esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Study of Cathode Shape
### Influence of Cathode Shape on Machining
#### 3.2.1 Influence of Cathode Shape on the Electric Field Distribution  
In order to explore the electric field distribution during processing, simulations of current density distributions at different processing times were performed for the four cathode structures shown in Figure 2, and the results are shown in Table 2. With cylindrical and hemispherical cathodes, the anode surface was equipotential at the beginning of machining, and the electric field lines passed vertically through the surface of the anode. The curvature radius of the sharp corner at the intersecting line of the holes was small, and the density of the electric field line perpendicular to the surface was higher, which resulted in the greater electric field intensity and higher current density in this area. Therefore, at the beginning of processing, the current density as highly concentrated in the sharp corners of the workpiece, and the sharp corners were removed at a fastest rate, which was beneficial for improving the processing efficiency. However, after the fillets were formed, the current density distribution on the surface of the fillets was excessively concentrated in the middle of the fillets, and the formed fillets gradually became oblique corners. Using conical and concave cathodes, the current density was less concentrated on the chamfered part than with cylindrical and hemispherical cathodes. With machining, the sharp corners of the workpiece gradually formed fillets. With this process, the curvature radius of the chamfered part increased gradually, and the density of the electric field lines perpendicular to the  
Figure 2: Different shapes of cathode design. (**a**) Cylindrical cathode; (**b**) hemispherical cathode; (**c**) conical cathode; (**d**) concave cathode.  
surface decreased uniformly. As a result, the current density distribution on the surface of the formed fillets gradually became sparse and uniform, and the material removal speed of the chamfered parts was slowed, which was beneficial to accurately control the change of the fillet size during processing. Therefore, concave cathodes and conical cathodes are more suitable for electrochemically controllable machining of small intersecting-hole fillets.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 5 Simulation and Experiment Results
### Experiment  
In this work, an experiment with UAV-LS is carried out consisting of 6 quadrotors transformed from a random initial formation to a desired formation. A laptop equipped with i7-7700 hq CPU and 8 GB RAM is used as the GCS for animation design, simulation, and real-time monitoring and control.  
Real-time measurement data are sent from UAVs to the GCS at a frequency of 10 HZ and the collision-free reference trajectories are planned by the proposed dynamic formation transformation algorithm, which is sent back from the GCS to the UAVs at a frequency of 5 HZ. The attractive gain coefficient \(\eta\) is set to 0.8, and the repulsive gain coefficient \(k_{p}\) is set to 0.3. The influence radius \(\rho_{o}\) of other UAVs is set to 2 m.  
In this experiment, the multi-threaded programming feature of C# is used. The GCS computer has 4 cores and 8 threads, in which 6 threads are enabled, each of which computes reference trajectories for a quadrotor in parallel. The initial and target formation positions of the quadrotors are given in Table 6.
## 2 Methods
### Measures of Performance  
Two measures were used to check model performance: the R2 score and average relative error. The R2 score measures the goodness of fit for a regression curve and the average relative error shows accuracy of the model over the test data set. Both good fit and accuracy were the desirable qualities in our models.  
Figure 3: Example depicting a three-layer skin model with a beam directed at its surface, where each shade represents a different layer. From left to right: epidermis, dermis, and fat.  
R2 score helps determine whether the model is sensitive to the change in the dependent variable when the independent variables change,  
\[\text{R2 Score}=1-\frac{\sum_{n=1}^{N}(U_{n}-\hat{U}_{n})^{2}}{\sum_{n=1}^{N}(U_{ n}-\hat{U})^{2}}. \tag{9}\]  
Here, \(U_{n}\) and \(\hat{U}_{n}\) are the true and predicted values across the entire domain, respectively, while \(\hat{U}\) represents the average value of the true solution from PAC1D, \(U\). Scoring 1 in this test will indicate that the model has a perfect fit and, thus, has approximated the target solution well.  
The average relative error was used to determine the accuracy of all test point predictions as a percentage, and was calculated by  
\[\text{Average Relative Error}=\frac{1}{N}\sum_{n=1}^{N}\left|\frac{U_{n}-\hat{U} _{n}}{U_{n}}\right|\cdot 100. \tag{10}\]  
Here, as before, \(U_{n}\) is the true value and \(\hat{U}_{n}\) is the predicted value, while \(N\) represents the total number of samples. The closer the average relative error is to zero percent, the better the accuracy.  
Run failures were occasionally observed for specific test cases. A run failure is defined as a training instance of a model that resulted in a R2 score of zero or less. These runs were chosen to be ignored when calculating the overall performance due to their infrequent nature and their profound effects on the average and standard deviation of the performance values. Model performances were based on 26 realizations per case.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 5 Experimental Results
### RRT*-FDWA Global Path Planning  
First, RRT* was used to simulate the global planning path. As shown in Figure 6, for the random moving obstacle environment, dashed and solid lines represent the global path. The dashed line shows the process of the RRT* algorithm rewiring and selecting a new node for the first time; the blue circle represents \(p_{new}\) and the black objects represent the moving obstacles. A size [10,10] map with a start point of [1.2,1.2] and a target point of [9,9] was used.
# Data Imputation Based on Retrieval-Augmented Generation
## 3 Methods  
### Framework  
The retrieval-augmented generation (RAG) framework proposed in this study integrates advanced retrieval mechanisms with the generative capabilities of LLMs to facilitate precise and contextually relevant data imputation by leveraging external heterogeneous datasets. As shown in Figure 1, the framework is structured around three primary components:  
Semantic Data Lake Indexing: This component focuses on the characterization and indexing of heterogeneous data stored in data lakes. By constructing semantically based indexes, the framework ensures efficient and accurate retrieval of relevant data, even amidst diverse data formats and sources.  
Recall and Re-ranking Retrieval Mechanism: This component implements a two-stage retrieval process to maximize the relevance and precision of retrieved data. The recall stage retrieves a broad pool of potentially relevant data, and the subsequent re-ranking stage refines these results by prioritizing the most pertinent data. This approach minimizes noise and ensures that only the most relevant and high-quality data is fed into the LLMs for the downstream data imputation task.  
Cost-Constrained Reasoning Quality Optimization: This component addresses the trade-off between data imputation quality and computational cost by framing the task as a code generation problem. Leveraging the LLMs' powerful natural language understanding, data semantic analysis, and code generation capabilities, the framework generates task-specific code for data imputation. This approach reduces the frequency of LLM calls, thereby lowering the cost of data imputation. Task decomposition is employed to break down the complex data imputation process into smaller, manageable sub-tasks, each optimized individually. Iterative code generation and validation further enhance the system's ability, combining multiple code snippets to handle complex scenarios while maintaining cost efficiency.  
The subsequent sections delve into each of these components in greater detail, elucidating the methodologies and technologies.
## 2 Results
### Hs-SPME/GC-MS Analysis
#### 2.1.1 PDMS/DVB Fiber  
Headspace VOCs of three populations of _I. pseudopallida_, two populations of _I.__illyrica_ and one of _I.__adriatica_, were isolated and analyzed by HS-SPME/GC-MS, using PDMS/DVB fiber. A total of 77 compounds were identified, accounting for 84.00-96.22% of the total VOC content (Table 1). Oxygenated monoterpenes were the most dominant compounds in _I.__pseudopallida_ from Bast (_I.__pseudopallida_ B, 38.00%) and Topici (_I.__pseudopallida_ T, 34.40%), as well as in _I.__illyrica_ from Vir (_I.__illyrica_ V, 23.07%) and Zaton (_I.__illyrica_ Z, 36.26%) and in _I.__adriatica_ (43.24%), while norisoprenoids dominated in _I.__pseudopallida_ from Dubrovnik (_I.__pseudopallida_ D, 52.93%). The major identified compounds in _I.__pseudopallida_ B were perilla aldehyde (20.55%), myrtenol (6.33%), \(\alpha\)-copaene (5.14%), 2-phenylethanol (4.67%), \(\alpha\)-pinene (4.48%), and _trans_-caryophyllene (3.40%). The major identified compounds in _I.__pseudopallida_ D included _cis_-\(\alpha\)-irone (45.76%), _trans_-caryophyllene (7.24%), _cis_-\(\gamma\)-irone (7.17%), nonan-2-one (4.99%), 6-methylhept-5-en-2-one (3.59%), and pentanal (3.28%). Furthermore, the principal identified compounds in _I.__pseudopallida_ T were perilla aldehyde (17.76%), ethanol (6.86%), butan-2,3-diol (6.38%), acetic acid (6.29%), benzyl alcohol (5.46%), and nervl formate (4.02%). The major identified compounds in _I.__illyrica_ V were perilla aldehyde (19.72%), acetovanillone (7.12%), acetic acid (6.95%), butan-2,3-diol (5.29%), ethanol (4.79%), and \(\alpha\)-copaene (3.62%). The major identified compounds in _I.__illyrica_ Z were perilla aldehyde (30.00%), butan-2,3-diol (10.27%), 6-methylhept-5-en-2-one (9.47%), 2-phenylethanol (4.76%), ethanol (3.65%), and dihydromycenol (3.46%). The primary identified compounds in _I.__adriatica_ were perilla aldehyde (26.83%), limonene (4.79%), _trans_-caryophyllene (4.51%), 1,8-cineole (3.82%), acetic acid (3.74%), and camphor (3.25%) (Table 1).
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 4 Thermal Management of PICs
Once the TEC is powered-up, the PIC rapidly stabilizes to the set-point temperature (T\({}_{\rm{SP}}\)) programmed into the external proportional-integral-differential (PID) controller. A micro-bead or surface-mountable technology (SMT) thermistor is placed in close thermal proximity to the PIC, to provide feedback to the PID controller. For the ONU from the 'FABULOUS' project, the PIC stabilizes  
Figure 14: Thermal imaging of a photonic module and PIC; (**a,b**) A comparison of optical and thermal imaging of the optical network unit (ONU) packaged for the FP7-FABULOU project. Once powered-up, both the EIC and PIC experience a significant increase in temperature (34 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, respectively); (**c**) Schematic of the ‘thermal stack’ in the ONU, showing the conduction path from the EIC to to the baseplate; (**d**) Thermal microscopy of the EIC and PIC on the ONU. Silicon is transparent in the 3–5 \(\upmu\)m wavelength range at which the sensor operates, allowing the user to ‘see through’ the EIC substrates and image the metallic structures on the surface of the EIC and PIC. to \(\mathrm{T_{SP}}\)\(\pm\) 0.1 \({}^{\circ}\)C in 30 s, and \(\mathrm{T_{SP}}\)\(\pm\) 0.01 \({}^{\circ}\)C in 60 s. In this steady-state, where 1.1 W of electrical power is applied to the EIC, the TEC needs to draw 0.5 W to recover the PIC to a room-temperature set-point of 18 \({}^{\circ}\)C. This implies a CoP of 2.2 = 1.1 W/0.5 W, and shows that thermal management accounts for 30% = 0.5 W/0.5 W + 1.1 W of the operational power-budget for the module. Increasing the CoP of TE-cooling for PICs, by improving how heat can be distributed and dissipated across the module, is an active topic of research. Finite-element simulations in _COMSOL_ can be used to test and optimize new stack designs, which are then experimentally validated using the dynamic temperature measurements from thermal microscopy--see Figure 16.
## 4 Results and Discussion
### Discussion of Results by Means of Comparative Charts  
#### 4.3.1 Effect of SD on the Mixture before Exposure  
As can be seen in the below figure (Figure 9(a)), implementation of SD increased the strength of the mixtures. From the figure, increment of SD from 3% to 15% in 3 days curing time shows an increase in strength. In addition, the same trend was observed with increasing cement percentage from 3% to 7%. Similar to 3 days curing time, the same trend was observed for 7 days curing time. The strength increase is clear within the 7 days curing time. In 28 days curing time, the same trend of increase was observed. Similar to the other curing times, 90 days curing time exhibited the same trend as the others (Figure 9(b)-d).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{3}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-4}  & **3\% PC = 1028** & **5\% PC = 1405** & **7\% PC = 1648** \\ \cline{2-4}  & **UCS (kPa)** & **UCS (kPa)** & **UCS (kPa)** \\ \hline
3\% & 1297 & 1680 & 1906 \\
5\% & 1468 & 1946 & 2192 \\
10\% & 1702 & 2019 & 2358 \\
15\% & 1925 & 2240 & 2450 \\ \hline \hline \end{tabular}
\end{table}
Table 10: UCS results before sulphate exposure in presence of SD (90 days curing).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{3}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**3\% PC = 201**} & \multicolumn{2}{c}{**5\% PC = 450**} & \multicolumn{2}{c}{**7\% PC = 537**} \\ \cline{2-6}  & **UCS (kPa)** & **Improvement (\%)** & **UCS (kPa)** & **Improvement (\%)** & **UCS (kPa)** & **Improvement (\%)** \\ \hline
3\% & 250 & 24.3 & 1240 & 175.5 & 1700 & 216.5 \\
5\% & 407 & 102.4 & 1640 & 264 & 1985 & 269.6 \\
10\% & 690 & 243.2 & 1840 & 308.8 & 2201 & 309.8 \\
15\% & 885 & 340 & 1990 & 342 & 2400 & 346.9 \\ \hline \hline \end{tabular}
\end{table}
Table 11: UCS results after sulphate exposure in presence of SD (90 days curing).
## 4 Experimental Design and Results
### Comparison Algorithms  
Comparison algorithms refer to the benchmarking methods used to evaluate the performance of proposed algorithms against existing state-of-the-art techniques. These algorithms are typically well established in the literature and serve as a reference for fair performance assessment. Encoding algorithms primarily focus on transforming data into a structured format according to predefined rules. They generally reduce the size of the data but optimize its representation for various purposes, such as efficient storage, transmission, or security. However, compression algorithms are specifically designed to reduce the size of the data by eliminating redundancy while preserving essential information using encoding algorithms. Compression can be either lossless (where the original data are perfectly restored) or lossy (where some data are discarded to achieve higher compression). While encoding algorithms are often used as part of compression techniques (e.g., Huffman coding in ZIP or JPEG), compression algorithms are specifically designed to reduce data size, making them more application-specific.  
In this study, the comparison algorithms included the following:
# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 2 Materials and Methods  
### Subjects  
Sistxty-two (62) SCZs (males: 37 (58.7%), females: 25 (41.3%), age: 37.27\(\pm\) 8.98 (range: 17-56) years) as well as 70 HCs (males: 38 (54.3%), females 32 (45.7%), age: 37.74 \(\pm\) 16.57 (range: 18-81) years) were invited to participate in this study. All subjects were unpaid volunteers, who were recruited from Hamilton Psychiatric Hospital, Hamilton, Ontario, to investigate whether EEG data can differentiate SCZ from HC. The study was reviewed and approved by the Research Ethics Board of the Hospital. All participants in this study filled the informed consent and were aware of the nature of the study. All SCZs met the diagnostic and statistical manual of mental disorders, fourth edition (DSM-IV) criteria for schizophrenia [40]. The severity of the symptoms was measured for SCZs using the positive and negative syndrome scale (PANSS) score. The values of the PANSS sub- and total scores are as follows: PANSS positive sub-score 28.45 \(\pm\) 7.87(range: 13-48), PANSS negative sub-score 29.05 \(\pm\) 8.01 (range: 8-49), PANSS general sub-score 58.79 \(\pm\) 12.55 (range: 27-97), and PANSS total score 116.29 \(\pm\) 24.73 (range: 51-187). Furthermore, the age of symptom onset was 20.86 \(\pm\) 4.84 (range: 13-32 years), and the duration of the illness at the time of recording was 16.52 \(\pm\) 7.97 (range: 4-35.5 years).
## 3 Communication Performance Analysis of Hybrid FSO/RF System
### Ergodic Channel Capacity  
In time-varying channels, the ergodic channel capacity is the weighted average of the capacity of the system in each channel state [46]. The normalized ECC of the hybrid FSO/RF system can be obtained by the following equation [47]:  
\[\left\langle C\right\rangle=\int_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma^{MRC}} (\gamma)d\gamma. \tag{13}\]  
Using the relationship between functions: \(\log_{2}(1+x)=\frac{1}{\ln 2}G_{2,2}^{1,2}\bigg{(}x\bigg{|}\begin{array}{c}1,1\\ 1,0\end{array}\bigg{)}\) and \(e^{-x}=G_{0,1}^{1,0}\bigg{(}x\bigg{|}\begin{array}{c}-\\ 0\end{array}\bigg{)}\)[48; 49] and Equation (07.34.21.0011.01) in [44], by substituting Equation (6) into Equation (13), the ECC of the hybrid system can be obtained as  
\[\left\langle C^{Ly}\right\rangle=\frac{A_{1}}{\ln 2}\sum_{k=1}^{\beta}c_{k} \sum_{i=0}^{\infty}C_{1}G_{r+3,3r+3}^{3r+2,2}\bigg{(}E_{1}\bigg{|}\begin{array} []{c}1-D_{1},1-D_{1},\kappa_{1}\\ \kappa_{2},-D_{1},-D_{1},1-D_{1}\end{array}\bigg{)} \tag{14}\]  
According to Equation (07.34.21.0012.01) in [44], the ECC of the only FSO system can be obtained by substituting Equation (1) into Equation (13):  
\[\left\langle C^{FSO}\right\rangle=\frac{\xi^{2}A}{2^{r}\ln 2}\sum_{k=1}^{\beta} b_{k}H_{3,5}^{5,1}\bigg{(}\frac{B}{\left(\mu^{FSO}\gamma^{1}\right)^{r}}\bigg{|} \begin{array}{c}\kappa_{3}\\ \kappa_{4}\end{array}\bigg{)} \tag{15}\]  
where \(\kappa_{3}=(0\,1/r)\), \((1\,1/r)\), \((1+\xi^{2},1)\), \(\kappa_{4}=(\xi^{2},1)\), \((\,\pm 1)\), \((k\,1)\), \((0\,1/r)\), \((0\,1/r)\), and \(H_{\gamma}^{\prime}(\cdot)\) is the Fox-H function [50].  
According to Equation (07.34.21.0012.01) in [44], the ECC of the only RF system can be obtained by substituting Equation (3) into Equation (13):  
\[\left\langle C^{RF}\right\rangle=\frac{A_{m}}{\ln 2}H_{2,3}^{3,1}\bigg{(}B_{m} \bigg{|}\begin{array}{c}\kappa_{5}\\ \kappa_{6}\end{array}\bigg{)} \tag{16}\]  
where \(\kappa_{5}=(-C_{m}\mu_{m}\,C_{m})\), \((1-C_{m}\mu_{m}\,C_{m})\) and \(\kappa_{6}=(0\,1)\), \((-C_{m}\mu_{m}\,C_{m})\), \((-C_{m}\mu_{m}\,C_{m})\).
## 4 Conclusions  
Here, we demonstrate the successful co-culture of SA-MIPs with HT29 spheroids for the first time. The spheroids exhibit a high level of viability after 8-10 days of interaction with SA-MIPs in the in vitro cell cultures, suggesting the latter's potential as a valuable diagnostic tool for detecting cancer and providing accurate models of the cell-cell interactions in vivo. These novel models hold promise for advancing our understanding of cancer targeting, diagnostics, drug delivery, and intercellular dynamics that are important in cancer research.  
[PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] carried out the cell-based studies and performed experiments; [PERSON] designed and provided the polymer materials; [PERSON], [PERSON] and [PERSON] analysed the data; [PERSON] and [PERSON] provided advice and technical assistance; [PERSON] and [PERSON] wrote the manuscript with contributions from all other authors. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement number 721297, the European Union's Horizon 2020 research and innovation program grant agreement number 848098, the Malmo Cancer Center, Malmo, Sweden, Biofilms Research Center for Biointerfaces and Malmo University, Sweden.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Acknowledgments:** We thank [PERSON] for support with MIP synthesis and [PERSON] for fruitful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 2: HT29 cells and the SA-MIPs were grown according to the described protocol for spheroids and left for 8–10 days in 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). The spheroids were thereafter harvested and the binding pattern of the SA-MIPs were investigated. Left to right: DAPI (blue); SA-MIPs-NBD (green); phalloidin (red); merged (DAPI/SA-MIPs/phalloidin. One representative experiment out of three performed is shown. Scale bar = 50 \(\mu\)m. NBD is nitrobenzoxadiazole.