esa-sceva/satcom-chunk-collection huggingface dataset:
# Metadata Quality for Biobanks
## 6 Metadata Quality Characteristics and Metrics
#### 6.2.2 Calculating Metadata Accuracy Metrics  
We propose to limit metadata accuracy metrics by calculating only syntactic accuracy, i.e., _the degree of correspondence of the metadata values to their domain_. Assuming that the metadata values belong to the specific domain, it is calculated as follows:  
\[\begin{split} MSAcc_{mc}=\frac{n(I_{m})-\sum\limits_{i\in I_{m}}f _{m}^{dt}(i)}{n(I_{m})},m\in M_{c}\end{split} \tag{1}\]  
where \(M_{c}\) is a set of all domains for the metadata values describing data item attributes instantiated by the samples in a biobank collection \(c\), \(n(I_{m})\) is a cardinality of the set \(I_{m}\) which includes all metadata values belonging to the domain \(M\), \(f_{m}^{dt}(i)\) is a negative threshold function over a metadata value \(i\in I_{m}\) related to its domain \(m\in M\), which is equal to 0, if \(i\) belongs to \(m\), 1 otherwise. An example of the domain constraint served well by such a metric is the non-negativity constraint for the data item reliability ([PERSON] and [PERSON], 2019) value domain (_data item reliability accuracy metric_).  
Suppose there are 200 reliability values available for a collection \(c_{1}\) (describing different data item attributes), 14 of them being negative (violating the domain constraint). Then the reliability accuracy for this collection is calculated as follows: \(\begin{split} MSAcc_{(reliability)c_{1}}=(200-14)/200=0.93 \end{split}\).  
To calculate the metadata accuracy metric based on all domains defined for a collection, it is necessary, as for the data item accuracy metrics ([PERSON] and [PERSON], 2019), to apply the aggregate function (e.g., average or median) to all \(\begin{split} MSAcc_{m},m\in M_{c}\end{split}\), where \(M_{c}\) is a set of all domains defined for the metadata values describing data item attribute values instantiated by the samples belonging to a biobank collection \(c\). An example could be the _collection-level domain-average-based syntactic metadata accuracy_, which is calculated by applying the following formula:  
\[\begin{split} MSAcc_{c}=\frac{\sum\limits_{m\in M_{c}}MSAcc_{mc} }{n(M_{c})}\end{split} \tag{2}\]  
Suppose the collection-level reliability accuracy for the collection \(c_{1}\) is equal to 0.6, whereas the completeness accuracy is equal to 0.85, and the conformance to expectations accuracy is equal to 0.8. Then \(\begin{split} MSAcc_{c_{1}}=(0.6+0.85+0.8)/3=0.75\end{split}\).
# A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity
## 5 Numerical Results
### Computational Complexity
In the simulations, the number of UAVs is 1000. The assignment results and computation time of the initialization phase (including column reduction, reduction transfer, augmenting reduction) and the augmentation phase of the LAPJV algorithm are logged and analyzed. The statistics of assignments from the initial topology to the target topology are shown in Figure 11. From the simulation results, the average percentage of assigned UAVs in the initialization phase and the augmentation phase are 99.99831% and 0.00169%, respectively. Figure 10: Computation time statistics of the four algorithms in the case of 1000 nodes. (**a**) Computation time statistics for the LAPJV algorithm; (**b**) Computation time statistics for the KM algorithm; (**c**) Computation time statistics for the improved KM algorithm; (**d**) Computation time statistics for the Auction algorithm; (**e**) Computation time statistics comparison for the four algorithms in the same scale. That is to say, 99.99831% of the nodes have been assigned in the initialization phase with a computational complexity of \(O(N^{2})\). According to Algorithm 3, only nodes that have not yet been assigned will be processed in the augmentation phase, which means that only 0.00169% of the nodes will implement the augmentation phase with a computational complexity of \(O(N^{3})\)). Thus, even though the worst-case computational complexity of the LAPJV algorithm is the same as the improved KM algorithm, which is \(O(N^{3})\), the average computational complexity of the LAPJV algorithm is much lower.
## 2 Methodology
### Orbital Parameters
#### 2.1.3 Inclination (\(i\))  
The orbit's inclination angle is measured in reference to the equator of the planet as shown in Figure 4. It is defined as the angle between the satellite's orbital plane and the Earth's equatorial plane. Inclination values range from 0 degrees to 180 degrees [27].  
Figure 3: Orbit Variation as a function of Eccentricity [13].  
When \(i=0^{\circ}\) or \(180^{\circ}\), it is considered as an equatorial orbit. Inclination with angles \(0^{\circ}<i<90^{\circ}\) are knows as prograde orbits that move in the direction of earth's rotation. When \(i=90^{\circ}\) this means it is a polar orbit. Lastly, when the inclination angle is \(90^{\circ}<i<180^{\circ}\), this means that it is in a retrograde orbit which moves in the opposite direction of earth's rotation [29].
## 2 Materials and Methods
### Coupling of a-TiO\({}_{2}\)-Nh\({}_{2}\) NPs with DOPC-DOTAP Lipid  
The preparation of lipid-coated NPs (a-TiO\({}_{2}\)-NH\({}_{2}\)/DOPC-DOTAP) was performed through a solvent exchange method. An amount of 1.75 mg of 1,2-dioleoyl-\(sn\)-glycero-3-phosphocholine (DOPC) and 0.75 mg of 1,2-dioleoyl-3-trimethylammonium propane (DOTAP, positively charged) in chloroform (mass ratio of 70% DOPC and 30% DOTAP) were mixed together and dried under vacuum overnight and redispersed in a 1 mL mixture of 40% (\(v\)/\(v\)) ethanol and 60% (\(v\)/\(v\)) bidistilled water. From the prepared stock solution of DOPC-DOTAP, 100 \(\mu\)L was removed and added to 50 \(\mu\)g of a-TiO\({}_{2}\)-NH\({}_{2}\) NPs. To induce the self-assembly of the phospholipid bilayer on the titania NPs surface, 900 \(\mu\)L of bidistilled water was added to the previous suspension and vortexed for 5 min. The a-TiO\({}_{2}\)-NH\({}_{2}\)/DOPC-DOTAP NPs were washed twice with bidistilled water to remove the unbound lipids by centrifuging the NPs at 10,000 RCF for 5 min, discarding the supernatant and adding fresh water. For the EPR and PCD experiments, the whole procedure was scaled-up.  
To confirm the coupling efficiency between the NPs and lipids, two labeling steps were added before the fluorescence microscopy experiments. The a-TiO\({}_{2}\)-NH\({}_{2}\) NPs were labeled with ATTO550-NHS ester dye, at a ratio of 2 \(\mu\)g of dye per mg of NPs. The suspension of NPs with dye was kept in the dark under continuous stirring overnight and then the sample was washed twice with fresh ethanol to remove the unbound dye compounds. The lipid bilayer, once already self-assembled on the a-TiO\({}_{2}\)-based NPs, was labeled with DiOC\({}_{18}\) lipophilic carbocyanine dye by adding 0.5 \(\mu\)L of the fluorescent dye to the suspension of NPs/lipids and left in an orbital shaker at 37 \({}^{\circ}\)C for 30 min.
# A Survey on Satellite Communication System Security
## 5 Defense in Satellite Communication Systems
### Solutions for Integrity
#### 5.3.1 Physical Parameter Analysis Methods
[102] extends the power-distortion (PD) detector, focusing on monitoring received power and correlation-profile distortions to detect GNSS carry-off-type spoofing, jamming, or multipath interference. The improved PD maximum-likelihood (PD-ML) detector enhances the classification accuracy, differentiating spoofing from jamming and multipath-affected data. Finally, ref. [103] proposes a novel technique using differential code phase for the time difference of arrival (TDOA) estimation of spoofing signals. This method marks a significant shift from conventional cross-correlation-based TDOA estimation techniques, highlighting the importance of TDOA in localizing GNSS spoofing signals.
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 3 Results  
### Accuracy Assessment for InSAR DEM Based on ICESat-2  
Through the comparative study, the results are shown in the Table 3; the mean elevation difference between InSAR DEM and NASA DEM (6.62 m) is smaller than the mean elevation difference between InSAR DEM and ICESat-2 (7.48 m). However, in terms of the maximum and minimum elevation differences, the latter (Min = 31.21 m, Max = 23.44 m) outperforms the former (Min = 37.24 m, Max = 26.84 m). The elevation differences between InSAR DEM and ALOS DEM are relatively small, indicating a close similarity between these two datasets. Considering the different data acquisition times, it is observed that the surface morphology changes in the non-glacial area are stable, and their magnitude is negligible compared to glacier changes. By using ridge-based DEM registration to eliminate the influence of terrain errors, the measurement accuracy of glaciers with InSAR DEM would significantly improve.
## 4 Results
### Discussion
#### 4.2.3 Pareto Front Analysis  
Here, the scenario 3 is investigated. We focus on the bi-objective optimization problem (**P2**) for which we would like to estimate the best compromised solution among many others, that satisfy to the low power consumption, low EMF exposure and maximal user's coverage requirements. Given the convexity of the optimization problem, we rely on the Pareto analysis using the weighted sum method. The following combinations of \(\omega_{1,2}\) and \(\omega_{2,2}\) has been used. First, we vary \(\omega_{1,2}\) from 0.1 to 0.9 in steps of 0.1 and derive the corresponding \(\omega_{2,2}=1-\omega_{1,2}\). Then, \(\omega_{2,2}\) is varied from 0.1 to 0.9 in steps of 0.1 and derive \(\omega_{1,2}=1-\omega_{2,2}\). For each combination (\(\omega_{1,2}\), \(\omega_{2,2}\)), we run 40 simulations using the parameters described in scenario 3 and evaluate the power consumption and the total dose due to the network designed. All these points are plotted (Figure10) and the Pareto optimal concept is used to analyze the solution approaches which provide the best compromises between the constraints. Figure 10 shows that the most critical trade-offs are obtained between 1.2 kW and 2.1 kW, corresponding to a solution with 48 32-antenna BSs (\(\omega_{1,2}=0.2;\ \omega_{2,2}=0.8\)) and a total dose of \(1.54\times 10^{-4}\) (\(\frac{W\cdot\mathrm{s}}{\mathrm{Kg}}\)) and a solution with 37 64-antenna BSs (\(\omega_{1,2}=0.3;\ \omega_{2,2}=0.7\)) and a total dose of \(1.25\times 10^{-4}\) (\(\frac{W\cdot\mathrm{s}}{\mathrm{Kg}}\)), respectively. Within that interval, we obtain lower dose and lower power consumption simultaneously with 32 or 64 BS antenna elements. At lower power consumption (1.2 kW), a 39% reduction of the total dose is achieved, from \(2.56\times 10^{-4}\) (\(\frac{W\cdot\mathrm{s}}{\mathrm{Kg}}\)) obtained with a 16-antenna BS (\(\omega_{1,2}=0.1;\ \omega_{2,2}=0.9\)) to \(1.54\times 10^{-4}\)(\(\frac{W\cdot\mathrm{s}}{\mathrm{Kg}}\)) obtained with a 32-antenna BS. However, the solution with higher power consumption and lower value of total dose (39 BSs, 3.5 (kW) and \(1.043\times 10^{-4}\) (\(\frac{W\cdot\mathrm{s}}{\mathrm{Kg}}\)) (with \(\omega_{1,2}=0.4\) and \(\omega_{2,2}=0.6\)) is achieved with BSs equipped with 128 antenna elements and provides better user coverage (96% vs. 94%), compared to the most critical trade-offs described above. Since, the objective functions considered here (the power consumption and the total dose) are conflicting, there may be many solutions that satisfy the constraints of low power requirements, low DL and UL EMF exposure.  
Figure 9: Impact of the number of antenna elements on the DL and UL exposure (scenario 2, bi-objective optimization).
## 2 Materials and Experimental Procedure  
### Samples Preparation, Hardness Measurment, and the ECAP Process  
In the current research, commercial copper samples (Cu 99.95% purity) were used for testing. Specimens of 20 mm diameter and 140 mm length were prepared from extruded copper rods. The ECAP process (four passes) was conducted on the specimens at room temperature. A schematic of the ECAP process and details of the die are shown in Figure 2. The Vickers hardness (HV) of the copper samples before and after ECAP passes were measured according to ASTM E92-04 [35]. The hardness tests were carried out using a universal hardness tester (model: Zwick/ZHU 250, Ulm, Germany). The measurements were conducted along the cross-section and longitudinal planes of copper samples. Before carrying out the hardness test, the surfaces of copper samples were mechanically polished by using silicon carbide papers of 800 grit sizes and washed with ethanol. Then the samples were subjected to distilled water and dried in air.  
At each position the Vickers hardness (HV) was measured at three adjacent points with a step of 3 mm using a load of 10 kg\({}_{\text{f}}\) and a dwell time of 10 s. The average values of hardness were recorded for each pass. An ECAP die with 90\({}^{\circ}\) channel was designed and made from H13 steel. Four passes of ECAP (route B\({}_{\text{C}}\)) was conducted by using a 160 ton hydraulic press. The setup and details of ECAP process were previously presented [9]. During the ECAP process, MoS\({}_{2}\) lubricant was used to reduce the friction between the specimens and die walls. Small plungers made of H13 tool steel with 20 mm diameter and 25 mm height are used to push the specimen along the channel of the die. The tests were conducted at room temperature with a punch speed of about 2 mm/s. The ECAP parameters are presented in Table 1.
## 4 Results and Discussion  
### MSW Prediction Model  
#### 4.1.1 Accuracy Analysis  
In this experiment, we used six machine learning algorithms to compare garbage yield prediction models. The algorithms included multiple linear regression, Bayesian ridge regression, support vector regression, elastic network regression, gradient boosted (GBR) regression, and random forest regression (RF). Figure 2(a)-f represent these methods,respectively, showcasing their fitting curves and performance across various indices. Linear regression and Bayesian ridge regression are relatively simple models, and there will undoubtedly be a large gap in comparison with the fitting effect of other models. Support vector regression is less effective overall, with high accuracy in predicting smaller amounts of waste generated but very poor on data with larger yields. GBR and RF are models based on boosting and bagging, respectively, which have better generalization ability and are better at solving high-dimensional tasks, and both of them perform much better than the other algorithms. Figure 4 and Table 2 show the performance of the six algorithms in the four evaluation metrics of MAE, MSE, MAPE, and R\({}^{2}\). The lower the values of the first three metrics, the better the performance of the last metric. It is evident that gradient boosting regression (GBR) and random forest (RF) significantly outperform the other four models, with support vector regression (SVR) being identified as the least effective model. The table presents a comparative analysis of each model's performance based on various evaluation indicators. Notably, GBR surpasses RF across all metrics, achieving results of 2.346329, 11.635605, 7.242440, and 0.994516 in these four indicators, respectively. Consequently, GBR has been selected as the preferred method for modeling municipal waste generation across different districts in Beijing.  
Figure 3: _Cont._  
Figure 4: Comparison of regression metrics of machine learning algorithm models.  
Figure 3: Performance comparison of machine learning regression models for waste generation prediction. (**a**) Comparison of regression effects of linear regression models. (**b**) Comparison of regression effects of Bayesian ridge models. (**c**) Comparison of regression effects of SVR models. (**d**) Comparison of regression effects of elastic net models. (**e**) Comparison of regression effects of GBR models. (**f**) Comparison of regression effects of RF models.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 4 Case Study
### Visualization and Portrayal Testing  
To evaluate the applicability of the constructed dataset in a realistic navigational environment, we tested the visualization of marine casualty features using the S-100-based viewer developed by the Korea Hydrographic and Oceanographic Agency (KHOA) [34]. This viewer supports datasets encoded in GML format and requires the accompanying FC and PC to be loaded for proper structural interpretation and rendering.  
Initially, the 454 instances from the GISIS dataset were visualized to assess the system's capacity to render marine casualty data. As shown in Figure 6, all features were rendered on a single chart, each represented by a triangular warning icon annotated with an abbreviation indicating the incident type. While the rendering confirmed the syntactic correctness and successful symbol assignment, it also revealed a practical limitation: overlapping symbols in high-density zones significantly reduced readability. This issue was particularly evident in coastal waters and port approaches, where marine incidents tend to be densely concentrated.  
When additional records from the MAIB and MTIS datasets were loaded--raising the total number of feature instances to over 21,000--the symbol congestion became even more pronounced, effectively overwhelming the chart display. This result underscored the importance of applying contextual or spatial filtering strategies to ensure information accessibility in operational environments. To further explore this need, we simulated a  
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more operational and context-aware use case by assuming a navigational scenario in which a vessel is transiting between Korea and Japan. The target area focused on the East Sea, following a high-traffic corridor between the ports of Busan and Kitakyushu. Instead of displaying all available incident records, the dataset was filtered to include only features located within a defined corridor surrounding the ship's assumed route. As illustrated in Figure 7, this selective portrayal significantly enhanced readability and made the output more aligned with navigational needs.  
Figure 6: Visualization of 454 marine casualty features with symbol congestion.  
Figure 7: Route-based filtering of marine casualty features between Korea and Japan.  
This portrayal testing confirmed that the proposed model's structure and GML encoding are functionally compatible with S-100-based systems. It also demonstrated the importance of implementing spatial and context-based filtering strategies to reduce visual clutter and enhance information accessibility in real-world maritime applications. Although this study focused on a single navigational route scenario, future work will expand applicability testing to other settings--such as anchorage zones, port approaches, and busy straits--to further validate the model's adaptability across various operational contexts.
# An Automated Behaviour-Based Clustering of IoT Botnets
## 2 IoT Botnet Behaviour
### IoT Botnet Operation  
The IoT botnet operation has two main goals, botnet growth and botnet monetization. The botnet growth is achieved by identifying and infecting vulnerable IoT devices. An IoT botnet infection typically involves two steps: (1) exploiting a vulnerability to obtain access to the victim device and (2) downloading and executing bot malware. The bot malware is downloaded from a MD server configured by the botnet herder. When the malware is executed, it connects to a C2 server and starts listening for instructions sent by the botnet herder. After connecting to the C2 server, the bot malware usually starts self-propagating by scanning the Internet for vulnerable IoT devices and infecting the discovered vulnerable devices. Alternatively, it may report the discovered vulnerable devices to a loader server which is used by the botnet herder for executing infection attacks.  
In addition, the bot malware can also perform multiple actions to preserve the control over the enslaved devices. Examples of such actions include (a) establishing persistence on the infected device by modifying the initialisation system configuration, (b) detecting and killing competing malware by enumerating the running processes, (c) preventing re-infection by blocking the open port used to exploit the vulnerable service, (d) preventing infection remedy by blocking remote access to the device, and (e) removing and hiding infection evidence to evade detection.  
Over the years, the botnet herders have monetized IoT botnets in different ways. The most common IoT botnet monetization is through distributed DDoS attacks offered as a service by the botnet herder. The bot malware typically supports multiple different DDoS attack types [19]. To launch an attack, the DDoS service clients specify the target host, one of the supported DDoS attack types and the attack duration. Other ways of monetizing IoT botnets include crypto-currency mining [20], credential theft [21], social media fraud [2], etc.
# Nano Aerial Vehicles for Tree Pollination
## 3 Proposed Solution
### Communication  
Our goal was to develop a NAV capable of moving and pollinating autonomously. Communication is central in an autonomous vehicle so information about the surroundings is analysed and the NAV reacts in time.  
The Crazyflie 2.1 uses the NRF51822 and RFX2411N as the core components of the communication module. This hardware provides low-latency/long-range radio communication and Low-Energy (LE) Bluetooth. However, as these components were not available due to the pandemic, it was necessary to find another way to communicate wirelessly.  
The AI Deck 1.1 uses the NINA-W102 component to communicate via Wi-Fi. This component allows communication through various protocols. The only difference between the NINA-W102 and the NINA-W106 is the number of pins available. Since the NINA-W106 was the only one in stock, we chose this component.  
We chose to incorporate the NINA-W106 into the schematic. This device has the following advantages: the ability to use different communication protocols and the fact that it does not require additional components to ensure operation.  
The NINA-W106 is a standalone multi-radio MCU module consisting of a radio for wireless communication, an internal antenna, and a microcontroller. The radio supports Wi-Fi and Bluetooth communications. The NINA-W106 includes several components, making it a very compact standalone multi-radio module with dimensions of 10.0 \(\times\) 14.0 \(\times\) 2.2 mm ([[https://content.u-blox.com/sites/default/files/NINA-W10_DataSheet_UBX-17](https://content.u-blox.com/sites/default/files/NINA-W10_DataSheet_UBX-17)]([https://content.u-blox.com/sites/default/files/NINA-W10_DataSheet_UBX-17](https://content.u-blox.com/sites/default/files/NINA-W10_DataSheet_UBX-17)) 065507.pdf, accessed on 27 March 2023).  
The purpose of the NAB is to pollinate flowers so flower detection is essential. By adding the NINA-W106, a user can stream the images collected by the NAB over Wi-Fi and run the detection and control code off-board. The NINA-W106 can use the same communication protocol as the Crazyflie 2.1 components, without the need for additional elements.