esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 AE Monitoring at the 105-C Reactor Facility
### Results and Discussion
#### 2.3.1 Remote Monitoring at the Crane Maintenance Area
Sensors corresponding to channels 9 and 11 (both at the vertical column to roof interface location) detached from the concrete surface on 27 November 2014 and 23 November 2014, respectively. Localized spalling that occurred at these  
Figure 6: Amplitude and temperature versus time for broadband sensors: (**a**) Vertical column to roof interface location, (**b**) horizontal beam location, (**c**) control location, and (**d**) rain versus time. locations during this time period is presumed to be the cause of the detachment. Both sensors were reattached on 8 April 2015. Three seismic events occurred during the monitoring period: 14 September 2014 (M2.2), 19 September 2014 (M2.6), and 22 May 2015 (M1.96). Close inspection of data collected during this period did not reveal a correlation between these events and the AE data. Referring to the definition of acoustic emission (transient stress waves caused by a rapid release of energy within a material, [13], AE sensors would potentially be capable of detecting crack growth events caused by a seismic event provided the crack growth event or events that occurred within the range of sensitivity of the sensors. In the application at 105-C, the range of sensitivity for minor crack growth events (similar in energy to that caused by a pencil lead break) is in the range of 3 to 10 feet from each sensor. Due to the frequency range of AE sensors (30 kHz to 300 kHz), the sensors are not sensitive to global structural vibrations such as those potentially related to seismic activity.
## 4 Analyses of Numerical Simulation Results
### Flow Physics on Aerodynamic Performance Under Design Conditions
#### 4.1.2 Flow Field Analysis
Specifically, the integration is conducted over the region corresponding to each flow phenomenon, and the amount of each flow loss is evaluated. The entropy production rate \(\dot{S}_{\text{gen}}\) of internal flow is defined as  
Figure 12:2  
The entropy production rate can be a useful tool to calculate various loss-generation amounts [40]. Figure 18 shows the entropy generation rate contours within the vane passage of a supersonic compressor cascade, where the region of the first-passage shock root and its interaction with the boundary layer are marked with black dotted lines. The loss of the SWBLI region can be obtained by integrating the contour of the entropy production rate. The areas of the SWBLI region, the first-passage shock foot region, and the reflected shock region should be extracted separately from the entropy production rate contour. Then, the corresponding loss can be obtained by integrating the areas of each part. The variations in the loss breakdown in the interaction region (normalized by the baseline cascade loss) are depicted in detail in Figure 12:3 The boundary layer loss accounts for a large proportion of the total loss of the cascade, which is estimated to be more than 90%. Compared with the baseline cascade, the first-passage shock foot loss and reflected shock loss in the interaction region of the improved cascade are reduced to 9.1% and 33.3%, respectively. This means that a weaker incident shockwave and a lower stagnation pressure loss are present. The incompressible shape factor curves of the two cascades near the shockwave impact position reflect the behavior of the boundary layer, as illustrated in Figure 12:4 The general distribution tendency of incompressible shape factors \(H\) rises initially and then reduces across the interaction region. The maximum value occurs at the impingement position of the first passage shock. Compression waves originating from the negative-curvature vane profile of the improved cascade also lead to a slight increase in the boundary layer shape factor, but the effect is much weaker than that of the shockwaves. Figure 12:5 Static pressure coefficient distributions on vane suction side of both cascades. Compared to the baseline cascade, the shape factor of the SWBLI region of the improved vane suction surface is generally smaller. The maximum value of the shape factor at the first-passage shock incident position is reduced by 21.9%. Smaller boundary layer shape factors imply a fuller velocity distribution and a more stable boundary layer.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 2 Related Work
### Multimodal Models for VLM  
VLM models that use multimodal learning effectively involve the integration of image and text information to generate accurate responses. Transformer-based architectures, such as MCAN [8], LXMERT [9], and UNITER [10], have shown high performance since they process visual and textual features together.  
Recently, pre-trained VLM such as CLIP [2] and Flamingo [11] have been increasingly proposed for VLM tasks, highlighting the advantages of large-scale pre-training in enhancing scene-text understanding. Specifically, domain-adaptive models such as LLaVA have shown promise in autonomous driving scenarios where scene-specific reasoning is required.  
Knowledge-based VLM methods have also been investigated to improve reasoning in autonomous driving applications. These methods incorporate external knowledge sources such as traffic rules, road regulations, and driving policies to provide more accurate and contextually relevant responses.  
Knowledge-enhanced VLM frameworks, integrating structured knowledge graphs [12], can help refine model predictions and ensure that the generated responses match the real-world driving environment. This method improves the reliability and interpretability of VLM systems in autonomous driving applications, which can, in turn, facilitate the use of both visual and textual inputs by vehicles to make more accurate and safer decisions.  
This paper proposes a data preprocessing pipeline that can be used to convert structured autonomous driving datasets into natural language captions. Furthermore, it fine-tunes BLIP-2's Q-Former module based on LoRA to enhance domain adaptation performance specific to road environments, and it develops an optimized prompting strategy to generate consistent scene captions that reflect contextual information; such captions can improve the interpretability and safety of autonomous driving systems.
## 3 System Models
### Routing Model  
The routing protocol in our proposed model is based on the location of sensor nodes. Therefore, each node should know its coordinates and its neighbor nodes' coordinates as well. The routing protocol is selecting the next node on the path towards the sink based on the following equation:  
\[\mathbf{d}=\sqrt[2]{\left(\mathbf{x}_{c}-\ \mathbf{x}_{sink}\right)^{2}+\left(\mathbf{y}_{c}-\ \mathbf{y}_{sink}\right)^{2}} \tag{3}\]  
where \(\mathbf{x}_{sink}\) and \(\mathbf{y}_{sink}\) are the coordinates of the sink, \(\mathbf{x}_{c}\) and \(\mathbf{y}_{c}\) are the coordinates of the candidate node, and \(\mathbf{d}\) is the destination between sink and candidate node. The node with the smallest \(\mathbf{d}\) value will be the next hop on the path. The location based routing protocol is flexible, and it means if the selected candidate node is out of battery or has a physical damage, the routing protocol will select the second best candidate node that satisfies the minimum \(\mathbf{d}\). The whole process will be repeated until the packet is received by the sink. The developed routing protocol does not cause high power consumption. It is implemented to reduce the number of packets, which leads to less overhead. Flooding based routing is not used in this work, because it surges the number of overhead packets and causes high power consumption.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## Data Bus Blood Oxygen Sensor
### Wireless Sensor Network Infrastructure
Remote node; the device that patients use to continuously send their vital signs to the WSN using IEEE 802.15.4;
2. Cluster device; the device responsible for group conformation within the network which manages and receives information from remote sensing node using IEEE 802.15.4;
3. Gateway device; the device that acts as the communication link between cluster devices to forward information using IEEE 802.15.4;
4. Sink device; the device that receives and forwards all information from WSN infrastructure to the PlaIMoS server using IEEE 802.15.4, 802.11 and 802.3. Figure 16 shows a diagram of a conventional network using the PlaIMoS-Routing communications protocol. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Package\_id** & **Patient\_id** & **Sample 1** & **Sample 80** \\ \hline Int & Int & Float & **F** Float \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Electrocardiogram package. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Package\_id** & **Patient\_id** & **Temperature** & **Heart\_rate** & **Fall** & **Location** \\ \hline Int & Int & Int & Float & Int & Int \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Continuous sampling package.
## 3 Experimental Tests and Results
### THERA(tm) Machine Tests
#### 3.1.1 First Test: Low Ambient Temperature Variation
While the shop floor's climate control system maintained a stable temperature during working hours, colder nights, particularly on 1 November and during the weekend climate control shutdown, resulted in a significant temperature drop of 4.0 \({}^{\circ}\)C. Over this period, a total of 225 complete MIIM measurements were performed. The average ambient temperature during the eight-day experiment was 19.21 \({}^{\circ}\)C, with temperatures ranging from a minimum of 16.8 \({}^{\circ}\)C to a maximum of 20.9 \({}^{\circ}\)C. Highly stable conditions were observed during working hours due to the climatization system. Figure 11 illustrates the deviations at each of the 74 measured points (refer to Figure 2) across the 225 MIIM measurements conducted between October and November 2023. Geometric deviations along the X, Y, and Z axes are represented as Ex, Ey, and Ez, respectively. Table 6 presents the average and maximum standard deviation values for the X, Y, and Z axes, calculated based on the data shown in Figure 11.  
Figure 11: Measured points’ geometric deviation per THERA\({}^{\text{TM}}\) MT along axes Ex, Ey, and Ez under varying environmental conditions. Figure 10: Air temperature and ETVE in Z direction from 24 October to 27 October. Figure 12 shows the evolution of the ETVE in the Z direction at a specific point within the measurement point cloud, corresponding to a Y-centred position and maximum ram extension (Point P_25 in Figure 2). A clear correlation is evident between temporal fluctuations in the point deviation and the ambient temperature changes. As the temperature increases, the error grows in a positive direction, effectively making the machine appear larger, with a certain delay. Conversely, when the temperature decreases, the error shifts in a negative direction, making the machine appear smaller. The error remains stable in the absence of temperature fluctuations.
# On the Feasibility of Using Industrial Urea to Mitigate Thermal and Shrinkage Cracking in Concrete
## 4 Results and Discussion
### Compressive Strength  
The compressive strength measurements of three specimens were averaged for each low-heat mixture including the urea mixture ratio (Figure 3). Concrete with relatively low hydration heat is known to show slow strength development, and therefore, the compressive strength was measured for up to 91 days. Relative to the OPC mixture, the mixture with urea shows \(\sim\)60% strength with initial aging for three and seven days. This tendency becomes more distinctive as the urea mixture proportion is increased gradually. As in SCM or LHC, the hydration temperature is controlled by adjusting the reaction velocity (or reacting dose) of cement hydration or inducing the pozzolanic reaction. On the contrary, because mixing urea in concrete results in a temperature decrease as soon as it dissolves in water, it is considered to affect the speed of strength development through cement hardening and retardation. With long-term aging for more than 56 days, LHC containing urea showed a strength close to that of the standard OPC mixture. This result is attributed to the continuous reaction of cement particles over time that are not hydrated  
Figure 2: Setting time of the concrete mixture.  
sufficiently in the early stage, and it can be considered a typical strength property of a low-heat mixture. However, U15V and U15W mixtures showed ~78% strength development with long-term aging for 91 days compared to the standard OPC mixture. Considering the required safety factor for a given site, separate measures must be taken to enhance the concrete strength; a urea mixture proportion of within 10% is considered appropriate.  
Figure 4 shows the typical compressive strength gain ratio measured in the experiment. The compressive strength (\(f_{c}(t)\)) of a specimen with an aging of \(t\) days was normalized on the basis of the compressive strength (\(f_{cu}\)) at 28 days to examine the strength development with early aging (<28 days) and long-term aging (>28 days). The strength gain ratio at 3 days relative to the 28-day strength of a mixture with urea was between 0.42 (U15W) and 0.60 (U5), and that at 91 days was between 1.21 (U5) and 1.36 (U15V). The long-term strength gain ratio of all mixtures exceeded the conventional value of 1.05-1.2 for plain concrete as presented in ACI 318 [29], thus enabling the evaluation of strength development in low-heat mixtures.
## 3 Results
Significant differences were detected between exercises in mean power output until reaching the braking phase (F = 40.85, \(p\) < 0.001) (see Figure 2D). Peak acceleration was higher in BP-EC (F = 7.99, \(p\) < 0.001) than in other exercises (see Figure 3A). Significant differences were detected between exercises in terms of the portion of the concentric phase (%) at which peak acceleration was detected (F = 3.47, \(p\) = 0.16) (see Figure 3B), or acceleration up until peak velocity was observed (F = 7.83, \(p\) < 0.001) (see Figure 3C). However, no differences were observed between exercises in the portion of the concentric phase where acceleration up to the braking phase took place (\(p\) > 0.05) (see Figure 3D). The reliability of measurements was analyzed according to bar peak velocity and acceleration. ICCs recorded for PV and acceleration in BP and MP were significant (\(p\) < 0.001). In BP exercise (BP-EC vs. BP-C), ICCs were 0.94 (CI 95%: 0.92-0.96) for peak velocity and 0.88 (CI 95%: 0.84-0.91) for acceleration. In MP exercise (MP-EC vs. MP-C) ICCs were 0.96 (CI 95%: 0.94-0.97) for peak velocity and 0.93 (CI 95%: 0.90-0.95) for acceleration. The CVs for the PV measures were 7.9% in the BP and 6.2% in MP. The CVs for the acceleration were 9.2% in the BP and 4.2% in MP exercise. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**RM** & **BPE-MA** & **BPC-VA** & **BPE-PV** & **BPC-PV** & **BPE-BP** & **BPC-BP** & **MPE-MA** & **MPC-MA** & **MPC-VA** & **MPE-PV** & **MPC-PV** & **MPE-BP** & **MPC-BP** \\ \hline
30 & 17 \({}^{\dagger}\) & 23 & 57 \({}^{f}\) & 6 & 22 & 22 & 20 & 25 & 62 & 68 & 18 & 21 \\
40 & 12 \({}^{\dagger}\) & 18 & 63 \({}^{f}\) & 65 \({}^{f}\) & 16 & 16 & 13 & 19 & 67 & 71 & 13 & 16 \\
50 & 10 \({}^{\dagger}\) & 16 & 67 & 69 & 11 & 11 & 11 & 17 & 72 & 74 & 9 & 11 \\
60 & 12 \({}^{\ddagger}\) & 18 & 71 & 73 & 7 & 7 & 13 & 17 & 76 & 76 & 5 & 7 \\
70 & 17 & 22 & 75 & 76 & 4 & 4 & 19 & 21 & 80 & 78 & 3 & 4 \\
80 & 25 & 30 & 77 & 78 & 1 & 1 & 30 & 27 & 83 & 80 & 1 & 2 \\
90 & 37 & 41 & 79 & 79 & 0 & 0 & 45 & 36 & 85 & 81 & 0 & 0 \\
100 & 52 & 56 & 80 & 80 & 0 & 0 & 65 & 47 & 87 & 82 & 0 & 0 \\ \hline \end{tabular} Data are presented as percentage (%). Abbreviations used: RM = maximum repetition; BPE = bench press eccentric/concentric; MA = peak acceleration; BPC = bench press concentric; PV = peak velocity; BP = braking phase; MPE = military press eccentric/concentric; MPC = military press concentric. \({}^{\dagger}\) Significantly different from BPC-MA and MPC-MA (\(p\) < 0.05). \({}^{\dagger}\) Significantly different from MPC-MA (\(p\) < 0.05). \({}^{\dagger}\) Significantly different from MPC-VA (\(p\) < 0.05). \({}^{\dagger}\) Significantly different from MPC-VA (\(p\) < 0.01).  
\end{table} 
Table 2: Relative contribution (%) of the total concentric phase duration at the time of peak acceleration, peak velocity, and braking phase.
## 3 Results
### Molecular Docking and Residue Interactions  
The natural ligand of XO was removed from the complex PDB structure 3 NVW and the ligand-free protein was used for docking. The inhibitors were docked to XO with AutoDock Tools 1.5.6 software (The Scripps research institute, CA, US, 2009). The estimated binding free energy between chlorogenic acid and XO (\(-\)6.75 kcal/mol) was lower than that of scopoletin- XO (\(-\)5.65 kcal/mol), and the inhibition constant of CA (11.18 uM) was far  
Figure 4: Expression levels of GLUT9 (**a**) and URAT1 (**b**), detected in UA-induced HEK293T cells. Bar graphs represent the relative expression levels and were calculated by Image J, as well as the error bars that indicate the mean \(\pm\) SD.  
Figure 5: (**a**) Relative abundance of scopoletin (blue) and chlorogenic acid (yellow) on XO-catalyzed UA formation. (**b**) Inhibition of scopoletin (blue) and chlorogenic acid (yellow). IC\({}_{50}\): concentration of the compounds, which induced 50% inhibition of UA formation. Symbols represent the data under corresponding concentrations and error bars indicate the mean \(\pm\) SD.  
less than that of SP (72.80 uM), which was consistent with the inhibition tendency of the experimental data below 260 uM.  
The docking sites of complexes displayed in spheres intuitively revealed the binding pockets (Figure 6a,b). Accordingly, the interactions of SP within 4.00 A showed critical residues Glu1261, Glu802, Phe798, Phe914, Ala910, Ala1078, and Ala1079 (Figure 6a), which are common residues surrounding the xanthine binding pockets [18]. Furthermore, it formed three hydrophobic interactions with Ala910, Ala1078, and Arg912 (three Pi-Alkyl), one conventional hydrogen bond with Ala1079, two Pi-Anion interactions, and a carbon hydrogen bond with Phe798 (Figure 6a). Meanwhile, the residues of CA within 4.00 A found the similar residues (Ser1080, Ser1082, Glu1261, Thr1077, and Phe798) observed around the xanthine pocket as well (Figure 6b). Furthermore, five conventional hydrogen bonds with Ser1082, Ser1080, Thr1077, Gln1194, and Met1038, and one hydrophobic interaction with Phe798 were visualized by PyMOL (Figure 6b). The more hydrogen bonds between inhibitor and XO that there are, the more stable the enzyme and inhibitor system is. CA, apparently, compared to SP and CA, with more hydrogen bonds would more prefer to dock to XO.
## 2 Materials and Methods  
Back-gated field-effect transistors were fabricated on Si (highly \(p\)-doped)/SiO\({}_{2}\) (300-nm-thick) substrates with mechanically exfoliated PdSe\({}_{2}\)[7] and chemical vapor deposition (CVD) MoS\({}_{2}\) nanosheets [8,9]. The standard scotch tape method was applied to exfoliate single PdSe\({}_{2}\) crystals that had been synthesized via a thermal process of compressed tablets of selenium and palladium powders, mixed in an atomic ratio of 2:1 and held in a quartz tube at 850 \({}^{\circ}\)C and 7.5 \(\upmu\)Torr for 72 h. The thermal cycle was repeated twice, the second time after mixing the PdSe\({}_{2}\) tablets with Se powder in a mass ratio of 1:4.  
MoS\({}_{2}\) nanoflakes were obtained by CVD in a three-zone tube furnace, purged with Ar gas for 15 min to minimize the O\({}_{2}\) content. S powder was located upstream in a zone heated to 150 \({}^{\circ}\)C, while the Si/SiO\({}_{2}\) substrate and the MoO\({}_{3}\) precursor were placed in a downstream zone of the furnace, heated to 750 \({}^{\circ}\)C. The deposition process lasted 15 min, after which the sample was cooled down rapidly.  
PdSe\({}_{2}\) has a pentagonal and puckered structure (Figure 1a), while MoS\({}_{2}\) has a hexagonal and planar structure (Figure 1b) [10,11].  
Nanosheets for this study were initially identified by optical microscopy and then confirmed by Raman spectroscopy. Furthermore, scanning electron microscopy (SEM) and atomic force microscopy (AFM) were employed to characterize the nanosheets' lateral size and thickness. While exfoliated PdSe\({}_{2}\) nanoflakes were multilayers with thicknesses around 15 nm, MoS\({}_{2}\) nanosheets were mostly monolayer or bilayers with thicknesses below 1.3 nm. Details regarding the materials' fabrication and characterization have been reported elsewhere [7,  
Figure 1: **(a)** Puckered pentagonal configuration of palladium diselenide (PdSe\({}_{2}\)) and **(b)** hexagonal molybdenum disulfide (MoS\({}_{2}\)). **(c)** SEM top-view image of **(c)** PdSe\({}_{2}\)- and **(d)** MoS\({}_{2}\)-based field-effect transistors, fabricated on Si/SiO\({}_{2}\) substrates. PdSe\({}_{2}\) is exfoliated while MoS\({}_{2}\) is obtained by chemical vapor deposition. The two processes yield different flake sizes.  
lift-off process were used to realize Ti(5 nm)/Au(40 nm) metallic electrodes contacting the nanosheets. In Figure 1c,d, we report a top-view SEM image of a back-gated field-effect transistor fabricated using a PdSe\({}_{2}\) (MoS\({}_{2}\)) nanosheet as the channel and Ti/Au leads as the metal contacts.  
The electrical measurements were performed inside a scanning electron microscope chamber, equipped with piezo-driven nanoprobes connected to a Keithley 4200 SCS (a semiconductor parameter analyzer by Tektronix Inc, Beaverton, Oregon) working as multi-source-measurement units. Metal leads were used as the source and drain electrodes in a three-terminal configuration, the back gate being realized by the Si substrate. The fabrication of more than two top electrodes allowed us to characterize more than one transistor on the same nanosheet to verify the sample homogeneity.