esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
One limitation of this study is related to the limited dataset. On one hand, field-scale yield data (especially in Europe, where such information is often controlled in the framework of the EC aids in agriculture) is difficult to obtain from farmers. On the other hand, the analysis was limited to only 3 years of agricultural harvests. Considering that seven agricultural seasons have passed since the two twin Sentinel-2 satellites became fully operational in the second half of 2017 [42], and given that crop rotation is a mandatory (and necessary) practice in Italy [82], this limitation remains intrinsic to the use of Sentinel-2 data until the continuation of the mission increases the available dataset. Other limitations of this study include the need to run the method multiple times when using different indices or fields, treating them as separate variables. Additionally, the method does not directly account for temperature data, which, in cases of significant annual variation, can be important in determining the optimal period and should be addressed separately.  
Although many scientific reviews have explored the identification of the most representative forecasting period (e.g., [7; 29], our literature research does not reveal any model specifically designed to determine the optimal measurement period tailored to individual fields. In machine learning models, this is understandable, as it can be addressed for each model by adjusting input periods [50], which can help determine the most advantageous time to deliver forecasts to the end user [7]. However, the agricultural sector is constantly evolving, with frequent changes in field-level conditions (e.g., changes in practices, planting dates, etc.). Consequently, homogeneous data are often insufficient to train ML models [83]. In such cases, forecasts may also rely on pre-existing statistical models [7; 84; 85], potentially integrated with process-based models [36], and anchored by fixed parameters for key  
Figure 12: February (NDVI-based) Maximum Value Composite for the years 2018, 2020, and 2022.  
inputs, such as the optimal time interval for image acquisition based on the crop growth phase [29].
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 5 Publicly Available Datasets for Medical Video Analysis
### GastroLab Dataset  
The GastroLab dataset [31] includes various endoscopy videos for research and educational purposes. The captured videos are of different organs, e.g., the colon, duodenum, oesophagus, and stomach, and they show several medical conditions such as cancer, ulcer, [PERSON]'s disease, and adenoma. All the videos are of different sizes and resolutions.
## 5 Conclusions  
We determined the sediment provenance to understand the paleoenvironment in the inner slope of the Chukchi Rise using mineralogy and Nd isotope analysis. The major findings are as follows.  
Core ARA09C-St03 provides a continuous record of the late MIS 5 to the Holocene in the study area. The core was divided into eight sedimentary units based on a lithological description. The sediment provenances of core ARA09C-St03 were determined to be the East Siberian Sea and the North American region, and changes in sediment provenance are controlled by climate-dependent particle size.  
Evidence that sediment from Units 3, 5, and 8 corresponds to glacial/stadial periods was supplied mainly from the East Siberian Sea via meltwater-derived suspension. Units 2, 4, and 7 were characterized by abundant IRD and sourced from North America. The North American-derived materials were transported by icebergs and reflect the initiation and disintegration of the Laurentide Ice Sheet. During MIS 3-4 and 5, the units received sediment from the Canadian Archipelago, while the source area of Unit 2's sediment during MIS 2 was determined to be the Beaufort Sea. These differences in sediment provenance may be related to the scale of the Laurentide Ice Sheet. Interglacial sediments are of mixed origin from Eurasia and the Canadian Archipelago and may have been transported by oceanic current and seasonal sea ice. Considered in tandem with the results from adjacent cores, these periods were likely associated with the negative AO's intensification of the BG.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11146491/s1](https://www.mdpi.com/article/10.3390/app11146491/s1)]([https://www.mdpi.com/article/10.3390/app11146491/s1](https://www.mdpi.com/article/10.3390/app11146491/s1)).  
Conceptualization and methodology, H.-J.K., Y.-K.J., and H.-G.C.; investigation, H.-J.K., and H.-G.C.; data curation, H.-J.K.; writing--original draft preparation, H.-J.K.; writing--review and editing, Y.-K.J. and H.-G.C.; supervision, H.-G.C.; project administration, Y.-K.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Polar Research Institute (project numbers PM16050 and 1525011795) funded by the Ministry of Oceans and Fisheries, Korea.  
Not applicable.  
The datasets generated for this study are available from the Supplementary Materials.  
The authors declare no conflict of interest.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 7 Conclusions  
This paper proposes a new method for data downloading in challenged networks, as an alternative to direct file transfer or to custom scripts. It is based on DTNbox, a DTN application for directory synchronization, recently implemented by the authors. The advantage of the proposed approach is that it decouples data generation from actual transmission, now completely delegated to DTNbox. The only requirements are that the source application save its data as files (e.g., images or experiment results), and that it be delay-tolerant. Coping with the challenges of the network is left entirely to DTNbox. The validity of this approach has been assessed by considering a complex Mars to Earth scenario, representative of future space missions. The results obtained, using a virtual testbed, show that the delay added by DTNbox processing is negligible. This confirms the validity of the proposed directory synchronization approach, as an alternative to direct data download, in interplanetary environments.  
[PERSON] is the main developer of DTNbox; [PERSON] is the principal investigator and the research coordinator.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### Resource Management  
IEEE 802.1 Qcc [45] extends the SRP capabilities for resource management and network configuration purposes. In particular, IEEE 802.1 Qcc describes protocols to provide support for Configurable Stream Reservation classes and streams. The IEEE 802.1 Qcc standard defines three network configuration models, i.e., the fully distributed model, the centralized network/distributed user model, and the fully centralized model. In the fully distributed model, the end nodes (talker and listeners) of a stream communicate the user stream requirements directly through a protocol, and the entire network is configured in a distributed way. In the centralized network/distributed user model, the end nodes of a stream communicate the user stream requirements to a Centralized Network Configurator (CNC). The CNC calculates the configuration and propagates it to the bridges using a remote network management protocol. In the fully centralized model, one Centralized User Configuration (CUC) entity is in charge of discovering end nodes and application requirements to calculate the stream requirements that are in turn transmitted to the CNC. The latter is in charge of calculating and propagating the network configuration to the bridges. The fully centralized model is suitable for automotive applications, as the timing requirements of the flows depend on the physical environment under control.  
In the following, an overview on the TSN standards supporting Automated Driving is provided.
## 5 Space-Time Correlation between AE and Seismic Events
\(C^{+}\) (\(r\), \(\tau\)), for example, gives the occurrence probability of an AE burst followed by an earthquake in the next interval \(\tau\) and within a radius \(r\) of the monitoring site. We applied the modified integral (Equation 6) to investigate the space-time correlation between the two above-mentioned regional seismic sequences and the AE time series from the Asinelli Tower and the Varallo Chapel (see Figures 4 and 6). The AE signals originating from damage sources definitely localized in the building's masonry are filtered out. In this way, we can preliminarily distinguish between environmental contributions due to crustal trembling (external source) and structural damage contributions (inner sources localized by means of triangulation techniques [18]). Then, by applying the modified Grassberger-Procaccia correlation integral to the remaining AE data series and to the earthquake sequence, we obtained the cumulative probabilities \(C^{\pm}(r\), \(\tau)\) as functions of time \(C^{+}(\tau)\) for different values of range \(r\). This provides two-dimensional plots, which are easier to read than a three-dimensional representation of \(C^{\pm}(r\), \(\tau)\). Figure 9a,b show that \(C^{+}{}_{r}>C^{-}{}_{r}\) for all considered values of range \(r\), suggesting that AE bursts are more likely to precede earthquakes than to follow them. The interpretation of this evidence is that the monitored structures behave as receptors of microseismic precursive activity during the preparation of a seismic event, _i.e._, as sensitive earthquake receptors. Figure 9: _Cont._
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Route and Charging Scheduling of UAVs and UGVs  
UAVs with low battery state-of-charge should plan their trajectories on the basis of location, position and altitude in order to reduce battery consumption along with avoiding mission failure and collisions. Thus, efficient and stable charging mechanisms are required to support fast charging. Moreover, considering the high mobility of UGVs and UAVs and the associated network topology, a distributed approach to energy and route scheduling is required to carry out real-time decisions.
# Review of Weed Detection Methods Based on Computer Vision
## 4 Weed Detection and Identification Methods Based on Deep Learning  
The great progress and popularization of image-capturing devices have made capturing images easy. Meanwhile, the cost of computer hardware has been greatly reduced, and the computing power of GPU has been remarkably improved. Deep learning has been extended to the agricultural field [118; 119; 120]. Methods based on deep learning have achieved good results in weed detection and classification [121]. Although traditional ML methods are easy to understand and many improvements have been made, most of them are verified in low-density images. Occlusion, clustering, and changing lighting conditions in a natural environment remain major challenges in detection and localization [122].  
Deep learning has a unique network feature structure, and features extracted using various deep learning methods are more effective than manually extracted features. Higher-level features can be obtained by learning local features from the bottom and then synthesizing those features from the top. Diverse features at different levels can correspond to various tasks. In the field of weed detection, deep learning methods use spatial and semantic feature differences to realize the identification and detection of crops and weeds and effectively improve the accuracy of weed identification and detection. In recent years, commonly used deep learning networks to solve the problem of weed detection include CNNs and fully convolutional networks (FCNs). Various methods in semi- and unsupervised fields have also emerged to reduce the labeling cost. In many cases, classification results obtained using these deep learning algorithms are better than those generated using traditional algorithms [123]. The use of traditional algorithms to classify different types of crops with high accuracy is still difficult. Deep learning methods need to rely on a large number of datasets for training, and the difficulty of collecting crop and weed images also demonstrates the disadvantages of deep learning methods for weed identification.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 4 Graph-Based RA in Cellular Homogeneous and Het-Nets
### RA Methods with User and Cell Clustering
The channels assigned to each group are assumed to be orthogonal, and the algorithm's task is to allocate power to each UE in the group in accordance with its channel quality and PCE. A directed graph (pointing from the user with the highest channel gain to that with the lowest) is constructed, which includes all users, including a set of virtual users (each representing a user group), with the edges connecting only users in different groups. The edges' weights illustrate the PCE differences between two vertices. Power allocation is performed via a fast greedy algorithm that aims to find the minimum total edge weight between every two vertices. The solution provides fast convergence and significantly lower total network transmit power \(P_{T}\) than the alternatives. In a similar manner, the authors of [27] group the users in clusters, allocating orthogonal sets of RBs for each cluster (then, only the intra-cluster interference needs to be considered), and then solve the joint RA and energy efficiency (EE) problem for a NOMA system. Their focus is on matching the users to the RBs via a many-to-one bipartite graph i.e., each user is matched to a single RB, while each RB matches with \(L_{u}\) users which operate through NOMA (\(L_{u}=\lceil N_{UE}/N_{RB}\rceil\), where \(\lceil.\rceil\) indicates the ceiling and \(N_{RB}\) is the overall number or RBs). The UEs swap their RBs until maximum EE is achieved for each cluster (power is assumed to be known). Then, for each RB, a power allocation which aims to maximize the throughput with the constraint of the maximum transmission power is performed. This problem is solved analytically for the general case of two users per RB (i.e., just two transmission power levels), for variable numbers of UEs, RBs and maximum power levels, achieving decreased computational complexity. Convergence of the EE \(\varepsilon\) is reached at \(P_{max}=-5\) dBm.
## 5 Final Simulated and Measured Results
Our design is compact both in physical and electrical lengths. Moreover, the design has a very wide band with a good CP performance. All these parameters and results confirm the novelty of the design. Based on the results, the proposed design is a good candidate for the RADAR and satellite wireless application. Figure 14: Simulated and measured far field patterns at 10.5 GHz in (**a**) \(\phi=0^{\circ}\), (**b**) \(\phi=90^{\circ}\). Figure 8:0 Simulated and measured radiation patterns at 9.44 GHz in (**a**) XY plane (E-field), and (**b**) YZ plane (H-field) of the proposed design. Figure 8:1 Simulated and measured radiation pattern at 13.13 GHz in (**a**) XY plane (E-field), and (**b**) YZ plane (H-field) of the proposed design. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Size (mm\({}^{3}\))/\(\lambda^{3}\)** & **BW (GHz)** & **AR BW** & **AR BW** & **Gain** & **Max. Eff.** & **Ref.** \\ \hline \(90\times 90\times 15/1.88\times 1.88\times 0.31\) & 0.25 & 0.25 & 5.5 & 6–7 dBic & 96 & [30] \\ \hline \(63\times 75\times 6.1/1.05\times 1.25\times 0.10\) & 10.9 & 0.7 & \begin{tabular}{c} 17.28, \\ 11.56 \\ \end{tabular} & 7 dBi & 83 & [32] \\ \hline \(23\times 23\times 2/1.64\times 1.64\times 0.14\) & 3 & 2.5 & 27 & 6.5 dB & – & [33] \\ \hline \(40\times 40\times 18/0.94\times 0.94\times 0.42\) & 1 & 0.4 & 17.5 & 4.2 dBic & 87 & [34] \\ \hline \(40\times 32\times 14/1.04\times 0.83\times 0.36\) & 1.5 & 1.1 & 29.9 & 2.8 dBic & 94 & [35] \\ \hline \(80\times 80\times 5/2.65\times 2.65\times 0.16\) & 4.1 & 3.3 & 56.6 & 2.7 dBic & 80 & [36] \\ \hline \(44\times 45\times 11/2.65\times 2.65\times 0.16\) & 0.5 & 0.1 & 7.94 & 4 dB & 95 & [37] \\ \hline \(45\times 40\times 15/1.15\times 1.02\times 0.38\) & 0.9 & 0.4 & 12.3 & 5.2 dBic & 88 & [38] \\ \hline \(50\times 50\times 5/2.08\times 2.08\times 0.20\) & 3,1.1 & \begin{tabular}{c} 1.3, \\ 0.3 \\ \end{tabular} & \begin{tabular}{c} 19.34, \\ 3.15 \\ \end{tabular} & \begin{tabular}{c} 4.38 dBi, \\ 6.4 dBi \\ \end{tabular} & 80 & [39] \\ \hline \(50\times 50\times 10/3.95\times 3.95\times 0.79\) & 0.4,1.1 & \begin{tabular}{c} 0.2, \\ 0.2 \\ \end{tabular} & \
\begin{tabular}{c} 1.6, \\ 1.8 \\ \end{tabular} &
\begin{tabular}{c} 2.2 dBi, \\ 3.7 dBi \\ \end{tabular} & 90 & [40] \\ \hline \(50\times 50\times 16/1.29\times 1.29\times 0.41\) & 2.0 & 0.3 & 7.7 & 5 dBic & – & [41] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the proposed design with the state-of-the-art published work. Figure 17: Fabricated prototype: (**a**) top view, (**b**) bottom view, (**c**) measurement setup in anechoic chamber.
## 2 System Design  
A wireless sensor network can have different topologies and structures. The restrictive environment of a buried pipeline enforces many limitations on the overall structure of the UWSN. The RF transmission range in soil is significantly lower than in air, therefore communication between nodes is much more limited. This imposes limitations on routing protocols and the overall structure of the UWSN. Moreover, the topology of the network is restricted by the topology of the pipeline. Figure 1 illustrates the general schematics of the proposed UWSN for pipeline monitoring. In the proposed UWSN each node communicates with both nodes in front and behind itself via RF signals. For every 4-5 nodes (up to maximum of 10 nodes) there is a master node which has the capability to communicate with the sensor nodes via RF transmission. Moreover, these master nodes should be able to connect to the internet and transmit the received data from the nodes to the cloud. Data in the cloud can then be accessed via different devices with internet connectivity.
## 3 Proposed Method
### Data Pre-Processing  
The purpose of data pre-processing is to prepare the input data for the subsequent classification network. We conducted two branches in the proposed classification network to extract two different types of features of HSI, so we need to prepare two types of data for them in this step.  
The first feature extraction branch is a 2D-FCN which can be utilized to extract our proposed spectral correlation feature. The HSI always includes three dimensions, X, Y, and Z, where X and Y are the spatial dimensions, and Z is the spectral dimension. To extract the correlation information between different spectral bands, we try to compute the self-correlation matrix of the spectral vectors at each pixel. Each element of the self-correlation matrix describes the correlation of two spectral bands at that pixel. We compute the spectral self-correlation matrix corresponding to all pixels within an image patch and stacked them together to form new image data. When we perform a convolution operation on this matrix, we can correlate information between any two spectra even if the convolution kernel is small.  
The detail of SSDT is illustrated in Figure 4 given original input data A \(\epsilon R^{W\times H\times C}\), where \(W\times H\) refers to the spatial size and C is the number of spectral bands. For each pixel in the spatial dimensions, we can obtain a one-dimensional spectral vector \(v_{(x,y)}\epsilon R^{1\times C}\) and reshape it into \(v^{\prime}_{(x,y)}\epsilon R^{\times 1}\). Then, multiplication of the vector is executed between \(v^{\prime}_{(x,y)}\) and \(v_{(x,y)}\), and we obtain a spectral self-correlation matrix (SCM) B \(\epsilon R^{C\times C}\):  
\[B=v^{\prime}_{(x,y)}\times v_{(x,y)}, \tag{5}\]  
where \(\times\) is the multiplication of the vector, and the value of the matrix indicates a weighting with other spectral bands. Obviously, the matrix is symmetrically distributed, with the maximum value on the diagonal. the above operation is repeated, and we can convert the HSI data from spatial-spectral dimensions to spectral correlation spatial dimensions. The data size is converted from \(W\times H\times C\) to \(C\times C\times(W\times H)\). Meanwhile, as the hyperspectral data change, we also reshape the corresponding label vector into \(1\times 1\times(W\times H)\).  
Figure 4: The details of the spatial and spectral dimensional transformation (SSDT).  
The second feature extraction branch is a 3D-FCN, which can extract the spatial and spectral features simultaneously. A 3D convolutional kernel can cover the spatial and spectral dimensions at the same time, so we do not need to perform any additional operations on the input data of this branch; we can just use the data after the data partitioning. Therefore, the data size for the second branch is \(\mathrm{W}\times\mathrm{H}\times\mathrm{C}\), and the corresponding label size is \(1\times 1\times\mathrm{C}\).
## 4 Description of Proposed Protocol
### Security Analysis  
In this section, we analyze the secrecy performance of the proposed scheme. For the proposed system, there is an eavesdropper, denoted as Eve, that will eavesdrop the transmitted information. We denote the channels between \(N_{a}\) and Eve and between \(N_{j}\) and Eve are \(h_{a,e}\) and \(h_{j,e}\), respectively. We assume that \(N_{a}\) and \(N_{j}\) utilize the different codebook. Therefore, the wiretap rates in the first and second hops are  
\[C_{a,e}=B\text{log}_{2}\left[1+\frac{P(L_{a,e})^{-\gamma}|h_{a,e}|^{2}}{n_{0}B} \right]\leq R_{a,e}^{th}, \tag{19}\]  
and  
\[C_{j,e}=B\text{log}_{2}\left[1+\frac{P\big{(}L_{j,e}\big{)}^{-\gamma}|h_{j,e}|^ {2}}{n_{0}B}\right]\leq R_{j,e}^{th}, \tag{20}\]  
where \(R_{a,e}^{th}\) and \(R_{j,e}^{th}\) represent the maximized permitted wiretap rates in the first and second hops, respectively.  
Based on the above analysis, the security outage probability \(P_{o}\) is derived as  
\[P_{o} = 1-\left(1-\text{Pr}\left(C_{a,e}\leq R_{a,e}^{th}\right)\right) \left(1-\text{Pr}\left(C_{j,e}\leq R_{j,e}^{th}\right)\right)\] \[= 1-\exp\left(-\frac{\lambda\left(2^{\frac{R_{a,e}^{th}}{B}}-1 \right)n_{0}B}{P\big{(}L_{j,e}\big{)}^{-\gamma}}\right)\exp\left(-\frac{ \lambda\left(2^{\frac{R_{a,e}^{th}}{B}}-1\right)n_{0}B}{P\big{(}L_{j,e}\big{)} ^{-\gamma}}\right).\]  
Therefore, we can make full use of the physical characteristics of wireless channels that enables messages to be transferred securely and prevents eavesdroppers from decoding confidential information.