esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Fidelity Energy Efficient Path Planner for Unmanned Airships
## 3 Vehicle Model  
For vehicles with a constant input power, the lowest energy path will be the path with the shortest arrival time. However, with the introduction of a set time goal and 3D spatial WVF applied to a vehicle capable of zero-speed flight, such as airships, there is the potential of a velocity varying path (i.e., trajectory) that consumes less energy. Energy consumption estimates are dependent on multiple factors and are highly platform specific. Airships, like most UAVs, are under-actuated non-holonomic vehicles, which require additional energy to change their orientation, which cannot be neglected for medium to long-range path planning. Moreover, rapid changes in orientation can cause unstable behaviours. A recent study by [PERSON] et al. suggests that these changes, as well as incorporating a six-degree of freedom model affect path planning performance [8]. The model developed in this paper includes several significant differential constraints inherent to airships and other non-holonomic UAVs.  
The theoretical energy required to change orientation is negligible compared to that of forward motion for highly dynamic manoeuvring UAVs. Conversely, airships are typically under-actuated and under-powered and thus have large turning radii. This results in longer paths and increased flight times between two points. The additional time to travel between two points depends on two factors: the ratio of the minimum turning radius over the distance between nodes, \(r_{min}/d(n,n^{\prime})\), and the magnitude of the orientation change, \(\psi\). Figure 3 shows an example of an idealized kinematic reorientation that takes into account the minimum turning radius. The true distance, \(d^{\prime}(n,n^{\prime})\), is the arc length of the two tangent arcs; the first is tangent to \(v_{pg}(n)\) with radius \(r_{min}\), and the second is tangent to both the first arc and \(v_{pg}(n^{\prime})\). The orientation ratio, \(o\), is the ratio of the true distance over the idealized straight line distance, \(d^{\prime}(n,n^{\prime})/d(n,n^{\prime})\).  
The platform considered in this paper is shown in Figure 4. It has a fixed pair of forward facing electric thrusters complimented by a hybrid gas-electric power plant and uses two tail-mounted thrusters for directional control and roll stability [38; 39]. The minimum turning radius ranges from 0 m at zero linear velocity to 10 m at a cruise speed of 12 m/s. This was estimated empirically from the full dynamic model of the vehicle developed in [40]. Considering a resolution between nodes of 50 m, the increase in energy to reorient the vehicle can be upwards of 25% for a 90\({}^{\rm o}\) turn. The platform considered also exploits the use of air ballasts (ballonets) for  
Figure 3: Idealized reorientation between two nodes.  
stability. These actuators use an electric air pump to change ballast volume. The ballonets also provide two essential alternate functions of pressure regulation to ensure structural integrity and weight ballasting to compensate for the consumed fuel of the power plant.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 4 Experimental Test and Security Analysis  
In this section, several classic test images are used for encryption experiments, and the performance of this encryption scheme is proved by the experimental test results. The experiment was completed on a PC equipped with Intel Core i7-9700 @ 3.00 GHz processor, 16 GB RAM and MATLAB 2021a. The classic test images were from the image data set of USC-SIPI ([[https://ccia.ugres/cvg/dbimagenes/](https://ccia.ugres/cvg/dbimagenes/)]([https://ccia.ugres/cvg/dbimagenes/](https://ccia.ugres/cvg/dbimagenes/)) (which can be accessed on 24 November 2021)). The key parameters used in the experiment were \(a=4\); \(b=1.9\); \(x_{0}=0.23\); \(y_{0}=0.93\); \(N_{0}=57\). Other parameters were set as: C\({}_{0}=73\), \(m=6\), \(k=5\), \(T=1\).
# A Flame Detection Algorithm Based on Improved YOLOv7
## 2 Background and Related Work
### Background on Target Detection  
Target detection is a hot research topic in computer vision, and it can be divided into traditional target detection [23] and deep-learning-based target detection [24; 25; 26; 27; 28]. Traditional target detection methods mainly rely on feature extractors, which use sliding windows to extract image features and generate a large number of target candidate regions [29]. However, the above-mentioned methods are cumbersome and have some problems, such as serious window redundancy, slow detection speed, and low monitoring accuracy. Recently, convolutional neural networks have become widespread tools for image feature extraction and classification, which is the most popular architecture of deep learning [30; 31; 32; 33; 34; 35; 36]. The target detection technologies based on deep learning can adaptively learn high-level semantic information of images by using multi-structure network models along with their training algorithms.  
In 2014, [PERSON] et al. successfully applied convolutional neural networks to target monitoring and proposed the R-CNN algorithm [4], which combined AlexNet [37] with selective search algorithms [38]. The detection accuracy of the R-CNN algorithm reached 58.5% on the PASCAL VOC2007 data set, which is significant progress compared to traditional target detection algorithms. The YOLO series is a deep-learning-based approach to object detection in real time. Meanwhile, it is often used in flame detection. In 2015, [PERSON] et al. proposed the YOLOv1 algorithm, which integrates classification, positioning, and detection functions in one network [10]. Following that, the YOLO series has been developed over many upgraded versions. There are many flame detection algorithms based on YOLOv3, YOLOv4, and YOLOv5 [5; 6; 7; 8; 9]. YOLOv7 [15] is the latest version of the YOLO series, and in [16], it is proved that the performance of YOLOv7 is obviously better than YOLOv5 and YOLOv6 in the aspect of target detection. Unfortunately, as far as we know, YOLOv7 has not yet been applied to flame detection. In this work, we propose an improved detection algorithm based on YOLOv7, which is, to our knowledge, the first time that YOLOv7 has been applied in this field.
###### Abstract  
Nowadays, unmanned aerial vehicle (UAV) communication systems are commonly considered as one of the key enabling technologies for 6G. The hybrid free space optical (FSO)/radio frequency (RF) system has the advantages of both FSO and RF links to improve communication system performance, and the relay-assisted system adopts multi-hop transmission and cooperative diversity methods to extend communication coverage. Thus, a joint consideration of UAV-assisted UAV assisted relay in hybrid FSO/RF transmission is meaningful. In this paper, we aim to analyze the performance of UAV-assisted multi-hop parallel hybrid FSO/RF communication systems with and without pointing errors (PE) in terms of Bit Error Rate (BER) and outage probability. In our considered system, the FSO sub-link adopts the Exponential Weibull turbulence model and the RF sub-link suffers the Nakagami fading model. With these, new mathematical formulas of both BER and outage probability are derived under the UAV-assisted hybrid FSO/RF with different modulation methods. Through numerical evaluaumerical simulations, the performances of UAV-assisted hybrid FSO/RF systems are analyzed under different weather conditions, modulation methods, optical receiver aperture, RF fading parameters, pointing errors, and relay structures. The results demonstrate that (1) compared to hybrid FSO/RF direct links, UAV-assisted hybrid FSO/RF systems can further improve system performance; (2) the performance of UAV-assisted hybrid FSO/RF systems varies with different relay structures; (3) large receiver aperture and RF fading parameters can further improve the communication performance of hybrid FSO/RF direct links and UAV-assisted hybrid FSO/RF systems.  
UAV-assisted hybrid FSO/RF communication systems; Exponentiated Weibull turbulence; Nakagami-m fading; pointing errors; receiver aperture; average bit error rate; outage probability
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
## 5 Conclusions  
This paper proposes a new life prediction scheme which can predict the life when considering the mission profile switching. First of all, the reference degradation models are constructed according to the RVM prediction results of each constant operating condition. The RVM achieves a more complete description of the detail and trend information of the degradation curve simultaneously. Therefore, more accurate life prediction results under constant operating conditions are obtained by RVM. Then, the life of the constant operating conditions of the sample to be predicted is obtained by STM. Finally, according to the proportion of each constant operating condition in the mission profile, the final life is calculated through weighted aggregating the life of each constant operating condition of the sample to be predicted. Through the experiments organized using the NASA turbofan engine simulation dataset, it is proved that the proposed method has excellent predicted results with the given mission profile. In addition, the proposed method can obtain the corresponding life predicted results according to mission profile switching.  
Although some important techniques associated with the proposed method have been investigated in this paper, there are still a few questions that are worthy of further consideration. First, the similarity measurement method based on Euclidean distance is the simplest but not the best one. Second, switching  
Figure 11: The average Mean Squared Error (MSE) of the proposed method.  
of the mission profile with the same proportion of operating conditions is not considered in this paper. Therefore, the proposed method may be further improved by considering the above questions.  
Conceptualization, J.Y.; formal analysis, J.Y.; investigation, J.Y., H.Z., and Y.Y.; methodology, J.Y.; project administration, M.X.; software, J.Y. and Y.Y.; supervision, M.X.; validation, H.Z.; writing--original draft, J.Y.; writing--review and editing, J.Y., H.Z., Y.Y., and M.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 11172078.  
The authors declare no conflicts of interest.
## 6 Case Study: Application of the Method on LoA 4 Valet Parking with V2X
### Smart Infrastructure--AVP  
Infrastructure in AVP covers both the parking lot or garage, where the maneuvering takes place and the section of the infrastructure that connects the parking area with the public road, where the function is activated. This includes any ramp, gate, parking bay or the section of the public road, where the LoA 4 function may be active.  
For AVP, one of the most crucial V2X requirements is a the availability of local HD or feature maps, specific to the parking garage, provided in an appropriate format and available for storage on the vehicle. This is considered a _static_ information and serves as a base for global trajectory planning. This information is completed with _dynamic_ data from the infrastructure, which includes the list and location of free parking spots, any moving vehicle within the ODD and other information on the environment, such as temperature or humidity.
## 7 Conclusions  
In this work, an intra-satellite wireless network (ISWN) for ORS satellites is proposed. A COTS-based hardware and modularized structure is constructed, which effectively reduces the volume and weight of the satellite platform. Then, flexible design and cost reduction in satellite design and launching can be achieved. A hybrid TDMA/CSMA protocol is proposed, in which two basic mechanisms--an improved single CCA strategy and a traffic adaptive slot allocation method--are proposed and verified. A Markov model is established and a detailed calculation is given to quantitatively analyze the practical design issues. Simulation experiments are carried out, and the effects of several main parameters on network performance are identified. The simulation results indicate that the proposed hybrid protocol outperforms the existing schemes both in capacity and packet discard rate. Also, through the traffic adaptive slot division, both energy saving and performance guarantee are achieved. The proposed scheme is proven to meets the on-orbit data demands and will be a promising candidate to replace the conventional wired data bus. In future work, we will focus on the layout and shield design. Semi-physical simulation and on-orbit verifications also need to be carried out.  
Conceptualization, L.W. and Z.Y.; Methodology, L.W. and Y.L.; Software, L.W.; Validation, L.W.; Formal Analysis, L.W.; Investigation, L.W.; Writing-Original Draft Preparation, L.W.; Writing-Review & Editing, L.W. and Y.L.; Visualization, L.W.; Supervision, Z.Y.; Funding Acquisition, Y.L and Z.Y. This research was funded by the National Key Research and Development Program of China under grants 2016 YFB 0501100 and 2016 YFB 0501101.  
I would like to thank [PERSON] for her help and support through the manuscript writing.  
The authors declare no conflict of interest.
## 4 Discussion
### Landslide Detection
It is recommended to conduct further studies on datasets with different resolutions. Overall, the SAM method and the NDVI filtering method were the most reliable for measuring windthrow and landslides, respectively. In contrast, the SVM method results, processed in Google Earth Engine, performed below our expectations despite being generated by the most sophisticated algorithm. Even though the SVM classifier working with the linear kernel delivered a \"substantial level of agreement\", the usage of a different kernel could improve accuracy but would increase the processing time.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 5 Discussion
### Impact of Elevation Difference on the Susceptibility Assessment  
In addition to the above analysis, we analyze the impact of elevation differences on the susceptibility assessment and the relationships between the elevation differences and the differences in assessment levels. Therefore, the areas with varying level and elevation differences are presented (Table 7).  
Table 7 demonstrates that the decrease in elevation results in a decrease in the susceptibility assessment level. There is a positive correlation between the elevation difference and the difference in the assessment level. Regardless of the level difference, the elevation difference is much larger in the area ranging from \(-20\) to \(20\) m than in other areas.
## 4 UbiPriSEQ: The Proposed Framework
### Device Module (DevM)
#### 4.3.4 DevM: Reward Function (Utility)  
DevM in each time \(t\) observes various parameters from the device and network (newly generated tasks by the device, SINR, etc.), and stores them as the state in time \(t\). This state is used to compute the utility or the rewards (\(ud_{d}\)), which is a weighted function given by Equation (2). The reward/utility function provides a higher reward when the task offloading meets the objectives (privacy, latency, QoS) and a lower reward when an offloading is far from our objectives. It is computed by adding PrivacyMetric (given by Equation (1)) and SINR (\(sinr_{t}\)), and subtracting tasks' computational costs (\(c_{t}\)),tasks' communication and queuing delays (\(b_{t}\)), and the loss to the device due to the tasks' execution delays (\(\rho\)). All the parameters are weighted (\(\mu\), \(\omega\), \(\eta\)) based on their relative importance. DevM tries to achieve a higher utility, so parameters such as SINR are added while others such as costs are subtracted. DevM adds each utility for time \(t\), and its associated action (offload policy) to an Experience Sequence (i.e., the accumulated knowledge).  
\[ud_{d}\triangleq Pri_{d}(\mathbf{s_{t}},\mathbf{a_{t}})-\mu\cdot c_{t}-\rho-\omega\cdot b _{t}+\eta\cdot sinr_{t}. \tag{2}\]
## 3 D2D-Based Resource Allocation Algorithms
### Greedy Throughput Maximization Plus  
The greedy throughput maximization plus (GTM+) algorithm [21] aims to efficiently maximize the total throughput of a D2D system subject to transmission interference constraints in the uplink. It is designed to provide a fast solution to this maximization problem.  
The algorithm starts by randomly pre-allocating idle resource blocks. In cases where no idle resource block is available, a D2D pair is randomly selected for each resource block and treated as if it were a mobile user. The number of interactions is then selected to determine how to reuse all resource blocks from CUEs.  
All unallocated D2D pairs are grouped together to maximize their own utility before performing the iterations. The largest group is chosen, and the D2D pairs within it that best maximize the total utility are selected as candidates for reusing resource blocks from CUEs. To ensure that the SINR restrictions are met, one candidate is removed at a time until the requirement is satisfied. Once the group is marked as complete, the iteration is finished. This routine is repeated until all groups are marked as complete.  
The utility function \(u_{n}(m)\) in Equation (1) represents the flow of the system:  
\[u_{n}(m)=W_{m}\log_{2}(1+\frac{P_{m}G_{mB}}{\sigma_{n}^{2}+P_{n}G_{nB}})+W_{m} \log_{2}(1+\frac{P_{n}G_{m}}{\sigma_{n}^{2}+P_{m}G_{nm}}), \tag{1}\]  
where the \(D_{n}\) group reuses \(C_{m}\) blocks with the assumption that there are no other CUEs or D2D pairs inside the cell, and \(W_{m}\) denotes the allocated bandwidth, with \(P_{m}\) and \(P_{n}\) representing the respective transmit powers of the CUE and DUE. The noise power is denoted as \(\sigma_{n}^{2}\), and \(G_{nm}\) stands for the channel gain existing between the two endpoints of the DUE pair \(n\). Additionally, \(G_{nm}\) signifies the channel gain extending from CUE \(m\) to DUE pair \(n\), whereas \(G_{mB}\) characterizes the channel gain between CUE \(m\) and the serving base station. Lastly, \(G_{nB}\) pertains to the channel gain between the sender of DUE pair \(n\) and the associated base station.  
A conflict graph \(G_{(n^{\prime})}\) is formed for a group \(m\), with each vertex corresponding to a D2D pair and weights defined by the utility function of each pair. If the mutual interference between two pairs exceeds the defined threshold, an edge is added to the graph, indicating that the two pairs cannot reuse the same resource block at the same time.  
The conflict graph \(G_{(n^{\prime})}\) is then used to determine the candidates, with each vertex representing a D2D pair. The pairs that maximize the throughput of the system are kept, and those that cannot coexist are removed. This guarantees that at least one D2D pair will reuse idle resource blocks, leading to an efficient resource allocation.
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 2 Related Work
### Security Mechanisms  
To achieve the main security objectives, which are authentication, authorisation and secure data sharing in a 5G-enabled D2D communication network, multiple security techniques are deployed.  
Mobile networks adopted a service authorisation model that provides default services to every subscribed user, whereby implicit access authorisation is given to registered user equipment (UE) upon successful primary authentication. Service authorisation in legacy mobile networks, such as Fourth Generation (4G), was based on the static subscription of a user. Moreover, each UE's authorisation matrix is kept in the home network (HN) and then downloaded to the sn [6]. The SN then utilises the received permission matrix to grant the authenticated UE access to the services provided by the SP.  
The standardisation and adoption of a static SP-based authorisation model have proven beneficial from an interoperability standpoint when applied to a market with a limited set of services supplied via wireless networks managed by one or two MNOs. In 5G, the UE will be authenticated to access the HN and authorised to access services in the HN and data network (DN) to support multiple shareholders. For new services, the authentication mechanism was decoupled from authorisation, and new authorisation processes were established. Network slicing provided by Network Function Virtualisation (NFV) and Software-Defined Networking (SDN) technologies is used in 5G to provide a diverse collection of services. A service authorisation architecture that allows the delivery of services from several infrastructure providers using an SP-based authorisation mechanism to enable existing implicit service authorisation while also protecting SPs from unauthorised service access is desirable because of the projected large range of service products and connected devices [7].  
The authentication and authorisation mechanisms that were used in this research adopted Authentication Key Agreement (AKA) and access control methods to address security in 5G-enabled D2D communications networks. These protocols' security properties are derived from the security requirements of the system model which are secrecy, authentication, integrity, confidentiality and privacy [2].
## 4 Results and Discussion
While the traditional P&O study achieves a good performance in steady state conditions, thanks to its low fixed step size, due to this low fixed step size in dynamic conditions its performance is weaker compared to the fuzzy logic enhanced P&O, specially when only the solar irradiation value is changed. A working prototype of the converter must be built, to determine if the higher stability, faster adaptability and the higher generated power achieved by the fuzzy logic enhanced P&O variant, justify its use over the P&O considering other variables such as the ease of implementation, the cost of the prototype and computational resources needed.