esa-sceva/satcom-chunk-collection huggingface dataset:
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 4 Methods
### Overall Framework  
Our search, presented in Algorithm 1 and described in Figure 1, is as follows:  
1. Update entropy map using Bayesian filtering;
2. Generate waypoints from entropy map (Algorithms 2 and A1);
3. Perform Heterogeneous Clustering using _coverage competency_ (Algorithm 3);
4. Solve the Traveling Salesman Problem over clustered waypoints for each agent;
5. Select optimal paths using an information-theoretic approach;
6. If re-plan conditions are met, repeat steps (2)-(5).  
```
Input:\(M,pos\)\(\triangleright\) (Entropy Map, Current Pose) Output:\(\mathcal{W}=\{w_{1},w_{2},\ldots,w_{n}\}\) Waypoint Set \(queue_{m}\leftarrow\emptyset\) \(\mathcal{W}\leftarrow\emptyset\)  Initialize \(\textit{Visited}[M_{m=0.5}]=False\) \(c_{0}\approx M_{m=0.5}\) take the unknown cell \(\textit{Enqueue}(queue_{m},c_{0})\)\(\triangleright\) insert \(c_{0}\) into \(queue_{m}\) \(\textit{Visited}[c_{0}]=\textit{True}\) while\(queue_{m}\) is not empty() do \(c\leftarrow\textit{DEQUEUE}(queue_{m})\)\(\triangleright\) pop from \(queue_{m}\) for\(n_{c}\gets\textit{neighborhood}(c)\)do\(\triangleright\) 4-adjacent cells if\(M[n_{c}]=0.5\) and \(\textit{Visited}[n_{c}]=False\)then\(\triangleright\)\(M[n_{c}]\): occupancy value at \(n_{c}\) \(w\), \(\textit{Visited},l_{c}\gets\textit{GetWaypoint}(M,n_{c},\textit{Visited})\) \(\mathcal{W}.append(w)\) \(\textit{Enqueue}(queue_{m},l_{c})\) elseif\(\textit{Visited}[n_{c}]=False\)then\(\textit{Enqueue}(queue_{m},n_{c})\) \(\textit{Visited}[n_{c}]=\textit{True}\) endif endfor endwhile return\(\mathcal{W}(\text{Waypoint Set})\)
```  
**Algorithm 2** WaypointGeneration()  
```
Input:\(\mathcal{W},\mu_{1:n},SR_{1:n}=\{\eta_{1},\ldots,\eta_{n}\}(\text{normalized})\) (Waypoints, Centroids(Robot Poses), Sensing Capabilities) Output: A Partition \(\mathbb{C}=\{C_{1},C_{2},\ldots,C_{n}\}\) \(\textit{cost}^{-}=0\)  Initialize \(\preceq\) repeat \(\hat{\mathcal{C}}_{i}=\{w_{j}:\eta_{i}d_{2}(w_{j},\mu_{i})\leq\eta_{h}d_{2}(w_{ j},\mu_{h})\text{ for all }h=1,\ldots,n\}\) \(\triangleright\) assign all datapoints to the nearest cluster \(\mu=\frac{1}{|\mathbb{C}|}\sum_{w_{j}\in C_{i}}w_{j}\)\(\triangleright\) update centroids if required \(\textit{cost}=\sum_{j=1}^{n}\sum_{w\in C_{j}}||\eta_{j}d_{2}(w_{j})||\) \(\Delta\textit{cost}=|\textit{cost}-\textit{cost}^{-}|\) if\(\Delta\textit{cost}<\textit{c}\)then \(\mathbb{C}\leftarrow\hat{\mathcal{C}}\) break endif \(\textit{cost}^{-}\leftarrow\textit{cost}\) untilMAXLOOP return\(\mathbb{C}\)
```  
**Algorithm 3** HeterogeneousClustering()
# Design and Verification of a Large-Scaled Flapping-Wing Aircraft Named \"Cloud Owl\"
## Materials and Methods
### Avionics of the Cloud Owl  
\"Cloud Owl\" primarily uses its wings to provide lift and propulsion, and it adjusts the elevons to manage the aircraft's pitch and roll attitude. Without real-time control of the lightweight flight control system, which consists of a flight control panel, a galvanometer, a governor, GPS, airspeed, a receiver, and data transfer, stable autonomous flying cannot be performed. The flight control panel includes an integrated IMU (inertial measurement unit) and AHRS. The electronic apparatus is 80 g in weight. The PX4 firmware is utilized as the primary flight control system, while the flight control hardware is based on Pixhawk. The aircraft's flight attitude and mode can be adjusted in conjunction with the ground station, and real-time digital transmission communication is also possible. The avionics of the Cloud Owl are shown in Figure 18.
## 6 Solutions for Security and Trust in IoV
### Future Directions
#### 6.4.3 Blockchains  
In most IoV scenarios, blockchains can offer a wide variety of novel solutions. The majority of IoV scenarios involve the generation and interchange of a significant amount of data, and the majority of conventional technologies are not suited for efficient utilization in these types of scenarios. As blockchain research is still in its infancy, however, the blockchain could enhance IoV's trust, security, and privacy [165]. The use of blockchain technology in IoV has great potential for protecting privacy. This is due to its privacy-focused characteristics and the decentralized management of data. Future research should focus on resolving issues with scalability, performance, and privacy to fully utilize these advantages. It is essential to overcome these challenges to develop a secure and reliable IoV environment.
## 2 The CNES PPP-WIZARD Demonstrator
### Computation of the post-processed Phase Biases
The problem has been made simpler by the previous determination of the troposphere delay and the use of the ground station positions in the sinex file. In addition to this, since this process is not performed in real-time, the entire passages of the satellites over the ground stations can be considered to retrieve the integer nature of the ambiguities. Indeed, once the cycle slips have been corrected, the ambiguities must be constant over the whole visibility interval from each satellite to each station. This constraint enhances the precision of the phase-bias determination Step 4 uses a technique similar to the one presented in [16]. Using this process, the computed phase biases are compatible with the OEUFS method. Table 3 shows the different combinations of OmC values used for each constellation. The final product is combined with the code biases to form a BIA file, which is uploaded to the website of the project ([[http://www.ppp-wizard.net/daily.html](http://www.ppp-wizard.net/daily.html)]([http://www.ppp-wizard.net/daily.html](http://www.ppp-wizard.net/daily.html)), accessed on 12 July 2023). On this webpage, both the real-time and the post-processed phase biases are available. In the following sections of this article, the products computed with the real-time architecture will be denoted \"RT products\", whereas the ones computed with the exposed post-process procedure will be called \"POST products\".
## 2 Results and Discussion
### Characterization of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)
#### 2.1.2 Magnetic Property  
Figure 4 showed the magnetic performance of Fe\({}_{3}\)O\({}_{4}\) and C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\), which were analyzed by VSM. The magnetic hysteresis loops reflect that the saturation value of Fe\({}_{3}\)O\({}_{4}\) was 62.67 emu/g, and C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was 19.93 emu/g. The saturation magnetization of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was obviously lower than that of Fe\({}_{3}\)O\({}_{4}\), which was related to the organic polymer layer that coated the Fe\({}_{3}\)O\({}_{4}\) surface effectively. However, it had no remarkable influence on the rapid magnetic separation of the adsorbent from the sample solution. C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) can not only be evenly dispersed in a solution without an external magnetic field, but can also be rapidly separated from the solution via a magnet within 10 s.
# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 1 Introduction
### Data Preprocessing  
As mentioned before, different algorithms and methods used or made will have to be inexpensive in computing time at runtime. Digital images, as they are acquired from the camera, are very often unusable for image processing--they include noisy signals. To remedy this, as shown in Figure 2, we use the following pretreatments:  
* Resizing: Due to the large number of digital cameras sold, the development of sensor technology has led to the emergence of many new products, including those with an increase in the number of sensor pixels. Having a high-quality camera is good for the eyes, but image processing does not always benefit from larger image sizes. A large number of pixels will quickly cause a large number of mathematical operations, resulting in a large number of calculations on the computer. Resizing the image is desired to improve computation. Figure 3 shows an example of adjusting the image from \(240\times 360\) to \(190\times 290\). The subject in question is still clearly visible. This is because each pixel must be processed when starting to process the image.
* Noise removal: To manipulate an image, we work on an array of integers that contains the components of each pixel. The treatments always apply to grey-level images, and sometimes also to color images. To improve the visual quality of the image, we must eliminate the effects of noise (parasites) by making it undergo a treatment called filtering (Figure 4). The filtering consists of modifying the frequency distribution of the components of a signal according to given specifications. The linear system used is called a digital filter.  
Figure 1: Proposed model of the system.  
Figure 3: Image resizing.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 6 Synchronization by Conflict Resolution
### The Synchronization Algorithm
#### Complexity of Algorithm 1
Consequently the invariant \(B\Subset B^{o}\) remains true. _Invariant 2_. : We know that if a merger of \(A\) and \(B\) contains \(\sigma\), then it cannot contain any of the discarded elements of \(B\) (see Proposition 13). We also know that all mergers of \(A\) and \(B^{\text{ok}}_{\sigma}\) contain \(\sigma\) by Proposition 15 as it is not in conflict with any other command. Consequently the set of mergers of \(A\) and \(B^{\text{ok}}_{\sigma}\) consists of those mergers of \(A\) and \(B\) that contain \(\sigma\), and the invariant continues to hold. _Invariant 3_. : If \(M^{o}\) was specified for the algorithm, then we chose the winner accordingly, and so \(\sigma\in M^{o}\). A consequence of the discussion above is that \(M^{o}\) remains part of the set of mergers of \(A\) and \(B^{\text{ok}}_{\sigma}\). Finally we show that if \(M^{o}\) is specified, then one of the conflicting commands can be chosen from \(M^{o}\). By Proposition 15 if \(\sigma\in A\) is not in conflict with any command in \(B\), then  
Figure 7: Outline of Algorithm 2. The _Initialize_ step sets \(A\) and \(B\). The _Iterate_ step is executed until no more conflicts are found. Conflicts are resolved using the hint from the additional input \(M^{o}\), then the command sets \(A\) and \(B\) are reduced. If no conflict remains, the merger \(M=A\cup B\) is returned by the _Finalize_ step. \(\sigma\) is in every merger of \(A\) and \(B\); in particular, it is in \(M^{o}\) by the third invariant. Thus, if no conflicting commands remain in \(M^{o}\), but there is a conflict, then \(M^{o}\) would be a proper subset of the merger returned by the algorithm, which is absurd. **Complexity of Algorithm 2**. : Both the _Initialization_ and _Finish_ steps can be done in time and space that is linear in the input size. Updating the sets \(A\) and \(B\) in the _Iteration_ step discards some of their elements which will never be used again. It means the total time used by the updates is bounded by the number of elements in the original input set \(A^{o}\cup B^{o}\). Finding conflicting pairs in constant time, however, requires building a structure which stores the bipartite conflict graph. This requires time and space which is proportional to the number of nodes (commands in \(A^{o}\) and \(B^{o}\)) plus the number of conflicting pairs. It means that Algorithm 2 can be implemented with both time and space complexity that is linear in the input size plus the total number of conflicts. While the latter number can be expected to be comparable to the input size, in the worst case it can be quadratic, meaning that this implementation guarantees at most quadratic time and space complexity. We conjecture that a more efficient implementation of Algorithm 2 can be created using the structural properties of the conflict graph. Some of those properties are discussed in the next Section.
## 3 System Description
### Mobile Data Acquisition System (MDAS)
#### 3.2.1 Hardware Components for the MDAS  
To collect information from the mobile network signal present in the area, there are two components that are needed: on the one hand, a hardware device capable of receiving such signals with its required features, and, on the other hand, a program able to provide a readable output of such signals in a suitable manner (for example, text files formatted according to the received information). From the point of view of our proposal, the hardware device to be used can consist of a smartphone, as they provide a portable piece of hardware where a plethora of programs oriented to our goals are executable. However, if a smartphone is to be used, it must have two key features related to this proposal:  
1. It is of critical importance that the smartphone used is dual-band-enabled, so that the accuracy of the data obtained can be as great as possible.
2. The mobile phone should be compatible with Galileo as the GNSS. With this feature, the possible choices to use smartphones with those characteristics are significantly narrower. As already described, the use of Galileo as the GNSS enables a greater level of accuracy (Precise Point Positioning via High Accuracy Service, or PPP via HAS) and security (Navigation Message Authentication or NMA) that, to the best of our knowledge, cannot be matched with other global satellite systems, so there is an incentive to use it.  
In the end, it was decided that the Xiaomi mi 10 lite [33] was the best possible option due to several reasons: (a) it is Galileo-friendly, (b) it has a dual-band receptor and (c) it is economically affordable. It could also make use of the software programs that had been installed to measure, as exact as possible, mobile network coverage values based on signal strength values. The smartphone is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Requirement** & **Description** \\ \hline Functional Requirement 1 & The MDAS must be portable so it can be installed in a UAV. \\ Non-functional Requirement 1 & The MDAS must be fully operational with a regular smartphone. \\ Non-functional Requirement 2 & The MDAS must be able to collect information about coordinates. \\ Non-functional Requirement 3 & The MDAS must be able to collect information about altitude. \\ Non-functional Requirement 4 & The MDAS must be able to obtain information about signal power levels. \\ Non-functional Requirement 5 & The MDAS must be able to run the program used for signal logging. \\ \hline \hline \end{tabular}
\end{table}
Table 5: MDAS requirements.  
Figure 6: Xiaomi mi 10 lite (**left**) running the Network Cell Info program (**right**).  
Therefore, the Xiaomi mi 10 lite smartphone was tied to the UAV that had been built as the device measuring the signal power and features obtained from the environment. The overall appearance of the phone can be seen in Figure 7.
## 5 Use Cases of Attack Detection with Matana
### Use Case: Return-Oriented Programming Attack Detection
#### 5.3.4 Area Footprint  
The area overhead considering MATANA with our ROP detection pattern is 1.90% of Flip-Flops and 1.95% of Look-Up Tables utilization compared with the base system. This value is much smaller than in the previous use case, mainly because the Detection Module is synchronized with the target component.
## 1 Introduction
Given the benefits of VR in creating a better learning environment and a limited amount of research on the development of VR-based social skills training for children with ADHD, it is critical to develop a VR-based social skills intervention for children with ADHD. A VR-based social skills training program (Social VR) has been developed. Before initiating the pilot trial to evaluate the effectiveness of Social VR, comprehensive user feedback was gathered pertaining to the mechanics of Social VR, levels of satisfaction, and instances of motion sickness. This study demonstrated the initial phase of designing, developing, and testing the preliminary usability stage of Social VR in a small sample of children with ADHD. The goals and theoretical background were defined in the initial design phase; the prototypes were created in the development phase; the usability and satisfaction of the prototypes were tested. The main purpose was to collect feedback on the operation of Social VR and improve it. Therefore, the research questions framed for this study were as follows:  
* What were the design principles in developing the Social VR intervention? * What were the participants' perceptions and satisfaction levels regarding the mechanics of the Social VR intervention? * Were there differences in mechanics underlying the three conditions of Social VR among the three ADHD subtypes? * What was the level of participants' satisfaction with the Social VR intervention? * What was the severity of motion sickness experienced by participants during the Social VR intervention?
## 1 Introduction
### Paper Organization  
The first section of this paper introduces the background of lightweight cryptography and authenticated encryption, followed by an analysis of the related work in the same section. In Section 2, we provide a detailed explanation of the algorithm of the LED block cipher and PHOTON hash function, as well as their individual implementation. The design procedure of the proposed authenticated encryption is provided in Section 3, as well as the implementation of hardware architecture for the authenticated encryption, and a summary of the hardware resource utilization. Section 4 contains the results and a discussion of the proposed authenticated encryption. It discusses the outcomes of the simulation and resource utilization and benchmarking with the available related work. Finally, the summary and conclusions and the paper's key findings are provided in Section 5.
## 2 Related Work
### Object Detection  
In recent years, single-stage object detectors have emerged as the mainstream choice for real-time applications, owing to their simple architectures and fast inference speeds. The SSD (Single Shot MultiBox Detector), proposed by [16], utilizes multi-scale feature maps for bounding box regression and classification. Several improved versions have subsequently been developed, such as DSSD [17], which incorporates a deconvolution module to enhance contextual information and improve small-object detection performance; FSSD [18], which adds a feature fusion module to generate richer pyramid features; and PSSD [19], which introduces an efficient feature enhancement module to strengthen local detection capabilities and semantic expressiveness. RetinaNet [20] uses Focal Loss to address the imbalance between foreground and background classes, reaching accuracy close to two-stage methods while retaining high inference speed. RTMDet [21] enhances detection accuracy through the integration of an efficient Transformer module, demonstrating superior performance particularly in small-object detection and under complex background conditions.  
The YOLO series, as a hallmark of single-stage detectors, started with anchor-free detection in YOLOv1 and has undergone multiple structural evolutions. YOLOv3 [22] integrates ResNet and FPN to improve multi-scale perception; YOLOv4 [23] enhances feature reuse and path aggregation efficiency through CSPNet and PANet; YOLOv5 [24] optimizes the network with lightweight designs, achieving simultaneous improvements in both accuracy and speed, making it ideal for resource-constrained environments; YOLOv8 [25]introduces the C2f module to optimize semantic feature extraction, further enhancing detection performance. The latest YOLOv11 [26] builds upon YOLOv8 by introducing the more efficient C3k2 module and the SPPF (Spatial Pyramid Pooling--Fast) structure, along with the C2 PSA module that integrates spatial attention mechanisms, significantly strengthening the model's multi-scale perception and directional robustness. However, although YOLOv5 performs well in speed and model size, its simplistic feature fusion limits its precision on fast and small targets. YOLOv8 improves semantic features but struggles in multi-scale detection accuracy. YOLOv11 further enhances backbone extraction, yet coarse concatenation of multi-level features hinders edge precision for small objects. In contrast, YOLOv11-Dd maintains efficiency while incorporating Ghost Conv to reduce complexity and FA-Concat to align cross-level features. These enhancements improve detection precision and robustness for small fast-moving objects in challenging environments.
## 6 Results and Discussion
### Fatigue Life Assessment  
Based on the modified S-N curves and derived stress spectra, detailed investigations can be made to identify the fatigue life of RD joints in the novel OSD with TEUs and the traditional OSD with CUs. The four typical joints (i.e., U2\({}_{\rm L}\), U7\({}_{\rm L}\), U12\({}_{\rm L}\) and U13\({}_{\rm L}\)) have been used as the representatives since the effect of both the occupancy rate and in-lane position could be illustrated by the comparison on the four joints. Meanwhile, due to the lack of data on the traffic volume in the bridge site, the allowance of lifetime traffic volume has been chosen as the direct indicator. The results, which are unprecedented, are summarised in Table 4.  
As aforementioned, remarkable differences could be observed in the stress spectra of RD joints at various lateral positions, which could further lead to the significant differences in the fatigue life of those joints. Two notable features could be found in Table 4: (1) with the same in-lane position, the joints within the middle lane or slow lane have a shorter fatigue life than within the fast lane; (2) within the same lane, the fatigue life is lower in the RD joints close to frequent loading position of wheels. Thus, the RD joint with the above two features can be regarded as the fatigue critical joint, to which special cares should be paid in fatigue design. Since it is assumed in the JTG model and AASHTO model that heavy trucks seldom use the fast lane, the level of ranges is extremely low in the spectra of the joint U2\({}_{\rm L}\). As a result, the fatigue issue of the joint U2\({}_{\rm L}\) could be almost ignored. In all the joints except the U2\({}_{\rm L}\), the JTG model leads to the most conservative result, followed by the AASHTO model, and then the EC1 model. It is also worth noting that the fatigue life is significantly higher in the results solved with the observation model than in the results solved by the standard truck models. This finding is due to the inclusion of lightweight cars in the observation model, which have the largest proportion but little contribution to the fatigue damage in RD joints. At the same time, the results also suggest a lower fatigue life could be determined by the hot spot stress approach when compared with the nominal stress approach, indicating the conservativeness of this approach. Generally, the fatigue life of RD joints can be notably improved by the application of TEUs in OSDs. The prolongation rate is consistent under the three standard truck models, i.e., 141% in both the nominal stress approach and hot spot stress approach. Under the observation traffic model, the prolongation rate is from 141% to 143% in the nominal stress approach, and from 146% to 161% in the hot spot stress approach. In sum, this study for the first time demonstrates that the fatigue