esa-sceva/satcom-chunk-collection huggingface dataset:
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 3 Attitude and Orbit Determination Method with Angle-Only Measurements
### Results and Discussion
#### 4.2.2 Case II: The Orbit of \(S_{1}\) Is Known, and the Attitude of \(S_{1}\) Is Unchanged
Compared with the situation in which the angular velocity of \(S_{1}\) is known, it is slightly more difficult to estimate the system when the angular velocity needs to be determined. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epoch \(t_{k}\)** & **Rank(N)** & **Observability** & **1/cond(N)** & **In[1/cond(N) ]** \\ \hline
0 & 3 & False & - & - \\
1 & 6 & False & - & - \\
2 & 9 & False & - & - \\
3 & 11 & False & - & - \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
1027 & 18 & False & - & - \\
1028 & 19 & True & \(5.7892\times 10^{-13}\) & \(-28.1776\) \\
1029 & 19 & True & \(5.8186\times 10^{-13}\) & \(-28.1725\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Observability test results of case V.  
Figure 16: Orbit determination errors of case IV (nonperturbation). (**a**) Orbit estimation errors of spacecraft \(S_{1}\). (**b**) Orbit estimation errors of spacecraft \(S_{2}\). As shown in Figure 18, the conclusions are summarized that the perturbations have almost no influence on the observability of the system, although the angular velocity is unknown. Note that the system is sensitive to the attitude and the angular velocity of \(S_{1}\); hence, for case V, the numerical observability measures are computed from the initial simulation epoch to the end of the simulation (within a total of 4 h) with a measurement frequency of 1 per 0.5 s. The results are illustrated in Figures 19-21. Figures 19 and 21 show that the attitude and the angular velocity converge at approximately 1 h, while as expected, the orbit states converge at approximately 2 h (Figure 20). Even under the situation in which the angular velocity is to be estimated, the estimation quality of case V is still healthier than that of case III, indicating that the attitude maneuver is superior to the complex perturbation with respect to system observability. Figure 17: Observability simulation of case V (with nonperturbation). Figure 18: Observability comparisons of case V.  
Figure 19: Attitude determination errors of case V (nonperturbation) and the corresponding larger plot. Figure 20: Orbit determination errors of case V (nonperturbation). (**a**) Orbit estimation errors of spacecraft \(S_{1}\). (**b**) Orbit estimation errors of spacecraft \(S_{2}\). The above discussion of the observability of the attitude and orbit is summarized in Table 7. Note that in Table 7, the symbol \(\bigvee^{\prime}\) denotes that the corresponding item is known, while the symbol \(\bigvee^{\prime}\) states that the item is unknown. Moreover, one item is suggested to not exist if it is marked _'none'_. For example, subcase II of case III indicates that the \(J_{2}\) perturbation is considered, both the orbits of \(S_{1}\) and \(S_{2}\) and the attitude of \(S_{1}\) are estimated, and no attitude maneuver is executed.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 6 Developed System Algorithm
\(D_{2}\) is always kept in the middle of highest height \(D_{2H}\) and the lowest \(D_{2L}\) as \(D_{2}=(D_{2H}+D_{2L})/2\). We are shrinking and expanding low and high values as per the requirements. If many CCHs were found and we want to limit them by increasing the height the low value is shifted to the middle and the middle value is calculated again. The same treatment in reverse order is used if no CCH is found, and the data collection height is decreased by shifting the high value to the middle and the middle is calculated again. 7. This \(D_{2}\) height tuning process continues till: 1. The number of CCHs falls in the range (\(1\) to \(m-1\)) 2. \(D_{2}\) reaches the boundary condition extreme high or low 3. The distance between high and low becomes less than the GPS accuracy of let's say \(5\) m.  
The third option is the variation of the threshold \(Th\); by increasing \(Th\) from 0 to 0.5 roughly half of the nodes will withdraw themselves from CH selection. The UAV can increase \(Th\) at every iteration. 9. The UAV will localize the activated candidate cluster heads and select the best one as CH on the basis of \(P_{i}\) information received and the distance to the next waypoint. The node having highest \(P_{i}\) value and closer to the waypoint will be selected as CH. 10. The UAV will send CH messages to all activated nodes. 11. The nodes receiving a CH message will send a join request. The CH receives the join request and allocates a time slot. 12. The nodes receive the time slot and send its data. 13. The CH aggregates the data and transmits it to the UAV. 14. The UAV moves to next cluster. All active nodes finish their transmission, and go to sleep for a fixed time.
## 5 Results and Discussion
### Crash Prediction Model
Figure 5: **The correlation matrix of the safety–related variables. ** Note: a black cross on a number means the correlation coefficient is insignificant. Crash_tot is the total crash count in a grid. total near-miss-to-crash rate, we leverage two statistics: that 10% of cars have the Advanced Driver Assistance System (ADAS) installed [57], and Mobileye holds a 69% market share in ADAS [58]. From this, we estimate that 6.9% of vehicles in New York City are equipped with Mobileye 8-connected devices. This, in turn, allows us to calculate the total near-miss-to-crash ratio (vehicle-to-vehicle near-miss: 1 km/h \(<\) speed \(<\) 200 km/h, \(\text{TTC}\leq\) 2.7 s, vehicle-to-bicycle or -pedestrian near-miss: 1 km/h \(<\) speed \(<\) 50 km/h), \(\text{TTC}\leq\) 2.0 s) in NYC as roughly 1957:1. Previous studies have provided empirical values for the near-miss/conflict-to-crash ratio. For instance, [PERSON], et al. [59] suggested a ratio of approximately 20,000 conflicts to 1 actual crash, though the method for identifying conflicts was unspecified. A more recent study suggested a ratio of 14 conflicts (defined by a minimum TTC of less than 3.5 s) to 1 crash at regular signalized intersections [53]. While our results vary from the previous literature, they offer a reference near-miss-to-crash ratio for a dense urban environment using Mobileye-defined near-misses. Our analysis also identified three significant network and facility-related variables: the number of intersections, the number of bus stops, and the road length. An increase in the number of intersections results in a rise in estimated crash frequency by 0.13. This increasing trend aligns with previous research [60; 61]. This finding is reasonable given that intersections are spaces shared among various road users, potentially leading to higher crash exposure. Similarly, the presence of bus stops, where traffic disturbances and pedestrian conflicts are likely, raises the estimated crash frequency. An increase of one bus stop per grid enhances crash frequency by approximately 0.21, a finding trend in line with prior research [60]. The length of the road is positively associated with crash occurrences. An increase in road length by 100 ft augments the estimated crash count by 0.65, potentially due to increased exposure. In urban areas, longer roads often correlate with higher traffic volumes, a finding supported by the previous literature [62]. Conversely, two land use variables, the residential land use rate and the open space land use rate, negatively impact the estimated crash frequency. Residential areas usually have lower traffic volumes, possibly due to lower speed limits and more traffic calming measures, leading to fewer crashes [63]. Similarly, open spaces, with fewer vehicles and intersections, contribute to reduced crash rates, a finding supported by previous research [64]. However, these relationships can depend on numerous factors, such as road design and maintenance, and the behavior of drivers and pedestrians [65].
## 1 Introduction  
Cloud computing has become a generic term that describes an easy, flexible, and scalable delivery of resources and services over the Internet. This provides an emerging model in support of \"everything-as-a-service\"(XaaS). A new, spectacular phenomenon of data sharing and service integration is possible within the cloud computing environment. This paves the way for technological innovation when applying semantic technologies to cloud computing [1].  
Current cloud infrastructures do not provide enough self-managed services for their cloud users. Therefore, a cloud provider's internal employees must use manual service management, which requires intensive human intervention to explore and allocate available virtual resources for cloud users [2]. This is certainly not adequate when cloud resources are agile and deployed over the wide-scale of the Internet. We need self-managed Software-as-a-Service (SaaS) not only for the automatic allocation of various cloud available resources but also to enforce security and privacy policies without too much human intervention. _Law-as-a-Service (LaaS)_ further enhances automatic security and privacy policy to provide law-aware semantic policy enforcement in the cloud [3].  
In this paper, relational structured data are re-modeled as ontologies and used for data integration and exchange (see Figure 1). This leverages the abstract concept representation and reasoning of ontologies, which do not exist in the relational database [4]. Furthermore, stratified Datalog rules with exceptions handling extends ontologies to empower data protection and query services [5]. We have built a semantic data cloud for data integration and exchange. Furthermore, we have also applied Semantic Web technology to represent and enforce semantic legal policies for data protection in the cloud [6].  
Figure 1: Law-aware semantic policy infrastructure, where a TLD is a legal cage, designated as an SPD, to circumscribe the legal virtual boundary of data disclosure and usage. A TVD is a logical cage that provides security and privacy policies, corresponding to semantic legal policies.
# Antenna-on-Chip for Millimeter Wave Applications Using CMOS Process Technology
## 4 Discussion  
From the above parameter simulation, it can be known that the grounding part of GSG can achieve impedance matching through our hole design so that the design frequency band can reach the standard reflection coefficient of \(-10\) dB and achieve a smaller value of reflection coefficient (S11). By designing AMC in the M1 layer, the antenna-on-chip can achieve a better performance and widen the bandwidth. Coupled with the advantages of the high gain and high directivity of the patch antenna, it can complement the lack of performance of the antenna-on-chip. As shows in Table 4, the measurement results of the antenna designed in this paper are more comprehensive than the bandwidth of the literature [8, 9, 15, 17, 18, 19, 20, 21, 22, 23] and have more and broader bandwidth application advantages. In terms of gain, it has a more significant gain value than those in the literature [17, 23]. Furthermore, the area part is smaller than the area used in the literature [18, 20], using a smaller area to achieve a broader bandwidth.
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up
### Active Shape Model (ASM)  
ASM is a deformable model, which is generated from a statistical model containing variations created from a training set of labeled images. The proposed ASM is detailed in Figure 4.  
ASM consists of two main stages: (a) training or generation of the point distribution model (PDM) and (b) an adjustment stage. The PDM is trained on a set of annotated images; a perpendicular gray-level image model or a gradient profile is extracted for each PDM landmark, usually trained with the same set of annotated images called the training set. In each image of the training set, several reference points (landmarks) are annotated (manually) along the contour of the object of interest, as illustrated in Figure 5, where an image with 50 landmarks can be observed.  
Figure 4: The tweaking of the model was carried out in two phases: training and adjustment.  
Figure 3: Movement region (green rectangle) bounded by the extreme points: \(E1(x_{4},y_{1})\), \(E2(x_{2},y_{1})\), \(E3(x_{2},y_{3})\), and \(E4(x_{4},y_{3})\), of the lines \(L_{1}\) and \(L_{2}\), whose direction vectors are the eigenvectors \([\vec{v}_{1},\vec{v}_{2}]\).  
Afterward, a non-aligned set of images of the pedestrian marked with 50 landmarks in each one of the frames is obtained, as exemplified in Figure 6.  
The set of annotated contours is aligned to remove differences in the pose (translation at \(X_{t}\) and \(Y_{t}\), scaling \(s\), and rotation \(\theta\)). Then, principal component analysis is performed to identify the main modes of shape variation in the aligned set of annotated contours. Next, image models are built, obtaining the gray or gradient levels for each point of the PDM by sampling the image profile perpendicular to the contour of the object at each landmark (\(l\)) for each of the training images, as is illustrated in Figure 7. The mean image profile \(\overline{y}_{i}\) and the corresponding profile covariance \(\sum_{i}\) are calculated for each point \(l\) of the PDM. In our example, it is necessary to calculate 50 average profiles perpendicularly and thus obtain the covariance matrices of each image [35].  
Figure 5: Example of marking with 50 characteristic points (landmarks) in the PDM stage of the office scene (frame 23) of CDnet 2014.  
Figure 6: Representation of the misaligned shapes of the training set with 50 points marked.  
Figure 7: Representation of the misaligned shapes of the training set.  
In summary, the ASM-based algorithm consists of the following steps: (1) assignment of reference points or landmarks, (2) application of principal components analysis, (3)adjustment of the PDM, and (4) modeling of the local structure. Figure 8 shows a person with 50 manually selected landmarks on the contour of a training set. The necessary transformations for the alignments are determined in an iterative process.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 4 Experiment Results and Discussion
### Results and Discussion
#### 4.2.2 Warning Notification Result  
It can be concluded that if the \"danger score\" calculated in the previous section is above 1, the decision support system sends an SMS warning notification to the smartphones of crew members (as shown in Figure 14 green rectangular), and a warning notification pops up on the web application platform (as shown by the green rectangle in Figure 15).  
Figure 14: SMS warning notification.  
Figure 13: The predicted “danger score”.  
Except for the above warning for the vessel attitude motion's danger status, if the temperature and humidity exceed the threshold value, the decision support system also sends an SMS warning notification to the smartphones of crew members (as shown by the red rectangle in Figure 14), and a warning notification pops up on the web application platform (as shown by the red rectangle in Figure 15). In addition, the GPS module provides real-time location data, enabling vessel managers to monitor the vessel's position in real time. This helps in ensuring the vessel stays on the planned route and facilitates search-and-rescue operations if needed. The surveillance camera monitors the cockpit and deck areas. It can detect if the cockpit is unattended or if there are personnel near the deck edges, and alert the vessel managers in such situations, enhancing crew safety. These components collectively contribute to a more robust and multifaceted safety monitoring system.  
Statistical analysis of 20 field trials revealed that the average latency remained consistently below 5 s from generating the \"danger score\" to SMS delivery. This quick response allows for the crew to take prompt action for safety. Meanwhile, shore-based managers can monitor vessel status via the web platform for timely decisions. The system thus meets the real-time demands for effective maritime safety management.
## 3 Results and Discussion  
### Mesh Size Study  
The choice of mesh size is closely linked to the nature of the underlying mechanic phenomenon and the objectives of the simulation. This mainly results from its direct effectiveness both on the precision of the results and on the time necessary to carry out the calculation. Although the use of a coarse mesh makes it possible to reduce the calculation time, it results in lower precision of the results. On the contrary, using a finer mesh results in an increase in calculation time, but allows for better-quality results. Thus, during each simulation, it is essential to seek the right accommodation between the size of the mesh, the calculation time, and the precision of the obtained results. In this section, our attention was focused on the impact of mesh size on the components of the cutting force when modeling the milling process. To this end, eight simulations were carried out by adjusting the mesh dimensions, varying from 0.2 mm to 0.9 mm. The simulations were carried out  
Figure 6: (**a**) Milling planar representation of the NHC core; (**b**) Boundary conditions adopted in the numerical modeling [21].  
with the same cutting conditions, including a feed rate of 3000 mm/min, a spindle speed of 5000 rpm, and an amplitude vibration is 25 \(\upmu\)m. The obtained results are shown in Figure 7.  
The convergence range of the mesh is remarkably established between 0.2 mm and 0.6 mm; this observation remains consistent for the two components F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\). However, the cutting force components recorded a weak decrease between sizes 0.7 mm and 0.8 mm, followed by a sharp increase between sizes 0.8 mm and 0.9 mm. The noticed variation can be credited to the elastic-restoring forces of the NHC core walls. Thus, as soon as the element reaches its breaking point, it is deleted, which results in a loss of contact between the wall of the structure and the milling cutter, leading to a reduction in the F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\) components. Effectively, the force resulting from spring back due to the uncut walls resists the rotation of the tool, thereby increasing the cutting force components. Despite this, small elements have a lower capacity to resist deformation. Therefore, mesh distortion issues manifest themselves more quickly, which can lead to calculation failures. In principle, a mesh size of 0.2 mm seems to provide an ideal compromise between the accuracy of the results and the efficiency of the calculation. However, it was essential to re-evaluate this approach and adapt it to a size of 0.4 mm in order to achieve the best balance between mesh quality and the calculation time. The objective of this adaptation is to guarantee an appropriate resolution of physical phenomena while preventing the deformation of the elements.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### Existing ML-Based approaches
#### 8.2.3 Deep Reinforcement Learning  
Adapting to new or constantly evolving attacks is a major drawback in classical ML and DL algorithms due to their dependence on the fixed features of existing attacks provided by the dataset for the learning process, which limits the implementation of algorithms in applications that are vulnerable to dynamic intrusions [80]. Research activities have searched for a more efficient solution by integrating DL methods with RL, which has proved effective in various IDS applications for detecting sophisticated types of cyberattacks, especially in real-time and adversarial environments [80]. For instance, attacks that affect both the physical and MAC layers were effectively detected using a proposed deep reinforcement learning (DRL) model that relied only on partial observations. In [81], a new DRL-based IDS for WSNs was designed considering link invulnerability and node importance.
## 3 Results and Discussion
### Grain Yield and Protein Content
Certain microorganism species (plant growth-promoting rhizobacteria) can extract nutrients from the soil or the atmosphere and, consequently, contribute to improved plant nutrition [20; 21; 28; 69]; however, these may not be sufficient quantities to satisfy all plant nutrient needs [70; 71; 72]. In the authors' own study, the highest protein contents in grains were noted in 2018; lower contents were observed in other years (Figure 2, Table 4). [PERSON] et al. [73] showed that weather conditions, including the temperature, insolation duration and rainfall during the plant growth period, are the main factors that determine the wheat protein content and quality. Protein accumulation under high temperatures and no rainfall conditions is related to metabolism, transport, photosynthesis, responses to stress, detoxication and protein synthesis [74]. According to [PERSON] et al. [75], [PERSON] et al. [76] and [PERSON] et al. [55], mild stress due to water shortage can promote the stabilisation of carbon assimilates into the grains, thus speeding up the grain filling and ultimately improving the yield quality. The influence of microbial preparations on the protein content in wheat grain was not demonstrated (Figure 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **NPK** & **NPK + PA \({}^{*}\)** & **NPK + BM \({}^{**}\)** & **NPK + BS \({}^{***}\)** & **NPK + PA + BM + BS** \\ \hline
2017 & 103 \(\pm\) 2.36 b  & 108 \(\pm\) 4.43 b  & 108 \(\pm\) 4.24 b  & 109 \(\pm\) 5.57 b  & 109 \(\pm\) 3.30 b  \\
2018 & 121 \(\pm\) 4.78 a  & 121 \(\pm\) 3.21 a  & 121 \(\pm\) 3.30 a  & 121 \(\pm\) 6.39 a  \\
2019 & 109 \(\pm\) 2.50 b  & 108 \(\pm\) 3.56 b  & 106 \(\pm\) 3.77 b  & 110 \(\pm\) 2.89 b  \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Protein content in wheat grains after application of microbial preparations (mean value and standard deviation), interaction between the years and treatments, g kg\({}^{-1}\). Figure 2: Protein content in wheat grains after application of microbial preparations (mean value and standard deviation), (**a**) average for the years, (**b**) average for treatment, g kg\({}^{-1}\). Values followed by the same letters do not differ significantly in [PERSON]’s (HSD) test (_p_ < 0.05). PA—_Parenichcallus megatorium; \({}^{**}\)_BM—_Bacillus megaterium; BS—_Bacillus subtilis._
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 3 Idle/Busy Time Segmentation  
Airspace sectors in idle or busy time segments change air posture as the number, speed and heading of aircraft change. In order to accurately assess the airspace traffic and conflict situation, and to divide the sector to meet the requirements of busy/idle time operation, six topological indicators reflecting the local and global information of the airspace: total node degree, average node degree, average point strength, average weighted aggregation coefficient, network density, and network efficiency are selected as the influencing factors to divide the idle/busy time period.