esa-sceva/satcom-chunk-collection huggingface dataset:
# Femtosecond Laser-Inscripted Direct Ultrafast Fabrication of a DNA Distributor Using Microfluidics
## 3 Results and Discussions
### Microfluidic Distribution of the DNA  
Figure 5b shows a microscopic image of the fabricated DNA distributor using femtosecond laser ablation (energy dose of 5% at a laser beam pass of 5) based on the computer simulation results. The channel width observed from the top view was uniform, and the bulging phenomenon was minimal around the channel profile. However, at the beginning and end of laser processing, the micro-channels were slightly larger as the laser beam remained longer at the tip. Thus, the channels were further extended over the intersection to prevent laser-machining errors at the beginning and end of laser processing. During fabrication, we found that the dimensions of the crossings between two perpendicular channels became about 1.5 times larger due to double irradiation of the laser beam. However, the double laser-irradiated region was very small compared to the grid spacing (200 \(\upmu\)m). Thus, we assumed that the size effect of the double-irradiated crossings on microfluidic flow was negligible.  
Figure 5c,d represent a fluorescent image and relative light intensity graph of the YOYO-1 DNA solution distributed using the microfluidics at a flow rate of 5 \(\upmu\)L/min. For the microfluidics, the fluorescent intensities varied with the location due to various velocity gradients as the DNA solution passed through the micro-channels. When the fluorescence intensity was the lowest (Figure 5d), the fastest velocity profile was shown in the computational simulation (Figure 5c). Our assumption was that the longer DNA would show the higher fluorescent intensity based on the previously reported reference [30]. For a brighter fluorescent intensity, longer DNA would be distributed at a low flow velocity. The fluorescent intensity was analyzed using a line profile, as shown in Figure 5d. At the highest flow rate of 15 \(\upmu\)L/min, the relative light intensity was the strongest at the middle channels (90.84 \(\pm\) 15.56 at point IV) and the lowest at the outer channel (53.87 \(\pm\) 5.69 at point I). In this study, we tried to show the feasibility of DNA distributor fabricated by femtosecond laser micro-scribing, which can lead to a novel technique to easily and simply determine how various lengths of DNA are mixed in a solution.
###### Abstract  
This study investigates the effects of hull and impurity content on the efficiency of cold-pressing high-oleic sunflower seeds using a screw press. High-oleic sunflower oil is valued for its oxidative stability and health benefits, and optimizing pressing conditions is crucial for maximizing yield and maintaining oil quality. The identification of high-oleic sunflower oil was performed by analyzing its fatty acid composition, iodine value, and refractive index. Eleven seed samples with varying hull and impurity contents were processed to assess their impact on cake composition, pressing efficiency, and pressing oil yield. Oil yield ranged from 39.24% to 76.52%, with higher hull content contributing to increased yield due to its role in facilitating oil drainage. Multiple linear regression models were developed to predict moisture and oil content in the cake, as well as pressing efficiency, based on hull and impurity content, demonstrating strong predictive accuracy. These parameters were selected as they represent economically significant indicators, given that moisture and oil content indirectly reflect the protein content in the cake, while sunflower cake is primarily used as animal feed. Additionally, pressing efficiency indicates oil yield during pressing, which is the most critical economic parameter of the cold-pressing process. Cluster analysis identified three sample groups with distinct characteristics, revealing interactions between seed composition and pressing performance. The results highlight the significance of seed preparation in optimizing cold-pressing efficiency and provide insights for improving oil extraction processes. These findings support the industrial application of high-oleic sunflower seed pressing and contribute to the development of sustainable, high-quality oil production methods.  
high-oleic sunflower; screw press; cake; pressing oil yield; pressing capacity; cluster analysis; optimization Article
# Improving Performance of Key-Value Stores for High-Performance Storage Devices
## 1 Introduction
To address this issue, we focus on the compaction procedure--one of the background I/O operations occurring in the KV store--where multiple files need to be read to sort key-value pairs. This procedure involves numerous recursive read operations, but the existing implementation of RocksDB utilizes only a single thread for these read operations. To optimize this, we enable concurrent file read operations by creating a read buffer and prefetching files into a temporary read buffer. We implement our scheme within the existing RocksDB and evaluate its performance using the db_bench benchmark, varying the number of threads and using different machines. The experimental results demonstrate that our scheme improves the performance of KV stores by parallelizing I/O operations utilizing idle computing resources. In summary, our main contributions are as follows:  
* We analyze the I/O path and scalability within the existing KV store, RocksDB. * We design parallel read operations to accelerate background jobs, optimizing the KV store for multi-core CPUs and high-performance storage devices. * We demonstrate that our proposed scheme improves the overall performance of the KV store by involving idle computing resources in I/O operations. The rest of this paper is organized as follows:  
Section 2 discusses the related work. Section 3 describes the background and presents the analysis performance of RocksDB with high-performance storage devices using various configurations. Section 4 presents the design of our scheme to fully utilize hardware capabilities. Section 5 shows the experimental results. Section 6 explains the limitations of the study and discusses future work. Section 7 concludes the paper.
## 3 Experimental Results and Evaluations
### Evaluation of the Sensitivity of the Thresholds
The second analysis is about the accuracy of the laser data which determines the three thresholds such as buffer threshold, co-planarity test threshold, and accumulator cell resolution. Recall that the accuracy of the laser data is usually known or provided by a data vendor (0.15 m for this dataset). However, the segmentations are carried out for this analysis while intentionally changing the accuracy values in the process from 0.02 m to 0.3 m. When the laser data accuracy is set to 0.02 m, over-segmentation occurs; hence, lots of planar patches are acquired from the segmentation process (in Figure 23a). Similar but less segments are acquired for the case of the value of 0.05 m (Figure 23b). The phenomenon of the over-segmentation is reduced when the value applied is increased. On the other hand, almost same segmentation results are acquired when the values of 0.10 m to 0.30 m are applied (Figure 23c-f). With a closer look at the segmentation results in these figures, the size of the  
Figure 23: Segmentation results according to changing the accuracy of the laser data from 0.02 m to 0.30 m (searching radius is 2.0 m for all cases). The accuracy of the laser data: (**a**) 0.02 m; (**b**) 0.05 m; (**c**) 0.10 m; (**d**) 0.15 m; (**e**) 0.20 m; (**f**) 0.30 m.  
small planar patch at the right corner of the building is slightly increasing when the value of the applied accuracy is increasing. This means that it will be fine to apply the value of the accuracy from 0.10 m to 0.30 m. In other words, the range of the proper value of the laser data accuracy can be from little bit less than to greater than the value provided (_i.e._, 0.15 m for this dataset). However, one should note that the large value of the applied laser data accuracy causes more computational load since the number of points in the neighborhood will increases. Hence, to avoid the over-segmentation and keep the computational efficiency, the value of the laser data accuracy can be set close to the one provided.
## 1 Introduction
These requirements often lead to reprocessing cycles, even when incorporating discrete mechanical adjustment elements to compensate for anticipated design tolerances. This context drives the development of an innovative intermediate approach--the dielectric resonator-based microstrip filter (DRMF) [18]. The proposed architecture simultaneously addresses multiple challenges as follows: simplifying fabrication, enhancing microwave circuit integration, and enabling mechanical tunability. Most critically, the DRMF must maintain cavity-like performance--including precise alignment and advanced filtering features (high \(Q\)-factor, transmission/equalization zeros)--while preserving microstrip technology's inherent advantages. Figure 3 shows an X-band dielectric resonator cavity filter developed by Thales Alenia Space Espana (TASE). The design integrates isolators at both input and output ports to suppress reflected waves caused by impedance mismatches. Figure 3: X-Band DRCF, courtesy of Thales Alenia Space Espana (TASE).
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 3 Proposed Methodology
### Cross-Validation and Model Assessment  
To objectively assess generalization capability and robustness, we employed 10-fold cross-validation. The complete dataset is systematically partitioned into ten mutually exclusive subsets (folds) of roughly equal size. Iteratively, each fold is treated as the validation set exactly once, with the remaining nine folds combined as the training set.  
In each iteration, the RFB-NB classifier is trained according to the methodology described in Sections 3.1-3.4 exclusively using the training subsets. The trained classifier is then assessed on the withheld validation subset. This process is repeated until every fold has served as the validation set exactly once. The predictive performance is evaluated through two primary metrics, namely accuracy and the weighted-average F1-score. The weighted-average F1-score is particularly useful as it accounts for potential class imbalance by computing the harmonic mean of precision and recall, weighted by class support. Formally, the weighted-average F1-score (\(\text{F1}_{\text{weighted}}\)) is calculated as  
\[\text{F1}_{\text{weighted}}=\sum_{c=1}^{C}\frac{n_{c}}{N}\cdot\frac{2\cdot \text{Precision}_{c}\cdot\text{Recall}_{c}}{\text{Precision}_{c}+\text{Recall}_ {c}}, \tag{24}\]  
where \(C\) is the total number of classes, \(n_{c}\) represents the number of samples in class \(c\), \(N\) is the total number of samples, and \(\text{Precision}_{c}\) and \(\text{Recall}_{c}\) denote precision and recall for class \(c\), respectively.  
Consequently, the final reported accuracy and weighted-average F1-score represent the average metrics computed across the ten validation iterations, providing reliable and unbiased estimates of predictive performance on unseen data. Formally, the 10-fold cross-validation accuracy \((\text{ACC}_{CV})\) and weighted-average F1-score \((\text{F1}_{CV})\) are calculated as  
\[\text{ACC}_{CV}=\frac{1}{10}\sum_{i=1}^{10}\text{ACC}_{i}\text{ and }\text{F1}_{CV}=\frac{1}{10}\sum_{i=1}^{10}\text{F1}_{\text{weighted},i}, \tag{25}\]  
where \(\text{ACC}_{i}\) and \(\text{F1}_{\text{weighted},i}\) denote the accuracy and weighted-average F1-score obtained on the \(i\)-th validation fold, respectively.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Relate Works
#### 2.2.3 Blockchain-Based Access Control Schemes  
Another research direction looked at how to overcome the access control and privacy concerns of EHR sharing on the blockchain; when third-party organizations or individuals other than hospitals or patients attempt to obtain patient data, patient privacy may be compromised. Recently, the use of ciphertext-policy attribute-based encryption (CP-ABE) in access control methods for secure data sharing has received a lot of attention. The authors of [39] proposed BMAC, a Blockchain-based Multi-authority Access Control mechanism for the secure sharing of EHRs.  
[PERSON] et al. [40] suggested an encryption system based on an attribute based on the smart Ethereum contract. In this method, the data owner is able to distribute data users' keys which removes the key misuse phenomenon and assures private data protection. To implement fine-grained access control policies, the authors of [41] introduce a blockchain-based system for secure mutual authentication, called BSeIn. The proposed model includes a combination of multi-receiver encryption, message authentication code, and attribute signature. This model provides privacy and security assurances including anonymous authentication, auditability, and secrecy. On another side, proxy re-encryption (PRE) supports an efficient fine-grained access control for the patients' data. PRE is an encoding scheme allowing a third party (proxy) to modify a single party-encoded ciphertext to decode it by another party approved to do so. In this case, a two-party proxy key allows a semi-confident intermediate proxy to convert ciphertext, avoiding sender-side decryption and the re-encryption of data [3].
Additionally, the forecast of an increasing number of wells to be abandoned in the coming years and the operational challenges related to these operations reiterate the need for new strategies to carry them out safely and economically. One of the most prominent strategies is the development of alternative materials to be used as barrier elements. Portland cement is commonly used as a barrier material for the abandonment of petroleum wells [3; 4], despite its known limitations. During its placement inside the well, for example, the cement can become unstable and may result in crack formation. Additionally, it may undergo mechanical or chemical degradation. Furthermore, the operational costs of using Portland cement are higher, since a rig is required for pumping and placing the cement paste [1; 7; 8]. Therefore, alternative materials to cement that ensure operational safety and cost reduction in well abandonment have been considered [3; 4; 7; 8; 9; 10; 11]. These studies have primarily focused on elements that, like cement plugs, constitute mechanical barriers. Thus, the use of liquid barriers, consisting of columns of fluids with sufficient hydrostatic pressure to contain fluids from permeable intervals and prevent their flow into the well, despite being provided for in some regulatory guidelines, such as the Brazilian [12] and Norwegian [13] standards, is still sparsely explored in the literature. In the oil industry, the study of products and formulations of fluids is usually related to well drilling operations. In this scenario, the use of clay-based aqueous fluid formulations has been consolidated for many decades, and these formulations have gained prominence due to their commercial availability, simplicity in preparation, low cost, and compatibility with different types of formations [14; 15; 16]. Furthermore, when the clay mineral used to compose these fluids is palygorskite, it is possible to obtain advantages such as high thermal resistance and the ability to maintain rheological properties even with exposure to salts, which may be in the fluid formulation itself or be added from influxes of formation waters or via contact with saline formations [14; 17; 18]. Most of the work found in the literature that concerns palygorskite-based aqueous fluids has considered technical standards and operational aspects relevant to its application in drilling operations, such as its capacity for carrying cuttings [19; 20]. However, the use and qualification of this type of formulation as a barrier element in well abandonment is still sparsely explored in the petroleum industry, so there are no studies in the literature that address the possibility of this application, nor specifications or regulatory standards that guide its qualification. In this sense, this work proposes the development of a formulation of a salted water-based fluid added with palygorskite, with an adequate performance for application as a liquid barrier element in temporary abandonment operations of petroleum wells.
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.2.1 The Influence of Capillary Forces on Phase Equilibrium Calculations  
Referring to the mixture component properties of the Fifth SPE Comparative Solution Project [59], the PR-EOS was modified to incorporate capillary forces. The study compared the modified PR-EOS with capillary constraints (Equation (5) for pore radii of 15 nm, 30 nm, and 50 nm against the conventional PR-EOS without capillary constraints. Figures 7 and 8 present the phase diagram and the iteration count, respectively.  
Figure 6: Comparison of Runtime for the First 50 Computational Steps Using Different Solution Methods.  
Figure 7: Phase diagrams corresponding to different pore radii.  
As shown in Figure 7, both the bubble point and dew point lines decrease with the reduction of pore radius. The smaller the pore radius, the more pronounced the downward trend of both lines. During the pressure drop process, the capillary effect becomes more significant with decreasing pore radius, leading to a reduction in both the bubble point and dew point. As a result, the liquid phase fraction increases while the gas phase fraction decreases. Near the critical point, the calculation results for different pore radii converge, which is attributed to the interfacial tension approaching zero as the gas-liquid interface disappears, thus nullifying the capillary force. Therefore, capillary forces do not affect fluid phase behavior near the critical point. For larger pore radii, such as 50 nm and above, the capillary effect is negligible, and the phase diagram closely resembles that without considering capillary forces.  
As shown in Figure 8, the number of iterations for phase equilibrium calculations increases with decreasing pore radius, particularly near the dew point line. This indicates that the PR-EOS incorporating capillary forces introduces greater computational overhead, highlighting the need for a multiscale fast solution. As described in Section 2.1, the conventional PR-EOS does not consider capillary pressure effects, leading to consistent phase pressures across different phases. However, when capillary pressure effects are incorporated, phase pressures differ, with smaller pores showing more significant disparities. This physical phenomenon increases the number of iterations required during the phase equilibrium solution process.  
Figure 8: Contour map of iteration counts corresponding to different pore radii.
# SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
## 3 Proposed Method
### Spatial Attention  
In this section, spatial attention modules CBAM [45] and CA [46] are introduced, and we discuss how they are applied to the YOLOv4 and YOLOv7 frameworks with the intention of enhancing the accuracy and efficiency of object detection. Similar to the principles of SENet, CBAM examines the relationships among filters to ascertain which ones need encoding. This is not the full extent of CBAM's functionality; it also encodes pixel-wise attention across the entire filter, helping to determine the regions that require more focused processing. This targeted approach ensures that the most relevant information is captured. Ultimately, these encoded attention maps are amalgamated with the original input feature map, thus refining the overall feature representation. In contrast, CA serves a slightly different purpose. CA is a specific spatial attention mechanism designed to guide the learning model towards filters and positions that demand focus. CA achieves this by applying attention based on the values gleaned from the global average pooling along the horizontal and vertical axes of each filter. This method is not arbitrary; it simultaneously considers the importance and spatial information among filters. The outcome is an improved object detection accuracy, as the model is better informed about where to concentrate its resources. While conventional filter-pruning techniques employing attention modules have predominantly utilized channel attention modules, this study aims to implement pruning by combining spatial attention modules with GT Box. This preference stems from our belief that, in detection tasks as opposed to classification, spatial attention modules are more effective than channel attention modules.  
To effectively integrate these attention mechanisms into the YOLOv4 framework, they are strategically applied at the end of each CBM (Conv-Bn-Mish) and CBL (Conv-Bn-Leaky) module in the backbone and neck of the architecture since it enables to obtain attention maps from the feature maps of the convolution layer. Such integration is not merely an add-on; it is an integral part of enhancing the model's responsiveness to the spatial features that are used for accurate object detection. Figure 5 illustrates the structure of YOLOv4 with the Spatial Attention (SA) modules, showcasing how these SA modules blend seamlessly into the existing architecture. For the YOLOv7 framework, SA modules are applied to each end of the MP (Max Pooling), ELAN (Efficient Layer Aggregation Networks), and ELAN-H (ELAN with HorNet) modules in the backbone and neck to obtain attention maps from the feature map of the convolution layer, and Figure 6 illustrates the structure of YOLOv7 with  
Figure 4: Overview of the proposed method SAFP-YOLO.