esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area  
The study focuses on the Yangou watershed, a small catchment located in Yanan city in the north Shanxi province of China (Figure 1). It is enclosed within the latitudes 36\({}^{\circ}\)28\({}^{\prime}\) N and 36\({}^{\circ}\)34\({}^{\prime}\) N and longitudes 109\({}^{\circ}\)27\({}^{\prime}\) E and 109\({}^{\circ}\)33\({}^{\prime}\) E, covering an area of about 48 km\({}^{2}\). The catchment is typical of the gullied-hill zone and is located in the central area of the Loess Plateau, which is a sub-watershed in the Yanhe basin. The river is a secondary branch of the Yan River and flows from southeast to northwest. The watershed exhibits complex topographic variations, with a gully density of 4.8 km km\({}^{-2}\) and an elevation ranging from 988 m to 1404 m. The terrain gradient of the basin is mostly composed of steep hill slopes. The slope analysis showed that the slope distribution in the study area ranged from 0\({}^{\circ}\) to 51\({}^{\circ}\). The climate belongs to the transitional zone from warm semi-dry to semi-wet. The average annual precipitation is 550 mm, and 70% of the annual precipitation occurs from June to September. Generally, the southern section had higher values than the north. The geotechnical medium in the area can be divided into rock mass and soil mass, mainly sandstone and loess. The saturated bulk density (\(\gamma\)), Poisson's ratio, cohesion (C), and friction angle (\(\varphi\)) of sandstone are 2.68 g/cm\({}^{3}\), 0.22, 4.23 MPa, and 41.6\({}^{\circ}\), respectively. The moisture content (\(\omega\)), dry bulk density (\(\gamma\)), cohesion (C), and friction angle (\(\varphi\)) of loess are 15.6%, 1.51 g/cm\({}^{3}\), 39.48 MPa, and 26.95\({}^{\circ}\).  
Figure 1: Study area in China. (**a**) Shaanxi province in China; (**b**) Yan’an city in the Shaanxi province; (**c**) Yangou watershed in the Yan’an city.
###### Abstract  
With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with \(R^{2}\) ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.  
HJ CCD; GF-1 WFV; STDFA; phenology; time series high spatiotemporal resolution remote sensing 2015  
716293-16314; doi:10.3390/rs71215826
###### Abstract  
This work proposes a new circularly polarized (CP) 4 \(\times\) 4 magneto-electric (ME) dipole antenna array using metallic walls for 28 GHz band applications. The ME dipole element is surrounded by eight metallic walls and is excited fed by microstrip line. By introducing metallic walls, the 3-dB axial ratio (AR) bandwidth of the element is increased from 23% to 36.5%. The 4 \(\times\) 4 array is fed by a simple 1-to-16 microstrip power divider. In contrast to the conventional substrate integrated waveguide (SIW) power divider using two layers, the proposed microstrip divider only needs one substrate layer. The experimental 3-dB AR bandwidth of the array achieves 30.1%, ranging from 22.5 to 30.5 GHz, which falls inside the \(-\)10 dB impendence bandwidth. The measured maximum gain is 19.2 dBic.  
millimeter wave; circular polarization; magneto-electric dipole; wideband array; microstrip feeding network +
Footnote †: journal: _electronics_  
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Footnote 0: Copyright** © 2023 by the authors. Liconsse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)))
# Computing the Atom Graph of a Graph and the Union Join Graph of a Hypergraph
## 2 Preliminaries
A _join tree_ of \(H\) is a tree \(T\) whose node set is \(\mathcal{E}\), such that, for each vertex \(x\) of \(H\), the set \(\mathcal{E}_{x}\) of nodes of \(T\) containing \(x\) induces a subtree of \(T\), or equivalently, such that, for each pair \(\{X,Y\}\) of \(\mathcal{E}\), \(X\cap Y\) is a subset of each node of \(P_{T}(X,Y)\). \(H\) is \(\alpha\)_-acyclic_ if it has a join tree. A join tree of an \(\alpha\)-acyclic hypergraph \(H\) can be computed in \(O(s)\) time, where \(s\) is the sum of the sizes of the hyperedges of \(H\)[27]. **Property 8**. : _Let \(H=(V,\mathcal{E})\) be an \(\alpha\)-acyclic hypergraph, and let \(G\) be the 2-section graph \(2 SEC(H)\). Then, \(G\) is chordal, and if, moreover, \(H\) is a clutter, then \(\mathcal{E}=\mathcal{K}(G)\) (i.e., the set \(\mathcal{E}\) of hyperedges of \(H\) is equal to the set \(\mathcal{K}(G)\) of maximal cliques of \(G\))._It follows that the number of hyperedges of a clutter is bounded by the number of its vertices, since a chordal graph has, at most, \(n\) maximal cliques. The number of hyperedges of an \(\alpha\)-acyclic hypergraph that is not a clutter may be exponential in the number of vertices. A join tree of an \(\alpha\)-acyclic hypergraph \(H=(V,\mathcal{E})\) can be defined from its _weighted line graph_, where weights are defined as follows. The _set associated with_ a pair \(\{X,Y\}\) of \(\mathcal{E}\) is \(X\cap Y\), and its _weight_, denoted by \(w(XY)\), is \(|X\cap Y|\). Let \(K\) be a graph whose node set is \(\mathcal{E}\). The weight of \(K\) is the sum of the weights of its edges. When considered a weighted graph, \(K\) is denoted by \(K_{w}\). Thus, \(L_{w}(H)\) denotes the weighted line graph of \(H\). **Characterization 4** ([28]). : _Let \(H=(V,\mathcal{E})\) be an \(\alpha\)-acyclic (resp. connected a-acyclic) hypergraph. Then, the join trees of \(H\) are the maximum weight spanning trees of the weighted complete graph on \(\mathcal{E}\) (resp. of \(L_{w}(H)\))._  
In particular, the atom trees of a connected graph \(G\) are the maximum weight spanning trees of the weighted intersection graph of the atoms of \(G\), which is proved in the case of chordal graph in [19] (and extends to any connected graph through the chordal graph \(G^{+}\) by Property 4).
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
###### Abstract  
Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger population. Consequently, this has facilitated the ease with which counterfeiters can manipulate images. Convolutional neural network (CNN)-based feature extraction and detection techniques were used to carry out this task, which aims to identify the variations in image features between modified and non-manipulated areas. However, the effectiveness of the existing detection methods could be more efficient. The contributions of this paper include the introduction of a segmentation method to identify the forgery region in images with the U-Net model's improved structure. The suggested model connects the encoder and decoder pipeline by improving the convolution module and increasing the set of weights in the U-Net contraction and expansion path. In addition, the parameters of the U-Net network are optimized by using the grasshopper optimization algorithm (GOA). Experiments were carried out on the publicly accessible image tempering detection evaluation dataset from the Chinese Academy of Sciences Institute of Automation (CASIA) to assess the efficacy of the suggested strategy. The results show that the U-Net modifications significantly improve the overall segmentation results compared to other models. The effectiveness of this method was evaluated on CASIA, and the quantitative results obtained based on accuracy, precision, recall, and the F1 score demonstrate the superiority of the U-Net modifications over other models.  
U-Net; convolutional neural network; forged images; social media; grasshopper algorithm +
Footnote †: journal: 2023, 16, 399. [[https://doi.org/10.3390/a16090399](https://doi.org/10.3390/a16090399)]([https://doi.org/10.3390/a16090399](https://doi.org/10.3390/a16090399))
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Optimal Sizing of Buildings  
[PERSON] et al. (2014) analysed the energy impact, determining it to be the incorrect dimension of a healthcare centre ([PERSON] et al. (2014) and determined the optimal ratio between the area per user and the number of users. For a smaller number of users, more space is needed for each. This is because there are certain healthcare units that need a minimal space independently of the number of users. It is possible to achieve annual savings by optimizing the built surface area.  
In order to quantify the economic and environmental impact from energy saving by optimizing building sizing, a total of five hospitals and 30 healthcare centres were analysed. In this study, those over-dimensioned zones were identified by applying standard correlations between the area per user and the number of users.  
Table 1 shows the potential average energy saving per built surface area (m\({}^{2}\)), through the optimal sizing of buildings for healthcare buildings of less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds).  
Figure 7: Potential of energy saving derived from thermal insulation improvement.  
Table 1 shows that it is possible to obtain a mean saving value between 0.24 kWh and 0.30 kWh per built surface area in a four-year payback period.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 2 AI and Recent RA Enhancements in Literature
### Architectural Improvements for RA  
The unpredictable channel condition of mmWave networks results in a significant challenge of RA preamble detection in the physical layer. 5G and B5G services, including mMTC, evolved mobile broadband (eMBB), and URLLC, require efficiency and reliability of the initial attachment point. Because of severe congestion resulting from different mixed traffic sources originating from 5G networks, especially from mMTC, 3 GPP introduced the concept of access class barring (ACB), where some users that would request access are barred from the service. The major concern regarding RA is the methods in which the access point dynamically selects RA control parameters, such as the number of preambles/number of consecutive preambles sent by users, ACB, and UBW, depending on the load size of the cell. The load is estimated based on past channel history, and the ACB parameter should be updated dynamically depending on the load size of each cell. The calculation of load estimation and the barring rate was proposed in [5; 6].  
To reduce RA delay, proposals have considered shortening the RA procedure from a four-way handshaking system to a two-way handshaking system, as illustrated in Figure 2. For example, the authors in [35] introduced the concept of a grant-free RA (one-shot) system, where data signals are sent along with the RA request. This is a viable solution where the data size is low for mMTC. A two-phase RA was proposed in [36] where users are first grouped according to the selected random preamble; for instance, users who select a certain preamble are in one group. No collision occurs across the group; thus, in the second phase, a dedicated channel is assigned for each group to request access. The concept allows the base station to decide the number of channels that should be assigned.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 4 The Future of Farming: 6G-IoT Applications in Agriculture 5.0
### Sustainable Infrastructure and Systems  
Smart agriculture will play a key role in both economic growth and global agricultural output under the new vision of Industry 5.0, which prioritizes reducing energy consumption and promoting sustainable practices. Although 5G networks have provided a more viable solution to environmental protection and energy efficiency challenges than 4G networks, the 2030 goals for a sustainable society have not yet been met, and further holistic solutions must be explored [77].  
6G networks will provide a sustainable end-to-end network architecture, with essential green technologies such as energy harvesting and the utilization of renewable energy incorporated into their standards. Moreover, with the ubiquitous use of artificial intelligence, it will be feasible to dynamically monitor and control the end-to-end network in an agro-ecosystem in order to achieve both the desired performance and the overall dynamic management of energy consumption, embracing green principles. In accordance with the aims of Industry 5.0, it is anticipated that Agriculture 5.0 will be developed based on the 6G pillars and managed through sustainable practices [16].  
Regarding the reduction of agricultural inputs, there has been a recent shift towards the use of nano-materials such as nano-fungicides, nano-fertilizers, and synthetic nano-chemicals as a sustainable method for treating and preventing plant diseases. This approach not only substantially improves effectiveness, but also reduces the release of toxic chemicals into the environment [42]. Examples of eco-friendly nano-fungicides include metal oxides such as _Cu, TiO\({}_{2}\)_, and _ZnO_, as well as _Ag_-based nano-fungicides. These nano-fungicides have broad-spectrum antipathogenic activity and are highly effective with minimal administration [133; 134]. By enhancing sensing accuracy and reliability as discussed in Section 4.1, enabling precision agriculture even in the most remote areas (Section 4.3), and employing secure AI-based data analysis (Section 4.4), 6G-IoT will allow for a timely and precise diagnosis to be made with ultra-high-precision even for each plant individually, allowing for the best course of action to be determined.
## 3 Tire Marking Point Identification Network Structure
### YOLOv5s Model Improvements
#### 3.2.2 Loss Function  
The YOLOv5s model uses the IoU Loss, which was proposed in 2016, as the localization loss function. However, the original IoU Loss has several drawbacks. It fails to determine the distance between two frames when the predicted frame and the real frame do not intersect. Additionally, it cannot accurately determine the position and size of two frames when they have the same IoU value [21]. To address these limitations, [PERSON] introduced the GIoU (Generalized Intersection over Union) Loss in CVPR 2019 [22]. The GIoU Loss takes into account both overlapping and nonoverlapping regions, providing a more comprehensive measure of the overlap between predicted and real frames compared to the original IoU Loss. Following this, DIoU and CIoU were developed, each with their own advantages and disadvantages [23]. In an effort to generalize existing IoU loss functions, [PERSON] proposed a power transformation by adding a Box-Cox transformation to the IoU Loss [24]. By adjusting the value of the power, the Alpha-IoU Loss enables the utilization of the existing IoU to its full advantage, leading to improved target recognition accuracy. The transformation process is described by the Equation (12):  
\[L_{\alpha-IoU}=\frac{1-IoU^{\alpha}}{\alpha},\alpha>0 \tag{12}\]  
Most of the loss functions now available by conditioning \(\alpha\) include all of the loss functions mentioned above, and when using multiple \(\alpha\), can be extended to other loss functions that have \(\alpha\) conditioning; multiple \(\alpha\) values are generalized as in Equation (13).  
\[L_{\alpha-IoU}=1-IoU^{\alpha_{1}}+P^{\alpha_{2}}\left(B,B^{\delta t}\right), \alpha\to 0 \tag{13}\]It is found that Alpha-IoU Loss has better robustness in small data and in noisy interference, and the regression accuracy can be improved by simply adjusting the value of \(\alpha\). Therefore, in this paper, the localization loss function of the network is replaced by Alpha-IoU Loss to improve the network recognition performance.
## 5 Results
### PCA and HCA Results
#### 5.3.4. HCA-2 Results  
Using the scores from PC1 and PC2 of PCA-2, the HCA-2 analysis allows for the classification of samples into six distinct clusters (Figure 9). Cluster 1 (n = 29) consists of samples that exhibit bimodal to trimodal coarse silt characteristics. Cluster 2 (n = 60) group samples are categorized as silty to silty clay, often displaying skewness and platykurtic distribution. Cluster 3 is relatively small (n = 3) and comprises fine sand samples with some silt. The samples exhibit fine skewness to symmetry and platykurtic to mesokurtic distribution. Cluster 4 (n = 49) encompasses the finest samples, i.e., clay with fine silt. The samples exhibit fine skewness and are mainly platykurtic. Cluster 5 (n = 15) represents very coarse silt samples with sand content. The samples exhibit very fine skewness and a very leptokurtic distribution. At the least, Cluster 6 (n = 2) is associated with coarse sand and some silt. These samples show very fine skewness and a distribution ranging from platykurtic to mesokurtic.  
Based on the memberships of the samples to the clusters resulting from HCA-2, the core CHA01 can be divided into four major units as shown in Figure 5. Unit U1 (900-869 cm) is characterized by its heterogeneity and comprises a mixture of samples from Clusters 2, 3, 4, and 6. Unit U2 (868-488 cm) can be subdivided into two highly homogeneous subunits. U2a (869-755 cm) consists exclusively of Cluster 2 samples, while U2b (754-488 cm) includes only Cluster 4 samples. Unit U3 (487-189 cm) is dominated by samples from Clusters 2 and 5, and it is divided into three sub-units based on the proportions of samples from Clusters 1, 2, and 3. U3a (487-406 cm) is mainly associated with Cluster 5. U3b  
Figure 9: Principal Component Analysis: (**a**) scree plot, (**b**) circle of correlation, and (**c**) biplot of first and second principal components with cluster groups.  
(405-290 cm) shows variations between Clusters 1 and 2, with the inclusion of two samples from Cluster 5. U3c (289-189 cm) exhibits fluctuations between Clusters 2, 3, and 4, but the overall trend is clearly dominated by Cluster 2. Unit U4 (188-0 cm) is primarily composed of samples from Clusters 1 and 2. U4a (188-142 cm) is dominated by Cluster 1 samples, while U4b (141-65 cm) consists exclusively of Cluster 2 samples. The top portion of U4, U4c (64-0 cm), solely comprises samples from Cluster 1.
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 4 Network Layer Security
### Authorization Service--PBAC  
After verifying the message's legitimacy, the receiver node must decide if it will perform the requested action or not. The PBAC framework is used to implement this authorization functionality. The framework manages direct access to a smart device's resources as determined by a pre-defined collection of policies and rules that are modeled on the OASIS standards DPWS [43] and the eXtensible Access control Markup Language (XACML) [82]. PBAC consists of four components that are placed between the backend infrastructure and devices: The Policy Administrator Point (PAP) and Policy Information Point (PIP) that maintain the attribute values for creating and managing policies in a central repository, the Policy Decision Point (PDP) that runs on a trusted gateway node with sufficient computational capabilities, evaluates the request, and renders the authorization decision, and the Policy Enforcement Point (PEP) that enforces authorization at the end device and makes decision requests. These are combined to provide fine-grained, policy-based access control on assets from remote endpoints (like control stations, sensors, or cameras). Therefore, the specification of an active policy set can be used to define therights to access to acquired resources (e.g., sensed data and video/audio streams), the rights to update the settings, and even the rights to push notifications of emergency alerts (e.g., blocked routes and train crashes).