esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Enhanced Oil Recovery
### Countercurrent Imbibition  
[PERSON] et al. suggested a countercurrent water imbibition strategy aimed at enhancing oil recovery [72]. This approach involves a cyclical regimen of water injection, soak periods, and production phases, which can be reiterated until the influence of capillary forces wanes. The theoretical framework for this method hinges on the pronounced capillary effects observed in shale formations, which are further substantiated by attributes like low permeability, dual-phase wettability, and the coexistence of overpressure conditions with low water saturation levels.  
Laboratory tests were performed on core specimens sourced from diverse stratigraphic layers within the Vaca Muerta formation. Subjected to reservoir-temperature water, these cores exhibited a strong affinity for water-wet conditions. During the imbibition experiments, the cores regained between 40% and 60% of their original oil in place (OOIP).  
Field trials were also executed, involving the injection of over 9400 barrels of water into a tri-stage vertical well. Oil samples were collected from the wellhead after a 12-day interval. Downhole sensors revealed a consistent oil flow through the perforations, leading to a lighter fluid column during the shut-in phase. Numerical simulations, utilizing actual field data from the Vaca Muerta formation, were employed to assess potential outcomes and uncertainty impacts. The optimized EOR scenario demonstrated an incremental resource gain of roughly 40% when compared to a baseline case devoid of EOR interventions (as illustrated in Figure 11).  
Should this EOR technique prove effective, it holds the potential to significantly influence both the valuation and economic feasibility of similar projects. Not only could it enhance oil recovery at a relatively low operational cost, thereby converting resources into proven reserves, but it could also mitigate operational hurdles such as paraffin build-up, pressure sustainability, and the need for extensive acid treatments.
## 4 Conclusions  
In summary, this study investigates the dynamic behavior of thin aluminum plates subjected to the consecutive loading by fragment impacts and blast waves. To this end, two separate experimental setups were used. The aim of the first step was to examine the influence of projectile diameter, number of impacts, and hole spacing on plate deformation. The digital image correlation technique coupled with high-speed cameras in a stereoscopic setting was used to investigate the out-of-plane displacement and in-plane strain fields. The second step involved the application of a blast load to the perforated plates using an explosive-driven shock tube to investigate the effect of relative hole spacing on the initiation and propagation of cracks in the plates.  
The findings of this study indicate that the failure mechanism after single and triple fragment impact is plugging, with localized plastic deformation around the impact point. Larger fragments result in higher out-of-plane displacement and strains, whereas the orientation of the fragment's front surface influences the major principal strain amplitude. The interaction between strain patterns of each impact increases for closer impact holes. After blast loading, a \"hole enlargement phenomenon\" is observed, and the maximum damage is found when the holes are centered in the target. Cracks tend to appear in the zones of increased strain between the holes, and the hole spacing significantly affects the damage mechanism. The study confirms the ability of high-speed stereo-vision imaging and digital image correlation to measure out-of-plane displacement and in-plane strain data at high rates, providing researchers with the possibility to study a wide range of dynamic events, including impact, penetration, and blast loading.  
The authors recommend expanding the experimental study presented in this paper to include high-velocity impact from fragments with different shapes. The addition of irregularly shaped fragments may yield distinct crack patterns around the perforation holes, which may result in an increased loss of structural integrity of the material under impact loading. To gain a deeper understanding of the behavior of blast-loaded thin metallic plates, it is suggested that detailed numerical simulations of the experiments described in Sections 2 and 3 should be conducted.
# Braking Performance and Response Analysis of Trains on Bridges Under Seismic Excitation
## 2 Materials and Methods
### Deceleration Model  
During the operation of a subway train, when the driver receives a braking command, the actual braking behavior does not occur immediately. There is a certain time delay between the driver's reaction action and the establishment of effective braking force by the train braking system. In order to reasonably consider this response process, this paper adopts the method of arithmetic averaging the empty time and the braking establishment time to calculate the equivalent response time between the train receiving the command and the actual braking effect. According to related research and engineering experience, the equivalent response time is taken as 0.85 s in this paper's calculation to be closer to the actual operation situation.  
The braking process of a train usually includes multiple modes in engineering practice, such as common braking, emergency braking, service braking, etc., and there are some differences in the size of the braking force and the mechanism of action in different modes. However, in order to simplify the dynamic analysis process, this paper uniformly adopts the emergency braking mode for calculation in order to obtain the structural response under the most unfavorable conditions. In the modeling process, the possible fluctuation of deceleration speed with the change in train speed is ignored and simplified to a uniform deceleration process. The acceleration of the train during deceleration is assumed to be constant at 1.2 m/s\({}^{2}\) until the train comes to a complete stop on the bridge. This assumption provides clear boundary conditions and uniform control parameters for the subsequent analysis of the bridge structure and train response.  
In calculating the braking force during emergency deceleration, the study uses the viscous wheel-rail adhesion model expressed in Equation (4). Its sole empirical parameter is the wheel-rail friction coefficient \(\mu\). The Chinese standard TB/T 3346-2019 gives \(\mu\) = 0.41-0.55 for clean, dry rail and \(\mu\) = 0.35-0.45 for wet or polluted rail. Field data from Wuxi Metro Line S1 indicate that the 5 th-percentile dry weather value is 0.42. To remain conservative, we adopt \(\mu\) = 0.42 in all calculations. For simplicity we further assume that \(\mu\) does not drop below 0.42 during emergency braking, so skidding is neglected and the wheel-rail interface is treated as staying in the stick regime.  
\[F_{SubBra}=uG(t) \tag{4}\]  
In Equation (4):  
Figure 1: Subway speed reduction map.  
\(F_{SubBra}\) is the braking force of the train;  
\(u\) is the coefficient of viscous friction between the wheels and rails, which takes the value of 0.42;  
\(G(t)\) is the normal force between the wheel and rail, which varies with time t.
## 4 Discussion
### Insights on Local Forest Management under Forest Fragmentation  
Historically, forests in Zhejiang, China, have experienced severe logging disturbances, causing a grim status of forest fragmentation. However, the results of this study may still provide useful insights into local forest management. Firstly, a higher carbon uptake capacity per unit area exists in highly fragmented forest landscapes, reminding us to explore the potential of forest carbon uptake in the context of the widespread existence of forest fragmentation. In particular, as one of the most developed regions in China, Zhejiang is subject to intense human activities, such as rapid urbanization and economic development, which have significantly affected the distribution pattern of natural ecosystems, such as forests. The positive relationship between forest fragmentation and carbon sequestration provides an opportunity for Zhejiang to balance development and ecological protection. Secondly, although the forest carbon uptake may be enhanced under fragmentation, there is still a great need to protect the forests due to the fac that any loss of forest coverage will lead to a further loss of carbon stock. Finally, this relationship is also based on the implication that the increase in the maximum photosynthetic capacity may induce the enhancement of carbon uptake in highly fragmented forest landscapes. Therefore, it is necessary to explore the mechanism by which forest fragmentation changes the vegetation photosynthetic process, which will play an important role in formulating forest management measures. In addition, although subtropical forests in Zhejiang Province were selected as being representative in this study, considering the universality of research approaches and the widespread availability of research objects, our results have great potential values for understanding how the carbon uptake of subtropical forests responds to forest fragmentation in other subtropical regions.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### OBE Enhanced Pulse Peak Attenuator  
The proposed OBE aided PPA works exactly like OBE, which uses the noise information from 2-GMM distribution when the envelope of nonlinear input is greater than the peak threshold detection value (\(\alpha_{th}\)). This device does not operate on the complex data at the input and passes to the output as it is when the above-mentioned condition is not accomplished. This new estimator improves the pulse blanking method by attenuating the complex data rather than discarding the information present at that subcarrier. In another way, it reduces the complex computations in the employment of OBE by utilizing the knowledge of parameters obtained from 2-GMM modeling along with the details from pulse blanking. The operation of the proposed estimator can be expressed mathematically as follows:  
\[\hat{x}_{|OA|}(y)=\begin{cases}y&\text{if }|y|\leq\alpha_{th}\\ \beta_{o}(y).y&\text{otherwise.}\end{cases} \tag{25}\]  
It is possible to use this proposed OBE enhanced PPA for processing complex valued signal as well. The mathematical statement for this operation is as given in (26) without altering the definition of \(\beta_{O}\).  
\[\hat{x}_{kOA}^{*}(y)=\begin{cases}|y|&\text{if }|y|e^{iarg(y)}\leq\alpha_{th}\\ \beta_{o}(y).|y|.e^{iarg(y)}&\text{otherwise.}\end{cases} \tag{26}\]
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 1 Introduction
### The Importance of Treewidth
This led us to the following insight: if we could decide, _on a graph-by-graph basis_, which solver will compute the treewidth of the graph most quickly, we could contribute to a further reduction in the time required to compute treewidth by selecting this fastest algorithm. Traditionally, such an approach would involve a blend of comparing worst-case time complexities, analysing the 'inner workings' of the algorithms and an ad-hoc exploration of which types of graphs do and do not solve quickly on certain algorithms. Such a process, however, is time-consuming, requires algorithm-specific knowledge and may have limited efficacy due to the opaqueness of the algorithmic approaches used and/or highly complex correlations between input characteristics and algorithmic performance. Hence the question: can we, as far as possible, _automatically_ learn the relative strengths of a set of algorithms? This brings us to the domain of _machine learning_[17] and machine learning-driven _algorithm selection_ in particular. We note that, although interest has peaked in recent years due to the emergence of data science, there is already a quite extensive literature on the use of machine learning in combinatorial optimisation and integer linear programming in particular (see e.g, References [18; 19; 20]). In addition to algorithm selection, which we will elaborate upon below, this literature focuses on topics such as classification of instances by difficulty [21], running time regression [22; 23] and learning to branch [24; 25; 26]. We refer to References [22; 27] for excellent surveys of the area and References [28; 29; 30] for some recent case-specific applications in operations research.
## 4 Simulations and Results
### Humidity  
The simulation results of the indoor relative humidity (with the control) are reported in Figure 16. The dehumidification system (Figure 17b) was activated with a maximum value of 15 g H\({}_{2}\)O/min during the night in order to remove the water vapor accumulated in the indoor climate of the greenhouse. This was needed to maintain the inside humidity around its predefined set point of 80%, but this value could reach more than 97% without the control, as is reported in Figure 6. During the day, the internal humidity was relatively low (Figure 6) without the control due to the effect of the thermal load trapped inside the greenhouse (this reduced the air in the contained water). During this period, the humidification system was activated at a high rate (Figure 17a) to stabilize the humidity level around the set point of 70%.  
The simulation results showed the effectiveness of the FLC in controlling the indoor parameters of the agricultural greenhouse. A fuzzy controller was designed to control the inside climate using  
Figure 16: The variation in the relative humidity with the FLC controller.  
Figure 15: Heating rate (**a**) and ventilation rate (**b**) computed from the fuzzy controller.  
appropriate actuators. The results of the simulation showed the efficiency of the developed FLC in meeting the requirements of factories in terms of temperature and humidity and in obtaining a favorable microclimate for the agricultural production of tomato plants.
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 2 WiMAX Physical Layer Model
### Randomizer  
Coding speed may be improved by avoiding extended sequences of ones and zeros in a random sequence, and by randomizing the input data [6]. A 15-stage shift register with XOR gates in a feedback arrangement is used to eliminate mistakes, as depicted in Figure 1. Figure 2 shows the PRB generator for randomization.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
###### Abstract  
Extreme low-light image enhancement presents persistent challenges due to compounded degradations involving underexposure, sensor noise, and structural detail loss. Traditional low-light image enhancement methods predominantly employ global adjustment strategies that disregard semantic context, often resulting in incomplete detail recovery or color distortion. To address these limitations, we propose a semantic-aware knowledge-guided framework (SKF) that systematically integrates semantic priors for improved illumination recovery. Our framework introduces three key modules: A Semantic Feature Enhancement Module for integrating hierarchical semantic features, a Semantic-Guided Color Histogram Loss to enforce color consistency, and a Semantic-Guided Adversarial Loss to enhance perceptual realism. Furthermore, we improve the semantic-guided color histogram loss by leveraging multi-color space constraints. Inspired by human visual perception mechanisms, our enhanced loss function calculates color discrepancies across three color spaces--RGB, LAB, and LCH--through three components: \(loss_{rgb}\), \(loss_{1 ab}\) and \(loss_{1 cb}\). These components collaboratively optimize image contrast and saturation, thereby simultaneously enhancing contrast preservation and chromatic naturalness.  
Keywords:low-light image enhancement; semantic information; semantic-aware knowledge-guided framework; multi-color spaces constraints +
Footnote †: journal: Applied sciences
## 4 Discussion
### Interpretation of the Diversity Measures Detectable with Optical Sampling  
Across-scale studies are vital to verify that the spatial resolution of remote observations are matching with the grain size of field data, considering that the \"optimal\" pixel size is varying among biomes and communities ([PERSON] et al., 2016) and that actual spatial heterogeneity information may be hidden at the sub-pixel level ([PERSON] et al., 2016). Our study, showing that the optimal pixel size for detecting optical diversity and productivity relationships was mostly 10 meters, points out the potential of adopting coarser spatial data to analyze diversity and productivity spatial patterns in grassland ecosystems and is opening the way to further research on the nature of the observed diversity. The observed across-scale ecosystem diversity and productivity linear relationships may be linked to different measures of diversity. While the observed relationships at high spatial resolution (0.9 m) may be associated to grassland \(\alpha\)-diversity at the plot level (as the pixel size is more comparable with the plant size), for larger-scale observations the diversity-productivity relationships may be related to other measures of diversity, such as \(\beta\)-diversity ([PERSON] et al., 2016). In the investigated grasslands ecosystems, \(\beta\)-diversity may be related to small-scale spatial variability of ecological factors (e.g., soil properties, slope) and varying disturbance effects (e.g., land use intensity).
## 2 Related Work
### Deep Feature Fusion  
Deep neural networks have a strong expressive ability by extracting deep feature maps. SSD uses the pyramid structure of the convolutional network to predict the target and achieves high-precision detection. A network called FPN [7], which combines high-level semantic information with low-level high-resolution feature information, is proposed to achieve higher accuracy. FPN is a fully convolutional network and its backbone uses ResNet. FPN uses nearest-neighbor interpolation to sample from top to bottom and then connects it horizontally with the feature map after \(1\times 1\) convolution in the way of element-wise summation. The experiments of applying FPN to RPN and Fast R-CNN [28] respectively show that after fusing different feature layers, the accuracy of object detection is improved, especially the detection of small objects.  
Figure 1: The Single-Shot MultiBox Detector (SSD) model consisting of a VGG and additional convolution layers.  
To detect small objects quickly and accurately, a multi-level feature fusion method that introduces contextual information into the SSD is proposed in the article Feature-Fused SSD: Fast Detection for Small Objects [29]. [PERSON] et al. designed two different feature fusion modules: the connection module and the element summation module. Compared with SSD, its detection accuracy and speed have been improved to a certain extent. However, Feature-Fused SSD is also not suitable for embedded devices because of the increased computational complexity over SSD.
# CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis
## 3 New Method
### Architecture and Development of the Model  
The CNN model, i.e., CoroNet, was proposed to automatically detect BC from mammogram images according to Xception CNN architecture [42; 43]. Xception Extreme Inception architecture represents the major feature of Xception (the predecessor model). In addition, it consists of a 71-layer deep CNN architecture pre-trained on an ImageNet dataset. The major conception behind Xception is its depthwise separable convolution. Using this method, the operations' number is decreased using a factor proportional to 1/k. Xception employs depthwise separable convolution layers with residual connections instead of traditional convolutions. Separable in-depth Convolution replaces the traditional n \(\times\) n \(\times\) k convolution with a 1 \(\times\) 1 \(\times\) k point-wise convolution followed by a channel-wise n \(\times\) n spatial convolution.  
Residual connections represent \"skip connections\" whose authorized gradients flux directly via a network, without travelling via non-linear functions of activation; consequently, disappearing gradients are avoided. In the case of residual connections, the output  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Main Finding** & **Depth** & **Dataset** & **Error Rate** & **Input Size** & **Year** \\ \hline AlexNet & Utilizes Dropout and ReLU & 8 & ImageNet & 16.4 & \(227\times 227\times 3\) & 2012 \\ \hline VGG & Increased depth, small filter size & 16, 19 & ImageNet & 7.3 & \(224\times 224\times 3\) & 2014 \\ \hline \multirow{2}{*}{ResNet} & Robust against overfitting because of symmetry mapping-based skip links & 50,152 & ImageNet & 3.57 & \(224\times 224\times 3\) & 2016 \\ \hline \multirow{2}{*}{Xception} & A depthwise convolution followed by a pointwise convolution & 71 & ImageNet & 0.055 & \(229\times 229\times 3\) & 2017 \\ \hline MobileNet-v2 & Inverted residual structure & 53 & ImageNet & - & \(224\times 224\times 3\) & 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the architectures of CNN.  
of a weight layer series is combined with the original input and passed via a non-linear activation function.  
Out of the 33,969,964 parameters in CoroNet, 54,528 are non-trainable, and the other 33,969,964 are trainable. Xception represents the base model of CoroNet while adding a dropout layer, and two completely connected layers, ultimately. In Table 5, CoroNet's architecture, layer-wise parameters, and output shape are all depicted. In order to specify the overfitting problem, we used Transfer Learning to initialize the model's parameters.