esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Model
### Cost Model  
The network deployment cost model assumed in this study includes three basic elements:  
* The cost of leasing optical fibers for the realization of TPs;
* The cost of the MUXs used for wavelength aggregation and signal filtering purposes at the ends of TPs;
* The cost of the OADMs applied for wavelength aggregation at intermediate nodes of TPs.  
The calculation of the above cost elements is achieved by Constraints (21)-(23) in the MILP model, whereas the overall network cost adds up these elements in objective function (2). In the model, \(\kappa^{\text{F}}\) denotes the leasing price of a km of dark fiber, \(\kappa^{\text{M}}(m)\) is the cost of a MUX of type \(m\in\mathcal{M}\), and \(\kappa^{\text{O}}(o)\) is the cost of an OADM of type \(o\in\mathcal{O}\).
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 2: A 4U CubeSat
#### 4.2.1 Constellation Phase Times  
Figure 13 illustrates the phase times for CS1\({}_{3}\) across various \(\Delta v\) magnitudes. The trends closely follow those observed in the PQ8 case study, remaining within a similar range of phase times. This consistency suggests that the proposed algorithms scale reliably across different constellation sizes and configurations. In this case, a \(\Delta v\) magnitude of 0.45 m s\({}^{-1}\) provided the best phase times of approximately 331 days.  
The required \(\Delta v\) magnitude for the disengagement mechanism increased despite the proportional scaling of mass, with \(\Delta v_{thresh}\) almost double that of Case Study 1. This increase is attributed to the reduction in the number of disengagement sequences, from the three-stage process in the PQ8 case to the two-stage process in the CS4 study. This trend suggests that as the number of satellites in the constellation increases, the demand on the disengagement mechanism decreases. This outcome is intuitive, as a larger number of satellites results in more \(\Delta v\) instances, distributing the required impulse more effectively. However, when considering \(\Delta v_{thresh}\) alongside the collision avoidance trade-off discussed in the previous case study, it becomes evident that an upper limit exists on the number of satellites in the constellation. Beyond this, \(\Delta v_{thresh}\) falls below the minimum required to prevent collision.
## 3 Levelling Strategy and Analysis
### Levelling Limit Range  
The mobile platform is only levelled about the \(X\)- and \(Y\)-axes, so the maximum levelling angle of the mobile platform about the \(X\)- and \(Y\)-axes and diagonal is calculated as follows, with the equivalent three-dimensional rotation shown in Figure 4.  
When the \(X\)-axis is inclined, \(\text{P}_{1}\) and \(\text{P}_{2}\) outriggers rise to the highest point. At this time, \(\text{L}_{\text{max}}=0.2\) m, and \(\text{d}_{\text{X}}=1\) m. According to the trigonometric function, the maximum allowable inclination angle at this time is:  
\[\angle\alpha= \sin^{-1}\left(\frac{\text{L}_{\text{max}}}{\text{d}_{\text{X}}} \right)=\sin^{-1}(0.2) \tag{9}\]  
When the \(Y\)-axis is inclined, the \(\text{P}_{2}\) and \(\text{P}_{3}\) outriggers rise to the highest point. At this time, \(\text{L}_{\text{max}}=0.2\) m and \(\text{d}_{\text{X}}=1\) m. According to the trigonometric function, the maximum allowable inclination angle at this time is:\[\angle\beta= \sin^{-1}\left(\frac{\text{L}_{\text{max}}}{\text{d}_{\text{Y}}} \right)=\sin^{-1}(0.2) \tag{10}\]  
When inclination occurs on the diagonal, the P\({}_{3}\) outrigger rises to the highest point, and the P\({}_{1}\) outrigger height remains unchanged. At this time, L\({}_{\text{max}}\) = 0.2 m, and d\({}_{\text{X}}\)\(\sim\) = 2\({}^{1/2}\) m. According to the trigonometric function relationship, the maximum allowable inclination angle at this time is:  
\[\angle \delta= \sin^{-1}\left(\frac{\text{L}_{\text{max}}}{\text{d}_{\text{X}- \text{Y}}}\right)=\sin^{-1}\left(\frac{\text{L}_{\text{max}}}{\sqrt{\text{d}_ {\text{X}}^{2}+\text{d}_{\text{Y}}}^{2}}\right)=\sin^{-1}\!\left(\frac{0.2}{ \sqrt{2}}\right) \tag{11}\]  
By calculating the maximum theoretical levelling angle of the mobile platform, the maximum levelling tilt angle of the mobile platform in three directions can be obtained. According to the trigonometric function formula, the maximum allowable inclination angles of the mobile platform in three directions are 11.54\({}^{\circ}\), 11.54\({}^{\circ}\), and 8.13\({}^{\circ}\). Calculating the maximum levelling angle of the mobile platform is an important parameter to ensure that the mobile platform can complete the levelling angle limit range [11].
# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 8 Open Research Challenges
### IoT Data Collection and Aggregation for 5G Data Indexing  
Good quality data may increase the efficiency of data indexing. Therefore, data aggregation and corresponding incentives to increase data quality should be established. In the literature, several studies concentrate on data aggregation. In [272], [PERSON] et al. introduce the first distributed aggregation technique for duty-cycle wireless sensor networks, and [PERSON] et al. [273] describe a tripartite architecture for Mobile Crowdsensing (MCS) with fast data aggregation. [PERSON] et al. in [274] introduce a novel MCS system architecture that integrates a data aggregation and perturbation mechanism, and in [275], the authors suggest a payment mechanism that dramatically enhances the data quality in the MCS system. However, data aggregation issues, on the other hand, get more difficult as the number of data sources grows, necessitating more storage and computing capacity [276]. In addition, the expansion of the amount of digital data can result in reduced bandwidth, increased power consumption, and/or congestion imposed on the network, ultimately leading to network saturation due to a large amount of data being transmitted simultaneously over the network [49; 277].  
There are some new approaches such as cluster-based data aggregation algorithm for WSNs, but it's still ineffective because of their weak points like the problem of unbalanced energy dissipation [278]. With the development of future/5G networks and other emerging technologies such as Network Function Virtualization (NFV), the question that could be asked is, are these emerging technologies capable of developing improved and more energy-efficient approaches and improving the efficiency of data aggregation on IoT networks?
# Optimization of Chinese Chive Juice as a Functional Feed Additive
## 2 Materials and Methods
### Antioxidant Activity and Antioxidant Compound Contents of CC Juice
#### 2.3.2 Determination of Total Phenolic Content  
The total phenolic content (TPC) of the fresh and heated CC juice was determined using the Folin-Ciocalteu method with some modifications [21]. An aliquot (100 \(\mu\)L) of each sample, standard or blank was mixed with Folin-Ciocalteu (FC) reagent (10%, _v/v_) and vortexed thoroughly, followed by the addition of 800 \(\mu\)L of Na\({}_{2}\)CO\({}_{3}\) (700 mM) solution. The mixture was vortexed again and allowed to stand for 2 h at room temperature. The reaction absorbance was measured at 765 nm using a microplate reader after transferring 200 \(\mu\)L of the mixture to a 96-well plate. TPC was calculated using gallic acid (0-250 \(\mu\)g/mL) as the standard and expressed as the gallic acid equivalent (GAE) (\(\mu\)g/mL).
# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 3 Materials and Method
### Data Collection Module  
The Data Collection Module forms the foundational layer of the proposed AI-based system designed to enhance the quality and reliability of climate change discourse on social media platforms. This module systematically gathers a large volume of relevant public content, ensuring a rich and diverse dataset for subsequent analysis. The process begins with extracting posts and comments from the public APIs of platforms such as Twitter/X, Facebook, Reddit, YouTube, and Instagram. Using climate-related keywords and hashtags (e.g., #ClimateChange, #GlobalWarming, #NetZero), the system retrieves textual data and accompanying metadata, such as likes, shares, reposts, and comment threads. These APIs provide real-time and historical data access, allowing the system to track discourse dynamics over time while adhering to rate limits and privacy policies. The system incorporates robust web-scraping techniques when APIs are limited or do not offer sufficient access. These scraping modules are designed to extract structured content from blogs, forums, news articles, and other relevant online sources. Equipped with intelligent selectors and pagination handlers, these agents ensure accurate and ethical data retrieval, complementing the data acquired via APIs. To support advanced analysis, the module stores rich metadata for each collected item. This includes user engagement metrics (likes, retweets, view counts), temporal markers (timestamps), source-specific identifiers (e.g., post ID, anonymized user ID), and contextual tags (hashtags, mentions, and geolocation data when available). The metadata allow for detailed filtering, classification, and modeling across various pipeline stages, such as misinformation detection, sentiment analysis, and discourse trend forecasting. Ultimately, this module is built for scalability, adaptability, and ethical compliance, laying the groundwork for the system's broader objectives of combating misinformation and guiding constructive climate communication. Figure 2 shows the details of the presented data collection module.  
Figure 2: Detailed architecture of the Data Collection Module. This includes API-based and web-scraping components, keyword filtering, metadata enrichment, and ethical compliance controls for multi-platform data harvesting.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 2 Materials and Methods  
### Antenna Design  
Figure 1 illustrates the overview of the antenna design and modelling characteristics and associated analysis. A monopole antenna was designed on FR4 material with a dielectric constant of 4.3 and a thickness of 1.6 mm. The patch was designed with copper material with a thickness of 0.035 mm and a partial ground on the opposite side of the antenna. Further, a microstrip feedline was provided for the patch. Impedance matching was considered to feed the patch and the 50 \(\Omega\) probe line. Metamaterials were placed on both sides of the feed line. The fabrication was performed for the antenna with and without the AMC structure.
###### Abstract  
The strategic selection of suppliers and the allocation of orders across multiple periods have long been recognized as critical aspects influencing company expenditure and resilience. Leveraging the enhanced predictive capabilities afforded by machine learning models, direct lookahead models--linear programming models that optimize future decisions based on forecasts generated by external predictive modules--have emerged as viable alternatives to traditional deterministic and stochastic programming methodologies to solve related problems. However, despite these advancements, approaches implementing direct lookahead models typically lack mechanisms for updating forecasts over time. Yet, in practice, suppliers often exhibit dynamic behaviours, and failing to update forecasts can lead to suboptimal decision-making. This study introduces a novel approach based on parametrized direct lookahead models to address this gap. The approach explicitly addresses the hidden trade-offs associated with incorporating forecast updates. Recognizing that forecasts can only be updated by acquiring new data and that the primary means of acquiring supplier-related data is through order allocation, this study investigates the trade-offs between data acquisition benefits and order allocation costs. An experimental design utilizing real-world automotive sector data is employed to assess the potential of the proposed approach against various benchmarks. These benchmarks include decision scenarios representing perfect foresight, no data acquisition benefits, and consistently positive benefits. Empirical findings demonstrate that the proposed approach achieves performance levels comparable to those of decision-makers with perfect foresight while consistently outperforming benchmarks not balancing order allocation costs and data acquisition benefits.  
supply chain risk management; supplier selection; order allocation; machine learning; continuous training +
Footnote †: journal: Applied sciences
# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
## 1 Introduction
On 16 February 2023, the European Parliament published a resolution on developing an EU cycling strategy and called to designate 2024 as the European Year of Cycling [17]. Following this idea, on 25 February 2024, Metropolis GZM (associating 41 cities and municipalities with a total area of 2500 km\({}^{2}\), home to 2.1 million inhabitants) launchedMetropolitan Bicycle, Poland's most extensive urban bicycle system. The system will ultimately comprise 7000 bicycles and allow travel in 31 municipalities, starting with the eight largest cities in the region [18]. Metropolis GZM is a well-known European hotspot region for air pollution [19]. In total, 5 of the 15 most polluted cities in the EU are located in this region [20]. Given the individual and environmental benefits, the challenge for researchers is to promote cycling in countries such as Poland, where it is not common as a daily mode of transport. Apart from the need to provide adequate facilities for cyclists, one of the main problems is the perception of cycling as a safe mode of transport in regions of high air pollution. Hence, this article aims to investigate exposure to NO\({}_{2}\) and PM\({}_{2.5}\) while cycling/walking/driving during the heating and non-heating season. Literature reviews [21; 22; 23], including experimental studies that compare exposure to traffic air pollutants among active commuters (pedestrians or cyclists) and commuters using motorized transport, clearly indicate data gaps in Central and Eastern Europe. This study presents the first research findings on active commuting in the most polluted region of Poland, with a specific focus on continuous, individualized air quality monitoring to enhance the estimation of personal exposure levels. The principal aim is to address critical data gaps concerning exposure to NO\({}_{2}\) and PM\({}_{2.5}\) by applying a novel methodological approach based on deploying low-cost, portable sensors. Incorporating low-cost sensor technology constitutes a significant technological innovation, enabling the collection of high-resolution, real-time exposure data that reflect the dynamic nature of individual activity patterns and varying microenvironments. This approach represents a substantial advancement over traditional reliance on fixed ambient monitoring stations, which are limited in capturing the true variability of personal exposure during active commuting. The study seeks to provide a robust basis for comprehensive health risk assessments (HRA), including the estimation of inhaled doses of NO\({}_{2}\) and PM\({}_{2.5}\). By delivering individualized exposure data, the research aims to more accurately evaluate whether the health benefits of active commuting, such as cycling or walking, outweigh the associated risks of air pollution exposure. Ultimately, by demonstrating the utility of low-cost sensor technology in environmental health studies, this research aspires to contribute to the broader adoption of cost-effective monitoring solutions, thereby facilitating improved population health outcomes in urban areas.
## 3 Results  
Figures 3 and 4 show the accuracy of the performance evaluation for each batch size and learning rate. KoBERT-EDA20 and KoBERT-FastText30, which showed high-performance distributions for each parameter, were used as the classification and recognition measurement models for each intent and entity. During the extraction of the main information of the case from the victim's statement, it was important to recognize the entire entity, not only the tokens divided through BIO tagging. In Korean, nouns and verbs are mainly classified as \"B-tags\" and suffixes are classified as \"I-tags\". The personality of an entity might change depending on the combination of the B- or I-tag. Therefore, the accuracy measurement method in entity recognition was applied to the case in which all B- and I-tagged entities constituting one entity were predicted. Both data augmentation methods enhanced the accuracy of the intent classification and entity recognition. As Figure 3 shows in the intent classification, the EDA method achieved a higher accuracy than the FastText method. Moreover, based on an analysis, the EDA method, which applied a data augmentation based on internal data, facilitated excellent intent classification performance because the victims' statements exhibited similar patterns according to the types of intent. The FastText method had the highest accuracy in entity recognition, as shown in Figure 4. This result was obtained because the models trained various changes in words that were not included in the internal data by employing external data instead of internal data, considering the characteristics of the Korean language, where words easily change. The highest performance was achieved at a batch size of 16 for intent classification and entity recognition, and the optimal performance was achieved for learning rates of \(3\times 10^{-5}\) and \(5\times 10^{-5}\), respectively. For both intent classification and named entity recognition, the accuracy was higher when using different batch sizes and augmentation than using only  
original data. The lower performance on the original was due to the smaller data size in each classification for both intent and person names, which resulted in better training for classes with more data.  
Figure 3: Intent classification performance by the training parameters.
## Numerical Analysis
### Data Profiling and Filtering
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\% Missing** & **Attributes Contained in Training Set** & **Attributes Contained in Test Set** & **\# Attributes** \\ \hline \multirow{2}{*}{\(>\)30\%} & br\_000, bq\_000, bp\_000, bo\_000, ab\_000, & br\_000, bq\_000, bp\_000, bo\_000, ab\_000, & \multirow{2}{*}{10} \\  & cr\_000, bn\_000, bm\_000, bl\_000, bk\_000 & cr\_000, bn\_000, bm\_000, bl\_000, bk\_000 & \\ \hline \multirow{2}{*}{20–30\%} & ad\_000, cf\_000, cg\_000, ch\_000, co\_000, & ad\_000, cf\_000, cg\_000, ch\_000, co\_000, & \multirow{2}{*}{14} \\  & ct\_000, cu\_000, cv\_000, cy\_000, & ct\_000, cu\_000, cv\_000, cx\_000, cy\_000, & \\ \cline{2-2}  & cz\_000, da\_000, db\_000, dc\_000 & cz\_000, da\_000, db\_000, dc\_000 & \\ \hline \multirow{2}{*}{10–20\%} & ec\_00, cm\_000, cl\_000, ed\_000 & ec\_00, cm\_000, cl\_000, ed\_000 & 4 \\ \hline \multirow{2}{*}{5–10\%} & ak\_000, ca\_000, dm\_000, df\_000, dg\_000, & ca\_000, ak\_000, df\_000, dg\_000, dh\_000, & \multirow{2}{*}{13} \\  & dh\_000, dl\_000, dj\_000, dk\_000, eb\_000, & di\_000, dj\_000, dk\_000, dl\_000, dm\_000, & \\ \cline{2-2}  & di\_000, bx\_000, cc\_000 & eb\_000, bx\_000, cc\_000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Contained attributes by sorted missing percentages. \begin{table}
\begin{tabular}{c c c} \hline \hline
**CLASS** & **Training** & **Test** \\ \hline NEG & 56,127 & 14,878 \\ POS & 538 & 196 \\ IR = NEG/POS & 104.3 & 75.9 \\ \hline Pearson Chi-square & 3199.883 (\(p\)-values = 0.0000) & 1239.109 (\(p\)-values = 0.0000) \\ Maximum-Likelihood Chi-square & 1406.807 (\(p\)-values = 0.0000) & 555.167 (\(p\)-values = 0.0000) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The imbalance ratio (IR) check and Chi-square test for attribute ac\_000. Figure 2: Number of attributes by different missing percentages. with more than 10% missing, the remaining 70 attributes were assigned to a bin and defined as a control group (CTRL). The missing percentage of attributes contained in EXP1 was less than 30% (contains 88 attributes), in EXP2 was less than 20% (74 attributes), and in EXP3 was less than 5% (57 attributes).
###### Abstract  
Conventional routing protocols proposed for Vehicular Ad-hoc Network (VANET) are usually inefficient and vulnerable for multi-hop data forwarding due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) technologies, link stability can be better improved through the availability of global network information. Thus, in this paper, we present a novel software-defined network (SDN) based routing scheme for P2P connection under urban inter-vehicle networks that can find a global optimal route between source and destination. This is a cross-layer routing protocol in VANETs, which utilizes metrics not only considering the position and velocity of vehicles, but also channel allocation and link duration when selecting the relay vehicles. Consequently, it starts a route discovery process which can improve the network performance in terms of end-to-end delay and low overhead. Furthermore, packet loss is largely minimized by the relatively stable paths. With the help of realistic simulation, we show that the proposed routing framework performs better than other three latest SDVN and conventional VANET protocols in routing overhead, average end-to-end delay, packet drop ratio, and average throughput. Therefore, our routing scheme is more suitable for 5G-enabled vehicular ad-hoc networks in future.  
software-defined networking (SDN); inter-vehicle network; data routing; cross-layer design; performance analysis +
Footnote †: journal: Applied
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
## 1 Introduction
Midgard shares the same objectives as Asgard's; their difference lies in the way their reward functions are defined, which are used to train the RL agent during the learning process: Asgard uses the environment state, which is the Linux command and the action taken by the agent to define its reward function, while Midgard only uses the environment state in its reward function. Cowrie [9] is a MiHP that can emulate the SSH and Telnet protocols, and it is widely used to emulate Linux servers and IoT devices [10, 11, 12] to capture the attack on those systems. It allows attackers to log in and execute some Linux commands through a fake shell. The goal of the paper is (1) to demonstrate the effectiveness of Asgard, which can collect the attacker's tools while evading detection, and keep the system safe for as long as it can through blocking or substituting the execution of malicious programs, and (2) the benefit of using the environment state and the action in RL to define the reward function to allow the agent to effectively learn its objectives. Our contributions in this manuscript are threefold:  
* We describe the adaptive self-guarded honeypot Asgard that uses the environment state and action to define its reward function in the reinforcement learning problem that allows it to achieve its defined objectives. * We describe its design and its implementation using a proxy-based architecture, which allows Asgard to use a real system as a honeypot. * We compare the performance of Asgard from a real deployment with Midgard and two other conventional honeypots: Cowrie and a real Linux system. The rest of the paper is organised as follows: background information and related work are presented in Sections 2 and 3, respectively. Section 4 describes the honeypot systems used in our experiments, and their design and implementation are described in Section 5. Section 6 presents the experimental results and lessons learned. Finally, a conclusion and future works are provided in Section 7.