esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Correlation Analysis
Although CFP samples had high levels of acetates and a relatively low level of IBMP, the fruity notes in CFP samples were lower and the scores in 'green pepper' and 'herbaceous' were higher than in CFC samples. The results indicated that the correlation between wine aroma with single volatiles was not enough to explain the aroma expression. The sensory characteristics of CFP samples were possibly due to the high levels of C6/C9 alcohols in CFP samples. Many researchers found that the excessive concentration of C6/C9 alcohols would inevitably contribute to herbaceous notes and suppress the expression of fruity notes [5; 23]. On the contrary, CFC samples had the lowest sensory score of 'green pepper' even with the highest concentration of IBMP among three blending types (Figure 4). This might be due to the appropriate concentration proportion of other volatiles in CFC samples. In other words, CFC samples had ethyl esters with the highest concentration and C6/C9 alcohols and acetates with the second highest concentration. The presence of MPs at certain concentrations could also contribute to the complexity and typicity of some wines [12]. The proper balance between fruitiness and greenness was reported to be important to wine aroma quality [21], and the enhancing and masking effects between different volatile compounds are receiving more and more attention [6; 25].
# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
## 3 Breakdown Test Procedure  
This section outlines the breakdown testing procedure applied to the PCB-defective samples. Prior to each test, the surfaces of the samples and copper electrodes were thoroughly cleaned using precision wiping cloths to eliminate any dust or contaminants that could have compromised testing integrity. The tests were performed using a commercial Haefely machine, which provided a stable and reliable performance and ensured consistent and accurate breakdown measurements of the defective samples. Figure 3 shows the breakdown test process flow chart, illustrating the sequential steps involved in the testing process.  
For the PD breakdown test, the samples were connected to a coupling capacitor to ensure appropriate insulation and grounding throughout the test. Insulating ceramic supports were used to securely hold the samples in place, and specific adjustments were made to address potential issues with the FEs. To stabilize the measurements, one corner of the insulator was grounded using aluminum braiding, which helped prevent unwanted discharge behavior.  
Figure 2: Schematic of PCB samples with five distinct electrode designs. All electrode widths (bold wires) were 5 mm.  
The overall test setup was designed to facilitate an even charge distribution across the sample, with particular attention paid to the contact points between the electrodes and the grounding system. These measures ensured that the breakdown tests were conducted under controlled conditions, thereby providing accurate and reliable results.  
1. At the start of the breakdown test, the voltage increased very slowly, at a minimum rate, using a linear approach. Each discharge pattern was recorded for one min.
2. When PDIV and repetitive PDIV (RPDIV) events were observed during the voltage increase, the voltage was held steady for five mins to allow the discharge to stabilize. The discharge pattern was recorded, and after confirming the completion of the RPDIV pattern, the voltage was increased in 3 kV increments. After each increment, the pattern was recorded every two mins for a total of ten mins. This process was repeated until the next voltage increase.
3. Once the sample reached breakdown, the timing chart and discharge pattern during the test were categorized and saved.
4. After the power supply was turned off and the grounding rod was secured, the breakdown location of the sample was examined to verify whether it matched the designed defect position. If there was no match, the test proceeded to Step 5; if there was a match, the test proceeded to Step 6.
5. The sample and overall test setup were reviewed to identify any elements requiring correction. After clarifying the issues and making the necessary modifications, the breakdown test was restarted from Step 1.
6. If the breakdown test was successful, the same sample design and test setup were maintained and the test was repeated until eight successful samples were obtained, marking the end of the test.
## 2 Fabrication Process and Filter Design
### Filter Design and Fabrication
Configuration (c) shows the L3 design with identical resonator widths in both cap and bottom wafers (100% matching), maximizing the bonding interface area. Each schematic includes precise labeling of material layers (Au Plating: 1-2 \(\upmu\)m, Cu Plating: 2-3 \(\upmu\)m) and clearly demonstrates how the different configurations create varied electromagnetic coupling environments between the wafers while maintaining the same overall dimensions. These structural variations directly influence both the RF performance characteristics and mechanical reliability of the resulting MEMS filter. Figure 7 presents optical microscope images of the three fabricated MEMS filter configurations after the wafer bonding process. Each image shows the top view of a completed filter device with dimensions of 5680 \(\upmu\)m \(\times\) 190 \(\upmu\)m \(\times\) 820 \(\upmu\)m and a stripline length of 5180 \(\upmu\)m. The left image displays the L1 configuration, which features no resonator structure in the cap wafer (0% width ratio). The dark areas represent the bonded regions, while the lighter areas show the internal filter structure. The middle image shows the L2 configuration with the cap resonator width at 60% of the bottom wafer width, creating a visible interdigital filter pattern with partial width matching. The right image depicts the L3 configuration with complete resonator width matching (100%) between cap and bottom wafers, showing the most uniform appearance with clear visibility of the interdigital filter structure. These images provide visual confirmation that the fabrication process successfully produced the three distinct resonator width configurations as specified in the design phase. The photographs demonstrate the physical implementation of the theoretical designs shown in previous figures and verify that the post-processing structures accurately match the intended specifications before performance testing. Figure 6: MEMS filter with top Cu plate with a width (**a**) 0% (L1), (**b**) 60% (L2), and (**c**) 100% (L3) that of the bottom Cu plate, L1.
# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 3 Methodology
### Specifications of Instances Deployed
#### 3.4.1 AWS Evaluation  
As in Figure 1, the dataset was stored in an Amazon S3 bucket to make it accessible via the cloud. Next, a SageMaker notebook instance was used to create the model endpoint, with a SageMaker endpoint to deploy the model.  
It is important to note that the model deployment was conducted on an ml.g4 dn.xlarge instance, which has an Nvidia T4 tensor core GPU with 16 GB of VRAM. The use of a GPU is crucial since most machine learning models use a GPU for accelerated performance and is a key contributor for real-time inference.  
Finally, to make our machine learning model accessible to users, we set up a client application. This application needed a dedicated server to function as a web server to process incoming requests. Hence, we used Amazon's Elastic Compute Cloud (EC2) Infrastructure as a Service (IaaS) and deployed a virtual machine. Specifically, we used the t3.micro Linux instance as our client application was lightweight and highly efficient.
## 4 Discussion
### Dynamic SIF-T Relationships in Temperate ENF during the Fall Transition  
A number of recent studies have demonstrated that SIF and T share a strong positive relationship at multiple spatial scales [12; 14; 17; 30; 31]. We collected ENF canopy SIF and T at high temporal resolution during the fall in 2020 and observed correlations between SIF and T. However, the relationships of SIF and T varied across different months both at hourly and daily scales. SIF and T showed significant linear correlation in October. These dynamic relationships between SIF and T indicated that current satellite SIF products averaged at a weekly and monthly time resolution are not yet adequate to characterize the SIF-T relationship in the fall transition. It is necessary to incorporate the dynamic relationships between SIF and T at an unprecedented spatiotemporal resolution into the SIF-based T estimations during season transition.  
Several factors influence the SIF-T relationship in the season transition, such as the vegetation biochemistry (e.g., leaf pigments, water and dry matter), physiological status (e.g., canopy structure), environmental conditions [15; 32], and observation angles [33]. Canopy structure, including canopy LAI and leaf orientation influence the SIF scattering and re-absorption [34]. Evergreens hold relative constant canopy structure during the season transition [6; 7]. To shed more absorbed energy as nonphotochemical quenching (NPQ) over winter months, ENF gradually changes pigment ratios of carotenoids and chlorophyll with meteorological shifts in season transition [35; 36; 37; 38]. Solar radiation, air temperature and VPD triggered plant photosynthesis and transpiration recovery and reduction in the season transition [39; 40; 41]. Therefore, meteorological conditions are the first-order driving factor that influences SIF and T relationships of ENF in season transition. Our results showed the significant influence of SWC on the SIF-T relationship at hourly scale in November. This was probably caused by short-term meteorological droughs; soil water dominates dryness stress on ecosystem transpiration [42] and becomes the major factor influencing the SIF-T relationship.  
In this study, we investigated the SIF-T relationship based on the T value estimated by the uWUE separation method. It involved uncertainty in T estimation [24; 32] and thus in the analysis of the SIF-T relationship. T values estimated from field observation (e.g., sap flow) are needed to verify our results in further research. We reported slightly higher linear relationships between SIF and T compared with hyperbolic relationships at hourly and daily timescales without considering clear and diffuse sky conditions. This would probably weaken the linear relationship between SIF and T because canopy photosynthesis and transpiration can be enhanced under cloudy conditions [43] but SIF reduces under cloudy conditions [18].
# From Large Language Models to Large Multimodal Models: A Literature Review
## 2 Large Language Models
### Representative Pretrained LLMs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pretrained LMs** & **Architectures** & **Parameters** & \begin{tabular}{c} **Pretraining** \\ **Corpus Size** \\ \end{tabular} & \begin{tabular}{c} **Pretraining** \\ **Objectives** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **Providers** \\ \end{tabular} & \begin{tabular}{c} **Fine-Tuned** \\ **LMs** \\ \end{tabular} \\ \hline T5 & \begin{tabular}{c} encoder- \\ decoder \\ \end{tabular} & 11 B & 1 T & MLM & Google & T0 [53] \\ \hline GPT-3 & decoder-only & 175 B & 300 B & ALM & OpenAI & \begin{tabular}{c} ChatGPT [54] \\ WebGPT [55] \\ \end{tabular} \\ \hline CPM-2 & \begin{tabular}{c} encoder- \\ decoder \\ \end{tabular} & 198 B & 2.6 T & MLM & Tsinghua & - \\ \hline PaLM & decoder-only & 540 B & 780 B & ALM & Google & - \\ \hline PaLM-2 & - & \textless{}540 B & \textgreater{}780 B & Mixture & Google & - \\ \hline LLaMA & decoder-only & 65 B & 1.4 T & ALM & Meta & \
\begin{tabular}{c} Vicuna [56] \\ Alpaca [57] \\ LIMA [58] \\ \end{tabular} \\ \hline LLaMA 2 & decoder-only & 70 B & 2 T & ALM & Meta &
\begin{tabular}{c} LLaMA \\ 2-CHAT [9] \\ \end{tabular} \\ \hline OPT & decoder-only & 175 B & 180 B & ALM & Meta & OPT-IML [59] \\ \hline PanGu-\(\Sigma\) & decoder-only & 1.085 T & 329 B & ALM & Huawei & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of pretrained LLMs, including architectures, parameter counts, pretraining corpus sizes, objectives, providers, and notable derived fine-tuned LMs
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
###### Abstract  
Large-scale high-dimensional many-objective optimization problems (LaMoOPs) are prevalent in fields such as autonomous driving, cloud resource scheduling, and smart grids. LaMoOPs involve a large number of decision variables and multiple conflicting objectives that need to be optimized simultaneously. The challenges posed by the curse of dimensionality due to the vast number of decision variables, and the conflict between convergence and diversity caused by the numerous objective variables, make traditional optimization methods inadequate. To address these issues, this paper proposes a two-population cooperative evolutionary algorithm based on large-scale decision variable analysis (DVA-TPCEA). This algorithm integrates quantitative analysis methods for decision variables to deeply examine their impact on each objective and introduces a contribution-based objective detection method. Additionally, a dual-population cooperative evolution mechanism is employed, with targeted optimization strategies designed for convergence and diversity populations, achieving synergistic and complementary optimization between the two populations. To validate the algorithm's effectiveness in practical applications, a large-scale container resource scheduling strategy based on the DVA-TPCEA algorithm is also proposed. The experimental results indicate that the proposed algorithm demonstrates significant advantages in both general datasets DTLZ, WFG, and LSMOP, and practical models.  
Keywords:dual-population cooperative evolution; decision variable characteristic analysis; large-scale optimization; high-dimensional many-objective; container resource scheduling +
Footnote †: journal: applied sciences
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 2 Related Work
### Learning-Based Methods
#### 2.2.1 Voxel-Based Reconstruction  
Voxel-based 3D reconstruction decomposes the 3D space into a volumetric grid of voxels that are considered occupied or empty and thus can be represented in a format suitable for deep learning frameworks. However, there are memory limitations that usually restrict the grid resolution to 10243 or less. The earliest works, like 3D-R2N2, used 3D convolutional networks paired with Long Short-Term Memory (LSTM) layers to output voxel-based representations from single or multiple images [12]. This approach uses an encoder, a 3D conv. LSTM, and a decoder to deal with multiple views of an object. Despite its innovative design, the model faces challenges; i.e., it has low resolution and a slow inference speed, which is mainly due to the sequential processing of images. To address these limitations, the OCTree Generating Network (OGN) introduced an adaptive voxel subdivision technique, which dynamically refines voxel grids to optimize memory usage, allowing for more detailed representations of 3D objects [22]. Further optimizations achievedhigher Intersection over Union (IoU) scores, even with fewer model parameters compared to previous methods, but as a result of the reduced number of model parameters, the resolution remains limited to 32\({}^{3}\) voxels [23]. Pix2 Vox++ further enhanced voxel-based reconstruction by incorporating a ResNet backbone, which not only reduced parameters by 25% but also improved accuracy by 1.5% [24]. Additionally, variational autoencoders (VAEs) have played a significant role in generating smoother, higher-resolution 3D models. By learning latent feature representations from images, VAEs enable the reconstruction of more realistic 3D structures [25].  
Recent approaches have further improved the accuracy and quality of 3D reconstructions. RNN-based discriminative networks, for example, improve the processing of sequential data, which is crucial for multi-view reconstruction [26], Generative Adversarial Networks (GANs) [27] have contributed to creating more detailed voxel grids through adversarial training, thus improving models' abilities to produce realistic 3D outputs, multi-scale context-aware fusion [23] enables models to capture both local and global features of objects, and transformers [28] provide robust capabilities in handling complex relationships between different parts of an object. Memory-based frameworks have also evolved to manage occlusions--situations where parts of an object are not visible in any provided view. These frameworks retain the memory of previously seen object parts, which helps to infer the structure of occluded areas [4]. Despite these advancements, voxel-based approaches continue to face challenges, such as low spatial resolution and high computational demands due to sparse voxel grids. These limitations continue to restrict their application in fields that require highly detailed and precise 3D reconstructions, such as medical imaging and virtual reality.
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 2 Related Work
### Color Mapping of Thermal Camera  
Thermal camera images differ significantly from standard RGB camera images. Thermal cameras measure the temperature of objects and generate images based on this temperature information. These images primarily detect infrared energy for temperature sensing, converting it into temperatures represented as visual data. The original images are essentially grayscale, composed solely of thermal information where brighter areas indicate higher temperatures and darker areas indicate lower temperatures. In order to aid user understanding, various color palettes are often applied to differentiate temperature variations through color. These color palettes typically use visible colors, assigning different colors to different temperature ranges.  
Figure 2 shows the application of various palettes on a thermal camera by ATN Corp. Since the perception changes with different palettes, adjusting the palette according to the usage environment can enhance performance. This flexibility allows users to optimize the thermal imagery for specific applications, such as wildlife observation, security, or search and rescue operations, by selecting palettes that best highlight the features relevant to each context. This ability to switch between different color mappings is crucial for maximizing the utility and effectiveness of thermal imaging technology in diverse conditions.  
[PERSON] et al. [24] utilized various color palettes in thermal cameras to visualize temperature changes, noting that different palettes can emphasize different features of the thermal images. [PERSON] et al. [25] evaluated the effectiveness of various color palettes used in tropical environments with thermal imaging. They highlighted how palettes can impact data accuracy and the ease of interpretation in environments with diverse and complex heat sources.  
In ophthalmology, images are often viewed in black and white or with a green filter. However, as demonstrated in other fields, utilizing a variety of color palettes to enhance structural features can improve interpretability and lead to greater accuracy.
## 5 Engineering Application
### RBDO for C4  
As shown in Figure 10, the NRRAP can be effectively reduced by adjusting the cable tension and the dead weight of the concrete girder and pavement. However, the NRRAP is caused by the forced closure of the concrete girder in actual engineering, and the concrete girder is closed while the pavement has not been constructed. As described in Section 2.2, it is infeasible to adjust the self-weight of the concrete girder, construct the girder counterweight, or optimize the cable tension in this case. Therefore, as shown in Figure 14, pavement segments P2-P7 and P15-P20 were selected as design variables X\({}_{\text{opt}}\), while the others were treated as random variables \(U\).  
The pavement optimization problem was formulated with the objectives of driving comfort and minimum construction cost. In addition, the reliability constraint for the RAP  
Figure 14: Design variables and random variables.  
Figure 13: Negative reaction risk assessment under the live load.  
was implemented in the optimization model. The optimization model was established as follows:  
\[\begin{array}{ll}find&X_{opt}=[P2,P3\cdots P7,P15,P16\cdots P20]^{T}\\ obj.&f^{1}{}_{obj}\big{(}X_{opt}\big{)}=\min(\max|P_{i}-P_{0}|)\\ &f^{2}{}_{obj}\big{(}X_{opt}\big{)}=\min\bigg{(}\sum P_{i}\bigg{)}\qquad i=2,3 \cdots 7,15,16\cdots 20\\ s.t.&f_{cont}\big{(}X_{opt},\ U\big{)}=\beta\big{[}G\big{(}X_{opt},\ U\big{)} \big{]}\geq 2.5\\ &P_{i}=510p_{i}+76.5;\quad[0.075m<p_{i}<0.125m]\end{array} \tag{14}\]  
where \(X_{opt}\) refers to the design variables, and \(U\) refers to the random variables. \(P_{0}\) and \(P_{i}\) refer to the pavement load before and after optimization, respectively. \(p_{i}\) refers to the thickness of the concrete pavement of each section after optimization, and the specification requires that the maximum deviation between \(p_{i}\) and the design value should not exceed 0.025 m [42; 43]. \(\beta\) is the reliability index of the RAP under the live load. NSGA-II was used to solve the multiobjective optimization problem, and the Pareto-optimal sets are as follows:  
During the evolution of NSGA-II, the Pareto front will continue to approach the optimal Pareto front. As shown in Figure 15, the Pareto frontier trend of the 10 th, 20 th, 50 th, and 100 th generations of adjacent algebraic populations fluctuates greatly but has a close trend. The trend of the Pareto front of the 100 th generation is completely consistent with that of the 200 th generation, and the difference is small. Therefore, it can be approximately considered that the Pareto front of the 200 th generation is the optimal Pareto front.