esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Phase 2: Indexing Pipeline  
This phase focused on transforming the structured corpus into semantically indexed content suitable for retrieval through vector similarity. The process involved selecting and configuring embedding models, integrating them with a scalable vector database, and developing the backend logic required for ingestion and storage.  
The first step consisted of evaluating appropriate embedding models capable of generating high-quality vector representations of the curated text. Two models were selected for different tasks within the pipeline. The model _text-embedding-ada-002_ from OpenAI was used for tasks involving semantic search and similarity computation, owing to its strong representational capacity and integration support. In parallel, the model _text-embedding-3-small_ was chosen for large-scale document ingestion [33]. This model was configured to produce vectors with a dimensionality of 768, facilitating scalable indexing while maintaining semantic accuracy.  
With the embedding strategy defined, the implementation of the backend indexing system began. The backend was developed using Python 3.10 and LangChain, leveraging its integration capabilities with the Qdrant vector database. The system was deployed in a Dockerized environment, which simplified configuration and scalability. Once initialized, the Qdrant client served as the primary interface for all document-related operations, including ingestion, modification, and retrieval. The backend functioned according to standard database operations--create, read, update, and delete (CRUD)--which ensures modularity and maintainability.  
A complete set of methods was implemented to manage vector data within Qdrant. These methods included the validation of collection existence, the creation and registration of new collections, the ingestion of documents along with metadata, and the retrieval of stored content in structured formats. In addition, the backend supported the deletion and update of both individual documents and entire collections, allowing for full control over the dataset. Documents were organized within collections based on instructional units and themes, with metadata assigned to support targeted retrieval operations.  
The development of indexing scripts was a critical deliverable of this phase. These scripts automated the loading of cleaned textual fragments, the generation of embeddings using the selected models, and the structured insertion of these vectors into the Qdrant collections. Each entry was enriched with metadata such as topic identifiers and the original source, supporting contextual filtering in later stages of the retrieval pipeline.
## 4 Methodology
### Numerical Simulation and Result Analysis  
#### 4.4.1 Simulation Analysis of the Initial Model  
The flow field analysis of the film stalk separation device mainly employs ANSYS' Fluent module to obtain pressure and velocity distribution maps of the comprehensive internal flow field. In Figure 9, the horizontal coordinate represents the length of the cavity in millimeters, while the vertical coordinate represents the pressure of the flow field in pascals. Similarly, in Figure 10, the horizontal coordinate represents the length of the hole in millimeters, and the vertical coordinate represents the velocity of the flow field in meters per second.  
Upon analyzing Figures 9 and 10, it is observed that the pressure inside the model gradually decreases, then increases, and exhibits a low-pressure zone at the lower end of the air inlet outlet and at the top of the front end of the model. The pressure values varied significantly in these zones. In the model's length range of X \(<\) 0.375 m, the pressure value starts to decrease, with the minimum pressure recorded at 3.00 Pa. On the other hand, the pressure value gradually increases within the scope of the model's size X \(>\) 0.375 m, with the maximum value at 14.5 Pa. The flow field pressure stabilizes towards the back end of the model, which is favorable for film impurity separation. The pressure state inside the model reveals a trend of decreasing and then increasing which benefits film impurity separation. The pressure values do not change significantly at the back end of the model. The fluid velocity inside the cavity displays a gradual decrease, with the velocity of the fluid not evenly distributed throughout the first half of the crater. The velocity at each point is relatively scattered and lacks sufficient density. However, the velocity distribution in the second half of the cavity is more even. Within the range of X \(>\) 400 mm, the velocity remains below 2 m/s.  
Figure 10: Velocity distribution in the flow field.  
Figure 9: Flow field pressure distribution.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 5 Performance Evaluation
### ERPL's P2P Routing and Data Forwarding Performance Evaluation
#### 5.1.4 Discussion  
The proposed ERPL protocol reduces the number of times a P2P packet is relayed in a network in order to reach its destination. This enhancement is achieved without any additional control overhead, hence ERPL reduces the amount of contention in a network. This not only positively impacts the performance of P2P communication, but also positively impacts the performance of MP2P and P2 MP communications. The protocol's better P2P routing and forwarding algorithms result in lower energy consumption as the protocol also demonstrates fewer re-transmissions. Placing the gateway at the edge of the network results in longer MP2P and P2 MP data forwarding paths, hence MP2P and P2 MP performances are negatively impacted. However, at the same time it results in a relatively bigger DODAG at each node, therefore nodes can discover more peer nodes. Consequently, the number of times a P2P packet is relayed through the gateway is lowered. Similarly, placing the gateway at the center of a network results in shorter MP2P and P2 MP data forwarding paths. It improves MP2P and P2 MP communications, however due to a relatively shallow DODAGs most P2P packets travel through the gateway, hence P2P communication is negatively impacted.
# Analytical Investigation of Vertical Force Control in In-Wheel Motors for Enhanced Ride Comfort
## 2 Dynamics Models and Methods
### Frequency Response of the Vehicle System  
Based on the transfer function of the road input to the vertical acceleration of the open-loop and closed-loop systems, as shown in Equations (18) and (34), the frequency response of both systems can be compared. To evaluate the frequency response, the velocity of the vehicle is assumed to be constant at 30 m/s. Given that the distance between the front and rear wheels is \(b+c\), the delay time, \(\tau\), of exact delay is 0.0991. The frequency response will be computed using MATLAB R2024a software [33]. An example of the frequency response of the open-loop system is shown in Figure 5.  
Figure 5: The frequency response of open-loop system by the exact delay and Pade approximation methods.  
Figure 5 compares the frequency response of the open-loop system when the delay time is modeled using the exact delay as in Equation (39) and approximated with the Pade approximation [34] with a Pade degree of 20. The figure shows that the frequency responses from both methods are the same up to a frequency of 60 Hz. Beyond 60 Hz, the frequency response from the Pade approximation cannot match the response from the exact delay. Therefore, the exact delay will be used for further evaluations in this case, even though it may be more complicated. The frequency response of the vehicle system is used to evaluate the system's response at each frequency and to calculate the ride comfort for both systems.  
Figure 6 shows the frequency response of both the open-loop and closed-loop systems. The proportional gain, \(K_{p}\), is varied from 100 to 500 to present the frequency response of the closed-loop system. Figure 6a compares the frequency responses of the open-loop and closed-loop systems. Figure 6b,c provide zoomed-in views focusing on the frequency ranges of 5 to 20 Hz and 0.1 to 5 Hz, respectively. It can be seen from Figure 6 that as the proportional gain, \(K_{p}\), increases, the amplitude response of the closed-loop system decreases compared to that of the open-loop system. Therefore, it can be concluded that the proportional ride-blending controller effectively reduces the vibration of the vehicle's sprung mass.  
Figure 6: The frequency response of open-loop and closed-loop system: (**a**) the frequency response from 0.1 to 1000 Hz, (**b**) the frequency response from 5 to 20 Hz, and (**c**) the frequency response from 0.1 to 5 Hz.
# Abnormal Ship Behavior Detection Based on AIS Data
## 3 Materials and Methods
### Detection of Abnormal Ship Behavior Based on Thematic Attributes  
#### 3.3.1 Nearest Route Matching  
Matching the maritime routes to the ship trajectory points can match the current trajectory points to the nearest and most suitable route segments. On the one hand, this may provide data support for the amplification of a maritime route. On the other hand, since different segments of routes have different traffic states, more fine-grained detection of ship patterns can be performed based on the characteristics of the maritime route segments.  
Considering the actual situation of the sea lane network, we segmented the sea lane network in a clockwise direction using the ArcGIS tool with the road intersection as the node. Finally, a unique identifier was assigned to the route segments after the segment. After the completion of route segmentation, the nearest route segment of each trajectory point was calculated according to the principle of the minimum distance and assigned as an attribute to the trajectory point.
## 3 Results and Discussion
### Confocal Microscopy Studies  
Selective staining of PHA granules is commonly carried out by dyes Nile Red and Nile Blue A [36, 50, 51]. The bacterial cells cultured for 100 h at 30 \({}^{\circ}\)C were used to prepare heat-fixed smears. The cells containing PHA granules showed orange-coloured fluorescence after Nile Blue A staining (Figure 4(A1,B1,C1,D1)). The accompanying differential interference contrast (DIC) images in black and white show these granules within the cells (Figure 4(A2,B2,C2,D2)). The merged images of stained cells and DIC images are exhibited in Figure 4(A3,B3,C3,D3). In comparison to cell membrane or lipid-containing cell component, Nile Blue A demonstrates higher affinity towards PHA showing fluorescence [36]. Fluorescence in _B. cereus_ cells after staining with Nile Blue A was not observed in agarose containing media, indicating the inability of _B. cereus_ cells to produce PHA while growing in co-culture with _Sde_ 2-40 on agarose (Figure 4(A1)). However, co-culture grown on xylan indicated fluorescence in _B. cereus_ cells (Figure 4(B2)). This suggested that _B. cereus_ also synthesized PHA along with _Sde_ 2-40 in xylan containing media. PHA synthesized by _Sde_ 2-40 while growing on agarose and xylan was clearly observed in Figure 4(C1,D1), respectively. Staining images also indicated a greater number of PHA containing cells in co-culture as compared to pure cultures. These results demonstrate that confocal microscopy can be an easy and inexpensive way to detect or differentiate the PHA accumulating cells in co-culture on the basis of cell morphology/phenotype.
## 4 Results
### Main Phase
#### 4.2.1 Comparison of Different Sentinel-2 Indices: Model Fit  
Having tested various indices for Chl-a modeling during the preliminary phase, Table 2 summarizes the R\({}^{2}\), RMSE, and MBE for linear regression models with seven indices for prediction of phycocyanin based on the testing dataset and contrasts this with the best Chl-a model. For both concurrent and one-day windows, the MPH model had the highest R\({}^{2}\) and lowest RMSE, although the MBE magnitude for the two-band ratio model was slightly lower than other indices. The SABI model had the worst values across all metrics. Based on the sign of the reported MBE values, Table 2 also shows that for the concurrent data MPH and NDCI overestimate phycocyanin concentrations, while the other indices underestimated concentrations. Using the one-day window, all models underestimated phycocyanin concentrations. The R\({}^{2}\) for the one-day time window tended to be similar to (or in some cases higher than) that for the concurrent data. The RMSE improved when moving to the one-day time window; conversely the magnitude of the MBE increased. The improvement in the model fit for the CI and MPH models is likely associated with the increase in data points available when the time window was expanded. This suggests that we can use a one-day time window between in situ and satellite data collection to expand our dataset without decreasing model fit.  
Figure 5: Scatterplot between in situ phycocyanin concentration above 8 \(\upmu\)g/L and Sentinel-2-based index.
###### Abstract  
Normal body tissue or lesion characteristics in T1 images have been evaluated; however, how external parameters effect the change in signal intensity by gadolinium-based contrast agent remains unknown. We investigated how contrast enhancement changed according to echo time (TE) in 3.0T magnetic resonance (MR) T1 imaging and determined the optimal settings for TE in contrast-enhanced T1 imaging. Since there are no guidelines regarding parameters for T1 enhancement when using MR-contrast agents, we analyzed results from varying TEs (between 25 and 7 msec) in both a phantom and clinical study. We obtained the following results: contrast percentage of fat to saline increased from 740.0-1003.6%, response start point increased from 30-90 mmol, max peak signal intensity increased from 1771-2425 a.u., max peak point increased from 2-4 mmol, enhancement percentage of the max peak signal intensity (MPSI) to saline increased from 1671.0-2065.2%, the average of SI on each mol as TE increased from 600.8-996.6 a.u., the average of SI as TE on each molar concentration increased from 378-845 a.u., the AEPSS increased from 44.3-140.3%, and the AEPSC increased from 224.3-647.8%. We confirmed that TE can affect contrast enhancement, and the lowest TE has faster and higher effects on contrast enhancement.  
3.0T MRI; contrast enhancement; gadoteridol; contrast media; echo time; T1 imaging 2018 8 1138 10.3390/app8071138
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 3 Results and Discussion
### The Measurement Results
#### 3.1.1 Measurements of the Level of Signals Reflected from the Rotors of Wind Turbines
It was also noted that wherever Annex 1 resulted in unacceptably high positive values for UFSR bias, in case of Annex 2 the respective values were negative (pessimistic model) and kept at a moderate level from approx. \(-5\) dB to \(-20\) dB. This confirms the applicability of the Annex 2 model in this case. Further in this section, the authors will explain in which situations to use a specific model (Annex 1 or Annex 2) based on the ITU-R BT.1893-1 recommendation [11]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Measurement**} & \multicolumn{3}{c}{**Measurement**} \\ \multirow{2}{*}{**The Peak** in Table 3**} & \multicolumn{3}{c}{**Number from**} & \multicolumn{3}{c}{**Considering**} & \multirow{2}{*}{**Theory— Annex 1**} & \multirow{2}{*}{**Measurement— Theory Annex 2**} & \multirow{2}{*}{**Theory Annex 1**} & \multirow{2}{*}{**Theory Annex 2**} & \multirow{2}{*}{**Theory Annex 2**} & \multirow{2}{*}{**Theory Annex 2**} \\  & **Number from** & & & & & & & \\  & **Which the** & **Signal Was** & **Antenna** & **[dBm]** & **[dBm]** & **Difference [dB]** & **Difference [dB]** & **Difference [dB]** \\  & **Reflected** & **Characteristics** & & & & & & \\  & & & & & & & &  
Based on the measurements and theoretical calculations using the ITU-R BT.1893-1 model (presented in Tables 3-6), the coherence bandwidths for individual cases was determined and listed in Table 7. It should be emphasized here that the coherence bandwidth calculation for the measured radio channels was performed in two ways (using two different FSR values). The first one based on the measured FSR value (power of the signal received directly from the transmitting station). However, the FSR measured in this way is subject to an error due to the following: (a) The propagation between the transmitter and the receiver took place close to the ground, so the diffraction had a greater effect than in the case of reflection from the wind turbine; (b) Terrain obstacles occurred in the first Fresnel zone; (c) The receiving antenna was positioned in the opposite direction to the main radiation beam of the transmitting antenna. Consequently, the calculations of the FSR were later adjusted, assuming the power level received from the known transmitter at its main antenna beam and considering solely free space propagation losses. The result of this \"recalculation\" can be seen in the last column of Table 7 and in the first row of Tables 4-6 (the \"Measurement\" field). Scenario 1 and 2 refer to measurements made in the northern part of the Jasna wind farm consisting of 25 wind turbines with a height of 117 m. The results obtained for both
# Isoflavone Protects the Renal Tissue of Diabetic Ovariectomized Rats via PPARy
## 2 Materials and Methods  
### Animals, Experimental Design, and Treatments  
Our study sample consisted of 60 adult, virgin female rats (Rattus norvegicus Albinus), three months of age, weighing between 180 and 210 g. The in vivo experimental protocol was approved by the Ethics Committee (Research Ethics Committee 001676/09) of the Federal University of Sao Paulo. The animals were donated by the Center for the Development of Experimental Models of the Federal University of Sao Paulo (CEDEME). All animals were kept in plastic cages with controlled light (12/12 h light/dark cycles) and temperature (22-24 \({}^{\circ}\)C), received water and food ad libitum, and were fed a specific proportion of soy-free food (Formula Labina Especial for rodents), developed by Agribrands-Purina, Sao Paulo, Brazil.  
After a one-week adaptation period, the rats were anesthetized with an intraperitoneal injection of 10% ketamine (0.08 mL/100 g/p.p; RompunVR, Sao Paulo, Brazil) and 2% xylazine (0.04 mL; KetalarVR, Sao Paulo, Brazil). They then underwent bilateral ovariectomy immediately after surgery. One day after bilateral ovariectomy, type 1 diabetes in animals was induced by a single-dose intraperitoneal injection of freshly prepared 60 mg/kg streptozotocin (STZ--Sigma-Aldrich, St. Louis, MO, USA) dissolved in a 0.1 mol/L citrate buffer (pH = 4.8). Hyperglycemia was confirmed 48 h after the STZ injection by measuring the tail vein blood glucose levels using a blood glucose monitoring kit (Accu Check ACTIVE Roche(r)). Only animals with mean plasma glucose levels \(>\) 250 mg/dL were accepted as having diabetes mellitus [21].  
Treatment with soy isoflavone and 17\(\beta\)-estradiol was performed for 30 days after T1D induction. Was used the concentrated extract of soy isoflavone NovasoyVR (Archer Daniels Midland, Decatur, IL, USA) containing 40% of total isoflavone in the ratio of 1.3:1:0.3 genisteinistein: daidzein: glycitein, respectively, 7-12% protein, 4% ash, and 6% moisture, and the remaining 41% consists of the other soy phytocomponents. In addition, we used 17\(\beta\)-estradiol (Sigma-Aldrich). Administration of soy isoflav or 17\(\beta\)-estradiol was initiated 30 d after the ovariectomy. Soy isoflavunes (150 mg/kg) were administered by gavage, and 17\(\beta\)-estradiol (10 mg/kg) was administered subcutaneously.  
After 30 days of experimentation, all animals were anesthetized with xylazine (RompunVR, SP, Brazil) 15 mg/kg associated with ketamine (KetalarVR, SP, Brazil) 30 mg/kg, intraperitonealally. A portion of the endometrium and kidney was immediately immersed in liquid nitrogen e and then stored in the \(-80\)\({}^{\circ}\)C freezer for qPCR assay. Another portion of the endometrium and kidney were immersed in paraformaldehyde4% for histological processing.
## 2 Methods: Experiment and Numerical Simulation
### Numerical Models
#### 2.2.2 Failure Criterion  
The 3D Hashin failure criterion accounting for fiber failure and matrix failure is adopted in Abaqus/Explicit using a user-defined subroutine (VUMAT) to analyze the damage mechanisms of the composites. The tensile and compressive failure of fiber, and that of matrix can be expressed as [22; 23; 24]:  
Fiber tension failure, \(d_{ft}=1\):  
\[\left(\frac{\sigma_{11}}{X_{T}}\right)^{2}+\left(\frac{\sigma_{12}}{S_{12}} \right)^{2}+\left(\frac{\sigma_{13}}{S_{13}}\right)^{2}\geq 1,\ (\sigma_{11}>0) \tag{7}\]  
Fiber compression failure, \(d_{fc}=1\):  
\[\left(\frac{\sigma_{11}}{X_{\text{C}}}\right)^{2}\geq 1,\ (\sigma_{11}<0) \tag{8}\]  
Matrix tension failure, \(d_{mt}=1\):  
\[\left(\frac{\sigma_{11}}{2X_{T}}\right)^{2}+\left(\frac{\sigma_{12}}{S_{12}} \right)^{2}+\frac{\left(\sigma_{22}\right)^{2}}{Y_{T}Y_{\text{C}}}+\frac{ \sigma_{22}}{Y_{T}+Y_{\text{C}}}\geq 1,\ (\sigma_{22}+\sigma_{33}>0) \tag{9}\]  
Matrix compression failure, \(d_{mc}=1\):  
\[\left(\frac{\sigma_{11}}{2X_{T}}\right)^{2}+\left(\frac{\sigma_{12}}{S_{12}} \right)^{2}+\frac{\left(\sigma_{22}\right)^{2}}{Y_{T}Y_{\text{C}}}+\frac{ \sigma_{22}}{Y_{T}+Y_{\text{C}}}\geq 1,\ (\sigma_{22}+\sigma_{33}<0) \tag{10}\]  
where \(d_{ft}\), \(d_{fc}\), \(d_{mt}\), and \(d_{mc}\) are the damage variables for evaluating the tensile failure and compressive damage of the fiber and matrix, respectively. \(\sigma_{ij}\) (\(i,j=1,2,3\)) are the Cauchy stress tensor components. \(X_{T}\), \(X_{C}\), \(Y_{T}\), and \(Y_{C}\) are the tensile and compressive strengths in the longitudinal and transverse directions, respectively. \(S_{12}\), \(S_{13}\), and \(S_{23}\) are the shear strength in the fiber and transverse direction, fiber and thickness direction, and transverse and thickness direction, respectively. In this work, the initiation failure criterion is modeled based on 3D Hashin failure criterion by using a user-defined subroutine (VUMAT) that is adopted in Abaqus/Explicit to analyze the damage mechanisms of composite laminates. To ensure the stability during analysis, the failure elements will be eliminated from geometry, and will be not considered in further calculations. The material parameters of composites used in finite element (FE) simulation is listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Yield stress (MPa) & 440.3 & 455.1 & 453.1 & 455.1 & 451.4 & 453.1 & 453.4 \\ Equivalent plastic strain & 0 & 0.032 & 0.045 & 0.060 & 0.074 & 0.082 & 0.092 \\ \hline Yield stress (MPa) & 457.1 & 492.6 & 624.1 & 772.2 & 952.4 & 1111.9 & 1226.3 \\ Equivalent plastic strain & 0.102 & 0.111 & 0.121 & 0.130 & 0.141 & 0.151 & 0.160 \\ \hline \end{tabular} Note: Density of SMA = 6417 kg/m\({}^{3}\), Young’s modulus = 20.2 GPa, Poisson ratio = 0.3.  
\end{table}
Table 2: Yield stress versus equivalent plastic strain for the Ni–Ti SMA wires.  
Figure 2: Stress–strain diagram of Ni–Ti shape memory alloy wires.