esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biological Potential
#### 3.3.3 Immunomodulation  
Disturbances in the process of intestinal inflammation can precipitate severe intestinal conditions, such as inflammatory bowel diseases (IBDs). The development of IBDs is closely linked to the involvement of inflammatory mediators, with IL-18 playing a significant role [18; 37; 38]. This investigation assessed how CNC-CTAB delivery systems containing CBG, as well as free CBG and the carrier itself, influenced the inflammatory response in Caco-2 cells triggered by IL-18. The research concentrated on the levels of cytokines IL-6 and IL-8, using betamethasone as a standard reference for anti-inflammatory efficacy. The results are presented in Figure 6.  
From Figure 6, significant (\(p<0.05\)) reductions in cytokine secretion were observed for CBG-treated cells, indicating a relevant anti-inflammatory effect. CBG reduced the secretion of IL-6 and IL-8 cytokines by ca. 60% and 40%, respectively, compared to the stimulated control cells. CBG has been found to modulate inflammatory processes by significantly reducing \(\mathrm{I\kappa B}\alpha\) phosphorylation, thus reducing the transcriptional activity of NF-\(\mathrm{\kappa B}\), which is responsible for the transcription of pro-inflammatory cytokines such as TNF\(\mathrm{\kappa}\) and IL-18 [3; 32]. Furthermore, CBG has been reported to reduce the activity of diacylglycerol lipase, the enzyme responsible for the biosynthesis of 2-archidonovylglycerol, the most abundant ligand for CB1 and CB2 cannabinoid receptors, as well as the activities of COX-1 and COX-2, which are involved in the metabolism of arachidonic acid and production of prostaglandandins, lipid mediators associated with in inflammation [3; 39]. In addition,CBG exhibits a significant activity against several receptors from the transient receptor potential (TRP) family, acts as a potent agonist of \(\alpha\)2 adrenergic receptors, and moderately blocks 5-HT1A receptors, which, combined with the effects mentioned above, potentiates its biological activity [2, 40]. Although CNC-CTAB in its free form demonstrated a slight pro-inflammatory effect, leading to an approximate 20% increase in the production of IL-6 and IL-8, encapsulation did not significantly impact CBG's inflammatory response in terms of IL-6 and IL-8 secretion levels in IL-18 stimulated Caco-2 cells (\(p>0.05\)) (Figure 6). CBG-encapsulated systems were capable of lowering cytokine production to levels comparable to those achieved with the anti-inflammatory agent betamethasone, indicating the potential anti-inflammatory activity of these systems.
## 2 Inductive-Based Digital Isolation System
### Rx Design  
The Rx circuit consists of a high pass filter (HPF), peak/bottom levels hold, a differential amplifier, and a hysteresis comparator. Figure 5 presents the schematic of the Rx with its operation waveforms. The isolation transformer's secondary side is connected to the Rx front side using two bond wires between the corresponding pads.  
The received signal (V_pulse) with a frequency of 3-5 GHz is first filtered from low-frequency noise components caused by common mode transients (CMT) using the HPF. The diode circuit is formed by implementing two series diode connected BJTs adopted to detect positive and negative impulses according to the Vbias threshold applied between its  
Figure 4: Non-ideal transformer equivalent circuit model [28].  
Figure 5: Circuit diagram of the implemented receiver (Rx) and the operation waveforms.  
terminals through resistances \(\mathrm{R_{a}}\) and \(\mathrm{R_{b}}\). The capacitor \(\mathrm{C_{b}}\) helps widen the detected pulses to a 10 MHz frequency so that they can be handled by the differential amplifier.  
The single-stage amplifier design compares both inputs, \(\mathrm{V_{\_pk}}\) and \(\mathrm{V_{\_bot}}\), with the reference voltage \(\mathrm{V_{\_}c}\)[38; 39], as shown in Figure 6. The reference current of the differential amplifier is settled by a Cascode current mirror, where the latter is referenced by the \(\mathrm{V_{\_}pm}\)-biased external current source. This amplification stage is designed to further reject the magnitude of the slow swing pulses that exceed the threshold voltage of the diodes. The purpose of capacitive coupling elements (\(\mathrm{R_{\_}c}\) and \(\mathrm{C_{\_}c}\)) at the amplifier output \(\mathrm{V_{\_}O}\) is to establish the DC level of the comparator input \(\mathrm{V_{\_}com}\) at \(\mathrm{V_{\_}pm}\).  
The last block of the Rx is the hysteresis comparator, which consists of three stages: a differential input pair, a decision stage, and an output buffer [40]. Figure 7 shows the circuit of the hysteresis comparator. The cross-coupled differential stage transistors provide positive feedback to the decision stage inputs and control the switching point of the comparator. The tail current of the differential stage is adjusted by the current mirror biased at \(\mathrm{V_{\_}pm}\). Thus, if the comparator's differential input signal exceeds/falls down this threshold level (i.e., positive/negative peak voltage), the output signal OUT switches to logic \(1/0\).
## 5 Conclusions  
In this work, single element AVA antenna type-A, type-B, and type-C were designed. The former two designs type-A and type-B were evolved to later type-C for the advantage of additional coverage range of lower frequency spectrum of the Ku-band. The single element type-C AVA antenna covered three frequency bands that is, 14.44-20.98 GHz, 24.34-29 GHz, and 33-40 GHz. The Ku-band from 12-18 GHz is designed by the IEEE and ITU for radar and satellite communications, while 28 GHz and 38 GHz are allocated to 5G mobiles communications. Although, single element type-C AVA has good characteristics like stable radiation patterns, wide bandwidth, light weight, and easy to fabricate but still, it is not suitable for the 5G applications due to having less gain values 2.5 dBi, 3.4 dBi, 3.6 dBi, and 7.4 dBi at the targeted frequencies of 15 GHz, 18 GHz, 28 GHz, and 38 GHz, respectively. To meet the high gain requirements of the 5G antennas operating in the mmW frequency bands, the single element type-C AVA was extended into two, four, and eight element AVA arrays. The proposed eight elements AVA array showed high measured gain values of 10 dBi, 10.5 dBi, 11.5 dBi, and 10 dBi at 15 GHz, 18 GHz, 28 GHz, and 38 GHz, respectively. A prototype of the proposed antenna design was fabricated and experiments were conducted. The simulated and measured results were found closely matched. The proposed eight elements antenna is showing higher gains, stable radiation patterns, and wide bandwidths at all operating frequencies. Owing to the aforesaid characteristics, the proposed antenna could be considered a candidate for 5G mmWave communications and Ku-band applications.  
Conceptualization, R.U. ([PERSON]) and S.U.; methodology, R.U. ([PERSON]), D.-y.C. and R.U. ([PERSON]); Software, R.U. ([PERSON]), D.-y.C. and F.F.; Validation, S.U., D.-y.C., A.A., B.K.; Funding acquisition, D.-y.C. and A.A.; Investigation, S.U., D.-y.C., F.F., and B.K.; Resources,D.-y.C. and A.A.; Writing-review & editing, D.-y.C. and A.A.; Supervision, S.U. All authors have read and agreed to the published version of the manuscript.  
The BrainKorea21 Four Program supported this research through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (4299990114316). Additionally, this research was also partially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1F1A1058128).  
The authors declare that there is no conflict of interest regarding this publication.
## 3 Results and Discussion
### Carbon Dioxide Emissions from the Manufacturing of Building Materials
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Material Quantity (unit/m\({}^{2}\))**} & \multicolumn{3}{c}{**Emissions of CO\({}_{2}\) (unit: kg CO\({}_{2}\) eq./m\({}^{2}\))**} \\ \cline{3-10} \multicolumn{1}{c}{**Materials**} & \multicolumn{1}{c}{**ORC**} & \multicolumn{1}{c}{**FPS**} & \multicolumn{1}{c}{**VDS**} & \multicolumn{2}{c}{**ORC**} & \multicolumn{2}{c}{**FPS**} & \multicolumn{2}{c}{**VDS**} \\ \cline{3-10} \multicolumn{1}{c}{} & \multicolumn{1}{c}{**ORC**} & \multicolumn{1}{c}{**FPS**} & \multicolumn{1}{c}{**VDS**} & \multicolumn{1}{c}{**Amount**} & \% & \multicolumn{1}{c}{**Amount**} & \% & \multicolumn{1}{c}{**Amount**} & \% \\ \hline
**Ready-mixed concrete** & 0.28 & 0.29 & 0.24 & 120.9 & 50.7 & 124.5 & 53.9 & 102.5 & 47.6 \\
**Rebars** & 32.95 & 30.35 & 26.69 & 112.0 & 46.9 & 103.2 & 44.7 & 90.7 & 42.1 \\
**Forms** & 2.00 & 1.05 & 0.65 & 6.5 & 2.4 & 3.4 & 1.4 & 2.1 & 1.0 \\
**Steel decking** & N.A. & N.A. & 0.61 & N.A. & N.A. & N.A. & 19.9 & 9.2 \\
**Void formers** & N.A. & N.A. & 0.02 & N.A. & N.A. & N.A. & N.A. & 0.1 & 0.1 \\
**Total** & 35.32 & 34.29 & 28.21 & 238.4 & 100 & 231.1 & 100 & 215.3 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Material quantity and carbon dioxide emissions per square meter. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CO\({}_{2}\) Emissions (kg CO\({}_{2}\) eq.)**} & \multicolumn{3}{c}{**Reduction**} \\ \cline{3-10} \multicolumn{1}{c}{**Materials**} & \multicolumn{1}{c}{**ORC**} & \multicolumn{1}{c}{**FPS**} & \multicolumn{1}{c}{**VDS**} & \multicolumn{2}{c}{**ORC-FPS**} & \multicolumn{2}{c}{**ORC-VDS**} \\ \cline{3-10} \multicolumn{1}{c}{**Ready-mixed concrete**} & 1,631,735 & 1,679,253 & 1,382,080 & 47,518 & 2.91 & \(-\)249,656 & \(-\)15.3 \\
**Rebars** & 1,510,906 & 1,391,616 & 1,223,834 & \(-\)119,290 & \(-\)7.9 & \(-\)280,072 & \(-\)19.0 \\
**Forms** & 88,303 & 46,335 & 28,709 & \(-\)41,969 & \(-\)47.5 & \(-\)59,594 & \(-\)67.5 \\
**Steel decking** & N.A. & N.A. & 269,554 & N.A. & N.A. & 269,554 & N.A. \\
**Void formers** & N.A. & N.A. & 1387 & N.A. & N.A. & 1387 & N.A. \\
**Total emissions** & 3,230,944 & 3,117,204 & 2,905,564 & \(-\)113,741 & \(-\)3.5 & \(-\)325,381 & \(-\)10.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Total carbon dioxide emissions and reduction of building materials. to enhance the nominal shear strength of flat plate slab at the critical section around the columns. Consequently, the voided slab system would have the least environmental impact compared to the other slab systems, even though it has additional building material, such as steel decking and void formers (see Figure 6).
## 5 Performance Analysis
### Experiment 5  
The following experiment was designed to assess the performance of the proposed system under conditions of high transmission rates and vehicular channel selectivity. Table 3 shows the system configuration parameters. The proposed system is compared against a conventional MIMO-OFDM system with two different constellation sizes: \(M=16\) and \(M=64\).  
The results presented in Figure 7 illustrate the main advantages and disadvantages of the proposed system. The primary advantage is the comparable performance between the proposed system with \(M=64\) and \(N_{c}=2\) and the OFDM-IM system with MMSE detection. However, it should be noted that the proposed system requires lower computational complexity. Compared to the conventional OFDM system with \(M=64\), the proposed system with \(N_{c}=2\) achieves a gain of approximately 2 dB with reduced complexity. However, achieving this performance requires a sacrifice of 1.2 in spectral efficiency. Additionally, for the case of \(N_{c}=8\), the proposed system achieves a gain of approximately 2.5 dB, indicating that an increase in the symbol block \(N_{c}\) helps mitigate ICI in the detection process.
## 3 Retrieval
### Setup
#### 3.1.4 State Vector \(\vec{x}\)  
The state vector consists of all quantities which we retrieve from the measurement and is of dimension \(n\times 1\) with \(n=36\). The dry-air mole fractions of water vapor (H\({}_{2}\)O) and CO\({}_{2}\) are retrieved from both CO\({}_{2}\) fit windows within five layers splitting the atmosphere into parts containing the same number of dry-air particles. This means, each CO\({}_{2}\) and H\({}_{2}\)O layer spans over four atmospheric layers used for the discretized RT calculations. The CO\({}_{2}\) and H\({}_{2}\)O concentrations are homogeneous within each of the five layers. XCO\({}_{2}\) and XH\({}_{2}\)O are not part of the state vector but are calculated during the post processing from the layer concentrations.  
SIF at 760 nm is derived from the SIF fit window by scaling the SIF reference spectrum \(F^{0}_{SIF}\). The scattering parameters pressure (i.e., height) of the scattering layer \(p_{s}\) (in units of the surface pressure \(p_{0}\)), scattering optical thickness at 760 nm \(\tau_{s}\), and Angstrom exponent A are derived from all fit windows simultaneously.  
Within the SIF fit window, FOCAL additionally fits a first order polynomial of the spectral albedo \(\alpha\)P\({}_{0,1}\) and shift and squeeze of the wavelength axis \(\lambda_{\text{sh,sq}}\). Within the other fit windows, FOCAL additionally fits a second order polynomial of the spectral albedo \(\alpha\)P\({}_{0,1,2}\), shift and squeeze of the wavelength axis, and a squeeze of the instrumental line shape function ILS\({}_{\text{sq}}\).  
We estimate the first guess zeroth order albedo polynomial coefficients \(\alpha\)P\({}_{0}\) from the continuum reflectivities \(R_{0}=\pi\,\zeta_{0}\,I/F_{0}\) using up to nine spectral pixels at the fit windows' lower wavelength length ends. The first guess profiles of H\({}_{2}\)O and CO\({}_{2}\) are obtained from ECMWF (European Centre for Medium-Range Weather Forecasts) analysis fields and SECM2016, respectively. SECM2016 corresponds to the simple empirical carbon model described by [PERSON] et al. [PERSON] et al. (2016) but trained with version CT2016 of the CarbonTracker model ([PERSON] et al., 2016). All other first guess state vector elements are scene independent and the a priori state vector \(\vec{x}_{a}\) equals the first guess state vector \(\vec{x}_{0}\).  
Table 1 summarizes the state vector composition including the used fit windows, a priori \(\vec{x}_{a}\) and first guess \(\vec{x}_{0}\) values, a priori uncertainties \(\sigma\vec{x}_{a}\), and typical values of a posteriori uncertainties \(\sigma\vec{x}\) and the degrees of freedom for signal \(d_{s}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**State Vector Element**} & \multicolumn{4}{c}{**Fit Window Sensitivity**} & \multirow{2}{*}{\(\vec{x}_{a}\), \(\vec{x}_{0}\)} & \multirow{2}{*}{\(\sigma\vec{x}_{a}\)} & \multirow{2}{*}{\(\sigma\vec{\hat{x}}\)} & \multirow{2}{*}{\(d_{s}\)} \\ \cline{2-2} \cline{5-8}  & **SIF** & & & & & \(\mathrm{R}_{0}^{\mathrm{SIF}}\) & 0.1
## 3 Results  
### Patient Characteristics and Mortality Rates  
A total of 27,238 adult patients were included in this study (see Supplementary File S1 for flowchart). The mean age was 58.5 years (SD 22.2). According to the Manchester Triage System (MTS), the majority of patients were classified as green (60.5%), followed by yellow (21.6%), orange (9.5%), blue (7.9%), and red (0.4%) (Table 2). Regarding arrival time, 74.7% of patients presented during the day, while 25.3% arrived at right. Most patients (60%) arrived by self-transport, 34.8% by ambulance, and 5.2% via emergency medical services (Table 2). Hospital admission occurred in 19.8% of cases, with ICU admission required in 0.9%. Short- and medium-term mortality was as follows: 122 patients (0.4%) died within 48 h, 265 (1%) within 7 days, and 643 (2.4%) within 30 days (Table 2). The NEWS parameters recorded in triage are listed in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Variable** & **N** \\ \hline Patients, n (\%) & 27,238 \\ Age, years, mean (SD) & 58.5 (22.2) \\ MTS urgency, n (\%) & \\ Blue & 2155 (7.9) \\ Green & 16,491 (60.5) \\ Yellow & 5891 (21.6) \\ Orange & 2582 (9.5) \\ Red & 119 (0.4) \\ Time of arrival, n (\%) & \\ Day & 20,349 (74.7) \\ Night & 6889 (25.3) \\ Mode of arrival, n (\%) & \\ Self-arrival & 16,338 (60) \\ Ambulance & 9477 (34.8) \\ Emergency medical services & 1423 (5.2) \\ Admission in hospital ward, n (\%) & 5381 (19.8) \\ Admission in intensive care unit (\%) & 246 (0.9) \\ Mortality, n (\%) & \\
48 h & 122 (0.4) \\
7 days & 265 (1) \\
30 days & 643 (2.4) \\ \hline \end{tabular} Abbreviations: n = number; SD = standard deviation; MTS = Manchester Triage System.  
\end{table} 
Table 2: Characteristics and outcomes of the patients enrolled in this study.
# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 3 Methodology
### Prototype Architecture: Modular Agent-Based Design
Users can define custom weights (from 0 to 1) for cost, time, and emissions, prioritize specific transport modes, and exclude countries (e.g., due to risk or regulation). In practice, for each possible route, the algorithm calculates a combined score by summing the weighted values for cost, time, and emissions, plus an additional penalty if a non-preferred mode is included, as described by Equation (1). The score for each route is calculated as follows:  
\[score=w_{costs}.costs+w_{duration}.duration+w_{emissions}. emissions+w_{mode}.penalty \tag{1}\]  
where \(w_{costs}\), \(w_{duration}\), \(w_{emissions}\), and \(w_{mode}\) are user-defined weights for the respective criteria. Penalty reflects the predefined cost of using a non-preferred transport mode. This weighted cost function aggregates all relevant factors into a single scalar value per route, enabling comprehensive trade-off analysis. Each execution produces both a step-by-step route trace of the selected path and a summary of cumulative impact metrics. It is important to note that the explained approach is only one possible realization of the SELECT task. The USE task is highly context-dependent and refers to how an organization integrates the prepared KG into business processes to generate operational or strategic value. In this study, a dedicated Streamlit dashboard, illustrated in Figure 4, was developed to demonstrate user interaction. The dashboard not only visualizes the current state of the supply chain KG, but it also enables key functionalities that support data-driven logistics decision-making; the following are examples:  
* Import data enables the mass import of nodes, relationships, and predefined routing demands. The user can trigger the full orchestration process with a single action. * Create additional routes that allow on-demand route creation for a user-specific start-end combination, using the orchestration process. * Visualize graph content offers both a dynamic network graph and a georeferenced map to explore entities and transport connections. * Route analysis and benchmarking leverage the SELECT task to find the best route between two selected nodes. Users can define custom preferences (e.g., exclude nodes in specific countries, prioritize certain transport modes) and compare routing alternatives across criteria such as cost, time, and emissions. This interactive dashboard operationalizes the IAES concept by demonstrating how the enriched transport graph data can be made visible, actionable, and decision-relevant.
## 2 Literature Review  
### Particulate Matter and Health  
PM refers to tiny particles suspended in the air, categorized by size into PM10 (particles with a diameter of 10 \(\upmu\)m or less) and PM2.5 (particles with a diameter of 2.5 \(\upmu\)m or less) [13]. These particles originate from both natural and anthropogenic sources. Natural sources include volcanic eruptions, wildfires, and dust storms, while anthropogenic sources include vehicular emissions, industrial activities, and construction sites. Smaller particles pose greater health risks as they can penetrate deeper into the respiratory system.  
PM adversely affects human health through various pathways. It is primarily associated with respiratory diseases, as PM can reach the alveoli in the lungs, impairing lung function and exacerbating conditions such as asthma and chronic obstructive pulmonary disease (COPD) [14]. Additionally, PM exposure is associated with cardiovascular diseases, increasing the risk of hypertension, heart attack, and stroke. Beyond these conditions, PM can cause skin and eye irritation and weaken the immune system, and long-term exposure is associated with an increased risk of cancer.  
Certain populations are particularly vulnerable to the health effects of PM. Older adults, with weaker immune systems and pre-existing health conditions, face heightened risks of PM exposure [15]. Children are more susceptible due to their faster breathing rates, leading to higher PM intake, which can negatively affect their growth and development [16]. Individuals with chronic diseases also face significant risks as PM can worsen their symptoms. Protecting these vulnerable groups is crucial for mitigating health risks and improving overall well-being.  
The effective management of PM is essential for reducing its health impacts. Critical measures include providing real-time information on PM levels, installing air purifiers, and recommending limited outdoor activities during periods of high PM levels [17]. Installing PM monitors in areas with high concentrations of vulnerable populations, such as near hospitals, schools, and senior centers, can provide timely alerts and enable swift action to protect public health [18]. These measures are vital for minimizing the health effects of PM and improving the quality of life for vulnerable populations. Globally, PM is recognized as a significant environmental and public health issue, necessitating a comprehensive understanding and management [19]. Systematic research and the implementation of effective strategies, particularly for protecting vulnerable populations, can mitigate the adverse health effects of PM and enhance quality of life.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 4 Hardware Architecture
### Parallel Architecture with a Single Hash Table  
Since the parallel architecture has multiple copies of the basic IP address lookup modules, the block-RAM (BRAM) utilization is rapidly increased when the number of modules is increased. The BRAM thus becomes the bottleneck in increasing the degree of parallelism. Since the hash table is infrequently accessed in our proposed IP address lookup architecture, multiple VBF blocks can share one hash table. In other words, we can separate the hash table from the basic IP address lookup module and make multiple VBF search blocks share a single hash table.  
Figure 5 shows the parallel architecture with a single hash table, in which two VBF search blocks share a single hash table. The hash table search block is now composed of a _Hash Table Queue_ and the hash table.  
The _Hash Table Queue_ is a waiting place before accessing the hash table in preparing the case that two or more input addresses need to access the hash table at the same time. If the output port for an input is not determined by the VBF search block, the index of the input and two hash indexes for the hash table are stored in the _Hash Table Queue_. When the input arrives to the front of the queue, the stored indexes for each input are used to access the shared hash table. The input is removed from the queue after accessing the hash table.