esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
## 3 Results
### Implementation Details  
_Data collections._ In order to recognize the pose of the antenna support correctly, we collected ten videos (5679 frames) of the antenna support in total as the training dataset, two videos (1096 frames) for evaluation, and another 5 videos (3105 frames) as the validation dataset. For each video, we manually selected some key points on the 3D model of the the antenna support, as shown in Figure 3b. The 2D correspondences in the first frame of the  
Figure 3: Preparation for the training and testing dataset of the antenna support. (**a**) The appearance of the antenna support. (**b**) Selected key points from 3D mesh model of the antenna support. (**c**) Correspondences in the 2D image. (**d**) Final ground-truth poses of the antenna supports calculated through PnP.  
video were then pointed out (Figure 3c) and the ground truth of the objects were calculated through PnP method, as shown in Figure 3d.  
The Aruco markers were used to calculate the pose of the camera with respect to the board. The property of relevant stills among frames in the same video were used to calculate the poses of each objects with respect to the camera for the rest of the frames. To enhance the performance of the model, we further rendered 20,000 synthetic images through the BOP [34] renderer for training, as shown in Figure 4. We also added data augmentation to the original images including random cropping, resizing, 3D rotation, and color jittering during training.  
Model settingsWe used EfficientDet-D2 as the object detection backbone in terms of the balance between detection accuracy and memory usage. The dimension \(D\) of the pixelwise and nodewise deep features was set to 128. The hyperparameter \(\zeta\) to balance the loss of the segmentation and the loss of similarity matching was set to 0.01 through cross validation on the evaluation dataset. All the objects detected by the EfficientDet were resized to \(256\times 256\) for further calculation by the U-Net.  
Training strategyWe used Pytorch [35] to implement our framework. The network was trained on two Nvidia RTX 3090 graphics cards with 24 GB RAM. The batch size was set to 16. We utilized the Adam optimizer [36] to process gradient decent of the parameters. The initial learning rate was set to 0.001 and divided by two for every twenty epochs. The model was totally trained for two hundreds of epochs and evaluated for every ten epochs. The model with the best score in the evaluation dataset was chosen as the final model for testing.  
Meshl model SimplificationTo reduce the memory usage of our model, we simplified the 3D mesh model of the antenna support to possess less than 8000 triangular patches and 4000 vertices through quadric edge collapse decimation in MeshLab [37]. The average of the node-pixel matching error is less than 0.5 pixel under this setting.
# Smoke Back-Layering Phenomenon under the Combined External Wind and Stack Effects in a Staircase
## 3 Results and Discussion
### Smoke Back-Layering Distance under the External Wind
The dimensionless smoke back-layering distance, \(L^{*}\), can be expressed as  
\[L^{*}=L/H \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Wind Velocity (m/s)**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline
**Opening Location** & **HRR(MW)** & **0, 0.5, 1.5, 2.5** & **3** & **3.5** & **4** & **4.5** & **5** & **5.5** \\ \hline
1F & 3 & & T & & N & N & - \\  & 4 & & T & & N & N & - \\ \hline
3F & 2 & & T & & N & N & - \\  & 3F & 3 & & T & & & N & - \\  & 4 & & T & & & N & - \\ \hline
5F & 2 & & T where \(H\) is vertical height between the fire floor and the top of the staircase. The relation between \(\tau^{\prime}\) and \(L^{\star}\) can be seen in Figure 14. As shown in Figure 14, the dimensionless smoke back-layering distance is linearly proportional to the new dimensionless back-layering time in double logarithmic coordinates. However, the wind effect is neglected. The downward moving smoke inside the staircase is mainly resulted from the external wind and influenced by the HRR of the fire. Then the correlation will be modified to make it more reasonable. The smoke back-layering phenomenon usually occurred in the narrow and long subway tunnel. The back-layering distance is important for the ventilation system design. When the longitudinal velocity is high enough, the smoke back-layering upstream of the fire will be prevented. Prediction correlations for the back-layering distance showed that the dimensionless back-layering distance was proportional to the dimensionless HRR and inversely proportional to the dimensionless ventilation velocity [25; 26; 27]. Based on the studies for the smoke back-layering phenomenon in the subway tunnel, the dimensionless back-layering time can be modified as  
\[\tau_{\text{mod}}=\frac{t}{t_{o}}\bigg{[}\frac{Q}{\rho_{a}T_{a}C_{p}\sqrt{g}L ^{2.5}}\bigg{]}^{1/3}(\frac{v^{2}}{gL})^{\alpha} \tag{12}\]  
Correlating experimental data with the modified dimensionless back-layering time and dimensionless smoke back-layering distance, we obtain Figure 15. It can be seen that, in double logarithmic coordinates, the dimensionless smoke back-layering distance are linear related to dimensionless back-layering time when \(\alpha=-1\). Figure 12.0 Dimensionless back-layering distance \(L^{\star}\) versus dimensionless back-layering time \(\tau^{\prime}\). Figure 12.1 Longest smoke back-layering distance with different open stair doors and wind velocities. (a) \(3.5\) m/s. (b) \(4\) m/s.
###### Abstract  
The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11 ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities.  
CNN-LSTM; MANET; VANET; beamforming; MU-MIMO; DSR; 802.11 ax
# The Effects of Methane Storage Capacity Using Upgraded Activated Carbon by KOH
###### Abstract  
In this study, a feasible experiment on adsorbed natural gas (ANG) was performed using activated carbons (ACs) with high surface areas. Upgraded ACs were prepared using chemical activation with potassium hydroxide, and were then applied as adsorbents for methane (CH4) storage. This study had three principal objectives: (i) upgrade ACs with high surface areas; (ii) evaluate the factors regulating CH4 adsorption capacity; and (iii) assess discharge conditions for the delivery of CH4. The results showed that upgraded ACs with surface areas of 3052 m\({}^{2}\)/g had the highest CH4 storage capacity (0.32 g-CH4/g-ACs at 3.5 MPa), which was over two times higher than the surface area and storage capacity of low-grade ACs (surface area = 1152 m\({}^{2}\)/g, 0.10 g-CH4/g-ACs). Among the factors such as surface area, packing density, and heat of adsorption in the ANG system, the heat of adsorption played an important role in controlling CH4 adsorption. The released heat also affected the CH4 storage and enhanced available applications. During the discharge of gas from the ANG system, the residual amount of CH4 increased as the temperature decreased. The amount of delivered gas was confirmed using different evacuation flow rates at 0.4 MPa, and the highest efficiency of delivery was 98% at 0.1 L/min. The results of this research strongly suggested that the heat of adsorption should be controlled by both recharging and discharging processes to prevent rapid temperature change in the adsorbent bed.  
adsorbed natural gas; activated carbon; potassium hydroxide; heat of adsorption; gas delivery +
Footnote †: journal: Applied Physics Letters B
## 1 Introduction  
The Hall thruster, advanced propulsion technology, is increasingly favored for diverse space exploration missions. This preference stems from its attributes, including a lightweight design, high specific impulse, robust reliability, and extended lifespan [1,2]. Nevertheless, its plume exhibits non-uniform plasma characteristics, potentially causing various issues when interacting with the satellite's near-field space environment. These issues encompass interactions with the near-field plasma, electromagnetic, and thermal environments. These interactions may result in adverse effects on the spacecraft, including charging and discharging effects, deposition contamination, sputtering corrosion, and other detrimental impacts [3]. These effects, in turn, impact the performance of spacecraft surface materials and disrupt the normal operation of the payload. Notably, spacecraft charging and discharging effects are prominent, contributing to approximately 50% of spacecraft anomalies in the space environment. Consequently, evaluating surface charging risks is crucial [4,5]. The effects of spacecraft charging and discharging reflect the charging conditions of satellite platforms within specific orbits, moments, materials, and structural designs. Due to the non-direct applicability of different structures and platforms, it becomes essential to analyze the charging of a specific spacecraft configuration.  
Apart from a thorough investigation into the plume-induced charging effects, this study specifically focuses on how the Hall thruster plume affects spacecraft surface charging at various locations, particularly examining variations under different mission conditions.  
This paper aims to enhance our comprehension of the potential environmental impacts of these thrusters in practical scenarios. Conducting a comparative analysis of the plume effects produced by thrusters at distinct locations on spacecraft surface charging allows us to develop a more thorough understanding of their behavioral characteristics in the space environment. This not only unveils the intricate interactions between the plume and the spacecraft but also furnishes a comprehensive guide for future spacecraft design and selection tailored to specific missions. Hence, this study not only underscores the influence of thruster plumes on spacecraft charging but also integrates an in-depth exploration of spacecraft surface charging by thrusters at various positions, offering a more dependable assurance for the successful execution of space exploration missions and making a substantial contribution to the advancement in future propulsion technologies.  
In this paper, we utilize SPIS (Spacecraft Plasma Interaction System) to simulate the surface charging potential, surface current, and distribution of various particles in the thruster plume of a spacecraft with a complex satellite structure. This allows us to comparatively analyze the impact of the thruster plume on spacecraft surface charging, as well as the effects of thrusters at different positions on surface charging and the distribution of various particles in the surrounding environment.
# CenterInst: Center-Based Real-Time Instance Segmentation
## 3 Methods
### Dual-Path Decoder
#### 3.3.3 Lightweight Inference Architecture  
To further improve the inference speed of the model, we introduce an innovative lightweight inference structure.  
During training, we used the updated content queries and center positions from the previous stage as inputs. However, unlike FastInst, the pixel feature input only comes from the neck, not the previous stage. Subsequently, we updated the content queries and pixel features through a dual-path decoder. The detection, classification, and segmentation tasks were then completed through the regression and detection heads, with the detection head utilizing a structure comprising a 3-layer MLP. Predictions from each stage were supervised through respective losses. Regression loss employed GIOU loss and L1 loss; classification loss employed cross-entropy loss; and segmentation loss combined dice loss and cross-entropy loss calculations.  
During inference, owing to the design of the dual-path decoder, only content queries and center positions are updated and used for the next stage at each step. Additionally, predictions for center points and updates to content queries do not depend on updates to pixel features. In other words, we only need to update pixel features in the final stage and utilize the updated pixel features to calculate the segmentation and classification results in inference. For the other stages, only updates to content queries and center point coordinates are necessary, without the need for computing segmentation and classification results.  
Through this lightweight inference structure, we significantly reduced the computational load associated with decoding pixel features in multistage scenarios, thereby further shortening the inference time. Our experimental results demonstrated that this operation not only reduces inference time but also preserves model accuracy.
# Recall Mechanism and Multi-Head Attention for Numerical Reasoning
## 2 Related Works
### Question and Document Representation  
Early approaches to numerical parsing relied on language parsers, such as the Stanford CoreNLP suite, to identify variables, operands, and their relationships in questions and documents [14; 15]. With the rise of deep learning, word embeddings became the dominant method for representing questions and documents. Early embeddings, such as skip-gram, CBOW, and GloVe, used static word vectors, where each token was represented by a single vector [16; 17; 18]. However, these approaches struggled with polysemy, as tokens like words can have multiple meanings.  
To address this limitation, contextualized word embeddings were introduced. Early efforts, such as ELMo, used bidirectional LSTMs to generate context-aware embeddings [19]. The advent of transformer-based models, such as BERT, RoBERTa, and LLaMA [20], revolutionized the field by enabling dynamic, context-sensitive representations. These models have been widely adopted in numerical reasoning tasks. For example, neural-symbolic reader (NeRd) uses BERT as its encoder [21], while models like QDGAT [22], MHST [23], and ELASTIC [6] leverage RoBERTa for encoding questions and documents. Additionally, models like LayoutLM and LayoutLMv2 [24] extend these approaches to visually rich documents by incorporating text, image, and layout information. When the reasoning process involves images within a document, certain image feature techniques can also be applied. For example, [PERSON] et al. use the combined channel and spatial attention (CCSA) mechanism, which emphasizes capturing both spatial and channel-wise features while effectively preserving essential high-frequency details [25].
## 3 Results and Discussion
For nanostructured Si surfaces, passivation was performed in QHY/MeOH and \(\mathrm{BQ}\)/MeOH with \(\mathrm{1}\) h, \(\mathrm{2}\) h, and \(\mathrm{3}\) h dipping. The results of the \(\tau_{\text{eff}}\) measurements (Figure 8b) show that \(\tau_{\text{eff}}\) from \(\mathrm{BQ}\)/MeOH increased as the dipping time increased with the highest \(\tau_{\text{eff}}\) of \(\mathrm{81}\)\(\upmu\)s with \(\mathrm{3}\) h dipping. In contrast for QHY/MeOH, the best \(\tau_{\text{eff}}\) of \(\mathrm{64}\)\(\upmu\)s was measured at \(\mathrm{1}\) h, and \(\tau_{\text{eff}}\) decreased with longer dipping times. From the experiments of nanostructured Si surface passivation, the trend of \(\tau_{\text{eff}}\) with dipping time was well-matched with the earlier results from the plane Si surface passivation. Therefore, for complex Si surface passivation, \(\mathrm{HQ}\) deteriorates surface passivation quality because, most probably, it impedes the formation of a conformal passivation layer, as discussed earlier. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Passivation Solution** & **Dipping Time** & \(\tau_{\text{eff}}\) **(\(\upmu\)s)** & **SPV (meV)** \\ \hline QHY & \(\mathrm{1}\) h & \(\mathrm{2813}\) & \(\mathrm{317}\pm\mathrm{26}\) \\ (\(\mathrm{BQ}+\mathrm{HQ}\)) & \(\mathrm{3}\) h & \(\mathrm{2195}\) & \(\mathrm{249}\pm\mathrm{8}\) \\ \(\mathrm{BQ}\) & \(\mathrm{1}\) h & \(\mathrm{1522}\) & \(\mathrm{263}\pm\mathrm{13}\) \\ \(\mathrm{3}\) h & \(\mathrm{3}\) h & \(\mathrm{3398}\) & \(\mathrm{357}\pm\mathrm{19}\) \\ \(\mathrm{HQ}\) & \(\mathrm{1}\) h & \(\mathrm{68}\) & \(\mathrm{13}\pm\mathrm{24}\) \\ \(\mathrm{3}\) h & \(\mathrm{91}\) & \(\mathrm{20}\pm\mathrm{22}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SPV values measured by Kelvin Probe system compared with \(\tau_{\text{eff}}\) from different dipping times and passivation solutions. Figure 7: (**a**) Measured \(\tau_{\text{eff}}\) after dipping in \(\mathrm{BQ}\) and \(\mathrm{HQ}\)/MeOH for \(\mathrm{10}\) min to \(\mathrm{180}\) min. \(\mathrm{HR}\)-AFM images of Si surface after dipping in QHY/MeOH (**b**,**c**) and \(\mathrm{BQ}\)/MeOH (**d**,**e**) for \(\mathrm{1}\) h and \(\mathrm{3}\) h, respectively.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 4 Experiment Results
### Recognition Accuracy of Two Classifiers  
Owing to the finite dimensions of the baseline features, the comparison under the two classifiers between the conventional features and the unconventional features is listed firstly. Then, the dimensions of the unconventional features are enlarged to verify the performances of the two classifiers further.
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 4 Taxonomy Mapping Toolkit
### Automated Literature Analysis Tool (ALAT)  
In addition to the TMT, during the iterative taxonomy development, the study group encountered instances where changes required re-mapping the literature to ensure consistency. This process proved to be both time-consuming and prone to oversight. The need for a tool in the toolkit to streamline this process led to the creation of the Automated Literature Analysis Tool (ALAT).  
The ALAT automates searching for relevant terms in a folder of PDF documents and outputs the results in a structured Microsoft Excel file. While it does not replace manual validation, ALAT is a guideline for highlighting relevant terms and linking them to specific documents. It was developed with three primary objectives:  
1. Automating term identification: To streamline the process of identifying relevant terms in an extensive collection of PDF documents based on a set of pre-defined search terms.
2. Summarizing occurrences: To provide a structured and concise summary of the frequency and distribution of these terms across all analyzed documents, enabling better insights into their relevance.
3. Guiding manual review: To serve as a guideline for researchers by highlighting key terms within each record, allowing for faster and more focused manual validation.  
ALAT inputs a Microsoft Excel file, where each row specifies a primary term and its associated sub-terms. ALAT then generates a new Microsoft Excel file with multiple sheets:  
* Literature list: Lists analyzed records with their reference numbers.
* Summary: Summarizes main term and sub-term occurrences across all records.
* Details: Provides detailed term counts for each record.
* Word references: Maps terms to the records they appeared in.  
While ALAT reduces the time required for record mapping, the output should not be taken as an absolute truth; instead, it serves as a guideline for manual review. Highlighting relevant terms and linking them to records enables faster, more focused analysis while still requiring human validation for accuracy.  
Combined, these tools support the methodology outlined in this study by improving collaboration, enhancing transparency, and reducing manual overhead in taxonomy development. By releasing the tools as open-source software and hosting publicly available instances, the research team ensures that the broader cybersecurity community can inspect, reuse, and build upon our work.  
\begin{table}
\begin{tabular}{c l l l l} \hline
**Level** & **Name** & **Description** & **ID** & **Path** \\ \hline
0 & Root Category & Description of root category & 1, 2, 3 & Root Category \\
1 & Subcategory A & Description of subcategory & 4, 5, 6 & Root \(\rightarrow\) Subcategory A \\
2 & Subcategory B & Detailed description & 7, 8, 9 & Root \(\rightarrow\) Subcategory A \(\rightarrow\) Subcategory B \\ \hline \end{tabular}
\end{table}
Table 14: Example structure of the _Categories_ sheet.