esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Paleoclimate  
Geochemistry plays a pivotal role in reconstructing paleoclimate. Soluble elements typically tend to be lost during intense chemical weathering, while refractory elements are retained. Based on this pattern, the chemical index of alteration (CIA) is widely used to evaluate weathering intensity and climate of fine-grained siliciclastic rocks [83; 84; 85; 86; 87]. The CIA is calculated as  
\[\text{CIA}=100\times\text{Al}_{2}\text{O}_{3}/(\text{Al}_{2}\text{O}_{3}+ \text{CaO}^{*}+\text{Na}_{2}\text{O}+\text{K}_{2}\text{O}) \tag{1}\]  
where oxides are expressed in molar units, and CaO* represents CaO in silicate, corrected as CaO* = CaO \(-\) (10/3 \(\times\) P\({}_{2}\)O\({}_{5}\)) [83; 88]. CIA values provide insights into paleoclimate: 50-70 indicates cold and arid climates, 70-80 corresponds to warm and humid climates, and 80-100 reflects hot and humid climates [83; 85; 89; 90].  
During Stage II, CIA values fluctuated significantly. SQ3 (78.48-81.03, mean 79.66) indicates warm and humid climates, SQ4 (64.12-81.03, mean 75.21) reflects a marked attenuation of warm and humid conditions and an ephemeral arid climatic perturbation, and SQ5 (77.59-84.78, mean 82.49) marks a shift to hot and humid climates (Figure 11). The _Cyathidines-Cibotiumspora-Disacciatrileti_ spore-pollen assemblage identified in the Yangxia Formation (SQ3-5) exhibits a non-acme occurrence of _Cyathidines minor_, establishing biost stratigraphic correlation with the Fuxian Formation of the Ordos Basin [91]. Previous palynological studies constrain the Fuxian Formation to the Early Jurassic Pliensbachian-Toarcian, with the _Classopolis_-dominated assemblage being stratigraphically associated with the Toarcian Oceanic Anoxic Event (T-OAE) [13]. This correlation suggests the Yangxia Formation spans equivalent Pliensbachian-Toarcian strata, where the abrupt proliferation of _Classopolis_ and _Kluisporites_ in SQ5 potentially records paleoclimate perturbations linked to T-OAE [24; 92; 93; 94]. In Stage III, CIA values stabilized: SQ6 (76.77-83.76, mean 81.10) reflects weakening hot and humid climates, while SQ7 (77.44-80.58, mean 78.99), SQ8 (79.04-79.29, mean 79.17), and SQ9 (77.93) indicate warm and humid climates with decreasing humidity (Figure 11).  
Although CIA is extensively used to assess chemical weathering and paleoclimate in lacustrine catchments [83; 84; 85; 86; 87], uncertainties in CaO correction associated with phosphates and carbonates can affect its accuracy [39; 40]. Therefore, In (Al\({}_{2}\)O\({}_{3}\)/Na\({}_{2}\)O) is often used as a complementary weathering index for cross-validation [40; 41; 42]. High In (Al\({}_{2}\)O\({}_{3}\)/Na\({}_{2}\)O) values indicate warm and humid climates with intense weathering, whereas low values reflect cold and arid climates with weak weathering [40; 42]. The In (Al\({}_{2}\)O\({}_{3}\)/Na\({}_{2}\)O) trend closely parallels the CIA curve (Figure 11), confirming the CIA's reliability in reconstructing paleoclimate variations in the study area.
## 3 Proposed Approach
### Backbone Architectures
#### 3.2.1 Vision Transformer (ViT)  
ViT divides an input image into fixed-size patches and flattens each patch into a 1D vector. These vectors, also referred to as patch embeddings, are then concatenated to form a sequence of tokens--much like words in natural language processing--so that the transformer can process them in parallel. To preserve spatial information, each patch embedding is enriched with a trainable position embedding, allowing the network to differentiate where each patch originates in the image. ViT also adds a dedicated \"class token\" at the start of the token sequence, serving as a global representation that aggregates features from all patches. In downstream tasks, this class token becomes the output from which final predictions are derived [23].  
In this study, the combination of YOLOv10 and ViT is referred to as YOLOv10-ViT. Specifically, ViT replaces the typical convolution-based backbone in YOLOv10, generating tokenized feature representations that the YOLO detection head can use to localize heavy equipment (e.g., loaders and trucks) and assign confidence scores in real time, as shown in Figure 2.  
Furthermore, the transformer encoder applies multi-head self-attention across these patch tokens, enabling it to model both local and long-range dependencies without relying on traditional convolutional filters. This direct attention to each patch's relationship with all others captures a global context early in the network. After the attention layers, a Multi-Layer Perceptron (MLP) head processes the aggregated feature representations to produce final predictions. Moreover, the self-attention has quadratic complexity with respect to the number of patches, which may lead to significant computational overhead for high-resolution images [24]. By replacing local receptive fields with global self-attention, ViT can capture broad contextual cues in fewer layers than CNNs. However, due to its reliance on large training corpora and substantial computation, practitioners must weigh the benefits of superior global modeling against the hardware and data demands.
# Methods Improving Energy Efficiency of Photovoltaic Systems Operating under Partial Shading
## 3 Efficiency Estimation of PV Installation
### PV Installation with Individual Matching Converters and Micro-Inverters
The introduction of micro-inverters in the photovoltaic industry is considered one of the largest technological advances in the industry today [34]. Manufacturers guarantee an increase in the power generation of solar installations by 5-25% due to their use [35]. The leader in the implementation and promotion of these devices is the American company Enphase Energy, which created the first successful commercial micro-inverter. At present, many companies have appreciated the advantages of this development, as a result of which they began to independently develop and produce micro-inverters [36; 37; 38]. The principle of operation of a micro-inverter is similar to a central inverter; the main difference is only the power. The positive effect of micro-inverters is achieved due to the fact that, such as ICPs, they optimize the operation of each module separately, allowing them to work at the maximum power point under any conditions. The absence of a central inverter also has a positive effect on the reliability of the installation and makes it more flexible in the possibilities of subsequent expansion, simplifies installation work, and increases the service life due to the absence of active cooling systems. Despite all the advantages of using micro-inverters, they have a number of disadvantages, and the main ones are:  
1. High cost. The use of micro-inverters instead of the central one in industrial power plants is on average 50-85% more expensive. Reduced energy efficiency with uniform illumination of solar cells. This is due to the fact that the efficiency of micro-inverters is less than that of the central one by an average of 2.5-4%, as well as the fact that the voltage in the connecting wires has lower voltage values. 2. Lack of possibility of implementation in existing power plants with a central inverter to solve the problem of uneven illumination.
## 3 Results  
Table 1 shows the performance metrics for the \"Both\", \"VF\", and \"Disc\" models in detecting the progression of POAG from longitudinal color fundus photos on the hold-out test set. A plot of each model's achieved AUC is presented in Figure 2.  
The \"Both\" model achieved the highest AUC (0.939, \(p\) = 0.0048), as well as precision, recall, and F1-score (0.984, 0.895, and 0.963, respectively). Of note, the \"VF\" model achieved the highest accuracy (0.959), followed by the \"Both\" model (0.942) and the \"Disc\" model (0.911). The \"VF\" model also demonstrated a higher AUC than the \"Disc\" model (0.911 vs. 0.894, respectively; \(p\) = 0.118), precision (0.813 vs. 0.646), and F1-score (0.804 vs. 0.743). However, the \"Disc\" model outperformed the \"VF\" model in terms of recall (0.875 vs. 0.565, respectively).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **“Both” Model** & **“VF” Model** & **“Disc” Model** \\ \hline AUC & 0.939 & 0.911 & 0.894 \\ Accuracy & 0.942 & 0.959 & 0.911 \\ F1-Score & 0.963 & 0.804 & 0.743 \\ Precision & 0.984 & 0.813 & 0.646 \\ Recall & 0.895 & 0.565 & 0.875 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the performance between the “Both”, “VF”, and “Disc” models in detecting glaucoma from longitudinal color fundus photos.  
Figure 2: The “Both” model (**top left**) achieved the highest AUC (0.939), followed by the “VF” model (0.911; **bottom left**) and “Disc” model (0.894; **top right**).  
Figure 3 shows the results of the heatmaps generated by Grad-CAM.  
Two Grad-CAMs from each of the three models on follow-up fundus images are provided. Areas of greater importance are highlighted in red, while areas of less importance are highlighted in purple. In the earlier layers (e.g., the \"expand_conv_depthwise\" layer), all models predominantly focused on retinal vasculature. In later layers (e.g., the \"out_relu\" layer), the models' attention shifted towards the optic nerve head (ONH), consistent with its well-documented significance in glaucoma diagnosis. This progression of focus from vascular features to the ONH suggests that the models are effectively capturing both early and advanced biomarkers of glaucoma.  
Figure 3: Grad-CAMs generated by the “Both”, “VF”, and “Disc” models are on images of patients diagnosed with both functional and structural glaucoma, just functional glaucoma, and just structural glaucoma, respectively. Panels (**A**) and (**B**) show the features selected by the “expand_conv_depthwise” and “out_relu” layers, respectively. All models focus on vasculature in the former layer and develop towards the ONH in the latter layer.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 4 Methodology Development
### Technological Components
#### 4.1.3 Image Preprocessing  
Each image was initially scaled to 224 \(\times\) 224 pixels to meet the input specifications of the convolutional backbone. We subsequently implemented geometric augmentations--random horizontal flips and rotations within \(\pm 20^{\circ}\)--to incorporate orientation diversity and reduce overfitting. Augmented images were transformed into three-channel PyTorch tensors, and their pixel intensities were standardized utilizing the conventional ImageNet statistics (mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]) to ensure that the pretrained network's feature detectors function on data distributions aligned with their initial training.
# Resolution Enhancement of Spherical Wave-Based Holographic Stereogram with Large Depth Range +
## 4 Reducing Pixel Size \(\Delta x_{1}\) with Aperture Filtering
To further improve the reconstruction quality within the total depth range, a partial aperture filtering technique is proposed here. That is, part of the EIs are converted to hogels without aperture filtering to ensure good reconstruction quality near the CDP, and the rest of the EIs are converted to hogels with aperture filtering to improve the reconstruction quality away from the CDP. This classification process can be easily realized in a simple 3D scene such as Figure 5a. The bee model and the school badge are located at the left-half and right-half spaces, respectively. Thus, the EIs mainly contributing to the two objects can be simply classified to the left half and the right half. Then, the left half EIs are transformed to hogels without aperture filtering, while the right half EIs are transformed to hogels with aperture filtering. It is noted that the reconstruction brightness is decreased due to the aperture filtering. Thus, to balance the brightness of the left and right halves, the calculated right half hologram needs to multiply with \"2\". Through this simple classification, the negative influence of aperture filtering on the reconstruction near the CDP can be relieved, as shown in Figure 8d. Figure 9 shows the optical reconstruction results, which verify the conclusion in Figure 8.  
Figure 8: (**a**) Numerical simulation results with pixel size \(\Delta x_{1}=5~{}\upmu\)m. (**b**) Numerical simulation results with pixel size \(\Delta x_{1}=2.5~{}\upmu\)m. (**c**) Numerical simulation results with pixel size \(\Delta x_{1}=2.5~{}\upmu\)m and aperture filtering. (**d**) Numerical simulation results with pixel size \(\Delta x_{1}=2.5~{}\upmu\)m and partial aperture filtering. Figure 9: (**a**) Optical reconstruction results with pixel size \(\Delta x_{1}=5~{}\upmu\)m. (**b**) Optical reconstruction results with pixel size \(\Delta x_{1}=2.5~{}\upmu\)m results with pixel size \(\Delta x_{1}=2.5~{}\upmu\)m and aperture filtering. (**d**) Optical reconstruction results with pixel size \(\Delta x_{1}=2.5~{}\upmu\)m and partial aperture filtering.
# Dynamic Initial Weight Assignment for MaxSAT
###### Abstract  
The Maximum Satisfiability (Maximum Satisfiability (MaxSAT)) approach is the choice, and perhaps the only one, to deal with most real-world problems as most of them are unsatisfiable. Thus, the search for a complete and consistent solution to a real-world problem is impractical due to computational and time constraints. As a result, MaxSAT problems and solving techniques are of exceptional interest in the domain of Satisfiability (Satisfiability (SAT)). Our research experimentally investigated the performance gains of extending the most recently developed SAT dynamic Initial Weight assignment technique (InitWeight) to handle the MaxSAT problems. Specifically, we first investigated the performance gains of dynamically assigning the initial weights in the Divide and Distribute Fixed Weights solver (DDFW+Initial Weight for Maximum Satisfiability (DDFW+InitMaxSAT)) over Divide and Distribute Fixed Weights solver (DDFW) when applied to solve a wide range of well-known unweighted MaxSAT problems obtained from DIMACS. Secondly, we compared DDFW+InitMaxSAT's performance against three known state-of-the-art SAT solving techniques: YalSAT, ProbSAT, and Sparrow. We showed that the assignment of dynamic initial weights increased the performance of DDFW+InitMaxSAT against DDFW by an order of magnitude on the majority of problems and performed similarly otherwise. Furthermore, we showed that the performance of DDFW+InitMaxSAT was superior to the other state-of-the-art algorithms. Eventually, we showed that the InitWeight technique could be extended to handling partial MaxSAT with minor modifications.  
maximum satisfiability; stochastic local search; artificial intelligence; dynamic local search; optimization MaxSAT +
Footnote †: journal:  
0
Footnote 0: Copyright**: © 2021 by the authors. Licence MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 2 Introduction of Wireless Body Area Network  
### WBAN Architecture  
With the rapid development of the wireless communication technology and sensor technology, Wireless Body Area Network (WBAN) has been developed as a new technology. WBAN is a three-tier architecture body network [4], as shown in Figure 1. The tier-1 is composed of sensors attached to the body surface or implanted into the body. Its function is to collect and transmit various physiological information about the human body. The tier-2 is composed of smart phones, personal computers, or other intelligent electronic devices. The information sent by sensors is forwarded to the terminal data center by a wireless mode. In tier-3 of WBAN, the terminal data center is mainly composed of remote servers providing various applications. Its function is to collate and analyze the received data, to provide a dynamic response. Especially when the sensor node collects abnormal data, it will carry out emergency transmission and alarm, which can speed up the emergency processing and rescue.
# A Simulation of Contact Graph Routing for Mars-Earth Data Communication
## 2 Model of CGR  
We develop a model of Contact Graph Routing for data transmission from rovers on planet Mars (Curiosity and Perseverance), relay satellites (MRO, TGO, Odyssey, and MAVEN), and DSN antennas (USA, SPAIN, and AUS). As the time reference, we use a Martian day, called a sol, which is slightly longer than an Earth day, at approximately 24 h, 39 min, and 35 s. The proposed model consists of the following five parts:  
1. Arrangement of the contact graph: determination of the number of nodes being used, possible routing among \(n\) nodes, storage capacities of satellites, the observed time per minute in a Martian day, contact window schedule between rovers and satellites, and between satellites and DSN antennas.
2. Arrangement of data: generating random data size per minute, distributing data into \(d\) packets whose maximum size is 256 Kbytes.
3. Routing management for the combination of rover and satellite.
4. Routing management for the combination of satellite and DSN antenna.
5. Plotting the graph.  
To evaluate the performance of the built model, we show that the process of sending data from Mars to Earth is satisfactory through the relay satellite, and also that the data storage is successfully carried out by the relay satellite according to its capacity. The stored data can be shown to be distributed to the antenna on Earth when the contact window is first opened. By using randomly generated data for a Martian day, a given schedule of contact windows, and a particular relay satellite with quite limited storage capacity, we calculate what percentage of the data amount is sent from Mars and successfully sent to Earth.
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
## 5 Conclusions  
In ophthalmic images, the tortuosity change of curvilinear organs is associated with a number of ocular diseases. The assessment of tortuosity plays a significant role for diagnosis. However, it still has two problems. First, there is no general evaluation standard. Second, most of the tortuosity grading methods are highly dependent on the results of image pre-processing. The poor results of pre-processing and the method of tortuosity calculation itself will lead errors.  
We employed a local phase tensor-based filter to improve the visibility of curvilinear structure, and a new exponential curvature estimation based tortuosity metrics for curvilinear structures is also illustrated. The proposed method has been validated on confocal corneal microscopy and retinal fundus dataset. The comparison results of tortuosity estimation with three datasets demonstrate that the proposed method has superior performance over the conventional measures.  
Conceptualization, Y.Z.; methodology, J.Z.; validation, B.C., D.Z.; formal analysis, H.C., B.C., D.Z.; investigation, D.Z., H.C., B.C.; original draft preparation, H.C. and B.C.; writing--review and editing, H.C., J.L. and Y.Z.; supervision, J.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Zhejiang Provincial Natural Science Foundation of China (LZ19F010001), Zhejiang Provincial Key Research and Development Program (2020C030360), Chinese Postdoctoral Science Foundation (2018M640578), National Natural Science Foundation of China (61906181), Ningbo 2025 Science and Technology Major Projects (2019B10033, 2019B10061). Guizhou Provincial Joint Funds LH[2017]7007, Ningbo Natural Science Foundation (2018A610055), and Zhejiang Postdoctoral Scientific Research Project (ZJ2019167).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Enhancement**} & \multicolumn{2}{c}{**IPACHR**} & \multicolumn{2}{c}{**U-Net**} \\ \cline{2-5}  & **FDR** & **Sen** & **FDR** & **Sen** \\ \hline Raw & 0.394 \(\pm\) 0.007 & 0.738 \(\pm\) 0.010 & 0.428 \(\pm\) 0.008 & 0.749 \(\pm\) 0.013 \\ Vesselness filtering [8] & 0.375 \(\pm\) 0.006 & 0.753 \(\pm\) 0.004 & 0.367 \(\pm\) 0.010 & 0.552 \(\pm\) 0.019 \\ WSF [13] & 0.363 \(\pm\) 0.007 & 0.759 \(\pm\) 0.004 & 0.353 \(\pm\) 0.009 & 0.788 \(\pm\) 0.004 \\ Ours & **0.357 \(\pm\) 0.010** & **0.760 \(\pm\) 0.005** & **0.338 \(\pm\) 0.007** & **0.807 \(\pm\) 0.005** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Segmentation results of different enhancement methods with IPACHR and U-Net on nerve fiber images.  
Figure 7: Comparison of nerve fiber segmentation results between raw image and our enhanced image with U-Net.
# Meshing Stiffness--A Parameter Affecting the Emission of Gearboxes
## 2 Materials and Methods
### Determination of Stiffness  
Due to the complex shape of the teeth, the theoretical determination of deformation and stiffness is difficult. Many theses are devoted to this issue, some dealing with the influence of tooth processing on the meshing stiffness. Calculating the stiffness of a spur gear with straight teeth by the method of layers (beams placed on a flexible base) was proposed in the literature [20]. Nevertheless, the question of determining the exact stiffness and deformation of the teeth still remains unsolved at the required level; therefore, experimentally determined values are usually used.  
Previous experimental procedures dealing with evaluating the stiffness of gears are based on the static measurement of the deformation of a gear loaded at a constant force or by seismic measurement of offsets at slow rotation. Other experimental procedures were based on the measurement of optical phenomena. The method of planar photoelasticimetry in particular had wide practical use [21; 22; 23; 24].  
Recently, with the ever-evolving computational technology that performs extensive calculations, it is easier to find modern numerical methods for determining the meshing stiffness of a wide range of gears in the available literature. These methods include the Finite Element Method, which is one of the numerical methods of mathematics widely used to solve problems of elasticity and strength, dynamics of flexible bodies, heat transfer, and many other engineering problems [25; 26; 27].  
Stiffness results are based on gear deformation. Gear deformation was determined using the Finite Element Method. To verify the results of deformation and gear stiffness solved by the Finite Element Method, tooth deformation was experimentally determined by static measurement of tooth deformation at the point of load with a constant force (Figure 4).  
Figure 3: The course of meshing stiffness of helical gearing. (**a**) Single-pair contact (meshing stiffness \(c_{p}\)); (**b**) resulting meshing stiffness.