esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These standards contribute to the integration of automated irrigation management systems by enabling seamless communication and data exchange between various components, such as sensors, controllers, and decision support systems. However, challenges such as data quality, scalability, reliability, and security need to be addressed to fully realize the potential of interoperability and standardization in automated irrigation management systems. Addressing these challenges is crucial for enabling the integration of components within the automated irrigation management pipeline, which is essential for achieving fully autonomous, scalable irrigation management. A comprehensive evaluation of the interoperability and standardization's role in this integration is necessary to guide future research and implementation efforts. The primary objective of this systematic review is to critically evaluate the current state and future potential of real-time, end-to-end automated irrigation management systems that integrate IoT and machine learning technologies for enhancing agricultural water use efficiency and crop productivity. The focus on these objectives is crucial because fully automated, integrated irrigation management systems have the potential to significantly improve water use efficiency and crop yields, which are essential for addressing the global food challenge. By examining the automation of each part of the irrigation management pipeline and the seamless integration of each section, this review aims to identify the current state of technology and the gaps that need to be addressed to achieve fully autonomous irrigation management. Analyzing the effectiveness and efficiency of integrated end-to-end automated irrigation systems will provide valuable insights into the benefits and limitations of these systems, guiding the future research and development efforts. Investigating the role of interoperability and standardization is essential for understanding how to enable the integration of components within the automated irrigation management pipeline, which is crucial for achieving scalable and reliable systems. Finally, by identifying gaps and proposing solutions for integration across the automated irrigation management system, this review aims to provide a roadmap for future research and implementation efforts towards fully autonomous, scalable irrigation management that can contribute to addressing the global food challenge.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 5 Role of Network Slicing in 6G Technology
### Artificial Intelligence in 6G Network Slicing  
The rapid rise of sophisticated terminals and infrastructures, as well as a wide range of applications with various needs, pose a threat to the ability of current networks to fully satisfy the demands of the quickly growing traffic. Using a novel paradigm known as artificial intelligence, 6G elevated networks have been developed and optimized. Data retrieval, smart strategy and planning, automatic network adjustment, and growth are all made possible by AI-enabled intelligent architecture for 6G networks, which is divided  
Figure 6: Network Slicing in 6G network.  
into 4 layers: an intelligent-sensing layer, an analytic and data mining layer, an intelligent control layer, and an intelligent application layer [57].  
1. Deep Learning: Network slicing and deep reinforcement learning are essential 6G network technologies. Multiple network slices from various tenants may be present in a 6G network. To enable intelligent and effective resource management, network providers must provide slices that fulfil the 6G use cases and quality of service and experience standards. Intelligent and efficient resource management necessitates anticipating tenant demand for services and achieving autonomous slice behavior. DRL allows for the analysis of techniques based on the optimization objective, network emphasis (core, edge, and end-to-end networks, for example), state space, action space, algorithms, and network architecture [58].
2. ML: ML-based algorithms make it feasible for app-based traffic direction between networking devices, dynamic network segmentation for addressing various situations with variable quality of service needs, and pervasive connection across diverse 6G communication channels [59]. In 6G wireless communication networks, machine learning technologies are used. Supervised, unsupervised, and reinforcement strategies are among these methods: * Supervised learning: A labelled data set is used to train supervised learning algorithms. The system is aware of both the desired output data and the input data needed to make a supervised prediction. To be effective in any application, supervised learning requires a sufficient amount of data [60]. * Unsupervised learning: Unsupervised-learning algorithms must correctly anticipate the output from a set of unlabeled inputs, which is the main difference between supervised and unsupervised learning algorithms. These methods are most commonly employed for grouping and aggregation problems, but they can also be utilised to solve regression problems with excellent results [60]. * Reinforcement Learning: A performance indicator obtained from the model's environment is used to accomplish RL. The model maximises the reward indication in an effort to produce the best performance level. Combining supervised and unsupervised learning techniques is what is known as RL [60].
## 4 Vibration Data Analysis
### Peak Acceleration Analysis
To verify the relationship between the Z-direction acceleration and displacement, we use the correlation coefficient. The correlation coefficient, commonly referred to as [PERSON]'s correlation coefficient, measures the linear relationship between two sets of data, with values ranging from \(-1\) to \(+1\), where \(+1\) indicates a perfect positive correlation,  
Figure 8: Decay relation of train acceleration in each direction. \(-1\) indicates a perfect negative correlation, and 0 indicates no linear relationship. The formula for calculating the Pearson correlation coefficient is as follows:  
\[r=\frac{n\sum(X_{i}Y_{i})-\sum X_{i}\sum Y_{i}}{\sqrt{\left[n\sum X_{i}^{2}-\left( \sum X_{i}\right)^{2}\right]\left[n\sum Y_{i}^{2}-\left(\sum Y_{i}\right)^{2} \right]}}\]  
where \(X_{i}\) and \(Y_{i}\) represent individual data points from two sets of data, and \(n\) is the number of data points. After calculation, \(r\) = 0.776, which indicates a strong positive correlation between the acceleration and displacement data sets. This means that as the acceleration increases, the displacement also tends to increase, and vice versa, with a fairly strong linear relationship between the two variables. To validate the accuracy of the study's results, a comparative analysis was conducted with previous research and theoretical models. For example, referencing the study by [PERSON] et al. [27] in a similar environment, it was found that the results of this research show consistency and reliability. The findings demonstrate that the tunnel structure has a significant impact on the propagation of vibration waves, particularly in terms of the amplification and attenuation of high-frequency vibration components. These results are of considerable significance in the context of the research objectives. Understanding the influence of tunnel structure on vibration propagation helps in developing effective vibration control strategies. For instance, considering the application of high-damping materials in tunnel lining design could effectively reduce the propagation and amplification of high-frequency vibrations. By identifying the primary propagation paths and amplification zones of high-frequency vibrations, targeted structural safety measures for surrounding buildings can be implemented to mitigate the potential hazards of vibration. Reducing the propagation and amplification of high-frequency vibrations can also improve the quality of life for nearby residents and reduce the interference of vibrations in daily life.
A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Iles-de-Boucherville National Park, Quebec, Canada  
[PERSON]  
Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A-0B9, Canada; [EMAIL_ADDRESS]  
[PERSON]  
Correspondence: [PERSON]###### Abstract  
A new ground truth dataset generated with high-accuracy Global Navigation Satellite Systems (GNSS) positional data of the invasive reed _Phragmites australis_ subsp. _australis_ within Iles-de-Boucherville National Park (Quebec, Canada) is described. The park is one of five study sites for the Canadian Airborne Biodiversity Observatory (CABO) and has stands of invasive _P. australis_ spread throughout the park. Previously, within the context of CABO, no ground truth data had been collected within the park consolidating the locations of _P. australis_. This dataset was collected to serve as training and validation data for CABO airborne hyperspectral imagery acquired in 2019 to assist with the detection and mapping of _P. australis_. The locations of the ground truth points were found to be accurate within one pixel of the hyperspectral imagery. Overall, 320 ground truth points were collected, representing 158 locations where _P. australis_ was present and 162 locations where it was absent. Auxiliary data includes field photographs and digitized field notes that provide context for each point.  
that they can more easily be eradicated or prevented from spreading using sensible and appropriate methods.  
As part of the Canadian Airborne Biodiversity Observatory, airborne hyperspectral imagery (HSI) was acquired with the Compact Airborne Spectrographic Imager-1500 (CASI-1500) over the entire extent of lies-de-Boucherville National Park in 2019. One of the purposes of the imagery was to identify and map invasive _Phragmites_ across the entire park. For this approach, a ground truth dataset is needed to both train target detection algorithms and assess their resulting accuracies. The airborne HSI is geo-corrected with a resampled pixel size of 2 m \(\times\) 2 m. The reported positional accuracy of the HSI is an average error in easting and northing of 0.91 m and 0.67 m, respectively (Table 1). However, as described by [7], only 55.5% of the signal from a given pixel originates from materials within its spatial boundaries, while the remaining signal is from the materials within neighboring pixels. For the CASI-1500 HSI, the net point spread function (PSF) extends approximately 3 m in the along-track direction and 2 m in the cross-track direction. Based on the characteristics of the imagery, for this application, ground truth points with less than 2.25 m error in both northing and easting were required. The requirement for precision of the ground truth points had been set to less than one half standard deviation of the net PSF in the cross track (i.e., narrower) direction (16.5 cm). Therefore, ground truth data needed to be collected using a high-precision GNSS (Global Navigation Satellite Systems) instrument, in order to ensure that the points are geolocated correctly within the HSI.
## 2 Related Work  
### Clustering  
Clustering is the process of dividing objects into groups. Specifically, the objects that belong in the same cluster exhibit greater similarity (based on a predefined similarity metric) to each other when compared to those belonging to other clusters. At present, there are different clustering approaches in accordance with the techniques employed. The main categories are partition-based, hierarchical, and density-based. Partition-based clustering approaches (such as _K-Means_[8] and _K-Medoids_[9]) use the method of extracting a center point for each cluster, which is used for distributing data points to clusters. _K-Means_ is based on the calculation of the center of data points conducted by an iterative procedure until some convergence criteria are met. _K-Medoids_ and _K-Means_ share comparable philosophies, with the only difference between them being that _K-Medoids_ has the ability to process discrete data. For each cluster, a medoid is extracted, which serves as a representative candidate. High computing efficiency and low time complexity are the main advantages of these approaches. However, there are also some disadvantages, such as their lack of efficiency in handling non-convex data, in the sense that outlier data points can affect the clustering procedure. Furthermore, a predefinition of the exact number of clusters is required.  
Hierarchical clustering approaches are based on the extraction of the hierarchical relationships among data. The first step of this approach is to correspond each data point with an individual cluster. These models operate by combining two clusters into a new one iteratively, based on a predefined metric, until convergence is reached. Some of the most well-defined approaches include _ROCK_[10] and _BIRCH_[11]. Density-based approaches share a basic principle, which dictates that data points that belong to the same cluster should form a high-density region in the data space. _DBSCAN_[12], falls into this category of approaches. These approaches excel in the efficiency of the clustering process while handling arbitrarily-shaped data. On the other hand, there are some disadvantages, with the most concerning one being that they produce low-quality clustering results when the density of data space is not even.  
Research has indicated recently that word/document embeddings (i.e., real-valued vector representations of words/documents) can be employed to facilitate clustering applications [13]. By employing embeddings, the task of clustering a corpus of documents is simplified by clustering data points with real-valued features that capture the semantical and structural semantics of the documents at hand. The majority of the approaches utilize a variation of the _Word2 Vec_ model [14].
## 4 Working Mechanism of ESRVR
### Data Transmission and Void-Hole Detection and Mitigation
#### 4.3.2 Void-Hole Presence and Detection  
A void-hole is the absence of a node in the direction of sea surface to advance information. The presence of a void-hole is sketched in Figure 4, in which two sensor nodes, X and Y, are shown. Every node transmission range is shown by the circle, in the center of which that node is present. That is, the circle with a solid circumference shown by the solid line is the transmission range of Node X, while the dashed circumference represents the transmission range of the circle Y. Node Y is the neighbor of Node X. Therefore, the latter forwards information to the former. However, Node Y does not possess any neighbor node further towards the water surface for data forwarding. Therefore, the information that X sends to Y cannot be forwarded towards the water surface beyond Y. This means a void-hole is present beyond Y.  
Figure 5 again sketches the scenario when a void-hole is present. A node's transmission range is represented by the bigger circle around it with the node being in the center of the circle. Node C is the source node that has packets ready to transfer. Node C has three neighbors: B, D, and E. However, Node C knows that Node B bears lower water pressure than Nodes D and E. Furthermore, Node C knows that Node B has further a second hop neighbor in the form of Node A. Nodes D and E are not the more suitable neighbors for Node C than Node B because the water pressure on these nodes is higher than Node C, which makes B closer to the water surface (lower depth). Therefore, Node B becomes the suitable neighbor for Node C. As a result, Node C forwards data to Node B, as shown by the tick mark. However, Node B knows that Node A has no neighbor in the second hop that has lower  
water pressure (depth) than Node A. That is, a void-hole exists beyond Node A. Therefore, Node B avoids forwarding data to Node A, as shown by the cross sign.  
To avoid complexity in Figure 5, the transmission range of Nodes D and E is not shown. Moreover, neighbors' information is obtained by nodes in the neighbors' recognition phase discussed above.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 7 Conclusions  
In this paper we have proposed a novel co-design-based data transmission protocol to achieve low-latency, energy-efficiency and reliability for UWSNs. Based on the co-design method, CDTP not only maintains the multi-layer structure and neighbor information of the network, but assists in conflict avoidance. With the help of neighbor information, fewer control packages and a novel holding time mechanism, CDTP reduces end-to-end latency, energy consumption and multi-copy problems simultaneously. The simulation results show that CDTP can perform better in a dense UWSN environment.  
**Author Contributions:** Conceptualization, X.W. ([PERSON]) and H.G.; Formal analysis, Project administration and Writing-original draft, H.G.; Methodology, X.W. ([PERSON]); Data curation, X.W. ([PERSON]); Resources, C.W.; Writing-review editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. U19A2061, 61772228 and 61902143), National key research and development program of China (Grant No. 2017 YFC1502306).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Average E2E Delay.  
Figure 9: Energy consumption.
## 4 Discussion
### Application of the IJP Sensor  
In addition to its accuracy, the IJP sensor is a low-cost sensor with skin-like characteristics that enhances the wearability of the sensor without limiting the daily activities of the patients. All the aforementioned characteristics of the IJP sensor makes it suitable for remote and continuous RR monitoring in low-resources settings and refugee camps, decreasing the pressure on the healthcare facilities in these settings which suffer from limited healthcare resources [37]. Continuous RR measurement in different postures is clinically significant especially for the monitoring of hospitalized patients. Currently, the RR monitoring relies on inaccurate manual counting in a short period (often 30 s or 1 min) or is neglected [38]. The mask-based or contact RR sensors, such as the e-Health sensor in this study, could cause uncomfortableness which makes them inappropriate for long-term continuous RR monitoring. The IJP sensor could accurately and continuously measure RR without any skin allergy or other clinical events, which makes it possible for clinical use. RR measurement at different body postures is important for patients with different physiological conditions. For example, some patients under physiotherapy or with plastic cast need to keep in mainly supine or lateral recumbent postures. Therefore, the IJP sensor provides the possibility of continuous RR monitoring in different patients.  
The IJP RR sensor is also applicable for RR monitoring at home without disturbing the users' daily activities. Wearable sensors could provide RR monitoring during the night at the supine and lateral position to inspect the sleep quality and contribute in early diagnostic of several sleep-related issues such as sleep apnea and insomnia [39]. Moreover, such wearable sensors could be used to reduce sudden infant death syndrome by providing comfortable and continuous RR monitoring [40]. Additionally, wearable sensors can be integrated into safety belts in automobiles which eases the monitoring of the RR during driving [11; 41]. The IJP sensor accurately measured the RR at all the inspected postures which paved the way for its further application in daily life.
# Radioactivity in Soils of Kosovo and Radiological Implications
## 3 Results and Discussion
### Assessment of Radiological Hazards  
Statistical descriptors of Ra\({}_{\rm eq}\), I\({}_{\rm Y}\), and H\({}_{\rm ex}\) radiological hazard indices were determined according to SGS-ANN simulation data for all Kosovo. The smallest and highest average Ra\({}_{\rm eq}\), I\({}_{\rm Y}\), and H\({}_{\rm ex}\) values were determined in Gjilan (Ra\({}_{\rm eq}\) = 73 Bq/kg, I\({}_{\rm Y}\) = 0.55, H\({}_{\rm ex}\) = 0.20) and Mitrovica (Ra\({}_{\rm eq}\) = 87 Bq/kg, I\({}_{\rm Y}\) = 0.66, H\({}_{\rm ex}\) = 0.24), respectively.  
Figure 3: Frequency distributions of \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs concentrations for actual and simulation data.  
Figure 4: The cross-validation diagrams generated as a result of comparing the predicted values with the observed values for \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs.  
Figure 5: Distributions of radioactivity concentration for \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs.  
In Kosovo soil samples, the mean value of Ra\({}_{\text{eq}}\) was found to be 80.1 Bq/kg. This value is lower than the worldwide average of 370 Bq/kg [28]. Additionally, when the average Ra\({}_{\text{eq}}\) values were compared with the values of other countries, they were found to be lower than the obtained values of 166 Bq/kg in Firtuna Valley [47], 112 Bq/kg in Artvin [12], and 232 Bq/kg in southwestern Nigeria [48]. The mean I\({}_{\gamma}\) calculated for the Kosovo soil samples had a mean value of 0.61, which is less than the mean value of the world, which is 1, according to the European Commission (European Commission, 1999). The average value of Hex was found to be only 0.22, so no harmful effects to the residents can happen due to radiation hazards. Figure 6 shows distribution maps of radiological hazards for Kosovo. When these maps were examined, it was seen that radiological hazards are at low levels, especially in the province of Gjilan and in the inner parts of Prishtina. They are at high levels in the Mitrovica province, north of Prishtina, south of Prizren, and western parts of Gjakova and Peja.