esa-sceva/satcom-chunk-collection huggingface dataset:
# Accelerated Testing and Reliability of FDM-Based Structural Electronics
## 4 Discussion  
An unresolved issue remained to explain the mechanism that caused the progressive increase in resistance of the samples. To the authors' knowledge, two types of phenomena leading to the increase in resistance are possible: changes in the geometry of the conduction path or chemical changes in the conductive material. Path dimension change could be caused by electromigration of silver [30; 31; 32] or degradation of the polymeric vehicle of the paste, which can lead to loss of integrity. In order to determine the geometry of the samples after the accelerated tests, X-ray computed tomography (CT) of samples with the highest resistance change was performed (Figure 4). The \"X\" marks visible in Figure 4 are CT artifacts representing the polymer substrate and do not affect the current flow. The CT observations and measurements indicated only a slight difference in the path dimensions, which may have been caused by the uneven application of the conductive material or due to the measurement accuracy (\(\pm\)0.05 mm). A 0.1 mm reduction in dimensions did not explain the two-order of magnitude increase in the resistance value.  
After eliminating changes in the geometry of the conductive paths, the next step was to measure and analyze the resistance of each sample. The results of these measurements are presented in Figure 5. It is clear that a significant increase in resistance occurred only  
Figure 3: Initial resistance of different groups of 3D printed electronics. The designation “+ res” indicates paths with a 0 \(\Omega\) resistor.  
for samples with embedded resistors. This means that the phenomenon causing this failure occurred at the contact between resistor terminals and printed paths. Void generation was initially excluded because of the low curing and testing temperature of the conductive material, and furthermore, no voids were observed with X-ray tomography. The most likely phenomenon that could cause such an increase in resistance is the formation and growth of an intermetallic layer. The main component of the conductive material was silver micro powder, while the resistor pads were plated with tin base alloy (which was confirmed in energy-dispersive X-ray spectroscopy). These two metals can form alloys with resistivity of up to two orders of magnitude higher than that of pure silver, which was observed while developing Ag-based resistors [33] and microelectronics films [34]. Furthermore, diffusion at the junction of these two metals was observed below the soldering temperature, and the resistivity of such a junction increases in time at an exponential rate [35], which is important for long-term processes.  
Figure 4: X-ray computed tomography model of sample after accelerated life testing. Initially, the printed trace had about 0.5 mm. The visible X marks are CT artifacts representing the polymer substrate.  
Figure 5: Change of resistance of 3D printed samples after accelerated aging test. R\({}_{t}\) represents resistances after test and R\({}_{0}\) means initial values. The designation “+ res” indicates paths with a 0 \(\Omega\) resistor.
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 3 Results
### Reconstruction Metrics  
Example sagittal slices of intermediate pipeline reconstruction outputs are shown in Figure 6, with corresponding reconstruction metrics. NYU-Knee AI and particularly UglyBarnacle produced intermediate reconstruction outputs with strong fidelity to ground truth, recovering fine details lost to aliasing. On the other hand, K-nirsh yielded an image with more distinct tissue boundaries, but with noise and pixel intensity distributions that clearly differed from the ground truth. Complete metrics of reconstruction performance are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cartilage**} & \multicolumn{2}{c}{**Bone**} & \multicolumn{2}{c}{**Full**} \\ \hline
**Team** & **Femoral** & **Tibial** & **Patellar** & **Femur** & **Tibia** & **Patella** & **Weighted DSC** \\ \hline K-nirsh & 0.904 \(\pm\) 0.014 & 0.899 \(\pm\) 0.015 & 0.910 \(\pm\) 0.034 & 0.989 \(\pm\) 0.002 & 0.985 \(\pm\) 0.004 & 0.966 \(\pm\) 0.012 & 0.910 \(\pm\) 0.021 \\ \hline UglyBarnacle & 0.895 \(\pm\) 0.016 & 0.890 \(\pm\) 0.017 & 0.903 \(\pm\) 0.032 & 0.984 \(\pm\) 0.004 & 0.980 \(\pm\) 0.004 & 0.961 \(\pm\) 0.015 & 0.903 \(\pm\) 0.021 \\ \hline FastMRI-AI & 0.845 \(\pm\) 0.124 & 0.862 \(\pm\) 0.126 & 0.843 \(\pm\) 0.124 & 0.964 \(\pm\) 0.078 & 0.952 \(\pm\) 0.138 & 0.834 \(\pm\) 0.306 & 0.849 \(\pm\) 0.123 \\ \hline NYU-Knee AI & 0.798 \(\pm\) 0.029 & 0.756 \(\pm\) 0.04 & 0.796 \(\pm\) 0.043 & 0.980 \(\pm\) 0.004 & 0.975 \(\pm\) 0.005 & 0.939 \(\pm\) 0.014 & 0.795 \(\pm\) 0.030 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segmentation performance across test set (n = 50) for each of the top 4 pipelines, stratified by tissue compartment. Results are presented mean \(\pm\) 1 s.d. K-nirsh showed the strongest results in each tissue compartment and overall, and is shown in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Team** & **nRMSE** & **PSNR** & **SSIM** \\ \hline K-nirsh & 31.2 \(\pm\) 4.26 & 19.7 \(\pm\) 0.68 & 0.217 \(\pm\) 0.059 \\ \hline UglyBarnacle & 2.07 \(\pm\) 0.25 & 31.5 \(\pm\) 0.87 & 0.693 \(\pm\) 0.043 \\ \hline FastMRI-AI & 3.05 \(\pm\) 0.68 & 29.8 \(\pm\) 0.99 & 0.681 \(\pm\) 0.061 \\ \hline NYU-Knee AI & 2.18 \(\pm\) 0.33 & 31.3 \(\pm\) 0.87 & 0.672 \(\pm\) 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard reconstruction metrics for intermediate pipeline outputs from all top submissions across the released test set (n = 50). Results are presented mean \(\pm\) 1 s.d. The top pipeline by each of these metrics was UglyBarnacle, shown in bold.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 4 In-Tube Solid-Phase Microextraction (In-Tube SPME)  
In-tube SPME is a SPME technique introduced in 1997 by [PERSON] and [PERSON], to overcome the coupling troubles observed when standard SPME fibers were used together with HPLC. These included low fiber capacity, high fragility, instability to solvents and poor sensitivity. In its original format, in-tube SPME consisted in the employment of a piece of a capillary GC column (e.g., an open capillary column containing an inner stationary phase), or a packed LC microcolumn, used as an extraction device coupled on-line to an HPLC system [60]. In in-tube SPME, in contrast to SPME fibers, the analytes equilibrium process occurs at the inner wall of the extraction device while their desorption is done by a solvent [61]. Thus, when the sample flows through the capillary device, the analytes interact with the extraction phase and then are eluted by the mobile phase and transferred to the HPLC system. So extraction, concentration, separation, and detection are carried out in a single step [62]. This technique is frequently used in the analysis of thermolabile or non-volatile compounds in aqueous samples.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 4 Equivalent Algorithm for Predicting Coal Seam Gas Content
### Calculation of the Influence of Coal Seam Gas Content  
The extent of influence on coal seam gas content is determined by the surrounding rock and geological structure, which adds value to the gas content. The primary target of this influence is the free gas within the coal seam, and the degree of impact is indicated by the magnitude of the influence coefficient. Its calculation formula still uses formula \(K_{mh}=\prod k_{ij}\), where \(K_{mh}\) is the influence coefficient of equivalent layer coal seam gas content.  
It should be noted that:  
1. The gas content prediction solution relies on numerical values derived from the raw coal state, thus, it only accounts for the impact of natural factors, including the surrounding rock of the coal seam and geological structures. It does not take into consideration the effects of adjacent layers or mining activities.
2. The measurement of coal seam gas content is expressed in units of m\({}^{3}\)/t. Given that the sole measurement of gas emission during mining is also conducted in m\({}^{3}\)/t, the influence coefficients utilized within the prediction algorithm are inherently shared with those employed for predicting gas emission in the mining face.
# Sketch-Guided Topology Optimization with Enhanced Diversity for Innovative Structural Design
###### Abstract  
Topology optimization (TO) is a powerful generative design tool for innovative structural design, capable of optimizing material distribution to generate structures with superior performance. However, current topology optimization algorithms mostly target a single objective and are highly dependent on the problem definition parameters, causing two critical issues: limited human controllability and solution diversity. These issues often lead to burdensome design iterations and insufficient design exploration. This paper proposes a multi-solution TO framework to address them. Human designers express their stylistic preferences for structures through sketches which are decomposed into stroke and closed-shape elements to flexibly guide each TO process. Sketch-based constraints are integrated with Fourier mapping-based length-scale control to enhance human controllability. Solution diversity is achieved by perturbing Fourier mapping frequencies and load conditions in the neural implicit TO framework. Adaptive parallel scale adjustment is incorporated to reduce the computational cost for design exploration. Using the structural design of a wheel spoke as a case study, the mechanical performance and diversity of the generated TO solutions as well as the effectiveness of human control are analyzed both qualitatively and quantitatively. The results reveal that the sketch-based constraints and length-scale control have distinct control effects on structural features and have different impacts on the mechanical performance and diversity, thereby enabling fine-grained and flexible human controllability to better balance conflicting objectives.  
topology optimization; structural design; innovative design; free-hand sketch; human control; design diversity Article
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 3 Analysis of Experimental Results
### Effect of Vibrating Parameters on Segregation of Slipform Concrete  
Presently, there is no commonly accepted principle for dissociation testing, and there are few methods for evaluating the dissociation degree. This study defined the \(SC\) value as the segregation rate of the degree of bone and paste separation based on the distribution of coarse aggregated area on the cutting surface of slipform concrete specimens (Equation (2)). The smaller the \(SC\) value, the lower the degree of homogeneity of aggregate segregation in the vertical direction of the vibratory forming of slipform concrete specimens.  
\[SC=\frac{2(P_{b}-P_{t})}{P_{b}+P_{t}} \tag{2}\]  
where \(P_{t}\) is the area of coarse aggregate within 300-450 mm at the top of the column height, and \(P_{b}\) is the area of coarse aggregate within 0-150 mm at the bottom of the column height.  
The \(SC\) values of the segregation rate of concrete under the mold with different vibrating parameters were calculated according to Image-Pro Plus image processing (Figure 8).  
Figure 6: Image analysis of the cut surface of slipform concrete.  
Figure 7: Aggregate size distribution coefficients of concrete under different vibrating parameters.  
The segregation rate's _SC_ value of the slipform concrete increased continuously with increasing vibration time and frequency (Figure 8). These observations showed that the slipform concrete underwent the actions of the high-frequency bounding of the vibration of the vibrating rod near the liquefaction area. These results showed that the large size of the aggregate in the vibration force and self-weight increased its sinking speed. In contrast, the light mass of mortar and some smaller aggregates floated at the top, decreasing the homogeneity of the concrete. Therefore, the proportion of large-size aggregates at the bottom of slipform concrete increased, the mortar and small-size aggregates at the top of concrete increased, and the _SC_ value of the segregation rate increased.  
The test results of the compressive strength of slipform concrete under different vibrating parameters showed that the segregation rate _SC_ value influenced the compressive strength of slipform concrete. When the segregation rate _SC_ value was <0.15, the compressive strength of slipform concrete was affected less. However, when the segregation rate SC value exceeded 0.15, the compressive strength of slipform concrete decreased. Therefore, the _SC_ value < 0.15 should be used for the slipform paving with large thicknesses of concrete.  
When testing large thicknesses of hardened slipform concrete pavement, the core samples can be analyzed to calculate their segregation rate _SC_ values and determine whether the quality of the concrete is affected by over-vibration.
## Case Studies  
### Selection of Case Studies  
Two distinct case studies, Edmodo and Duolingo, demonstrate the implementation and efficacy of the Data-Driven Personalized Learning Model (DDPLM) [42]. These platforms were selected for their extensive user bases and robust data generation capabilities, which provide a rich context for applying and evaluating the DDPLM. Each case study offers unique insights into how the model adapts to different learning environments and user needs, showcasing the model's flexibility and scalability.  
Edmodo serves as a case study due to its widespread use in educational institutions around the globe [43]. It functions primarily as a classroom management tool that facilitates communication between teachers and students and offers functionalities for distributing and grading assignments. The platform's comprehensive data collection capabilities allow for detailed student progress and engagement tracking, making it an ideal environment for implementing the DDPLM [44]. By integrating DDPLM, Edmodo's existing educational tools are enhanced to provide personalized learning experiences based on individual student data.  
Duolingo, on the other hand, is chosen for its focus on language learning through interactive activities. It is renowned for its gamified approach to education, which engages users in language tasks that adapt to their learning speed and proficiency level. Duolingo's data-rich environment captures many learner interactions, from completed exercises to time spent on tasks. Applying DDPLM to this platform demonstrates how big data can fine-tune learning activities and paths, ensuring that each user's experience is optimally challenging and engaging.  
These case studies were selected not only for their alignment with DDPLM's goals but also for their potential to illustrate the model's application across different types of educational technology platforms. By analyzing these implementations, we can evaluate DDPLM's practical benefits and challenges in real-world educational settings.
## 1 Introduction
### Literature Review
[PERSON] and [PERSON] [32] investigated a morphing wing by combining variable span morphing and variable camer variable chord morphing mechanisms. Their study included wind tunnel and flight tests to evaluate the aerodynamic changes and operational stability of the morphing mechanisms. The results confirmed increased lift coefficients and lift-to-drag ratios for the morphing mechanisms, especially when the morphing mechanisms were combined. However, challenges such as surface deformations of the latex skin during tests were noted, highlighting the need for further material optimization. Investigations into local morphing applications, such as morphing ailerons, have further highlighted the potential for aerodynamic gains. [PERSON] et al. [33] designed, manufactured and tested a wing-tip model featuring a morphing upper surface and interchange-able ailerons. The experimental results demonstrated that the morphing aileron extended the laminar flow over the wing surface by up to 9% of the chord length and achieved a drag coefficient reduction of up to 9%. These findings underscored the potential of morphing technologies to enhance aerodynamic efficiency by promoting laminar flow and reducing drag. In addition to structural innovations, bio-inspired approaches have also emerged. [PERSON] et al. [34] explored the benefits of combining two bio-inspired technologies: wing morphing and distributed pressure sensing. A wind tunnel model was developed with an avian-inspired wing capable of sweep motion, and pressure sensors were distributed across the wing surface. The results showed that the wing sweep could be an effective method for pitch control, enhancing the aircraft's control authority at high angles of attack. Additionally, the pressure sensing system effectively detected the onset of aerodynamic stall, which could be used to implement an early warning stall system. Finally, recognizing the broader environmental and economic benefits of morphing technologies, [PERSON] et al. [35] developed a validation methodology for the morphing wing, aiming to enhance fuel efficiency and reduce environmental impact. They conducted aerodynamic simulations and wind tunnel testing to assess the wing's morphing capabilities and optimize the wing aerodynamic efficiency. The validation results confirmed that the morphing wing design performed as expected, with the experimental data. These findings validated the effectiveness of the morphing wing in achieving higher aerodynamic performance and lower fuel consumption.
## 3 Case Study
### Collection of Processing Data Based on CM  
We opted to conduct a dynamic risk assessment for cable fires in the power distribution room during the operational hours between 2 P.M. and 3 P.M. on 22 November 2021, during which data processing took place. The assessment data for root nodes X1-X4 were derived from the continuous monitoring data collected by four sensors, with readings recorded every three seconds. The fluctuations in these four sets of monitoring data are visually depicted in Figure 8. In addition, the evaluation data of root nodes X5-X8, which can be updated irregularly, were obtained using dynamic expert scoring. Given the brief time frame of this case study, the expert scores remained unaltered throughout the hour, as detailed in Table 4. The expert scoring method is a technique employed to quantify qualitative descriptions, wherein experts assign scores based on their experience and evaluation criteria. In this paper, X5-X8 (insufficient laying spacing, insulation aging, insulation damage, and poor connection quality) are not real-time data and cannot be measured multiple times in a short period. Instead, their risk levels are derived from experts' observations and accumulated work experience. These four risk factors exhibit minimal changes over short timeframes, resulting in scoring results with extended validity that do not necessitate frequent updates. Consequently, based on the aforementioned factors, it can be inferred that the absence of updates within a 60 min interval would not significantly impact the results.  
Figure 7: Schematic diagram of GeNIe-based DBN modeling.
# Surface Topographical Control of a Liquid Crystal Microlens Array Embedded in a Polymer Network
## 2 Materials and Methods
### Focal Length Measurement  
The focal length of the fabricated microlens array was measured experimentally under POM equipped with a hot stage (Tokai Hit, TP-S-100, Tokyo, Japan) by removing the analyzer, sending a linearly polarized light at 90\({}^{\circ}\), and increasing the temperature. First, the base of the microlens array was focused and this distance was denoted as \(L=0\)\(\upmu\)m. The stage was adjusted until the irradiated light was focused on the microlens array, and the distance moved was defined as the focal length. Then, the temperature was increased by 1 \({}^{\circ}\)C each time, and the stage was adjusted until the light was again focused. At each temperature, a new focal length was determined and recorded.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 7 Conclusions and Future Directions  
On-demand data broadcast has been demonstrated as a promising way to disseminate information to a large population of clients in wireless communication environments. Conventional scheduling algorithms cannot best explore the broadcast efficiency of wireless bandwidth. In this study, we apply network coding to enhance the scheduling performance of existing on-demand scheduling algorithms. Based on the derived CR-graph, we propose a heuristic coding-based approach, which can efficiently transform existing scheduling algorithms to their respective network coding versions while preserving their original scheduling criteria. In particular, based on the classification of on-demand scheduling algorithms, we select several representative algorithms in each group for performance evaluation and enhancement, including two real-time algorithms (i.e., EDF and SIN), four non-real-time algorithms (i.e., FCFS, MRF, LWF and R\(\times\)W) and two stretch optimal algorithms (i.e., LTSF and STOBS). We describe the detailed implementation of the network coding version of each algorithm and investigate their potential performance improvement using a running example. We design a number of metrics and build a simulation model to compare the performance of the non-coding version with the coding version of each algorithm. The comprehensive simulation results under various circumstances conclusively demonstrate that network coding can effectively improve the performance of on-demand scheduling algorithms with different objectives.  
In our future work, we will further investigate the network coding strategy in heterogeneous service environment where data items are with different sizes. Specifically, since the benefit of network coding largely depends on the size of the encoded packet, which is determined by the largest size of data item which is encoded. It is interesting to strike a balance between encoding large and small data items to maximize the overall system performance.  
This work was completed with contributions from all the authors. [PERSON] and [PERSON] proposed the research idea with regular feedbacks of suggestions and supervision. [PERSON] helped in the conceptualization of the project, conducted further simulations and wrote the initial manuscript. [PERSON] conducted the initial simulation studies. [PERSON] did the funding acquisition and supervised the whole project. All authors did edit, review and improve the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EDF & Earliest Deadline First \\ SIN & Slack time Inverse Number of pending requests \\ FCFS & Fist Come First Served \\ MRF & Most Requested First \\ LWF & Longest Wait First \\ RxW & Number of Pending Request Multiply Waiting Time \\ LTSF & Longest Total Stretch First \\ STOBS & Summary Table On-demand Broadcast Scheduler \\ CR-Graph & Cache-Request-Graph \\ \end{tabular}
## 3 Results and Discussion
### Optimization Results Using PSO without and with DG integration
#### 3.2.6 Scenario 6: Three DGs Placed at Buses 18, 33 and 32 Based on the CF Ranking Method
It can be seen from Figure 5 that the identified locations for DG placement as predicted by the CF method (cases 2, 4 and 6) showed a significant improvement in voltage profiles.