esa-sceva/satcom-chunk-collection huggingface dataset:
# Value of Information Based Data Retrieval in UWSNs
## 4 Path Planning Algorithms
### Lawn-Mower Path Planner-LPP  
The LPP algorithm is based on a pre-computing strategy. It determines the route before-hand and it does not take into account the Vol profiles of the sensor nodes for its path planning decisions. The crux of this algorithm is to find the most optimal tour in terms of time traveled. This, in essence, is like solving the traveling salesman problem (TSP). The title Lawn-Mower is motivated by the analogy that the AUV will cover the area in parallel tracks in the same fashion as the grass is moved down on a lawn using a lawn-mowing engine. A potential path planned using the LPP algorithm is shown with the blue colored trail in Figure 1.  
Please note that this path planner does not explicitly take Vol into account but it should give effective results as it essentially takes into consideration the minimization of time \(t\), which is a parameter in determining Vol as suggested by Equations (17), (19) and (20). Therefore, this algorithm implicitly improves Vol by taking care of overall visitation time.  
Algorithm 1 highlights the steps involved for the Lawn-Mower path planner. The AUV maintains a history of nodes that it has already visited and the ones that it has yet to visit. Besides this, it also maintains a direction priority list. This list contains elements related to all the sensor nodes such that each element contains the information about which node among the neighbors of a sensor node needs to be visited next. For our implementation, we prioritize the selection of the next neighbor that is located on the East of the current node, then West, followed by South and finally North. The complexity of LPP is \(\mathcal{O}(n)\) where n is the number of sensor nodes.  
A more generic implementation of this algorithm, for any topology other than a mesh, would be to use an algorithm that solves the TSP. However, for a mesh, instead of using a computationally expensive TSP algorithm, one can just simply use the LPP.  
```
1:procedureLPP(\(S\))
2:\(S\leftarrow\{s_{1},s_{2},...,s_{n}\}\)\(\triangleright\) Set of sensor nodes
3:\(V\leftarrow\emptyset\)\(\triangleright\) Visitation sequence
4:\(P_{D}\leftarrow\{(East,West,South,North)\}\)\(\triangleright\) Direction priority list
5:\(i\gets s_{1}\)\(\triangleright\) Tour starting node
6:while\(S\
eq\emptyset\)do
7:\(N\leftarrow\textsc{neighborhood}(i,S)\)
8:\(j\gets s_{x}\) from \(N\) in the direction given by \(P_{D}\)
9:\(\textsc{toReculidean}(i,j)\)
10:\(V\gets V+j\)
11:\(S\gets S-j\)
12:\(i\gets j\)
13:endwhile
14:return\(V\)
15:endprocedure
```  
**Algorithm 1** Shortest Path Lawn-Mower Path Planner-**LPP**.
## 2 Signal Model
### Imaging Geometry Model  
As shown in Figure 1, the hypersonic platform SAR moves along an approximate arc trajectory. Accordingly, an arc band-shaped observation scene can be illuminated by fixing the antenna beam pointing. Here, \(L\) represents the turning radius of the platform, \(\omega_{s}\) denotes the angular velocity, \(r\) stands for arbitrary position of target \(p\) in the imaging scene, and \(r_{cen}\) is the center of the scene.
# Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications
## 1 Introduction
The numerical calculation and measurement results reveal that the proposed antenna covers various mobile and wireless communication systems such as 5G NR n1 band (at 2.09 GHz), Wi-Fi (IEEE 802.11a/n/ac), Bluetooth (5 GHz), WLAN (at 5.3 GHz), Commercial Wi-Fi (at 5.3 GHz), Long Term Evaluation (LTE) (at 2.15 GHz), 3G (UMTS), and ISM band (at 5 GHz).
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 3 Algorithm
### Detection of Type 1 Vibrations
#### 3.3.2 Refinement of Type 1 Vibrations  
For each identified Type 1 vibration \(V_{1,j_{1}}\), all corresponding peaks \(\hat{p}_{i,n}\) in the linear-scale spectrogram were identified. If fewer than five peaks were identified, the vibration \(V_{1,i}\) was considered invalid and removed from the result set. For each remaining Type 1 vibration, the set of all peaks \(P_{v1,j_{1}}\) was created with elements \(P_{v1,j_{1}}=\{\hat{p}_{i,n}\}\) for \(k_{v1,j_{1}}-w_{v1,j_{1}}\leq n\leq k_{v1,j_{1}}+w_{v1,j_{1}}\) and \(i=1,...,L_{G}\). Those peaks were used to derive the dependency on the reference variable \(\hat{r}\) by fitting the first-degree polynomial  
\[f_{v1,j_{1}}(\hat{r})=c_{0,j_{1}}+c_{1,j_{1}}\hat{r} \tag{8}\]  
to identified peak locations \(l_{j,n}\in P_{v1,j_{1}}\) by using robust linear least squares fitting. Here, \(c_{0,j_{1}}\) and \(c_{1,j_{1}}\) corresponded to the fitting parameters for vibration \(j_{1}\) and estimated functions \(f_{v1,j_{1}}\) represent the variation of discrete frequencies \(k_{v1,j_{1}}\) with the reference.  
Resulting functions \(f_{v1,j}(\hat{r})\) were stored for further analysis. The majority of peaks corresponding to each Type 1 vibrations (67%) were removed from the set of all identified peaks \(P_{all}\). However, the most prominent 33% of peaks corresponding to each Type 1 vibration \(j\) were kept in the dataset to account for potential overlaps of Type 1 and Type 2 vibrations. All peaks related to Type 1 vibrations \(P_{v1}\) are displayed in Figure 6 (right) in  
Figure 7: Detection of Type 1 vibrations: (**Left**) Linear-scale spectrogram with identified peaks \(P_{all}\) visualised; and (**right**) cumulative prominence \(C_{cum}(k)\) of identified peaks with highlighted locations \(P_{C}\) corresponding to locations of identified Type 1 vibrations.  
red colour. In addition, peak locations corresponding to Type 1 vibrations are displayed in Figure 7 (right).
## 2 Theory and Analysis
### The Classification and Identification Method
#### 2.2.2 Roadmap of Radiated Emission Spectrum Classification  
In order to obtain better classification results, the overall similarity parameter needs to be obtained. The unified similarity \(S(A,B)\) is used to characterize the degree of dissimilarity between the two groups of data, as shown in the following formula:  
\[S(A,B)=\omega_{1}*S_{1}(A,B)+\omega_{2}*S_{2}(A,B)+\omega_{3}*S_{3}(A,B) \tag{17}\]  
where \(S_{1}(A,B),S_{2}(A,B)\), and \(S_{3}(A,B)\) represent normalized similarity values of peak components, broadband components, and trend components. Finally, the density-based spatial clustering of applications with noise (DBSCAN) [45] can be used for the cluster analysis of \(S(A,B)\).
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 9 Multidimensional Evaluation
### Average Time a Single Message Remains in a Unique Nodes Buffer
#### 9.3.1 Black-Hole Attack  
The graphs (Figure 9) show two different averages for the buffer times of the nodes, both the mean and the median. The buffer time is the amount of time that a message is in a node's buffer. For Spray and Wait it was consistently far larger than that of the Epidemic, MaxProp and PRoPHET algorithms. This reflected the fact that during the wait phase a message can spend a very long time in the buffer until direct contact with the destination node was achieved, whereas the others were more efficient in achieving final delivery and then deleting copies of the message.  
The quadratic trend lines for the spray and wait algorithm meant that the rate at which the buffer time increases gets larger with an increasing number of black holes.  
Both of the trend lines for the MaxProp protocol showed that the buffer time was unaffected by black-hole nodes. This was due to the nature of the algorithm dropping messages once the buffer is full. This was a more controlled way to manage network traffic as it meant that nodes were not carrying excessive amounts of unnecessary data.  
Interestingly, the Epidemic and PRoPHET protocols were very similar and also far less effected by black holes than Binary Spray and Wait. It did however show a slight increase in buffer time with an increase in the proportion of black holes. This suggested that although it was definitely more resilient than the Binary Spray and Wait algorithm, Maxprop still outperformed them both.  
Both types of averages were included because, as can be seen in the graph, throughout the experiment the mean was larger than the median without fail. This showed that the data were positively skewed. The skewness showed that the majority of the buffer times were smaller than indicated in the mean, it was just a few unusually large results that increased the mean.  
Figure 9: Two averages for the buffer times throughout the experiment.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 2 Systematics and Characteristics of _Trichoderma_ [PERSON] et al. [28] documented that more than 375 species of _Trichoderma_ have been identified and characterized molecularly and morphologically. According to the current MycBank classification [29], these fungi belong to the domain _Eukaryota_, kingdom _Fungi_, subkingdom _Dikarya_, division _Ascomycata_, subdivision _Pezizomycetina_, class _Soradiomycetes_, subclass _Hypocreomyctidae_, order _Hypocreales_, and family _Hypocreaceae_. Membership of the _Ascomycata_ family is associated with the presence of asexually reproducing individuals (anamorph) and sexually reproducing individuals (teleomorph). The Centre for Agriculture and Bioscience International (CABI) has divided the species belonging to _Trichoderma_ according to their developmental form and classified the teleomorphs as a new genus _Hypocrea_[30].  
The vast majority of _Trichoderma_ species exhibit the ability to reproduce asexually by producing large numbers of single-celled conidia and thick-walled chlamospores, which act as spores. Dark green (e.g., _T. aggressivenessum, T. asperellum_) to grey (e.g., _T. erinaceum_, _T. tomentosum_) or brown (e.g., T. _melanomagna_) conidia [19; 31] are produced by multiple branched conidiophores, which are terminated by one or more fialids [32]. Circular conidial zones are made up of loose or collapsed conidiophores [33]. In contrast, _Hypocrea_ species exhibit the ability to reproduce sexually by producing haploid ascospores in the ascus that form the ascocarp (perithecia) [34]. The surface of the colony takes on different colors (most often shades of green, e.g., _T. longibrachiatum, T. reesei_), is smooth or rough, has a grayish-white or yellowish tinge, and resembles cotton wool (e.g., _T. koningiopsis, T. spirale_) [35; 36] (Figure 1). In addition, the species _T. atroviride, T. harzianum, T. koningii_, and _T. viride_ can produce volatile 6-pentyl-\(\alpha\)-pyrone (6 PP), which gives their colonies a characteristic hazelnut and coconut smell [19; 31; 37].
# Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
## 2 Materials and Methods
### Experiment Setup  
**Two-Dimensional Pose dataset.** The datasets employed in this research are self-collected infant pose data and the MS COCO dataset [25], specifically a collection of infant datasets sourced from YouTube. The MS COCO dataset comprises a diverse range of human activities and postures, featuring 200,000 image data points segregated into train/validation/test sets. The training and validation sets are annotated with up to 1.7 million labeled keypoints. One of the major challenges that we had to contend with was the unavailability of suitable data on infants. Infant data, due to its sensitive nature, is not openly available in existing base datasets. Furthermore, experimental research and studies have been conducted using adult images, and pre-training with adult datasets is believed to be advantageous in carrying over keypoint features, but with larger dimensions. In order to overcome this challenge, we performed a manual data collection of infant data from YouTube and subsequently carried out manual labeling. The dataset that was obtained comprises seven brief video segments, featuring seven distinct infants in the supine position. The videos were formatted in various resolutions and frame rates.  
**Infant Data Annotation.** Infant data were collected by the authors on YouTube. The dataset includes 8 videos with 10 s per video corresponding to 8 distinct newborns/infants (Table 1). They are labeled to include 18 keypoints, and the labeled file is saved in.json format, with occluded body joint points excluded. Following the labeling process using the VaticJS tool, we derived a dataset of 1952 images, each with approximately 35,000 labeled keypoints. The dataset was segregated into train/validation/test sets, with the test set being a distinct dataset that featured a single infant.
## 4 The Third-Order Beam Theory
### Exact Solutions for Bending
In the RBT, one is required specify \(-dw/dx\) in addition to \(\phi_{x}\) (or their dual variables, \(P_{xx}\) and \(\bar{M}_{xx}\), respectively), providing the required eight boundary conditions. The second-order derivative appearing in Equation (102) comes from \(P_{xx}\) of Equation (92). If we neglect the second-order derivative of \(w\) in Equation (102), by reasoning that they are higher-order terms (i.e., very small compared to the other terms in the equation), we obtain  
\[N_{xx}=K_{1} \tag{94}\]3 \[N_{xx}=K_{1} \tag{94}\]4 \[N_{xx}=K_{1} \tag{94}\]5 \[N_{xx}=K_{1} \tag{94}\]6The solution to these equations is (after a lengthy algebra)  
\[N_{xx}=K_{1} \tag{94}\]7 \[N_{xx}=K_{1} \tag{94}\]8 (108) \[N_{xx}=K_{1} \tag{94}\]9 \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]0 (109) \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]1 \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]2  
The corresponding solutions obtained using the first-order shear deformation theory (TBT) of beams are:  
\[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]3 \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]4 (111) \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]5  
\[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]6 \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]7 \[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]8  
A comparison of Equation (112) with Equation (109) shows that the shear correction factor predicted by the \"simplified\" RBT is \(K_{s}=14/17\), which is slightly smaller than the value suggested for rectangular cross-section beams, which is \(K_{s}=5/6\). Of course, the TBT solution is different from the RBT solution. Particularly, the expression for \(\phi_{x}\) in the RBT contains a term due to transverse shear coefficient. As an example, we present here the exact solutions using the simplified RBT of a functionally graded beam with both ends _pinned_ and subjected to a uniformly distributed load of magnitude \(q_{0}\). We take the origin of the \(x\)-coordinate at the left end of the beam, (i.e., \(0\leq x\leq L\)). For this case, the boundary conditions at both ends, \(x=0\) and \(x=L\), are:  
\[\bar{N}_{xz}=\alpha\frac{dP_{xx}}{dx}=-\int^{x}q(\xi)\,d\xi+K_{2}\]9Use of these boundary conditions yield  
\[M_{xx}=-\int^{x}\int^{\xi}q(\eta)\,d\eta\,d\xi+K_{2}x+K_{3}\equiv F (x). \tag{96}\]0 \[M_{xx}=-\int^{x}\int^{\xi}q(\eta)\,d\eta\,d\xi+K_{2}x+K_{3}\equiv F (x). \tag{96}\]1 \[M_{xx}=-\int^{x}\int^{\xi}q(\eta)\,d\eta\,d\xi+K_{2}x+K_{3}\equiv F (x). \tag{96}\]2 (115) \[M_{xx}=-\int^{x}\int^{\xi}q(\eta)\,d\eta\,d\xi+K_{2}x+K_{3}\equiv F (x). \tag{96}\]3 \[M_{xx}=-\int^{x}\int^{\xi}q(\eta)\,d\eta\,d\xi+K_{2}x+K_{3}\equiv F (x). \tag{96}\]4
## 2 RS Systems
### Active
#### 2.2.7 HF Radar  
HF radar systems are mainly land-based RS instruments that utilize high-frequency radio waves. These systems carry out Near Real-Time (NRT) measurements of the ocean surface parameters in coastal regions over relatively large areas from a few kilometers up to about 200 km from coastlines, regardless of the weather condition. Typically, HF radar operates in electromagnetic bands between 8 and 37 MHz, corresponding to wavelengths of 8-37 m. These systems transmit moderated radio waves and receive the backscattered signal, which is mainly from ocean waves spreading along the radar look direction [66]. HF radar measurements primarily rely on the Bragg scattering mechanism [67]. A growing number of HF radar systems are being installed and utilized worldwide for deriving many different oceanographic parameters, including OSC [67], OSW [68], OWH [69], tidal processes [67], ships [70], and tsunami [71]. Despite the remarkable advantages of HF radars, including high temporal and spatial resolution in multiple applications, several restrictions should be considered while using HF radar data. One of the limitations is the smaller coverage than satellite observations, which can be resolved through installing a network of HF radar antennas covering larger study areas [67]. Furthermore, HF radars provide only information from the surface of the ocean with at least a few centimeters to 1-2 m depth [66]. Another consideration is the relatively high uncertainty of the data associated with various hardware operational issues and uncertainty in the radial velocities [66].
## 3 Results
### Quantitative Assessment of Grassland Degradation in Eastern Inner Mongolia  
The annual mean GDI changes with fluctuations from 2.29 to 1.61 in eastern Inner Mongolia from 2000 to 2017, and presents a weak decreasing trend, which indicates that the grassland degradation situation of the whole region is changing from moderately degraded to lightly degraded. The non-degraded grasslands accounted for about 42.4% of the total area, while the area of lightly degraded, moderately, and seriously degraded grasslands were about 22.0%, 28.8, and 6.8% of the total area respectively. The yearly percentages of different degradation levels are shown in Table S1.  
The annual mean GDI in Xilingol League, Xing'an League, and Hulunbuir City were 2.41, 1.18, and 1.50, respectively. The situation of grassland degradation was most serious in Xilingol League, where the seriously and moderately degraded grasslands accounted for about 11.0% and 41.9% of the total area, respectively. Nevertheless, seriously degraded grasslands have little distribution in the other two regions, with proportions less than 1.0%. The situations of grassland degradation in Xing'an League and Hulunbuir City were close to the slight degradation grade, with the slightest degradation in Xing'an League, where 84.9% of the area is at the non-degradation level, and 12.3% was at the slight degradation level. While the grassland degradation in Hulunbuir City was slightly more severe than that in Xing'an League, areas of moderately and lightly degraded grasslands accounted for about 12.1% and 23.4% of its total area, respectively. The spatial distribution of grassland degradation in the year 2017 is shown in Figure 8.  
Figure 7: Spatial distribution of calculated multi-year averaged HAI (**a**), variation of total grasslands \(\mathrm{NPP_{Luc}}\) and \(\mathrm{HNPP}\) (**b**), and variation of annual mean HAI (**c**) over the period of 2000 to 2017 in eastern Inner Mongolia.
## 4 Discussion
### Maximum Packing Density Prediction
It is also worth mentioning that although the current model and its correction functions [Equations (3) and (4)] were originally developed for poured packing condition [41], the model predictions match with experimental data for sand-fines mixtures prepared under both poured and tapped packing conditions (Figure 3 and Table 1). Although the poured mixtures seem to have slightly lower \(p\) values (0.13-14 as for mixtures M1, M2 and M4), compared to tapped mixtures (0.15-0.17 for mixtures M3, M5 and M8), the difference is too small that using an average \(p\) of 0.15 works well for all mixtures (Figure 3). Compaction reduces the void ratio and impacts packing characteristics of sand-fines mixtures [65, 66, 67, 68]. Nevertheless, [PERSON] et al. [65] showed that knowledge about the void ratio of unmixed pure sand and fines at any given compaction condition is essential for the void ratio prediction of sand-fines mixtures prepared at the same condition. It might be possible that the impact of compaction/tapping on the packing of the examined mixtures is largely accounted for by using porosity or void ratio of the unmixed compacted pure sand and pure fines as inputs in the current model. Supporting the same argument, [PERSON] et al. [69] showed that \(e_{max}\) (loose packing) for a sand-fines mixture could be predicted reasonably well using the same correction functions and parameters as for the \(e_{min}\) (dense  
Figure 7: Measured maximum packing density versus predicted values obtained using three different approaches: ideal packing (black squares), incorporating \(R\) only (blue open diamond) and current model (red circles). The prediction of the three methods are close to each other only for mixtures M10 and M11 which have near-ideal packing and are characterized by high maximum packing density (\(>\)0.75). packing) case. This is given that void ratio of the pure sand and pure fines at each packing condition are used as inputs in the model [69]. Same behavior was observed for mixtures M4 and M5 where both are composed of the same sand and fines materials (Table 1) but one was prepared under loose packing condition (M4) while the other (M5) had dense/tapped packing. The void ratio of both mixtures could be however predicted using the same correction functions and parameters (Figure 3). It should be noted that similar to existing models for example, References [15; 33; 65], the current model predicts porosity or void ratio of sand-fines mixtures as function of fines fraction, given that the porosity of end members is measured or known. The new contribution here is to account for the cohesive packing in the dry binary mixtures which improves the prediction when fines have particle sizes less than 0.15 mm. The estimation of porosity for the pure unmixed end members is not the focus of the current study and the reader is referred to other relevant studies about mono-sized or narrowly graded particles packing (e.g., References [70; 71; 72; 73]).