esa-sceva/satcom-chunk-collection huggingface dataset:
# An Indoor Space Model of Building Considering Multi-Type Segmentation
## 3 Indoor Space Extension Model Based on IFC
### Entity Extension
#### 3.2.1 The Relationship Involved in the Indoor Space  
There are three relationships between components and indoor space: containment relationship, boundary relationship, and segmentation relationship.  
(1)  
The containment relationship refers to the relationship between the indoor space and the indoor furniture, home appliances, and facilities inside. Example: The relationship between the sofa, potted plants, and the living room (Figure 9).  
(2) Boundary relationship  
A boundary relationship is a relationship between different indoor spaces and indoor components. As the boundary element that defines the indoor space, the internal  
Figure 9: Containment relationship diagram.  
components play the function of enclosing the indoor functional subspace. For example, the screen is the boundary between the hall space and the living room space (Figure 10).  
(3) Segmentation relationship  
The segmentation relationship is the association relationship between indoor components and indoor space. Indoor components play a role in the physical or virtual division of indoor space, dividing a single indoor space into several lower-level indoor subspaces. Such as the division of single room space by light partition wall; Virtual segmentation of different functional areas by floor and ceiling.  
There are decomposition, aggregation, adjacency, and connectivity relationships among different indoor space objects.  
(1) Decomposition relationship  
The decomposition relationship is the relationship between the indoor compound space and the single space, and it is an expression of the relationship between the whole and the part. For example, the relationship between floor and room space, and the decomposition relationship between building space and floor space.  
(2) Aggregation relationship  
It is the relationship between the room space and the different functional subspaces. For example, the aggregation relationship between the living room and the meeting area, leisure area, audio-visual area subspace, etc., and the aggregation relationship between the bedroom and the sleeping area, dressing area, storage area, audio-visual area, and other sub-spaces.  
(3) Adjacency relationship  
This relationship refers to the adjacent relationship between different functional subspaces in the room. For example, the meeting area has an adjacent relationship between the screen and the porch.  
(4) Connectivity relationship  
Connectivity relationship refers to the interconnection relationship between different indoor spaces in the room. For example, the meeting room is connected through the door and the corridor.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
The algorithm employs the modified Intelligent Driving Model (IDM) to adjust advisory speeds, considering HCV acceleration limitations and intersection clearance constraints. The advisory speed \(V_{a}\) for the platoon leader is calculated as:  
\[V_{a}=\frac{d_{a}}{T_{g}} \tag{1}\]  
where:  
* \(V_{a}\) is the advisory speed of the lead HCV in the platoon;
* \(d_{a}\) is the distance to the stop line;
* \(T_{g}\) is the remaining green time. If the computed speed exceeds a safe acceleration threshold for HCVs, the algorithm adjusts it to prevent excessive fuel consumption, engine strain, or unstable braking patterns. The signal phase selection and advisory speed-coordination steps operate in parallel, ensuring that both signal allocation and speed recommendations are synchronized. This proactive approach allows for real-time adjustments, unlike traditional actuated control methods that reactively change signals based on vehicle detection. By optimizing both signal timings and advisory speeds simultaneously, the HCV platoon-based adaptive signal-control algorithm effectively reduces delays, idling time, and fuel consumption, while enhancing the efficiency of logistics operations.
## 3 Results and Discussions
### Mechanical Properties in Hardened State
On the other hand, it can be observed that when using WFT and WFR fibers, the mortar has the ability to transfer stress even after the formation of cracks and reaching maximum load. This is evident from the steady decrease in load experienced after reaching the maximum load and has been reported by previous research [26; 40; 41]. The toughness of a test specimen can be determined by measuring the areas under the load-deflection curve. This measurement reflects the specimen's ability to absorb energy and is influenced by its geometric properties and the way it is loaded. The test method reported by ASTM 1018-97 [42] evaluates the toughness properties of fiber-reinforced CM by measuring the area under the load-deflection curve of a beam supported at two points and loaded at a third position. This test method evaluates the toughness indices that indicate the material's behavior until it reaches the point of deflection. These indices are generated by dividing the area under the load-deflection curve up to a given deflection threshold by the area up to the first crack (ASTM 1018-97). These indices provide residual strength factors that quantify the average amount of load retained after a crack has occurred, expressed as a percentage of the load at the initial crack. ASTM 1018-97 also defines the residual strength factors R\({}_{5,10}\) and R\({}_{10,20}\) by the following equations. \[R_{5,10}=\frac{100}{5-10}\;(I_{5}-I_{10}) \tag{4}\]  
Figure 13.0  
Figure 13.1 Load–mid-span deflection curve for controlled mortar and mortar prepared using WFT-20 and WFR-20 fibers. The results for toughness indices and residual strength factors are given in Figure 13.2  
The mortar made with both types of fibers has commendable toughness. MWFT-20 exhibits superior toughness compared to MWFR-20.
## 2 Modeling
### Energy Balance  
Temperatures in Equation (24) can be determined using the first law of thermodynamics, i.e., the principle of energy balance. Assuming that the gasification process is under adiabatic conditions and that there is no change in pressure, the energy balance is written in Equation (29) (29).  
\[\Delta H = \sum_{j = \text{ product}}H_{j}-\sum_{i = \text{ reactant}}H_{i} \tag{29}\]  
Considering \(\Delta H\) = 0, the reactant enthalpy on the right side of Equation (29) can be defined as follows (29).  
\[\sum_{i = \text{ reactant}}H_{i} = h_{f,\text{bio}}^{o}+m\left(h_{f,\text{H}_{2}\text{O}}^{o}+ \Delta h_{T,\text{H}_{2}\text{O}}\right) \tag{30}\] \[h_{f,\text{bio}}^{o} = \text{HHV}_{\text{bio}}+\left(h_{f,\text{CO}_{2}}^{o}+\frac{x}{2 }h_{f,\text{H}_{2}\text{O}}^{o}\right) \tag{31}\]  
The calorific value of the formation of the biomass with a higher heating value (\(\text{HHV}_{\text{bio}}\)) is calculated using a correlation developed by [PERSON] and [PERSON] (2018) as follows.  
\[\text{HHV}_{\text{bio}} = 0.3491\text{C}+1.1783\text{H}+0.1005\text{S}-0.1034\text{O}-0.0 151\text{N}-0.0211\text{Ash} \tag{32}\]  
where \(\text{HHV}_{\text{bio}}\) (MJ/kg) is the HHV value of biomass, and C, H, S, O, N, and ash are the mass percentages of carbon, hydrogen, sulfur, oxygen, nitrogen, and ash content of the biomass, respectively. The mass percentages of C, H, O, N, S, and ash in Equation (32) are  
Figure 1: Plot depicting the relationship between the equilibrium constants \(K_{i}\) and temperature.  
determined using ultimate and proximate tests of the dried biomass. Further, the lower heating value of the biomass (\(\mathrm{LHV_{bio}}\), in \(\mathrm{MJ/kg}\)) is calculated using Equation (33) [22]  
\[\mathrm{LHV_{bio}}\ =\ \mathrm{HHV_{bio}}-h_{\mathrm{gas}}\bigg{(}\frac{9\mathrm{ H}}{100}+\frac{\mathrm{MC}}{100}\bigg{)} \tag{33}\]  
where \(h_{\mathrm{gas}}\) is the latent heat of steam (2260 \(\mathrm{MJ/kg}\)). The enthalpy of the producer gas on the right side of Equation (29) is calculated using Equation (34) [29] as follows.  
\[\sum_{j\ =\ \mathrm{product}}H_{j}\ =\ \sum_{j\ =\ \mathrm{product}}n_{j} \bigg{(}\overline{h}_{f,j}^{0}+\Delta\overline{h}_{T,j}^{0}\bigg{)} \tag{34}\]  
were,  
\[\Delta\overline{h}_{T,j}\ =\ \int_{298}^{T}\overline{C}_{p}(T)dT\ =\ aT+bT^{2}+cT^{3}+dT^{4}+\kappa \tag{35}\]  
The values of \(\overline{h}_{f,j}^{0}\) in Equation (34) and coefficients, \(a\), \(b\), \(c\), and \(d\) in Equation (35) are provided in Table 3, except for the enthalpy of carbon enthalpy because the referred reference [19] does not have the enthalpy of carbon in its datasets. Therefore, the enthalpy of carbon is calculated using the following Equation (36) [30].  
\[\Delta\overline{h}_{T,C}\ =\ \int_{298}^{T}\bigg{(}16.336+0.60972\times 10^{-2}T- 0.64762\times 10^{-6}T^{2}-\frac{836340}{T^{2}}\bigg{)}dT \tag{36}\]
# Upregulation of Neuronal Rheb(S16H) for Hippocampal Protection in the Adult Brain
## 3 Rheb-mTOR Signaling in the Brain
A recent study showed that Rheb overexpression-mediated synaptic growth was morphologically and functionally different from that in the presence of the TSC mutant, indicating that TSC may be able to regulate the neuromuscular junction synapse independent of the Rheb-mTORC1 pathway [68]. In addition, Rheb augments the expression of NTFs such as BDNF, CNTF, and GDNF through the activation of mTORC1 in the brain [11; 69]. NTFs support the cell growth, survival, synaptic plasticity, and differentiation of both developing and mature neurons [70]. On the other hand, Rheb activation inhibits the non-selective degradation of protein aggregation mediated by autophagy, resulting in cell death due to the accumulation of misfolded proteins [19]. Autophagy dysfunction can be implicated in the pathogenesis of neurodegenerative disorders including PD and AD [20; 21; 22; 23]. However, it is still unclear whether the alteration of autophagic regulation by neuronal Rheb upregulation is involved in neurotoxicity or neuroprotection in the adult brain with neurodegenerative diseases. Therefore, further studies are needed to determine the connection between neuroprotection and Rheb-mediated autophagy in neurodegenerative diseases such as PD and AD.
## 2 Methodology
### Scenarios  
As mentioned previously, different constructive solutions were investigated, and the results were compared with the reference ones. The considered constructive solutions are thoroughly described in Table 4. The same internal partition walls were used in all the simulations.  
For the enhanced solution, PCM with different melting temperature values were tested to reduce the high levels of overheating. Commercial solutions provided by BioPCM(r) were selected (Table 5), considering PCM with different melting points within the same group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Scenarios**} & **Building Element** & **Constructive Solution** & \multicolumn{1}{c}{_U-Value_} \\ \hline \multirow{8}{*}{B} & \multirow{4}{*}{External walls} & Massive granite solution with 80 cm of thickness. & \multirow{2}{*}{2.09} \\  & & In the inner surface of the wall 2 cm of mortar was & & \\  & & considered. & & \\  & & Pitched roof & Ceramic roof tiles supported by a wood structure. & 2.35 \\  & & Ceramic floor tiles supported by a miscellaneous & & \\  & & Ground floor slab & of materials including gravel, stone and air lime & 2.26 \\  & & with a thickness of 20 cm. & & \\  & & Metallic modular system with 5 cm of thermal & & \\  & & insulation (expanded polystyrene) plus 5 cm of & & \\  & & acoustic insulation (glass wool) coated by wood & & \\ C & & panels. & & \\  & & Metallic modular system with 6 cm of thermal & & \\  & & insulation (extruded polystyrene) plus 4 cm of & & \\  & & acoustic insulation (glass wool) coated by wood & & \\  & & panels in the inner surface and metallic sheet in & & \\  & & the outer surface. & & \\  & & Coronet floor slab & Concrete slab with 6 cm of thermal insulation & & \\  & & (extruded polystyrene). & & \\  & & Metallic modular system with 5 cm of thermal & & \\  & & insulation plus 5 cm of acoustic insulation coated & & \\  & & by wood panels and BioPCM (a non-toxic, & & \\ Enhanced solution & & non-corrosive, biodegradable patented family of & & \\  & & phase-change materials) in the inner surface. & & \\  & & Metallic modular system with 6 cm of thermal & & \\  & Pitched roof & insulation plus 4 cm of acoustic insulation coated & & \\  & & by wood panels in the inner surface plus BioPCM & & \\  & & and metallic sheet in the outer surface. & & \\ Ground floor slab & & Concrete slab with 6 cm of thermal insulation & & \\  & & (extruded polystyrene). & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenarios constructive solutions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**PCM Reference**} & **Thickness (m)** & **Melting Point (°C)** & \multicolumn{1}{c}{
\begin{tabular}{c} **Total Energy Storage** \\ **(J g\({}^{-1}\))** \\ \end{tabular} } \\ \hline BioPCM® M\({}_{91}\)/Q\({}_{25}\)\_0.037 & & 25 & 322 \\ BioPCM® M\({}_{91}\)/Q\({}_{27}\)\_0.037 & 0.037 & 27 & 322 \\ BioPCM® M\({}_{91}\)/Q\({}_{29}\)\_0.037 & & 29 & 350 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Used phase-change material (PCM): main properties.
## 3 Engineering Case Study
### Landslide GWL Monitoring  
Inclinometers and water level gauges were installed in a series of boreholes (Figure 2). Inclinometers JC1 (40 m depth), JC3 (40 m depth), and JC8 (43 m depth) are positioned at elevations of 180 m, 198 m, and 225 m, respectively, with water level gauges installed at their bases. Figure 3 shows the GWL variations in three monitoring boreholes of the landslide in response to fluctuations in RWL. Rainfall has little effect on the fluctuation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Hole Number** & **Test Depth (m)** & **Materials in Test Section** & **Permeability Coefficient (cm/s)** \\ \hline ZK4 & 0.5–0.7 & Gravel soil silty clay & \(1.38\times 10^{-5}\) \\ ZK10 & 4.3–4.5 & Gravel soil & \(6.4\times 10^{-2}\) \\ ZK11 & 6.1–6.3 & Gravel soil & \(0.5\) \\ ZK1 & 6.4–6.6 & Gravel soil & \(1.5\) \\ ZK2 & 10.0–10.3 & Sandstone (highly weathered) & \(2.5\times 10^{-2}\) \\ ZK6 & 10.4–10.6 & Mudstone (highly weathered) & \(6\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Infiltration test results for the Majiagou landslide.  
Figure 2: Geological engineering and monitoring layout of the Majiagou landslide.  
of GWL because the rainfall is less than 300 mm/month, and much of it becomes surface runoff rather than infiltrating the landslide.  
According to the monitored results (Figure 3), the annual variations in GWL exhibited consistent patterns. Due to the high permeability of the surface deposits and the proximity of piezometer JC1 to the reservoir, the GWL at JC1 fluctuated synchronously with the reservoir level. However, the GWL at JC3 and JC8 lagged about two months behind the reservoir level decreases. However, monitored data can only reflect the changes in GWL at monitoring points in the landslide and cannot reflect the overall seepage field. Therefore, the following text will use an inverse analysis to obtain the permeability coefficient of the landslide and subsequently simulate the evolution law of the seepage field.
# Modeling and Control Simulation of Power Converters in Automotive Applications
## 4 Software-in-the-Loop Simulation Analysis
### Switching Devices Thermal Simulation Analysis  
The thermal behavior of the switching components of the previously examined AC/DC converter is depicted in Figure 27, with reference to the \"intermediate\" working state, which calls for an AC supply of 230 V & 50 Hz and a DC bus of 650 V. One branch of the active rectifier operates at a high switching frequency, while the other operates at the grid frequency, as was previously mentioned in relation to converter operation. Since the two branches are typically powered by the same current, their conduction losses are comparable; in contrast, the high-frequency branch has much larger conduction losses. The simulation results align perfectly with this observation.  
Figure 26: Variations in the output capacitor and the primary-side inductance of the transformer have an impact on the DAB converter’s voltage regulation.  
Figure 25: Impact of the \(V_{in}\) fluctuation on the DAB converter’s output voltage control, when \(V_{ref}=450\) V.  
Consequently, for the two branches operating at distinct frequencies, a stable state value of \(T_{j_{HP,final}}\cong 75\) [\({}^{\circ}\)C] is identified for the high-frequency branch devices, while a regime value of \(T_{j_{LP,final}}\cong 61\) [\({}^{\circ}\)C] is observed for the grid-frequency devices within the branch. Additionally, two transients with differing rise times are identified for each branch. The thermal transient of the SiC-integrated devices is displayed in Figure 28 for the chosen DAB converter. The switching losses (as well as those for switching) differ greatly because, on average, the primary and secondary stage branches are not crossed by the same current due to the galvanic isolation transformer. Specifically, the average current flowing through the active rectifier's components crosses the first conversion stage (DC/AC).  
In fact, the operating temperature of the SiC primary components is comparable to that of the rectifier's high-frequency components, with a steady state average value of \(T_{j_{p},final}\cong 82\) [\({}^{\circ}\)C]. Since the resonance frequency is higher than that of high-frequency components, the switching losses weigh much more, leading to overheating at higher speeds. On the other hand, because the transformer is employed to decrease the effective voltage value, a greater current passes through the components of the AC/DC secondary circuit, making them susceptible to noticeably higher conduction losses.  
The average junction temperature in secondary SiC devices has a steady-state value of \(T_{j_{s},final}\cong 109\) [\({}^{\circ}\)C]. It is noteworthy that the steady-state values are within the manufacturer's stated operating limitations and are consistent with the design and size decisions made during the early modeling stages.  
Figure 28: Thermal transient of the primary and secondary side switching devices in the DC/DC converter.  
Figure 27: Thermal transient of the high-frequency and low-frequency switching devices in the active rectifier.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 5 Discussion  
### Ability Assessment of ML and Remote Sensing on Predicting Single Source Emission  
Most existing RS-ML research focuses on the general large-scale spatial distribution of NO\({}_{2}\) concentration and did not explore micro-resolution single-point emission for several reasons [33]. ML can address these concerns by directly simulating the relationships between remotely sensed data and accurate ground emission sources. As shown in Section 4, at most times there was a significant correlation between TROPOMI and EPA ground observations which indicates that the relationship is very likely to help inferences from one to another. Even though the curve of TROPOMI was relatively flat compared to the EPA observations, the ups and downs in both time series resonated with each other. Regarding the emission transportation, normally the operational power plants are running constantly with short occasional breaks (e.g., EPA orders shutdown after exceeding an emission threshold), the NO\({}_{2}\) plume should be close to the power plants and can be captured by the imagery grids even though its spatiotemporal resolution is coarse. The experiment results have validated that ML could overcome the challenges at some level and produce a reliable estimate on specific emission sources. However, certain conditions are required to allow the ML models to work in their best mode. There should be no other major second source of NO\({}_{2}\) close by, e.g., metropolitan cities or other massive-emitter facilities. We have not experimented on the boundary of the noise sources and temporarily recommend using 100 miles to single out power plant emissions and clean the training data. In the future, with more availability of higher resolution satellite products, we will be able to use ML to predict all power plant emissions without such spatial restraint.
## 3 Results and Discussion
### Antioxidant Activity  
To further characterize the ripening process, three in vitro assays were used to determine the potential for antioxidant activity of protein hydrolysis products in meat and brine (Table 6). Marinades obtained at 2 and 12 \({}^{\circ}\)C showed the highest FRAP reduction capacity, 10% higher than using marinades at 7 \({}^{\circ}\)C. FRAP activity in meat was mostly from the 5-10 kDa or \(<\)5 kDa fraction for herring marinated at 2 or 12 \({}^{\circ}\)C, respectively. The ability to inhibit lipid peroxidation in unfractionated brine ISP extracts was 26.6-31.2 mM FeSO\({}_{4}\)/100 mL, which was as high as in marinated meat 30.4-34.9 mM FeSO\({}_{4}\)/100 g. FRAP activity in the brine was derived almost entirely equally from the two fractions 5-10 and \(<\)5 kDa. The fractionation of ISP extracts did not significantly affect the sum of FRAP-reducing capacities, indicating that fractionation does not alter the synergistic action of peptides of marinades and/or the proportion of lysine and sulfur-containing amino acids in peptides of different molecular weights [46]. The antioxidant activity against the ABTS (the TEAC method) ranged from 107 to 136 \(\upmu\)M TE/100 g of the marinades; the highest was in marinades ripened at 2 \({}^{\circ}\)C, and the lowest at 7 \({}^{\circ}\)C (Table 6). Using the TEAC method, activity was mainly formed by the 5-10 kDa fraction for marinades at 2-7 \({}^{\circ}\)C, and the \(<\)5 kDa fraction for marinades at 12 \({}^{\circ}\)C, similar to the FRAP method, but the differences in activity between these fractions were much smaller. In the brine, the antioxidant activity was 34.3-36.6 \(\upmu\)M TE/100 mL; however, the sum of the activities of the three fractions was almost two times higher than the activity
## 2 Related Works
### Feature Point Detection and Matching  
Feature point detection is the process of finding parts that express important information or patterns within an image. This process aims to determine the local variation or structure of an image, helping the computer to identify specific points within the image. A typical procedure for feature point detection consists of five steps: image preparation and preprocessing, scale space setup, feature value calculation, keypoint selection, and duplicate removal and alignment. There are various representative algorithms, such as SIFT, SURF, and ORB [39; 40; 41]. Recently, algorithms which use deep learning, such as SuperPoint, D2-Net, LF-Net, and R2D2, have been used [42; 43; 44; 45]. Feature point matching is the process of finding a corresponding feature point pair between two images by comparing feature points extracted from different images or videos. There are various representative algorithms, such as nearest neighbor (NN), k-nearest neighbors (KNN), and fast library for approximate nearest neighbors (FLANN) [46; 47; 48]. Recently, deep learning-based feature point matching algorithms such as SuperGlue, DeepCompare, and GeoDesc have been developed and studied [49; 50; 51].