}/m^{3}\)} & \multirow{2}{*}{5.86} & \multirow{2}{*}{\(\rm m^{3}\)} & \multirow{2}{*}{2731.81} \\  & mortar DP M20.0 & \multirow{2}{*}{340.44} & \multirow{2}{*}{\(\rm kgCO_{\rm 2e}/m^{3}\)} & \multirow{2}{*}{21.23} & \multirow{2}{*}{\(\rm m^{3}\)} & \multirow{2}{*}{7229.11} \\  & Dry mixed plastering & \multirow{2}{*}{403.46} & \multirow{2}{*}{\(\rm kgCO_{\rm 2e}/m^{3}\)} & \multirow{2}{*}{15.31} & \multirow{2}{*}{\(\rm m^{3}\)} & \multirow{2}{*}{6177.45} \\  & mortar DS M20.0 & \multirow{2}{*}{4620} & \multirow{2}{*}{\(\rm kgCO_{\rm 2e}/m^{2}\)} & \multirow{2}{*}{4005.42} & \multirow{2}{*}{\(\rm m^{2}\)} & \multirow{2}{*}{16,061.75} \\ \hline Waterproof, heat insulation materials & Asphal waterproof coil & 4.01 & \multirow{2}{*}{\(\rm kgCO_{\rm 2e}/m^{2}\)} & \multirow{2}{*}{4005.42} & \multirow{2}{*}{\(\rm m^{2}\)} & \multirow{2}{*}{16,061.75} \\  & Squeeze polystyrene board & 4620 & & 9.56 & t & 44,162.12 \\ \hline Glass & Flat glass & 1130 & \(\rm kgCO_{\rm 2e}/t\) & 3.46 & t & 3905.70 \\ \hline Aluminum alloy &
\begin{tabular}{c} Broken bridge aluminum \\ alloy window \\ \end{tabular} & 194 & \(\rm kgCO_{\rm 2e}/m^{2}\) & 7.28 & \(\rm m^{2}\) & 1412.32 \\ \hline Ballast & Gravel & 2.18 & \(\rm kgCO_{\rm 2e}/t\) & 120.08 & t & 261.77 \\ \hline Solar photovoltaic panels & Solar photovoltaic panels & 1809.47 & \(\rm kgCO_{\rm 2e}/kW\) & 8.29 & kW & 15,003.62 \\ \hline Total carbon emission & \multicolumn{6}{c}{293,208.17 kgCO\({}_{\rm 2e}\)} \\ Carbon emissions per unit area & \multicolumn{6}{c}{408.94 kgCO\({}_{\rm 2e}/m^{2}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 5: Architectural geometric model.
## 5 Discussion
First, digital inclusion serves as a driver for economic growth,particularly by enhancing access to ICTs in underserved areas. Regions with robust digital connectivity experience a noticeable increase in productivity, as businesses gain access to digital marketplaces, e-commerce, and remote work opportunities. This digital integration has been shown to stimulate local economies, reduce operational costs, and expand consumer reach. Furthermore, greater digital access allows for increased participation in the national and global economy, reducing regional economic disparities. Educationally, digital inclusion significantly impacts access to resources, particularly in rural or low-income areas where physical educational infrastructure may be limited. Students with access to the internet benefit from digital tools and platforms that provide enhanced learning experiences, online courses, and interaction with educational resources unavailable locally. The impact of digital inclusion on education in Mexico became especially evident during the COVID-19 pandemic when digital access determined students' ability to continue their education. On a social level, digital inclusion supports greater civic engagement and enhances access to government services, health care, and social support networks. It fosters an informed and connected citizenry, which is crucial for addressing socio-economic inequalities and promoting social mobility. However, in areas with limited connectivity, residents are often excluded from these benefits, exacerbating existing divides and reinforcing cycles of poverty. Therefore, digital inclusion in Mexico not only promotes equitable economic development but also plays a critical role in democratizing education, civic engagement, and access to essential services. Enhancing digital access is vital for Mexico's progress, ensuring that all citizens can fully participate in and contribute to the digital economy and society.
# Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
## 2 Materials and Methods
### Performance Assessment in an Upper GI Porcine Model  
To assess the instrument's performance and properties inside the GI tract, an experiment was performed using an ex vivo porcine model, consisting of an esophagus and stomach. The porcine model was placed on a polystyrene (PS) board with a cutout shaped to resemble the upper GI tract. The PS board cutout was curved at its beginning to resemble a mouth and throat, with a cavity with a leak opening 35 cm away from the beginning. To provide access to the leak cavity on the PS board, a 1 cm hole was made in the esophagus of the porcine model.  
The instrument was inserted through the esophagus and advanced toward the leak opening. A 10.5 mm gastroscope was inserted alongside the instrument to provide the vision of the distal tip and operating area (Figure 4).  
A series of tests were performed by a group composed of several physicians and engineers. A grading scale (0--insufficient, 1--limited, 2--sufficient, 3--good) was used to assess the performance of the prototype, as the properties of the instrument could not be accurately quantified inside the organs of the porcine model. Insufficient and limited properties were regarded as unacceptable for use and would require a significant improvement, while sufficient and good properties were regarded as acceptable for procedures in interventional endoscopy.  
The following properties were assessed and graded:  
Figure 4: Image of the instrument placed next to the gastroscope in the open stomach of the porcine model. The gastroscope was used to provide the vision for navigation of the instrument. The distal tip of the instrument is bent more than 180\({}^{\circ}\) and the grasping forceps are open.  
* Flexibility--the ability of the instrument to bend around the anatomical landmarks inside the porcine model.
* Resistance to buckling--the ability to avoid bending (buckling) when pushed through the organ or when advancing an object to the leak-opening area.
* Friction between instrument and organ surface.
* The maneuverability of the distal tip inside the porcine model.
* Torsional stiffness--the ability of the instrument to transfer the rotational movement from the handle to the distal tip.
# A Decentralized Multi-Agent-Based Approach for Low Voltage Microgrid Restoration
## 4 Proposed Decentralized Approach
### Information Sharing Process  
The local initial information of each agent is placed within an initial matrix. The initial matrices just have the local information of the agents. Agent \(i\) is initialized with a \(n\times 4\) matrix \(\mathbf{M_{i}}\) where \(n\) is the number of agents. In \(\mathbf{M_{i}}\), up to four non-zero elements may exist. These three non-zero elements are \(M_{i}(i,1)\), \(M_{i}(i,2)\), \(M_{i}(i,3)\), and \(M_{i}(i,4)\). \(M_{i}(i,1)\) can be either 0 or \(i\) to show whether the generation unit or the load assigned to agent \(i\) is connected or not. Each agent can realize which generation units or loads are disconnected by checking the position of the zeros. \(M_{i}(i,2)\) can be 0 or \(i\) to show that the disconnected load or generation unit is ready for restoration or not. In the case of MC agents, \(M_{i}(i,3)\) shows the amount of the power that the generation unit can produce while in the case of LC agents, it represents the amount of the load power that agent \(i\) will consume. \(M_{i}(i,4)\) can be 0, 1, 2, 3 that shows a pre-defined priority of the agents for restoration action. If \(M_{i}(i,4)=0\), the agent \(i\) has no pre-defined priority.  
For example, let consider the initial matrices \(\mathbf{M_{i}}\), \(\mathbf{M_{j}}\), \(\mathbf{M_{n}}\) for agent \(i,j\), \(n\), respectively, as follows:  
\[\mathbf{M_{i}}=\left[\begin{array}{cccc}0&0&0&0\\ \vdots&\vdots&\vdots&\vdots\\ 0&i&P_{G_{i}}&0\\ \vdots&\vdots&\vdots&\vdots\\ 0&0&0&0\end{array}\right]_{n\times 4},\mathbf{M_{j}}=\left[\begin{array}{cccc}0&0&0&0 \\ \vdots&\vdots&\vdots&\vdots\\ 0&j&-P_{L_{j}}&1\\ \vdots&\vdots&\vdots&\vdots\\ 0&0&0\end{array}\right]_{n\times 4},\mathbf{M_{n}}=\left[\begin{array}{cccc}0&0&0&0 \\ \vdots&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots\\ n&0&0&0\end{array}\right]_{n\times 4}. \tag{10}\]  
The above initial matrices present the following information. MC-agent \(i\) is disconnected; it is ready for restoration; if it is connected, it can produce power equal to \(P_{G_{i}}\); and it has no pre-defined priority. Similarly, the LC-agent \(j\) is disconnected; it is ready for restoration; if it is connected, it consumes \(P_{L_{j}}\); and it has the highest priority to be connected. The agent \(n\) is connected, and there is no need for the restoration. Each agent has a similar principle to make the initial information matrix. By using the ACA (Equation (1)), all initial matrices will converge to the same matrix that is available for each agent. A typical final converged matrix can be:  
\[\mathbf{M_{conv.}}=\left[\begin{array}{cccc}\vdots&\vdots&\vdots&\vdots\\ \dfrac{0}{n}&\dfrac{i}{n}&\dfrac{P_{G_{i}}  
to implementing the proposed method in which the function of agents and decision making process are described.
# The Impact of the Stringent Response on TRAF GTPases and Prokaryotic Ribosome Assembly
## 4 (p)ppGpp-Binding RA-GTPases Involved in 30S Assembly
### RsgA (YloQ/YjeQ)
A non-GTPase assembly cofactor for the 30S, RbfA (ribosome binding factor A), binds to the 5' end of the 16S rRNA, possibly during the 17S immature stage, to enable destabilisation during h1 development and, perhaps, RNase processing [66]. RbfA is a bacterial cold-shock protein, consisting of a type-II KH (K-homology) domain characteristic of nucleic acid binding proteins. As well as facilitating the formation of h1, RbfA binding is followed by dramatic alterations in h44 and h45 conformations to prohibit subunit joining [67]. The association of RsgA with the 30S can release RbfA when in the GTP-bound state, enabling RsgA-mediated positioning of h44 and h45 into the correct conformation [66]. This is consistent with previous studies showing that RsgA is unable to stably bind the 30S in the absence of GTP [59; 68]. Since release of RbfA was also observed when incubated with the RsgA in the presence of a non-hydrolysable GTP analogue GMPPNP, it is apparent that binding of RsgA to the 30S is enough to promote RbfA release and could be involved in controlling RbfA-mediated maturation events [66]. Figure 3: An overview of the ribosomal binding sites and structures of (p)ppGpp-binding RA-GTPases. (**A**) _E. coli_ 30S ribosomal subunit (adapted from PDB-ID 4 ADV [60]) with major domains and features labelled. The binding sites for RsgA and Era are indicated (structures adapted from PDB-IDs 5 NO4 [56] and 3 IEV [61], respectively). (**B**) _E. coli_ 50S ribosomal subunit (adapted from PDB-ID 3J5L [62]) with major structural features including the A-, P- and E-sites labelled. The binding sites for ObgE, RbgA (not present in _E. coli_) and HfIX are indicated (structures adapted from PDB-IDs 5M04 [63], 3 CNL [64] and 5 ADY [65], respectively). R-proteins are coloured grey, whereas rRNA is coloured blue for comparison. GTPases are coloured as a gradient from the N-terminus (blue) to the C-terminus (red) and are not to scale relative to the ribosome.
# Deep Reinforcement Learning Method for 3D-CT
###### Abstract  
Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.  
deep reinforcement learning; multi-scale processing; prior knowledge; nasopharyngeal cancer localization +
Footnote †: journal: Applied Sciences
## 5 Performance Assessment
### Comparison between D-ST and TSN-ST  
In the D-ST configuration, the two ST flows, i.e., the LiDARs and Ultrasonic flows, are mapped to the highest priority queue, which is reserved to the ST class, while the other flows are handled according to the EDF-based policy. Instead, in the TSN-ST configuration, the LiDARs and Ultrasonic flows are mapped to the ST class (i.e., queue 7), the Video flows are assigned to the SR class A (i.e., queue 6) and undergo the CBS, while the ED ADAS sensor flows are mapped to the highest-priority BE queue, (i.e., queue 5).  
Table 3 shows the maximum _e2 eDelay_ values obtained for all the flows.  
As shown in Table 3, for the LiDAR and Ultrasonic flows the maximum frame _e2 eDelays_ are the same with both approaches, as this traffic is isolated from the other traffic classes. The ADAS sensors flows with D-TSN obtained a maximum frame _e2 eDelay_ equal to 0.45 ms, and therefore no deadline miss occurred. Conversely, using TSN-ST, the ED traffic experiences deadline miss, as the maximum _e2 eDelay_ is equal to 1.56 ms, while the flow's relative deadline is 1 ms. This is because with TSN-ST the ED traffic is affected by the interference of both the ST traffic and SR traffic, which are mapped to the two highest-priority classes. Conversely, in both configurations, Video flows do not experience deadline miss. In the TSN-ST configuration, Video flows obtained a slightly lower maximum delay, as such flows have the second highest priority, therefore they do not suffer from the interference of ED traffic. On one hand, this results in lower delays for Video flows, but, on the other hand, this configuration determines deadline miss for the ADAS sensors flows.  
The relative deadline \(D\) of each frame of the video flow, here called \(D_{Video,f_{i}}\), is calculated according to the following equation:  
\[D_{Video,f_{i}}=\frac{D_{Video}}{Burst_{dim}}\times no_{f}, \tag{8}\]  
where \(Burst_{dim}\) is the number of frames a message of the Video flow is split in, and \(no_{f}\) is the frame id within the frame burst. The ST flows, i.e., LiDAR and Ultrasonic, experience a null jitter in both configurations.  
To summarize, comparing with TSN-ST, D-ST significantly reduces the end-to-end delay of ED flows (i.e., the ADAS sensors flows) thanks to the frame online EDF scheduling. Moreover, D-ST achieves the same performance for the jitter-sensitive ST flows (LiDAR and Ultrasonic), and better performance for the ED flows, without a significant impact on the periodic RT traffic (i.e., Video).
# Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria
## 2 Intracellular Bacteria That Replicate Inside a Vacuole during Infection  
As briefly mentioned previously, vacuolar bacterial pathogens are defined as species that remain within the vacuole formed upon entry into the host cell, though not all vacuoles are created equally [22; 23]. The pathogen-containing vacuoles (PCVs) are very specific to each pathogen with differences in the suitable intravacuolar acidity for pathogen growth. The vacuole markers and composition are indicative of the stages of vacuole transport pathways that the PCV is derived from or halted in and the presence of intravacuolar nutrients and associated host cell organelles. Many vacuolar pathogens, including _Coxiella burneti_, _Chlamydia_ spp., _Mycobacterium_ spp., _Anaplasma phagocytosisphim_, _Ehrlichia chaffensis_, _Bruella abortus_, _Legionella pneumophila_, and _Salmonella_ spp., have been shown to manipulate host lipids during intracellular infection for an array of defined and speculated functions involved in bacterial and vacuolar membrane production, foam cell presentation and immune modulator production, and energy production.
## 3 Results and Discussions
### Chemical Kinetics of Photocatalytic Processes  
The results of the chemical kinetics of the degradation of AMX in aqueous solution for the heterogeneous solar photocatalysis process carried out under pH 4.15, show a first order reaction (n = 1) on both 0.1 and 1 m\({}^{2}\) FPR surfaces (Table 1).  
Table 1 shows the calculation of the photocatalytic rate constant (K\({}_{\text{phC}}\)) for AMX degradation, calculated by Equation (4) of Section 2.5, on both 0.1 and 1 m\({}^{2}\) FPR surfaces, with addition (4 mM) and in the absence of H\({}_{2}\)O\({}_{2}\), under a reaction at pH 4.15. It also shows the % degradation reached by the COD analysis, 80 min after the start of the reaction, and indicates the average radiation of each experiment in W/m\({}^{2}\). It also gives the half-life of the reagent, calculated in each case using Equation (5), Section 2.5.  
As observed in Table 1, AMX degradation in the absence of H\({}_{2}\)O\({}_{2}\) shows differences in photocatalytic rate constants for the different 0.1 and 1 m\({}^{2}\) FPR surfaces, agreeing with the indication of [PERSON] et al. 2012 [51], in that a greater exposed reaction surface  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FPR Surface** & **Rad. W/m\({}^{2}\)** & \begin{tabular}{c} **H\({}_{2}\)O\({}_{2}\)** \\ **(mM/L)** \\ \end{tabular} & \begin{tabular}{c} **COD** \\ **Degrad. (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **K\({}_{\text{phC}}\)** \\ **(min\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **t\({}_{1/2}\)** \\ **(min)** \\ \end{tabular} \\ \hline
0.1 m\({}^{2}\) & 951 & 0 & 46.9 & 0.013 & 54.02 \\ \hline
1 m\({}^{2}\) & 956 & 0 & 100 & 0.10 & 8.59 \\
963 & 4 & 72.0 & 0.044 & 15.89 \\ \hline \hline \end{tabular}  
* Rad: Solar Radiation Intensity; COD Degrad: AMX Degradation by COD analysis, after 80 min reaction.  
\end{table}
Table 1: Photocatalytic rate constants (K\({}_{\text{phC}}\)) and half-life time (\(t_{1/2}\)) for AMX degradation in aqueous solution with absence (0 mM) and added (4 mM) H\({}_{2}\)O\({}_{2}\), at constant pH 4.15 of reaction, for two FPR surfaces (0.1 and 1 m\({}^{2}\)).  
(FPR\({}_{1\text{m}}\)\({}^{2}\) = 10 FPR\({}_{0\text{-}1\text{m}}\)\({}^{2}\)) favors the degradation of AMX under similar reaction parameters, and also favoring the speed of the reaction. The higher the magnitude of K\({}_{\text{phC}}\), the lower the values of \(\mathbf{t_{1/2}}\) are obtained (Table 1), and higher percentages of AMX degradation; therefore the solar photocatalysis reaction in 1 m\({}^{2}\) FPR, the experimental magnitude of pH 4.15 and the absence of H\({}_{2}\)O\({}_{2}\), are the best AMX degradation conditions in this work.
## 3 Results
### Sensitivity of the Simulation Results to Forest Age
#### 3.3.2 Sensitivity Analysis of the 3-PG Model at the FSC Level  
The impacts of adjusted forest age on the simulated H and ACS for the 140 FSC show that the largest deviation for both occurs in the scenario of increased age of 12 months (Table 4), with RMSE increasing by 12.2% and 41.23%, respectively. The lowest RMSE of H and ACS are observed in the scenario of decreased age of 3 months and no adjustment scenario, respectively. The decreased forest age does not lead to much variation in ACS simulation at the SP level.
###### Abstract  
The sourcing of raw materials with low environmental impact, e.g., \"upcycled\" ingredients from short supply chains, has currently become necessary, and agri-food waste represents a very attractive hub to produce innovative cosmetic extracts. In this paper, an integrated approach considering all the different steps, starting from material selection, extraction, chemical characterization, biological activity evaluation, and environmental impact calculation, was adopted to obtain innovative, sustainable, and effective cosmetic raw materials from food waste. As case report, a supercritical CO\({}_{2}\) extract obtained from wild-strawberry-processing waste after jam production (WSWSCO\({}_{2}\) extract) was developed. The fatty acids profile of the waste material and WSWSCO\({}_{2}\) extract was investigated via a GC-MS method, and mainly polyunsaturated fatty acids (PUFAs) such as linoleic and linoleic acids were detected. Furthermore, the ability of the WSWSCO\({}_{2}\) extract to inhibit 5\(\alpha\)-reductase type 1 expression in skin fibroblasts was assessed, confirming significant efficacy at the dose of 5 mg/mL. Finally, in view of the eco-sustainability approach, the environmental impact related to WSWSCO\({}_{2}\) extract was calculated using a life cycle assessment (LCA) analytical approach, considering different parameters and indicators (e.g., carbon footprint) and verifying the eco-friendly approach in extract development and production. Although further research is needed, for example, to check the full composition of the extract and its effect on skin cells, these results suggest that the WSWSCO\({}_{2}\) extract may represent an innovative and sustainable ingredient for cosmetic applications especially in topical preparation for the treatment of some androgenic-related discomfort, such as acne and androgenic alopecia, reflecting the potentiality of the holistic and pioneering approach related to ingredient development presented in this study for the cosmetic sector.  
Keywords:wild strawberry; upcycling; supercritical carbon dioxide (SCO\({}_{2}\)); 5\(\alpha\)-reductase; cosmetic ingredient +
Footnote †: journal: Applied sciences