esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Architecture  
To estimate the inventory 3D position, we propose a novel 3D position estimation framework that includes QR code segmentation, outlier removal of estimated drone position, and error correction methods utilizing the characteristics of the video frame. Figure 3 represents the overall architecture of the proposed framework. The drone-mounted monocular camera capture QR codes attached to the floor and inventory, without a fixed path. The QR code decoding is performed after detecting and correcting the QR code area using the segmentation model on all the captured video frame. The drone's 3D position is estimated using the 3D position information stored in the ground QR code and the 2D coordinates of the ground QR code detected from the frame. The outlier of the estimated position is detected, and removed by utilizing the characteristics of the video frame in the estimated 3D position set of the drone. A straight line is derived that simultaneously passes through the drone's estimated 3D position and the inventory 3D position using the estimated 3D position set of the drone and the 2D coordinates of the inventory QR code detected from the image. The inventory 3D position is estimated from a pair of derived straight lines. Afterwards, the final inventory 3D position is derived by correcting the estimated inventory 3D position through an error correction method using the angle between the straight-line pairs. We detail the module of the proposed framework in the following subsections.
## Materials and Methods
### Architecture  
As outlined in the previous sections, the ASACAS includes the functionalities of SA and CA, and it provides an enhanced SitA to the remote pilot by classifying each surrounding vehicle based on the associated risk with respect to the ownship. The SA and CA functions are allocated to two different logical levels, since CA is considered an emergency function that is activated only when an emergency event is raised, whereas the SA function is in charge of guaranteeing safe separation among aircraft in tactical operations. The functional architecture of the ASACAS is reported in Figure 2[14].  
As indicated in Figure 2, the system receives traffic position and velocity data about surrounding ADS-B OUT-equipped aircraft from the on-board ADS-B IN surveillance sensor in order to predict future conflicts (intended as a predicted loss of separation) and potential future collisions between the ownship and surrounding traffic. The DAA system is supported by a DO-317 [24] surveillance processing application that is used to perform the processing of the raw data provided by the ADS-B IN surveillance system in order to allow for their use by the conflict/collision detection system. Furthermore, the functional architecture comprises the following functionalities, which are outlined in the following section:  
* Coarse filtering for both the collision avoidance and separation assurance functions.
* Conflict detection for the separation assurance function.
* Conflict resolution for the separation assurance function.
* Collision detection for the collision avoidance function.
* Collision prioritization for the collision avoidance function.
* Collision resolution for the collision avoidance function.
* TCAS compatibility check for the situational awareness function.  
Figure 2: ASACAS functional architecture [14]. SA: separation assurance; HMI: human–machine interface; CA: collision avoidance.  
* Severity assignment for the situational awareness function.
* ASACAS logic.  
The outputs of the system include the classification of the surrounding traffic in terms of conflicts (i.e., vehicles that constitute a potential loss of separation with respect to the ownship), threats (i.e., vehicles that constitute a collision risk with respect to the ownship), and traffic (i.e., vehicles that do not pose risks with respect to the ownship). Furthermore, the DAA system elaborates a suitable maneuver to restore the separation minima with respect to all the detected conflicts (if any) and a suitable maneuver to escape from the most dangerous collision vehicle detected (if any). The separation assurance maneuver is proposed to the remote pilot, who is in charge of providing his/her clearance and may also request the automatic implementation of the maneuver by the system. The collision avoidance maneuver is executed in an automatic way due to the emergency nature of such a maneuver and can be terminated by the remote pilot if needed.
# Automatic Medical Image Segmentation with Vision Transformer
## 3 Method
### Infection Segmentation Network  
#### 3.2.1 Framework Overview  
The overview of our framework's architecture is shown in Figure 1. Firstly, the input medical images are passed through two convolutional layers to obtain high-resolution feature representations with low-level semantic. An edge attention is then applied to explicitly enhance the representation of region boundaries.  
The obtained low-level features \(f_{l}\) are passed through three convolutional layers to extract high-level features \(f_{h}\). These high-level features serve two purposes. First, they are aggregated in parallel by a partial prediction decoder (PPD) \(f_{h}\) to generate a coarse global infection localization map \(S_{g}\). Second, \(f_{h}\) combined with \(S_{g}\) are passed through a cascade of reverse attention (RA) modules guided by the localization map \(S_{g}\). Notably, each RA module guides the subsequent RA module in the cascade. Finally, the output of the last RA module passes through a sigmoid activation function to predict the infection regions.  
Our network extracted multi-level features, utilized self-attention to model global context, and hierarchically refined localization via a cascaded decoder design for precise infection segmentation. The cascaded decoder mimics the workflow of radiologists, providing context and direction to focus the model on areas of interest. This hierarchical strategy tailors the model capacity to efficiently improve segmentation performance.
# Synthesis of Covalently Cross-Linked Colloidosomes from Peroxidized Pickering Emulsions
###### Abstract
The amphiphilic polyperoxide copolymers employed in colloidosome synthesis, poly(2-_tert_-butylperoxy-2-methyl-5-hexen-3-ine-_co_-maleic anhydride) (PM-1-MA) and poly[N-(_tert_-butylperoxymethyl)acrylamide]-_co_-maleic anhydride (PM-2-MA), were developed in the authors' groups. PM-2-MA contains primary-tertiary peroxide groups in the macromolecules, whereas PM-1-MA has tertiary peroxide groups. These peroxide groups have different reactivity and thermal stability levels. It was expected that the presence of carboxyl and peroxide functional groups (both derived from inisurf molecules) on the latex particles' surfaces would provide: (i) the required surface properties of particles (hydrophilic-lipophilic balance, HLB), sustaining a Pickering emulsion mechanism for synthesis of colloidosomes [10]; (ii) the initiation of free radical reactions, polymer formation, grafting, and cross-linking within Pickering emulsion droplets; and (iii) an opportunity to further functionalize colloidosomes in post-polymerization reactions. The Pickering emulsion was composed of an oil phase from a monomer mixture, an initiator (AIBN), and a hydrophobic solvent hexadecane (HD) that was emulsified in an aqueous phase where the peroxid oxidized latex particles, which exclusively acted as surface-active ingredients, were located. Once a Pickering emulsion was formed, it was heated to initiate free radical polymerization inside each oil droplet, yielding hollow (HD-filled) colloidosomes (Figure 1). It was demonstrated that the presence of functional groups on latex particle surfaces plays a crucial role in the stability of a Pickering emulsion (by controlling the HLB) and determines its success in colloidosome synthesis. By utilizing the peroxide functional groups of latex particles, colloidosomes can be further modified to obtain a covalently cross-linked shell and hollow (liquid) core.
# Surface-Codes-Based Quantum Communication Networks
## 4 Extending the Distance between the Nodes in the Proposed SC-Based QCN  
To extend the transmission distance between neighboring nodes in QCN, we propose to use the _quantum error correction (QEC)-based repeaters_. So far, QEC-based repeaters are based on two-dimensional QE-based repeaters, such as the dual-containing Calderbank-Shor-Steane (CSS)-codes-based repeaters [17] and surface-codes-based repeaters [18]. Unfortunately, dual-containing CSS codes are essentially girth-4 quantum low-density parity-check (LDPC) codes with poor error correction performance [19]. On the other hand, the surface codes proposed in [18] introduce large latency and are not compatible with the QCN proposed in the previous section. Here, we propose a different approach to interpret an intermediate node as an SC patch and apply the patch deformation approach due to Litinski and thus extend the logical qubit to two spatially separated patches, which is illustrated in Figure 6. In this example, three wavelengths are needed to interact remote patches. Once the logical qubit is extended to the intermediate node, we further perform product \(X\otimes Z\) measurements to entangle the logical qubits \(q_{1}\) and \(q_{2}\). This approach is applicable to several intermediate nodes, thus offering the potential to significantly extend the distance between any two desired nodes in the QCN.
## 6 Conclusions and Future Challenges  
### Conclusions  
In this paper, a comprehensive review on existing methods to estimate the road friction potential has been provided. This work aims at highlighting the main limitations derived from current slip-based methods, which are unable to provide an accurate estimation of the friction potential during free-rolling driving conditions.  
As has been evidenced, longitudinal dynamics and lateral dynamics slip-based approaches require significant grip utilisation levels, often close to the maximum road grip potential, in order to achieve an accurate estimation of the road friction potential. Despite some authors have presented the slip slope method as an alternative approach to reduce the longitudinal excitation levels, this methodology presents contradictory results. In brief, additional investigations are still needed to clarify whether this methodology can be applied reliably to rigid asphalt surfaces or, on the contrary, the changes in the slip slope are induced by the soft material present at the rubber-road interface. Under the previous hypothesis, the slip slope approach might be valid only to distinguish loose surfaces such as gravel or snow from rigid surfaces, such as dry asphalt.  
Among the slip-based group, the lowest excitation levels have been reported by works employing tyre SAT-based approaches. Although the grip utilisation levels are significantly lower than those observed in the previous slip-based methods, authors remark that lateral acceleration levels of at least 0.3 G are still required. Such lateral excitation levels suppose a 100% grip utilisation on low mu surfaces such as ice. In addition, these approaches require modelling the suspension and steering kinematics. While the total axle aligning torque can be obtained from the EPS current, the estimation of the tyre SAT requires the subtraction of the moment components derived from the inclination of the wheel rotation axle (e.g., mechanical trail). Compensating these components complicates significantly the accuracy of these approaches.  
These limitations, particularly the necessity of estimating the grip potential during reduced excitation levels, evidence the necessity of new friction monitoring solutions. Solutions to the above limitations have been proposed by means of vibration-based friction estimation methods. Low and high-frequency approaches have been revised in this survey. Among these methods, high-frequency solutions seem most promising. Nevertheless, despite relevant classifiers have demonstrated capable of distinguishing between road segments of different friction potentials, relevant investigations are still needed prior to commercial implementation. In particular, the derivation of the feature vector for the grip classifier is not-trivial and relies on a systematic trial-and-error process. Thus, further studies and metrics relating the tyre frequency responses and the road grip potential are to be investigated.
## 2 Methods and Materials  
### Mission Description  
#### 2.1.1 User Needs  
The main motivation for carrying out the AlfaCrux mission is to provide an opportunity for space educational training and scientific benefits in the context of small communication satellite missions. The AlfaCrux project fosters the educational collaboration and technical investigations carried out by researchers, professors, students, and radio amateurs, with the sole purpose of self-training in topics such as radio communications, technical management, system-level design, and the development, integration, and operation of small satellites. The need for training and allocation of human resources for better management and technological innovation in the space sector is a critical need in our  
Figure 1: AlfaCrux mission life cycle in the development process.  
country. Space activities need to be established in a sustainable model in which constant training plays a fundamental role.  
The specific context of the AlfaCrux mission also arises from another critical need in our society, which is to provide reliable narrow-band communication coverage. For instance, narrow-band communication via satellite can meet the needs of data collection systems in places without terrestrial infrastructure, such as underdeveloped areas, uninhabited areas, and areas that have been devastated by a natural disaster as well as provide tactical communication links. Several applications can be constructed based on a system that provides low data rate communication services.  
To achieve this goal, the following set of objectives are proposed:  
* Launch, operate, command, and control an amateur radio satellite.
* Provide a communication application to interact with the amateur radio community (demonstration of digital data repeater system).
* Provide communication between users and a set of generic data platforms (demonstration of data store-and-forward system).
* Implement and run an online open access database of in-orbit data for general investigations.
* Analyze narrow-band signal attenuation due to the effect of ionospheric scintillation (critical in low latitude regions, as in the case of Brazil).
* Analyze the satellite dynamic based on computer simulations and in-orbit data for studies and validation of Attitude Determination and Control System (ADCS) solutions and applications.
* Analyze and investigate digital twin modeling for aerospace applications.
## 4 Results
### Experiment in Abrupt Change Area (Flood)
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **STARFM** & **STRUM** & **FSDAF** & **EFSDAF** \\ \hline \multirow{8}{*}{AD} & B1 & 0.0104 & 0.0104 & 0.0096 & 0.0094 \\  & B2 & 0.0137 & 0.0141 & 0.0130 & 0.0127 \\  & B3 & 0.0165 & 0.0167 & 0.0155 & 0.0151 \\  & B4 & 0.0249 & 0.0259 & 0.0238 & 0.0227 \\  & B5 & 0.0487 & 0.0520 & 0.0423 & 0.0425 \\  & B7 & 0.0444 & 0.0472 & 0.0394 & 0.0392 \\  & Mean & 0.0264 & 0.0277 & 0.0239 & 0.0235 \\ \hline \multirow{8}{*}{RMSE} & B1 & 0.0152 & 0.0145 & 0.0137 & 0.0136 \\  & B2 & 0.0214 & 0.0207 & 0.0196 & 0.0193 \\  & B3 & 0.0260 & 0.0249 & 0.0236 & 0.0231 \\  & B4 & 0.0364 & 0.0371 & 0.0342 & 0.0326 \\  & B5 & 0.0643 & 0.0685 & 0.0560 & 0.0560 \\  & B7 & 0.0560 & 0.0595 & 0.0505 & 0.0502 \\  & Mean & 0.0366 & 0.0375 & 0.0329 & 0.0324 \\ \hline \multirow{8}{*}{CC} & B1 & 0.595 & 0.617 & 0.654 & 0.667 \\  & B2 & 0.607 & 0.628 & 0.665 & 0.672 \\  & B3 & 0.594 & 0.626 & 0.663 & 0.674 \\ \cline{1-1}  & B4 & 0.789 & 0.771 & 0.808 & 0.827 \\ \cline{1-1}  & B5 & 0.759 & 0.725 & 0.785 & 0.791 \\ \cline{1-1}  & B7 & 0.743 & 0.710 & 0.752 & 0.763 \\ \cline{1-1}  & Mean & 0.681 & 0.679 & 0.721 & 0.733 \\ \hline \multirow{8}{*}{SSIM} & B1 & 0.583 & 0.611 & 0.644 & 0.645 \\  & B2 & 0.577 & 0.613 & 0.643 & 0.653 \\ \cline{1-1}  & B3 & 0.565 & 0.610 & 0.642 & 0.648 \\ \cline{1-1}  & B4 & 0.778 & 0.761 & 0.796 & 0.815 \\ \cline{1-1}  & B5 & 0.748 & 0.714 & 0.768 & 0.772 \\ \cline{1-1}  & B7 & 0.705 & 0.669 & 0.714 & 0.719 \\ \cline{1-1}  & Mean & 0.659 & 0.663 & 0.701 & 0.709 \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative assessment of the Landsat-like images by STARFM, STRUM, FSDAF, and EFSDAF in dataset 2. Figure 8: Zoomed in scenes of the black square areas marked in Figure 7. From left to right, (**a**) true Landsat image of 26 November 2004 (base date); (**b**) true Landsat image of 12 December 2004 (prediction date); (**c**–**f**) predicted images of 12 December 2004 by STARFM, STRUM, FSDAF, and EFSDAF, respectively.
# Anatomical-Based Customized Cervical Orthosis Design in Automation
## 2 Materials and Methods
### The Design Workflow of Cervical Orthosis
#### 2.1.2 Neck Coordinate  
Based on the anatomical features shown in Figure 4, we defined a unique neck coordinate system for further orthosis design automation. According to the 12 feature points obtained from the previous section, the origin of the neck coordinate is set to the subsurface of the spinous process of cervical 7, C7. The detailed neck coordinate setup procedures are listed below.  
1. Define the neck \(z\)-axis from C7 to the external occipital protuberance.
2. Set the head \(x\)-axis to be the cross product of the neck \(z\)-axis and the vector from the external occipital protuberance to the chin.
3. Set the back \(x\)-axis as the cross product of the neck \(z\)-axis and the vector from the spinous process of C7 to the sternum.
4. Define the neck \(x\)-axis as the average vector of the head \(x\)-axis and the back \(x\)-axis.
5. Define the neck \(y\)-axis as the cross product of the neck z-axis and the neck \(x\)-axis.
## 8 Discussion and Future Work
### Future Work  
Future research endeavors should focus on addressing the identified drawbacks and exploring alternative methodologies that could mitigate the computational and scalability challenges. This includes the development of more efficient feature selection techniques that can further refine the feature set without compromising model performance. Furthermore, incorporating other datasets, like DrugBank, and investigating the possibilities of combining data from several sources could improve the model's precision and comprehensiveness. A more in-depth analysis of the trade-offs between model complexity and generalizability is warranted. This could involve investigating other machine learning and deep learning architectures that may offer improved performance or efficiency. Furthermore, the practical deployment of these models should be closely examined, with studies aimed at assessing their performance in diverse healthcare settings and their integration into existing pharmacovigilance workflows.  
In summary, while our study presents a significant advancement in predictive modeling for drug safety, it also opens up numerous avenues for further research and development. Addressing the limitations and expanding upon the theoretical and practical implications of our work will be crucial for harnessing the full potential of advanced machine learning techniques in the enhancement of pharmacovigilance systems.
## 3 CC-VC-UKF
### Space-Time Joint Cleaning Based on CC-VC-UKF Algorithm
#### 4.2.2 Cleaning Based on the Reference Sequence of TTU Measurement  
When the FTU and TTU measurement data are filtered, the FTU measurement data can be cleaned according to the TTU measurement data as a reference sequence. Taking the measured active power as an example, the power flow relationship between FTU and TTU is shown in Figure 7 in the measured data obtained from a city's electric power company.  
Therefore, the equation of state and the measurement equation can be set as follows:  
\[x_{k}^{b}=f\Big{(}x_{k-1}^{b}\Big{)}+w_{k}^{b} \tag{24}\] \[y_{k}^{b}=x_{k}^{b}+v_{k}^{b}\]  
Figure 7: Power flow relationship between FTU and TTU.  
where \(x_{k}^{b}\) represents the TTU measurement data value at time \(k,f\) represents the conversion formula of the TTU measurement data value at time \(k\) and time \(k-1,y_{k}^{b}\) represents the FTU measurement data value at time \(k\), \(w_{k}^{b}\) represents the deviation of the TTU measurement data value between time \(k\) and time \(k-1\), and \(v_{k}^{b}\) represents the spatial measurement deviation of time \(k\). The distribution histogram of \(v_{k}^{b}\) is shown in Figure 8, intuitively indicating that \(v_{k}^{b}\) is a non-zero mean non-Gaussian distribution at this time. In addition, spatial measurement deviations are theoretically inherent, including line losses between measurement devices and measurement errors in the measurement devices themselves. However, the CC-VC-UKF algorithm proposed in this paper is not designed to eliminate these deviations, but rather to eliminate some of the FTU anomalies through the trending of the TTU data in the form of CC-VC-UKF-based filtering.  
After filtering by the CC-VC-UKF algorithm, \(x_{k}^{\prime}\) is set as the measured data value of the TTU measurement data after filtering, and \(v_{k}^{\prime}\) is the spatial measurement deviation after filtering. Then, the measured data value of the FTU after joint spatiotemporal cleaning can be expressed as \(y_{k}^{\prime}=x_{k}^{\prime}+v_{k}^{\prime}\).
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 7 Guidelines for Practitioners  
Before concluding this study, we propose the following series of guidelines to practitioners of DLP, with the hope of further improving the evaluation of this task:  
1. One key conclusion is that evaluating DLP algorithms requires exploring the performance along several dimensions of the data, at the node, edge, and time interval level.
2. To do so, as much as possible, a good practice for the use of the DLP algorithm is to enable the saving of the list of scores of each positive event and negative event. Indeed, these scores can be the starting point of in-depth evaluations such as the one conducted in this paper. As suggested in [44], this type of standardized output format is critical in order to easily apply diverse evaluation methods, while minimizing evaluation error.
3. Birth-Death diagrams are important tools to understand the effect of time-based train-test splitting on the nodes and edges involved in the graph, and may help in hypothesizing the expected performance of a given method on a given dataset.
4. Simple baselines such as EdgeBank and Preferential Attachment baselines are indispensable in order to understand whether the model learns anything non-trivial from the data.
5. Finally, visualizing how the prediction performance evolves over time can be crucial in understanding the strengths and weaknesses of DLP algorithms.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Number of Vehicles
#### 4.3.5 Simulation Results of Rayleigh Propagation Model regarding Number of Vehicles  
The Rayleigh propagation model is a multi-path propagation channel; hence, the mRSU throughput appears to be better here. Additionally, the average latency of the mRSU is shorter in the Rician propagation model as compared to the sRSU. Because the mRSU is in motion with the nodes, the space between them is reduced, and therefore the latency is reduced. Furthermore, the throughput increases owing to the multi-path method since messages can be delivered from any of the hops.