esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Cluster Analysis
#### 2.2.4 Determining the Number of Clusters: Clustering Index  
'Calinski-Harabasz'-Criterion  
In order to find the structure contained in the data, it is necessary to specify a suitable and ideal number of clusters for hierarchical clustering. To determine this number, the following evaluation criteria were analyzed in more detail, the Calinsky-Harabasz, Davies-Bouldin, Gap, Silhouette and Cut-off criteria for inconsistent data. A detailed description of the application of the Silhouette coefficient can be found in [PERSON] et al. [27].  
The chosen Calinski-Harabasz index (known as Variance-Ratio criterion) is defined as the quotient of the degree of separation and compactness, using a normalization factor to limit the monotonous increase of the index with an increasing number of clusters. The Calinski-Harabasz index (CH-i) can therefore be written as:  
\[CH(n)=\frac{SS_{B}}{SS_{W}}\times\frac{n-k}{k-1} \tag{6}\]  
Figure 1: Algorithm for selecting the best suitable metric and writing the final clustered results.  
where \(n\) is the total number of data points (observed objects), \(k\) is the number of clusters, \(SS_{B}\) is the overall between (inter)-cluster variance or separation degree and \(SS_{W}\) is the overall within (intra)-cluster variance or compactness degree. Accordingly, the optimum number of clusters is achieved with the highest value of CH-i.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 2 The STEP Cube Lab-II CubeSat's Overview  
### Mission Objectives and System Descriptions  
Figure 1 illustrates the system architecture of the STEP Cube Lab-II CubeSat mission. The STEP Cube Lab-II CubeSat has been developed in a collaborative framework of university and start-up aerospace companies for educational and technological verification purposes. The STSL of Chosun University and a consortium team of eight domestic organizations are involved in this project to verify domestically developed space technologies and demonstrate unique nighttime video mode remote sensing capability through CubeSat. The CubeSat is equipped with three commercial off-the-shelf (COTS) remote sensing instruments onboard as the primary payload for earth remote sensing, such as an  
electro-optical camera (EOC), broadband infrared camera (BBIRC), and long-wave infrared camera (LWIRC). The primary objective of the STEP Cube Lab-II project is to carry out the nation's first multi-band earth observation mission by utilizing CubeSat's electro-optical (EO) and infrared (IR) images and videos on various targets of the Korean peninsula, including Mt. Paektu, which has recently shown signs of a volcanic eruption, observation of areas affected by forest fires, and the route of spread in areas where forest fires have occurred, analysis of the thermal island phenomenon in urban areas of metropolitan cities, observation of ship activities, and monitoring of the nuclear power plant's coolant discharge in seawater. Furthermore, one of the space technologies to be verified in this mission is an optimized data compression method in the payload data handling system (PDHS) and payload data transmission system (PDTS) for video data processing and transmission to the ground station. Additionally, this mission also tests a high-damping VMLSA to ensure structural protection of mounted solar cells in launch vibration environments by suppressing transmitted launch loads on the solar panel and a three-pogo pin-based burn wire triggering mechanism for high holding capability at the launch stowed configuration of the solar panel and reliable release action in space. Furthermore, an optimized SADA for the acquisition of solar power maximization is one of the secondary payloads. This study is mainly focused on the on-ground experimental validation of the design effectiveness and structural safety of the VMLSA combined with P-HRM.  
Table 1 presents the detailed specifications of the CubeSat system. For mission operation, the satellite orbit is selected as a sun-synchronous orbit of 700 km altitude from the earth surface and an inclination angle of 98.2\({}^{\circ}\) with a local time of descending node (LTDN) of 16:00 p.m. The ground station contact time of the satellite calculated from the orbital analysis is approximately 7.5 min, and access to the ground station occurs six times a day. The CubeSat mission operation life is set to one year.  
Figure 1: The system architecture of the Space Technology Experimental Project CubeSat Laboratory (STEP Cube Lab)-II CubeSat’s mission.
# A Deep Fusion Matching Network Semantic Reasoning Model
## 3 Methods
### Design of Reasoning Model Based on Deep Fusion Matching Network
#### 3.2.1 Sentence Coding  
In order to avoid the result's interference of the sentence representation on the judgment of the reasoning model, the coding layer of the deep fusion matching network is designed in this paper. The bidirectional long-short memory network is used to obtain the sentence embedded representation of the premise sentence pair \((p,q)\). If there is no clear indication in the following text, the premise sentence is represented by substitution, and use \(p\) instead of presupposition, use \(q\) instead of hypothetical sentences.  
Firstly, this paper preprocesses the \(p\) and \(q\) sentences, including English word segmentation and the removing of stop words, to obtain the word list \(p=(p_{1},p_{i},\ldots,p_{m})\) and \(q=(q_{1},q_{j},\ldots,q_{n})\), where \(m\) and \(n\) represent the number of words in the sentence and respectively.  
Then, the bidirectional long-short memory network combined with sentence context information is used to encode sentence semantic-information, and the hidden layer state \(h_{i}\) of the \(i\)th word in the sentence is obtained as shown in Formula (1).  
\[h_{i}=BiLSTM(e_{i},h_{i-1},h_{i+1}) \tag{1}\]  
Among them, \(e_{i}\) represents the \(n_{e}\) dimension word vector corresponding to the \(i\)-th word, which is generated by word2 vec technology; \(h_{i-1}\) and \(h_{i+1}\) represent the hidden layer state corresponding to the previous and the next word of the \(i\)th word, respectively.  
Combining the hidden layer state of each word in the sentence, we obtain the sentence embedding representation \(H\), as shown in Formula (2):  
\[H=(h_{1},h_{i},\dots,h_{L}) \tag{2}\]  
where \(H\in R^{1\times L}\), \(L\) is the length of the sentence. After the final sentence \(p\) and \(q\) pass through the coding layer, the sentences embedded are expressed as \(H_{p}=(h_{p_{1}},h_{p_{i}},\dots,h_{p_{m}})\) and \(H_{q}=(h_{q_{1}},h_{q_{j}},\dots,h_{q_{m}})\).
## 2 Architecture: The EMERGE System
These units' responsibility is to support more complex optimization, analysis, and coordination tasks, such as the optimization of routes traveled by vehicles, the selection of locations to be visited by vehicles, and massive data analysis for traffic predictions. The vehicle equipment can be of any of the following three types, named _OBU configurations_, with different sets of supported services:  
1. Configuration **EMERGE full:** * Supports emergency management services through dynamic and collaborative navigation applications with the use of preferential and/or dedicated lanes; * Facilitates efficient traffic flow management for \"everyday\" operations, mobility support services, and third-party services; * Includes modules for satellite communication, multi-standard terrestrial communication, advanced high-accuracy and high-integrity navigation techniques and algorithms, and 360-degree video surveillance of the vehicle's surrounding environment. 2. Configuration **EMERGE MEDIUM:** * Supports traffic flow management in \"everyday\" operations, mobility support services, and third-party services; * Includes multi-standard terrestrial communication, advanced high-accuracy and high-integrity navigation techniques and algorithms, and 360-degree video surveillance of the vehicle's surrounding environment. Figure 1: The EMERGE system: an overview of physical/hardware components and interactions. 3. Configuration EMERGE SMALL: * Supports traffic flow management in \"everyday\" operations, mobility support services, and third-party services; * Includes multi-standard terrestrial communication and Commercial Off-The-Shelf (COTS) GPS navigation systems; * Integrates 360-degree video surveillance of the vehicle's surrounding environment. Vehicles equipped with an EMERGE OBU are, if necessary, able to interface with external vehicles outside the EMERGE system equipped with V2V connectivity. In these cases, the reference V2X standards (DSRC or C-V2X) are used [14]. The terrestrial architecture consists of four main components:  
1. Roadside infrastructure and MEC, which includes processing and communication elements placed along the road (RSU, eNB, gNB, etc.) for the local processing of information and support for URLLC services. The roadside infrastructure may potentially interface with external vehicles outside the EMERGE system equipped with V2I connectivity. 2. Ground aggregation of heterogeneous information flows. 3. EMERGE Service Hub, with integrated processing capabilities for information from the field, both locally and remotely through the EMERGE Cloud, for the implementation of EMERGE applications and the provision of EMERGE services to third parties. 4. EMERGE Cloud, dedicated to the remote processing of information for specific dynamic and cooperative navigation applications, augmentation, and professional communications in support of EMERGE services. We refer the interested reader to [14; 15] and references therein for more details on some employed technologies and operational modes.
###### Abstract  
Object tracking has gained importance in various applications especially in traffic monitoring, surveillance and security, people tracking, etc. Previous methods of multiobject tracking (MOT) carry out detections and perform object tracking. Although not optimal, these frameworks perform the detection and association of objects with feature extraction separately. In this article, we have proposed a Super Chained Tracker (SCT) model, which is convenient and online and provides better results when compared with existing MOT methods. The proposed model comprises subtasks, object detection, feature manipulation, and using representation learning into one end-to-end solution. It takes adjacent frames as input, converting each frame into bounding boxes' pairs and chaining them up with Intersection over Union (IoU), Kalman filtering, and bipartite matching. Attention is made by object attention, which is in paired box regression branch, caused by the module of object detection, and a module of ID verification creates identity attention. The detections from these branches are linked together by IoU matching, Kalman filtering, and bipartite matching. This makes our SCT speed, simple, and effective enough to achieve a Multiobject Tracking Accuracy (MOTA) of 68.4% and Identity F1 (IDFI) of 64.3% on the MOT16 dataset. We have studied existing tracking techniques and analyzed their performance in this work. We have achieved more qualitative and quantitative tracking results than other existing techniques with relatively improved margins.  
object tracking; object detection; MOTA; real-time multiobject tracking +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 4 Experimental Evaluation  
### Experimental Setup  
#### 4.1.1 Data Preparation  
For the sake of verifying the robustness and availability of the proposed encryption application traffic identification method based on the CLSTM-Mean-Teacher collaborative learning across different traffic datasets, we selected two publicly available encrypted traffic datasets from the internet: ISCXVPN2016 and ISCXTOR2016 [26; 27]. These datasets include a variety of common encrypted application traffics, covering different types of web applications. We categorized the traffic into 14 label categories according to the type of application they belong to. During training, we divided the input dataset into training data, validation data, and test data at a ratio of 8:1:1. Among these, the training set was further divided into labeled and unlabeled data according to the proportions used in the experiments (0.1:9.9; 0.5:9.5; 1:9; 1:5:8.5; 2:8). This approach was used to assess the validity of encrypted traffic classification on VPN data. Tables 1 and 2 show the classification of the ISCXVPN2016 traffic dataset and the ISCXTOR2016 traffic dataset, respectively.
# Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
### Cervical Cancer Diagnosis  
Given that changes from normal cervical mucus to clinically clearly visible cancer in most cases develop over a period of several years, regular gynecological examination, PAPA test, colposcopy, and HPV typing give us enough time to detect these changes promptly, treat them adequately, and completely prevent the occurrence of cervical cancer. There are two main types of cervical cancer: squamous cell cancer (70-80%), and adenocarcinoma, which develops from glandular cells that produce mucus that lines the cervical canal. Adenocarcinoma is less common than squamous cell carcinoma, but in recent years, it has become more common and it now accounts for 10 to 15% of uterine cancers. Since adenocarcinoma arises in the cervical canal and not in the cervix itself, it is more difficult to detect through screening than squamous cell cancer of the cervix, but the treatment is the same [6; 7]. Cervical cancer is caused by certain types of human papillomavirus (HPV), and the main causative agents are high-risk types of HPV. It is important to emphasize that there are risk factors that can increase the risk of developing cervical cancer in women infected with HPV. Some of the risk factors are: smoking, early sexual intercourse, promiscuity, infection with genital herpes, immunosupppression, lower socioeconomic status, poor genital hygiene, and a higher number of births [8; 9]. Symptoms depend on the extent of the tumor and the stage of the disease, but the problem arises in the pre-stage of cancer because it is usually asymptomatic. Changes in the cervix are usually discovered by chance during regular annual check-ups. Advanced stages show clear symptoms in about 90% of cases. Irregular bleeding is the main symptom of cervical cancer. It occurs as:  
* spotting,
* brownish discharge,
* postmenopausal bleeding, or
* contact bleeding (bleeding during sexual intercourse or defecation).  
Another important symptom is the appearance of bloody discharge, usually with an unpleasant smell. In addition to these symptoms, pain in the lower abdomen can occur, usually in advanced stages of the disease, with the involvement of neighboring organs [10].
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 2 Unstructured Grid Finite Volume Method (FVM) CFD Solver
### Data Structure for Unstructured Grid  
For structured grids, the computational space can be represented by the index \(i\), \(j\), \(k\), which directly corresponds to the storage way of flow field variables in the computer. This feature can easily obtain adjacency relationships between grid entities (nodes, faces, and cells). However, unstructured grids do not have this property, and grid entities typically do not have a regular arrangement order, requiring additional data structures to store grid topology information. Unstructured grid data structures can be abstractly viewed as sets of grid entities. Physical quantities are defined corresponding to these collections, e.g., coordinates, velocities, and fluxes, and explicit connectivity data is needed to represent the adjacent info between entities. For cell-centered schemes, the numerical operation is mainly on the faces of the control volume, so it is natural to adopt a face-based data structure [21]. For example, when solving the decentralized control Equation (2), the flux at the face center of the control volume must be provided. As shown in Figure 1, to compute the flux at \(f_{0}\), pointers to the two cells (\(c_{0}\) and \(c_{1}\)) sharing \(f_{0}\) should be provided to access the flow variables associated with them.
## 4 Experiment Results and Analysis
### How Many Components Should Be Remained?  
To test how many principal components should remain, we tested on the two datasets mentioned above. For the Pavia University dataset, the number of principal component components retained changes from 1 to 103, and for the Salinas data set, the number of principal component components retained changes from 1 to 204. The corresponding run time and overall accuracy are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Classes** & **Total Samples** & **Training Samples** \\ \hline
1 & Asphalt & 6631 & 200 \\
2 & Meadows & 18,649 & 200 \\
3 & Gravel & 2099 & 200 \\
4 & Trees & 3064 & 200 \\
5 & Painted metal sheets & 1345 & 200 \\
6 & Bare Soil & 5029 & 200 \\
7 & Bitumen & 1330 & 200 \\
8 & Self-blocking bricks & 3682 & 200 \\
9 & Shadows & 947 & 200 \\ \hline Total & & 42776 & 1800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of training samples of the Pavia University dataset.  
As can be seen from Figure 4, in the Salinas data set, when the number of components is less than 25, the more principal components that are retained, the higher the overall accuracy is that can be obtained. While in the data set of Pavia University, when the number of components is less than 15, the more principal component components that are retained, the higher the overall accuracy. However, from then on, the accuracy did not improve with an increase in the number of components. This is because these components have retained the information more than 99%. However, as the retained components increase, the testing time increases linearly. To balance time and efficiency, we set the number of components to 25 for the rest of experiments. Of course, the number can also be calculated automatically, for example, the number of reserved components can be determined automatically by requiring more than 99% of the information to be retained.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
### GCI Analysis  
Since this study primarily analyzes the amplitude and acceleration of a three-degree-of-freedom low-frequency resonant mixer at the third-order natural frequency, and for such mixers, the motion state of the load component directly reflects the mixing effect, GCI analysis mainly focuses on the third-order natural frequency and the amplitude and acceleration of the payload assembly at that frequency.  
The same simulation method as before is used to change the grid division and obtain different data, as shown in Table 3.  
The grid convergence error \(\varepsilon\) is defined as the following:  
\[\varepsilon= \frac{f_{1}-f_{2}}{f_{1}} \tag{8}\]  
In the formula, \(f_{1}\) and \(f_{2}\) are the key data obtained under the fine grid and coarse grid, respectively. The key data of interest here refers to the third-order natural frequency,  
Figure 6: Amplitude–frequency response curve of each assembly of the mixer.  
as well as the amplitude and acceleration of the payload assembly at the third-order natural frequency.  
The ratio of the degree of grid division is defined as the following:  
\[r_{h}=\sqrt[3]{\frac{N_{h+1}}{N_{h}}} \tag{9}\]  
In the formula, \(N_{h}\) represents the total number of nodes in the grid.  
The grid convergence index \(GCI\) is defined as the following:  
\[GCI=F_{s}\frac{|\epsilon|}{r^{m}-1} \tag{10}\]  
In the formula, \(F_{s}\) is the safety factor. If three or more sets of grids are selected for GCI estimation, \(F_{s}\) is taken as 1.25; \(r\) is the encryption ratio of the grid; and \(m\) is the convergence accuracy index, which is taken as 1.97 [25].  
The data from Table 3 are substituted into Equations (8)-(10).  
The calculation results of the data used in the previous article are shown in Table 4.  
It can be seen that the GCI value is less than 3%, which meets the requirement of independence of grid division.
# Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis
## 1 Introduction
To address this gap, this study investigates the following research questions:  
1. To what extent can LLMs accurately respond to media analysis questions derived from structured codebooks? 2. Can agentic workflows enhance the performance of LLMs in codebook-based news content analysis? To this end, we compiled a dataset of 200 news articles related to the tariff policies announced by the United States in 2025. We then manually annotated these articles using a 26-question, tariff-specific analysis codebook developed for this study, comprising 122 possible codes. Based on the ground truth established by human annotators, this study evaluates the question-answering capabilities of LLMs through various model configurations and agentic workflows. To the best of our knowledge, this is the first work to systematically explore the applicability of LLMs compared to a multi-agent expert team approach for comprehensive codebook-based news content analysis. More specifically, the contribution of this work is multifold. First, we enable a reproducible evaluation of automated content analysis by publishing our annotated dataset and structured codebook on an open data repository. Then, we design and assess a prompting framework across seven state-of-the-art LLMs spanning multiple capacity tiers, achieving strong performance on direct annotation tasks. Finally, we develop and benchmark an agentic architecture utilizing Meta's Llama 4 Maverick model powering a multi-agent team of media, political, trade economist, and reviewer expert agents, demonstrating improved accuracy (F1 increasing from 0.757 to 0.805) and significantly enhanced cost efficiency relative to commercial models. The rest of this paper is structured as follows. First, related work is presented in Section 2. Then, we detail the dataset and the data annotation process and analytically describe the proposed method in Section 3. Next, we provide details of our extensive experimental study in Section 4. Finally, Sections 5 and 6 conclude the paper and discuss possible future research directions.