esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 2 DNA Data Storage Readout and Hardware Acceleration
### Fundamental Workflow of DNA Data Storage  
The process of storing data in DNA consists of four main stages: encoding, DNA synthesis (data writing), high-throughput sequencing (data readout), and data recovery (Figure 1). Data writing consists of the following steps. First, the raw data are encodedusing a specific algorithm and mapped to a base sequence based on a predefined bit-to-base mapping scheme. Next, an index sequence is appended, and primer pairs are added to both ends. Finally, high-throughput DNA synthesis is employed to generate DNA strands carrying the encoded information.  
The readout and data recovery process of DNA storage involves both biochemical operations of DNA samples and computational processing of sequencing reads [31]. The biochemical operations primarily include polymerase chain reaction (PCR) amplification and high-throughput sequencing, which produce many sequencing reads. To efficiently process these data, primer sequences are first identified. Then, read grouping is performed based on the index sequences. Next, edit-distance-based multiple sequence alignment is performed. Finally, the consensus is reassembled into codewords and decoded to recover the original file.  
This study focuses on accelerating the DNA storage readout process. Unlike prior studies that focus on optimizing clustering or multiple sequence alignment, we propose a comprehensive hardware acceleration approach for the full DNA data readout pipeline.
# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 4 Experiment Results and Discussion
### Ablation Study
#### 4.3.1 Ablation Study of the Proposed Module  
Tables 5 and 6 present the results of a comparative analysis of FCOS methods. It is clear that the proposed module is efficient, with improved accuracy and less computation. First, to verify the utility of the split lightweight module, we compared MBConv with the proposed network and found that the proposed method showed 0.7% higher accuracy with 1.1M more computations. Compared with the standard convolution method, ours had 2.4M fewer calculations and a 2.0% high accuracy. Therefore, it is clearly shown that the proposed method is efficient, with improved accuracy and fewer computations.  
Table 6 presents the results of the comparative analysis of kernel size and the LSM expansion ratio. Here, K denotes the kernel size, and D denotes the expansion factor. The lower the expansion ratio, the narrower the accommodation area; the larger the expansion ratio, the wider the accommodation area. According to the ablation study, if the expansion ratio increases beyond a certain level, the object information will be lost because of the empty area inside. The FCOS style of object detection, which is similar to semantic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parmas (M)** & **FLOPs (G)** & **mAP (\%)** \\ \hline LSM (K = 3, D = 1) & 29.7 & 83.40 & 79.9 \\ LSM (K = 3, D = 2) & 29.7 & 83.40 & 79.9 \\ LSM (K = 5, D = 1) & 29.7 & 83.44 & 79.4 \\ LSM (K = 5, D = 2) & 29.7 & 83.44 & 80.0 \\ LSM (K = 7, D = 1) & 29.8 & 83.48 & 79.8 \\ LSM (K = 7, D = 2) & 29.8 & 83.48 & 79.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of the proposed lightweight spatial module (LSM).  
Figure 10: Detection results with the MS COCO2017 minival dataset by (**a**) the baseline FCOS and (**b**) the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parmas (M)** & **FLOPs (G)** & **mAP (\%)** \\ \hline FCOS (Stdconv) & 32.2 & 88.40 & 78.5 \\ FCOS (MBconv [43]) & 28.7 & 84.90 & \\ FCOS (DLM) & 29.8 & 83.44 & 80.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of the proposed module.  
segmentation, leads to damaged adjacent pixels. Hence, the tests show the appropriate kernel size (K) and expansion ratio (D) needed to maximize the LSM detection accuracy. This shows that an inappropriate kernel size with an incorrect number of extensions leads to lower accuracy.
## 2 Materials and Methods
### Elastodynamic Algorithm for UGW Propagation  
The main algorithm of the proposed method for simulating UGW propagation within poly-crystalline materials is illustrated in Figure 4. The algorithm starts by considering the input file where the number (\(\#\)) of grains, the mean grain area, and the boundary dimensions of the micro-structure can be selected. Then, the micro-structure can be generated by choosing the desired VT algorithm. Furthermore, if necessary, each micro-flaw (e.g., inter-granular crack or void) was modeled and embedded within the micro-structure. After that, the micro-structure was discretized by creating the grain boundary elements mesh, as was explained in the section related to the BEM. The UGWs, of which the user can define the type, amplitude, and center frequency, were modeled in the Laplace domain. The selection of the number of Laplace parameters was pivotal for achieving optimal solution accuracy. It was observed that increasing the number of Laplace parameters leaded to higher solution accuracy. However, this improvement came at the expense of increased computational cost, and the relationship between the number of parameters and computational cost was non-linear. Therefore, it was essential to strike a balance between accuracy and computational cost to find the optimal compromise. Moreover, the structural mesh size needed to be carefully chosen to strike the correct balance between accuracy and computational cost. Additionally, the time step, which is influenced by the wave frequency, must be appropriately selected. The system of Equations (5) can be solved at this point. Moreover, with a reliable and fast post-processing, the boundary solutions can be computed. Based on data interpretation and processing, the desired outcomes were obtained.
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 4 Results and Discussion
### Chloride Ion Penetration Resistance of Waste Fiber-Reinforced Concrete  
The penetration of chloride into concrete structures can lead to corrosion and reduction in strength of rebar, resulting in early repair of the structure [49]. Therefore, the chloride ion penetration resistance of concrete is an important factor for evaluating the durability, and in general, the easier it is to penetrate chloride ions, the higher the total charge passed is measured. This experiment was conducted to evaluate the chloride ion penetration resistance of concrete in which waste fiber wool was added. Figure 28 shows a graph depicting the result of deriving the total charge passed by accumulating the charge passed measured in units of 30 min for 8 h on the 28-day specimens. First, the reference concrete was 3423 Coulomb, showing a moderate level of charge passed based on ASTM. The specimen to which waste glass wool was added showed passing charge amounts of 3619, 3582, and 3468 Coulomb with the addition rates of 0.5%, 1.0%, and 2.0% respectively, and it was confirmed that the total charge passed was similar as it showed a difference of up to 6% compared to reference concrete. However, the specimen to which waste mineral wool was added showed total charge passed of 3716, 4304, and 4547 Coulomb by addition rate, and as the addition rate increased, the total charge passed was also increased. When more than 1.0% of waste mineral wool was added, the level of total charge passed was higher than reference concrete, which sharply decreased the chloride ion penetration resistance. In general, when fibers were dispersed in concrete, they reduced the size of the pores in the cement matrix and formed micro pores that increased the density of the internal structure. As a result, it is known that durability may be improved through impermeability. However, if the fiber dispersion in the concrete is uneven or other substances in the fiber were present and mixed together, pores that may inhibit permeability may be formed, thereby deteriorating the concrete pore structure. Therefore, in the case of waste glass wool, there was no improvement in chloride ion penetration resistance through formation of fine pore structures in concrete, but it was expected that it can be applied as a fiber-reinforcing material according to similar permeability to reference concrete. But, specimens to which waste mineral wool was added, the total charge passed increased by up to 33% compared to reference concrete, which means that as the addition rate increased, harmful pores with a size that is easy for chloride ions to penetrate were formed in the cement matrix. Thus, it is considered that the addition of waste mineral wool to concrete cannot be expected to improve the durability aspect such as chloride ion penetration resistance.  
Figure 27: Concrete samples after 240 freezing–thawing cycles.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Interoperability and Standards
#### 8.3.6 Data Integration  
Data integration is equally important for user data and profiles, virtual currency and transactions, and communication and interaction data [275]. Standard formats for user profiles, preferences, and settings (JSON or XML) enable consistent user experiences across different platforms. Adopting blockchain protocols like ERC-20 or ERC-721 for virtual currencies and transactions ensures secure and interoperable management of digital assets [274]. In terms of communication and interaction data, standardized protocols like XMPP (extensible messaging and presence protocol) or WebRTC (web real-time communication) ensure that text, voice, and video communication services are interoperable across different platforms [276]. Using standard formats for describing virtual environments, such as XML-based X3D or JSON-based Scene Graphs, also ensures that different platforms can render and interact with these environments consistently [277].  
By adopting these common formats and data integration standards, the metaverse can achieve a higher level of interoperability, enabling a more cohesive and integrated user experience. This approach not only facilitates the seamless transfer of digital assets and data across different virtual environments but also enhances user engagement and satisfaction. As the metaverse continues to evolve, the importance of standardized data formats and integration protocols will only grow, ensuring that users enjoy a unified and immersive digital universe.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results
### Experimental Results and Analysis
#### 6.3.3 Comparative Experiment
In the \(-\)10 dB result, the YOLO series models incorrectly detect background signals as TT&C signals. Finally, in terms of detection confidence, TATR's detection results have the highest confidence compared to other methods, which further proves TATR has the advantage of a low miss rate and high detection accuracy in TT&C signal detection with complex backgrounds. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **AR (\%)** & **\(M_{s}\) (\%)** & **Parameters (M)** & **FPS** \\ \hline Faster-RCNN (MV2) & 86.23(\(\uparrow\)10.61) & 43.67(\(\uparrow\)16.54) & 41.29(\(\uparrow\)11.94) & 80.21(\(\uparrow\)2.02) & 0.049 & 82.3 & 17 \\ Faster-RCNN (R50) & 82.81(\(\uparrow\)14.03) & 42.47(\(\uparrow\)17.74) & 39.34(\(\uparrow\)13.89) & 79.65(\(\uparrow\)2.58) & 0.054 & 41.7 & 20 \\ \hline YOLOv5s & 94.21(\(\uparrow\)2.63) & 45.52(\(\uparrow\)14.69) & 45.14(\(\uparrow\)8.09) & 81.15(\(\uparrow\)1.08) & 0.044 & 7.2 & 53 \\ YOLOv5m & 94.28(\(\uparrow\)2.56) & 46.63(\(\uparrow\)13.58) & 45.38(\(\uparrow\)7.85) & 83.23(\(\downarrow\)1.00) & 0.041 & 21.2 & 43 \\ YOLOv5l & 95.19(\(\uparrow\)1.65) & 47.23(\(\uparrow\)12.98) & 46.67(\(\uparrow\)15.65) & 85.24(\(\downarrow\)3.01) & 0.034 & 46.5 & 37 \\ \hline DETR & 94.21(\(\uparrow\)2.63) & 46.75(\(\uparrow\)13.46) & 45.16(\(\uparrow\)8.07) & 80.03(\(\uparrow\)2.2) & 0.032 & 41.3 & 18 \\ \hline YOLOv7 & 95.64(\(\uparrow\)1.2) & 42.57(\(\uparrow\)17.64) & 43.25(\(\uparrow\)19.98) & 83.51(\(\downarrow\)1.28) & 0.046 & 36.5 & 14 \\ YOLOv7w & 96.85(\(\downarrow\)0.01) & 47.85(\(\uparrow\)12.36) & 46.81(\(\uparrow\)6.42) & 86.11(\(\downarrow\)3.88) & 0.035 & 69.8 & 8 \\ YOLOv7x & 97.12(\(\downarrow\)0.28) & 53.62(\(\uparrow\)16.59) & 46.92(\(\uparrow\)6.31) & 87.80(\(\downarrow\)5.57) & 0.032 & 70.8 & 7 \\ \hline TATR-4 layer & 95.78(\(\uparrow\)10.6) & 56.82(\(\uparrow\)33.99) & 50.21(\(\uparrow\)3.02) & 79.67(\(\uparrow\)2.56) & 0.031 & 35.5 & 30 \\ TATR-6 layer & 96.84 & 60.21 & 53.23 & 82.23 & 0.029 & 41.2 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of detection performance between TATR and traditional detection models. (MV2 represents the backbone feature extraction network using MobileNetv2, R50 represents the backbone feature extraction network using ResNet50. \(\uparrow\) represents the improvement of TATR on each model, and \(\downarrow\) represents the gap of TATR on each model.)
## 2 Materials and Methods
### Model Development and Training  
In order to evaluate the efficacy of the chosen landmarks we extracted earlier, we chose three classification models: Random Forest, Gradient Boosting and Ridge Classifier. For accurate testing, we compared the performance of three state-of-the-art models in the field, and we utilized cross-validation metrics, t-paired testing for statistical comparison of our model superiority, F1 scores, accuracy, precision, recall, and confusion matrices.  
* **Random Forest Classifier**: A commonly used machine learning algorithm that combines the output of multiple decision trees to reach a single result.
* **Gradient Boosting**: A technique based on functional space boosting. It gives a prediction model which contains an ensemble of weak prediction models. These models make very few assumptions about the data, in which most of the cases are simple decision trees.
* **Ridge Classifier**: A machine learning algorithm designed for multi-class classification tasks.  
The test-train split technique was applied in our dataset, with 80% of the data used for training, and 20% of the data used for testing. In order to handle missing values in our model, we used the mean imputation strategy provided by the simple imputer. Next, we performed a cross-validation for the three classification models using 5 folds to evaluate the models' performance and choose the model with the best performance ratio to advance.
## 3 Results and Discussions
### Gas Analysis  
Figure 7 shows the yield of gases produced during torrefraction. Until 200 \({}^{\circ}\)C the yield was almost negligible for all the gases. With an increase in the torrefraction temperature, the yield of gases increased significantly. After approximately 215 \({}^{\circ}\)C both CO and CO\({}_{2}\) start to increase with the increase in the torrefraction temperature. Traces of methane (CH\({}_{4}\)) can be observed after 250 \({}^{\circ}\)C, whereas Thermal Hydro-Carbon (THC) is released approximately around 200 \({}^{\circ}\)C. [PERSON] and [PERSON] [50] explain the production of CO\({}_{2}\) is caused by decarboxylation of acid groups, which refers to the CO production due to the reaction of CO\({}_{2}\) and steam with char at elevated temperature. Similar results are obtained by [PERSON] et al. [51]. It can be seen from the [PERSON] that although carbon is devolatized in the gaseous phase in the form of CO, CO\({}_{2}\), CH\({}_{4}\), and THC, there is an increase in the relative carbon content which may be associated with the mass loss being greater than the loss in carbon.  
In addition, the present study indicates that there is a significant loss of volatiles at temperatures above 300 \({}^{\circ}\)C. Since the loss of higher energy content volatiles may be disadvantageous for improving the energy at temperatures above 300 \({}^{\circ}\)C. This study concludes that 250-300 \({}^{\circ}\)C can be a suitable range for torrefraction of sewage sludge as the temperature above 300 \({}^{\circ}\)C results in significant loss of volatiles, resulting in higher relative ash content, which is not suitable for energy applications. [PERSON] et al. [52] also suggested that temperatures above 300 \({}^{\circ}\)C are not necessary for torrefying biomass.  
Figure 6: Changes of (**a**) H/C and (**b**) O/C molar ratios as a function of the torrefraction residence time.  
Figure 7: Gas analysis from torrefraction of sewage sludge as a function of the temperature.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Performance Discussion and Contribution of Phase-Based Features
#### 4.3.3 Impact of Phase-Based Features on Classification Performance  
To assess the contribution of the proposed phase-based image representation detailed in Section 3.3, we compared the classification performance of the proposed method with and without the use of phase features. These features, namely local phase, orientation, and phase congruency, are used to emphasize structural patterns in mammographic tissue that are not easily distinguishable in intensity-based representations. On the combined dataset (DDSM, MIAS, and CSAW-M), the EfficientNetV2M model combined with the stacking classifier yielded lower performance when excluding the phase features, with 84.65% accuracy, 74.53% precision, 84.65% recall, and an F1-score of 78.95%, compared with the results obtained with phase features, as shown in Figure 5a. On the CSAW-M dataset, the EfficientNetV2 XL model with the stacking classifier yielded 91.16% accuracy, 84.97% precision, 91.16% recall, and an F1-score of 87.93% when phase features were excluded, compared with the improved performance obtained with phase features, as shown in Figure 5b. These results confirm that phase-based features provide complementary structural information that improves the model's ability to distinguish between cancerous and non-cancerous regions. In particular, they enhance the representation of edges and texture patterns, which are often important for clinical interpretation. Therefore, the inclusion of phase features strengthens the overall discriminative capability of the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**True Label** & **Cancer** & **No Cancer** \\ \hline \multicolumn{3}{c}{(**a**)} \\ \hline Cancer & 379 (TP) & 61 (FN) \\ No Cancer & 102 (FP) & 4109 (TN) \\ \hline \multicolumn{3}{c}{(**b**)} \\ \hline Cancer & 243 (TP) & 18 (FN) \\ No Cancer & 34 (FP) & 2711 (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrices of the proposed method on: (**a**) the combined dataset (DDSM, MIAS, and CSAW-M; 4651 test images); and (**b**) the CSAW-M dataset (3006 test images).
## 3 Review of Previous Work
### Two-Stage CV Fusion Statistic  
Under a high SNR assumption, the received data \(z_{i}=h_{i}b_{i}+n_{i}\) at the FC can easily be detected as one or \(-1\). Hence, the optimal LRT rule can be approximated as:  
\[\Lambda_{\text{CV}}=\sum_{\text{sign}(z_{i})=1}\log\frac{P_{d_{i}}}{P_{fa_{i} }}+\sum_{\text{sign}(z_{i})=-1}\log\frac{1-P_{d_{i}}}{1-P_{fa_{i}}} \tag{2}\]  
The detailed derivation can be found in [20]. It assumes that the channel has high SNRs, and thus, the estimates of the received data are reliable. As the SNR decreases, the approach suffers a significant performance loss.
## 3 Materials and Methods
### Hot Mix Asphalts (HMAs)  
The hot mix asphalts used in the current study (dense asphalt concrete mixtures) had a maximum aggregate size equal to 20 mm (HMA20) in all cases. More specifically, the HMAs20 with 50/70 conventional bituminous binder, either with limestone or diabase aggregates, were coded as HMA20-5070-Lm or HMA20-5070-Db, respectively. Similarly, for the mixtures with SBS-modified bitumen, the following codes, HMA20-SBS-Lm and HMA20-SBS-Db, for limestone and diabase aggregates, respectively, were used. The specimens of all HMAs were compacted in the laboratory by means of an impact compactor (EN 12697-30). All the specimens were characterized by a diameter of 100 mm and an average thickness of 63.4 for the mixtures with the limestone aggregates and 63.7 mm for the mixtures with the diabase aggregates. In total, 69 specimens were prepared for the mixtures with limestone aggregates and 60 specimens were produced for the mixtures with diabase aggregates. Therefore, 129 specimens in total were used in the present research. The grading curves of the HMA20-5070-Lm and HMA20-SBS-Lm are reported in Figure 4. The grading curves of the HMA20-5070-Db and HMA20-SBS-Db are shown in Figure 5.  
Tables S2-S5 report the specimens' volumetric characteristics (EN 12697-8), Marshall stability (MS), and Marshall quotient (MQ) values (EN 12697-34) for each mixture investigated. The Marshall quotient was determined for each specimen, as the ratio between Marshall stability and Marshall flow. Despite having already been discussed as partial representativity of the Marshall results with regard to the bituminous mixtures' response [91; 92; 93; 94], such a test is currently used due to the huge experience gained all around the world.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{2}{c}{**Aggregate Type**} \\ \cline{2-3}  & **Limestone** & **Diabase** \\ \hline Los Angeles coefficient (\%), EN 1097-2 & 29 & 25 \\ Polished Stone Value (\%), EN 1097-8 & - & 55 to 60 \\ Flakiness Index (\%), EN 933-3 & 23 & 18 \\ Sand equivalent (\%), EN 933-8 & 79 & 59 \\ Methylene blue value (mg/g), EN 933-9 & 3.3 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Limestone and diabase aggregates’ characterization results.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{2}{c}{**Bitumen Type**} \\ \cline{2-3}  & **50/70** & **SBS Modified** \\ \hline Penetration (0.1 \(\times\) mm), EN 1426 & 64 & 45 \\ Softening point (\({}^{\circ}\)C), EN 1427 & 45.6 & 78.8 \\ Elastic recovery (\%), EN 13398 & - & 97.5 \\ Fraas breaking point (\({}^{\circ}\)C), EN 12593 & –7.0 & –15.0 \\
**After aging, EN 12607-1 (RTFOT):** & & \\ Retained penetration & - & 84 \\ Difference in softening point (\({}^{\circ}\)C) & - & –2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bitumen characterization results.