esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Applications in Agriculture: A Scoping Review
### Related Work  
Although there are various reviews regarding the use of blockchain in agriculture, only one is the most extensive, [PERSON] et al. [37], including 49 papers. The same author, in 2021 [38], also published a book chapter which, at that time, had 80 related papers. The rest of the reviews, e.g., [39; 40; 41; 42; 43; 44; 45], include a smaller number of papers, either due to more specific questions or mainly due to the early stage of the technology when conducting their research. This is mainly due to the recent explosion of blockchain use and the corresponding increase in the related scientific literature. There is also a difference in the exact focus area of each review paper, with some reviews answering questions about blockchain use in food [40], others about blockchain in agriculture and the food supply chain [37], or just about blockchain in agriculture [42; 44; 45].  
The present work, compared to previous related works, is novel in several aspects. First, our research is the most comprehensive literature review that has been conducted so far, including a total of 104 research papers. Second, our research area is focused not only on blockchain but also on distributed ledgers technologies in agriculture. Third, we apply the widely used PRISMA-ScR [46] methodology for systematic scoping reviews. To the best of our knowledge, this is the first systematic literature search in this field following a formal methodology. Finally, in our research, we examine a wider range of research questions, some of which have not been mentioned in the past in the existing literature. For example, we answer questions about application development beyond the blockchain technology used, the service area, the maturity level, the country, or the product to which the application refers. Other such questions were if the data were stored on-chain/off-chain (and also the off-chain technology used), the reason for using the blockchain, and the type of blockchain.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 7 Evaluation
Figures 9-11 show the results for packet sizes of 700, 800, and 900 bytes. It can be derived that the arrival times of the packets can be shaped in a deterministic manner only when the resources of the radio channel in one GCL period are sufficient to transport the required traffic within the respective GCL slots of the traffic classes. In the event that a packet cannot be entirely transmitted before the end of its GCL slot, the TSN requirements cannot be met anymore, leading jitter to increase drastically because the data has to be  
Figure 8: Arrival slots of TSN traffic classes with dynamic LCP. Figure 6: Density of arrival slots with dynamic LCP. Figure 7: Arrival slots of the TSN traffic classes with static LCP. queued until the next GCL period. The figures show that this turning point with respect to the number of available Resource Blocks depends on the packet size. This is an expected behavior, as larger payloads require more radio resources. Depending on the criticality of the application, a strategy for this case may have to be configured beforehand. Possible tools allowing the network to react by temporarily decreasing radio channel resources include the dropping of packets or the transmission of feedback to the application via the Application Function in the 5G Core.
## 2 Materials and Methods
### Path-Tracking Method Design  
#### 2.2.1 Pure Pursuit Model  
The pure pursuit model was set as a lateral motion control algorithm for vehicles that simulates the driver's experience. In Figure 4, X is the geometric center of the 4 WIS-4 WID prototype vehicle, line \(P_{1}P_{2}\) is the desired path, and the angle between vehicle forward direction and line \(P_{1}P_{2}\) is the heading deviation \(\theta\). Crossing the center point X, line XN is drawn perpendicular to line \(P_{1}P_{2}\) and point N is the intersection points, so XN is the lateral deviation \(d\) of the vehicle. If we set the length of MN equal to the look-ahead distance \(LD\), then point M is the look-ahead point of the prototype vehicle and XM is the look-ahead straight line. For the pure pursuit model, the steering center O of the prototype vehicle must be at the intersection of lines GO and XO. GO is the vertical bisection line of the look-ahead line XM, and XO is the extension line of the vehicle's lateral center. That is, OX is the steering radius \(R\) of the prototype vehicle under the current posture state.  
According to the geometric relationship in Figure 4, the steering radius \(R\) by Equation (1) is:  
\[R=\frac{LD^{2}+d^{2}}{2 LD\sin\theta+2d\cos\theta} \tag{1}\]  
where  
\(R\)--steering radius, m;  
\(d\) = the lateral deviation, m;  
\(\theta\) = the heading deviation, \({}^{\circ}\).  
Figure 3: Fuzzy control process.  
From Equation (1), when the deviation is fixed, look-ahead distance \(LD\) is the only parameter to determine the rotation radius and there is a positive correlation between \(LD\) and \(R\), i.e., the greater the \(LD\), the greater the value of \(R\) and vice versa.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Introduction of Smart Grids and Grid Edge
A study reports that 75% of enterprise data will be produced at the grid edge by 2025 [84]. Meanwhile, experts from Red Hat predict that within the next three years, IT budget costs will account for 30% of the total [84]. Research on the grid edge is necessary and well worth the investment. The primary goals of edge grid technology programs are to create new energy architectures that address decentralization and handle the increasing volume of data. Historically, electricity was generated by the centralized utilities and transmitted to end users. As modern power systems evolve, traditional energy models no longer suffice. Decentralized power centers at the grid edge offer similar benefits with fewer risks and lower costs, motivating both academia and industry to focus on decentralization and grid edge research. By shifting to smaller hubs near the consumer side, energy loss and risks can be reduced, and distribution centers can be more flexible and easily maintained. Additionally, the emergence of IoT, smart meters, and other technologies has led to a data explosion, overwhelming centralized systems with data collection, transmission, and analysis. Consequently, the power industry prioritizes grid-edge innovations. Significant projects such as the BrooklynQueens Demand Management program (BQDM) [85] and California's Demand Response Auction Mechanism (DRAM) [86] exemplify successful grid edge initiatives [84]. Well-designed energy models based on the grid edge can also decrease the costs of energy transportation and storage and improve the stability of the power supply. As enterprises and countries race to realize digital transformation in the energy sector and net-zero emissions, AI deployed at the edge (edge AI) becomes a popular technology. Edge AI can effectively manage distributed energy resources [87]. Noteworthy AI [88] uses AI-enabled vehicle-mounted cameras to help monitor pole-mounted assets. Small boxes also attach to trucks with computation resources and communications gear in them. These boxes link to a processing unit in the cab to execute machine learning tasks [89]. Siemens Energy uses AI deployed at the edge of power plants to realize autonomous management [90]. Many researchers are exploring the use of edge computing technology to enhance power system applications. The following applications have been reviewed in this paper:  
* Power Grid Distribution Monitoring;
* Smart Meters Management;
* Data Collection and Analysis;
* Anomaly Detection;
* Measurement Placement;
* State Estimation;
* Energy Storage;
* Resource Management;
* Security of Grid Edge Management System;
* Renewable Energy Forecasting.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 2 K-L-Based Local Path Planning Algorithm
### The Flow of the K-L Algorithm  
The K-L algorithm flow is shown in Figure 2. The main steps of the optimal path algorithm based on K-L are as follows:1. When the sensors find an obstacle, the K-L algorithm is activated:
2. First, the Kohonen network outputs the state set (\(S\)), which records the information collected by the sensors.
3. Then, the algorithm establishes a state-action mapping pair. The mapping pair is established by traversing the actions of each state (for example, the AGV moves forward). The mapping pair is represented by \(Q\) (\(S\), \(A\)) and is stored in the Q-table.
4. Then, the SSA-based exploration strategy selects the next action based on the Q-table. Finally, the AGV obtains the reward value of environmental feedback.
5. The AGV selects the next action according to the reward value of environmental feedback until it bypasses the obstacle.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 7 Conclusions  
In this paper, we have proposed a new efficient IP address lookup architecture using a Bloom filter. The proposed architecture is based on a vectored-Bloom filter, which stores an output port in each vector. We have also proposed the use of parallel architectures to improve the search performance. In the proposed parallel architectures, multiple VBF search blocks are implemented with sharing a single hash table, since the hash table is infrequently accessed. Hence the memory efficiency is increased. The performance of the proposed architecture has been evaluated at the behavior level using C and at the hardware level using Verilog. The behavior evaluation shows that the proposed architecture can perform the IP address lookup without accessing the off-chip hash table for a reasonably sized Bloom filter. The hardware evaluation shows that the proposed architecture provides the throughput of 4.92 million packets/sec on an FPGA operating at 100 MHz. The proposed hardware architecture can be a promising candidate to provide the wire-speed packet forwarding since a more degree of parallelism can be pursued when it is implemented with an ASIC operating at a higher frequency.  
Figure 9: Relationship between average number of cycles and throughput.  
**Author Contributions:** Conceptualization, H.B. and H.L.; methodology, H.L.; software, H.B.; hardware, Q.L.; validation, H.B. and Q.L.; investigation, H.L.; resources, H.B. and Q.L.; data curation, H.B.; writing--original draft preparation, H.B.; writing--review and editing, H.L.; visualization, H.B.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L.  
**Funding:** This research was funded by the National Research Foundation of Korea (NRF), NRF-2017R1A2B4011254.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 2 Overall Design Considerations for Photodiodes
### Principles of PIN-PD and UTC-PD
However, the introduction of quaternary system material (Figure 3b) might bring certain difficulties for epitaxial growth and the device fabrication process. Therefore, a dipole-doped regime, as shown in Figure 3b is incorporated to reduce the conduction band barrier, which uses a lower dipole-doping concentration (\(\leq 5\times 10^{18}\) cm\({}^{-3}\) to avoid out-diffusion) to fully suppress the conduction band offset only left with a spike-like barrier for electrons to tunnel through [50, 51, 52].
## 3 Foundation of Medical Image Analytics with Machine Learning
### Machine Learning Algorithms for Medical Image Analytics  
Medical imaging analytics refers to the application of software analytical methods to medical imaging data to extract useful medical information, including health conditions and ongoing diseases. ML plays a key role in developing high-performing medical image analytics systems. The essential types of ML algorithms for medical image analytics include the following:  
* CNN for image classification, segmentation, and object detection tasksRNN for analyzing time-series medical images or volumetric data
* GAN for image data augmentation, image synthesis, and image-to-image translation
* SVM for medical image classification and abnormality detection  
There are multiple ML algorithms for each type of ML model. For example, representative CNN algorithms that can be used for medical image analytics include U-Net, V-Net, DeepMedic, ResNet, Inception, and Mask R-CNN. The most optimal ML algorithm for a given diagnostic task should be identified and applied to yield high-performance analytics.
## 4 Discussion
### Human-Centered AI and Ethical Considerations  
Despite notable gains in personalized learning, the ubiquitous collection and real-time interpretation of behavioral data highlight potential ethical pitfalls. Student discomfort rises through the continuous video monitoring or the fear of being profiled unfairly by machine-learning algorithms [55]. Additionally, as [36] demonstrates, while advanced biometrics such as EEG or facial expression tracking can precisely gauge cognitive load or emotional states during class, this level of granularity raises fundamental questions about privacy and data minimization. Institutions must weigh whether capturing minute stress indicators or momentary confusion genuinely benefits learners or whether such monitoring infringes on autonomy and stress levels. Moreover, data governance in AI-driven systems requires constant vigilance, as [65] argues that state-level infrastructural control can inadvertently shift agency away from teachers and learners toward opaque algorithmic processes.  
Additionally, instructors and students alike express concern over the potential for algorithmic bias, mirroring broader debates in AI ethics [5]. When adaptive systems rely on training data which may lack diversity, they can unintentionally reinforce stereotypes or systematically disadvantage certain demographic groups. Opaque AI black boxes can also obscure how personalized recommendations are generated, limiting teachers' ability to interpret or override questionable outputs. Although best practices in Education informationization 2.0 suggest a human-in-the-loop approach, where educators retain decision-making authority, actual classroom implementation still varies widely, especially in teaching specific subjects [69].  
One avenue to address the tension between personalization and privacy is federated learning, which trains models locally on student devices or edge servers while transmitting only aggregated updates [12]. This method significantly reduces raw data flow to cloud servers, lowering the risk of large-scale breaches and affording students greater data sovereignty. It also respects diverse local policies; for instance, schools in regions with strict data-protection laws can maintain student information on-premise while still benefiting from collectively trained AI models. However, federated learning increases on-site computational overhead, making hardware capability an important factor in equitable adoption.  
The 5G-enabled smart education in enhancing moral education through Flash animation and multimedia-based learning was examined [70]. High-speed 5G networks facilitate real-time interaction and improve multimedia content delivery in ideological and political education. The findings indicate that 5G-enhanced education led to a 60% improvement in problem-solving skills and a 52% increase in active learning engagement across various subjects, indicating that 5G-empowered animation-based approaches can reinforce ideological education and cultural narratives.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 8 Conclusions  
This research offers pivotal insights for the tailored design and execution of QEC codes in line with the distinct noise challenges of modern quantum hardware. We have established that surface codes excel in quantum error protection, proficiently managing both common bit-flip and phase-flip errors, a contrast to basic repetition codes which only cater to bit flips. Among the surface code variations, rotated surface codes emerge superior due to their higher error thresholds, simplicity, and efficient qubit usage. Such findings guide researchers in choosing the most apt QECC structure, harmonizing with desired reliability and hardware constraints. Our extensive exploration of noise models, ranging from the stringent code-capacity to the pragmatic circuit-level models, offers a detailed portrayal of QECC durability. The study also underscores the value of fine-tuning QECC parameters, such as distance, to strike a balance between desired reliability and qubit economy. In essence, this research is a significant stride towards achieving scalable, fault-tolerant quantum computing, laying down the foundation for QECC designs attuned to specific quantum noise scenarios.  
Conceptualization, A.C.; data curation, S.D.; writing--original draft preparation, A.C.; writing--review and editing, A.C., S.D. and S.G.; visualization, A.C.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
The work is supported in part by the National Science Foundation (NSF) (CNS-1722557, CCF-1718474, OIA-2040667, DGE-1723687, and DGE-1821766) and gifts from Intel.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Discussion  
In this study, we introduced GBNSS, a novel approach for global biological network similarity search. The GBNSS method consists of three phases: the graph feature construction phase, the model training phase, and the similar network querying phase. GBNSS has been extensively evaluated on four species, considering both topological and biological aspects. Across multiple biological network databases, GBNSS consistently outperforms existing methods such as NSSRF and PathEmb in terms of topological accuracy. Notably, GBNSS retrieves more relevant pathways at various biological levels compared to these methods, demonstrating its utility in accurately identifying similar networks.  
Although GBNSS requires more model training time than NSSRF and PathEmb, it significantly reduces query time, making it more efficient for rapid searches. This trade-off between training time and query speed positions GBNSS as a suitable method for applications requiring fast similarity queries in large-scale biological databases.  
Due to its incorporation of multiple metrics for comparing the topological similarity of two networks, GBNSS effectively predicts topological similarity. However, there are currently limited metrics for assessing biochemical functional similarity.  
Figure 9: Visualization of (**a**) hsa05410 and (**b**) hsa04976.  
Beyond the current scope of biological network similarity search, GBNSS holds the potential for broader applications across several important fields. For example, in drug discovery, GBNSS could be instrumental in identifying similar biological networks related to disease pathways. In evolutionary biology, GBNSS can be applied by comparing biological networks across species, helping to identify evolutionary conserved pathways and functions.  
Methodology, Y.W.; Software, Y.W.; Validation, Y.W.; Formal analysis, Y.H.; Writing--original draft, Y.W.; Writing--review & editing, F.Z., C.H. and Y.H.; Supervision, Y.H.; Funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Guangxi Province (No. 2020 GXNSFAA159074) and the National Natural Science Foundation of China (No. 61862006 and No. 62261003).  
The raw data supporting the conclusions of this article will be made available by the authors on request. Data are available from: [[https://github.com/hyr0771/GBNSS](https://github.com/hyr0771/GBNSS)]([https://github.com/hyr0771/GBNSS](https://github.com/hyr0771/GBNSS)), accessed on 27 October 2024.  
The authors declare no conflict of interest.
## 3 Results
### Results and Analysis  
To evaluate the effectiveness of the RPC model-based satellite SAR image positioning considering atmospheric delay, three sets of experiments were conducted: (1) The satellite position was determined using the autonomous radar LOS vector intersection method. The results were compared with reference ephemeris data to assess the accuracy of satellite position inversion. (2) The atmospheric stratification approach was employed to examine the impact of ionospheric and neutral atmospheric stratification levels on the atmospheric correction of satellite images. (3) The RPC model reconstruction method, incorporating atmospheric delay, was used to analyze changes in the uncontrolled positioning accuracy of the SAR images before and after atmospheric correction.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### WSN Application Areas
#### 2.8.2 Medical Applications  
The health sector is increasingly using WSNs for healthcare needs, such as medical treatment, pre- and posthospital patient surveillance, and rescue operations [32]. In addition, sensor networks can help in solving important social problems, such as caring for the chronically ill and people with intellectual and physical disabilities. The field of healthcare includes patient follow-ups, invalid assistance, life-saving, biosurveillance, and intelligent environments. Furthermore, WSNs are used for monitoring physiological measurements [33]. They can also be used, for example, to monitor the vital functions of the elderly or people with health problems in order to alert medical devices. Similarly, we can analyze different physical measurements collected from athletes, the possibilities offered by a WSN to lighten data-collection devices.
## 3 Analysis of Output Characteristics Variation
### Mode 3: Charging Region  
The imaginary part of the output-voltage expression cannot be ignored in the charging region. The output voltage and output current exhibit non-linear changes, reaching the maximum-output power state in this interval. The resistance value when the output power reaches its maximum value is shown (24). Once the output power reaches the maximum value with the increase in the load resistance, it starts decreasing. It is necessary to consider the major charging regions of the battery.  
\[R_{L,P_{L}=P_{\text{max}}}=\frac{\omega M^{2}}{L_{m}} \tag{24}\]  
Figure 6 presents a graph of the output current, output voltage, and output power of the proposed topology for different loads based on Table 1. Figure 6 shows that the proposed design method has a self-current/voltage limitation function depending on the load fluctuations. The maximum voltage and maximum current are fixed, which prevents the expansion of accidents during short-circuit and open-circuit states.  
Thus, the proposed design method does not require additional components, which makes it suitable for low-cost WPT charging systems. In addition to the parameters proposed in Table 1, the short-circuit current and open-circuit voltage can be calculated and designed by modifying inductance, operating frequency, input voltage, and coupling coefficient. Battery management (BM) converter attachment is required to satisfy the output characteristics that vary depending on the battery SoC and the parameter tolerance [35].