esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Security and Privacy
#### 4.5.5 Anomaly or Intrusion Diagnosis and Suppression
Moreover, a secure framework consisting of an intrusion detection system, using Random Subspace Learning and K Nearest Neighbor (RSL-KNN) to detect falsified orders and a blockchain-based integrity-ensuring system for preventing misrouting attacks, has been studied in [353]. Alternatively, when assessing a device's credibility for identifying insider assaults in cooperative attack detection using Conditional Generative Adversarial Networks (CGANs) in UAV KDN, it leverages blockchain to verify data immutability and distributed federated learning to ensure privacy and collaborative learning [354]. Furthermore, for knowledge-based SD-IoT networks, a Distributed Denial of Service (DDoS) attack recognition and suppression system has been feasible thanks to the security provided by distributed blockchain and threat detection using Artificial Neural Networks (ANNs) [355]. Driven by the uncertainty issues of deep learning techniques, an intelligent neuro-fuzzy inference system built on blockchain has been utilized to detect threats in IoT networks, where meta-heuristic algorithms have been leveraged to optimize threat detection error [356]. Similarly, an attack recognition system built using fuzzy logic and a private blockchain known as PBFL-ADS detects attacks by processing multimedia information in IoMT networks, where the purpose of the blockchain is to improve trust management efficacy [357]. Moreover, a framework called BFT-IoMT uses fuzzy logic to detect Sybil attacks in an IoMT network where transactions are implemented on a blockchain to improve security [358]. Anomaly detection--A DAG blockchain is utilized to mitigate illegitimate packets generated due to multiple handovers in KDN. In particular, 5G users are authenticated using hash generation, where a DAG at the controller stores these hashes of users and hashes of traffic rules for verification, while authentication of dubious packets is carried out using a soft actor-critic algorithm and classification of packets is performed by a capsule neural network [359]. Alternatively, a framework known as brain-chain, which detects domain name system amplification attacks in permissioned blockchains using flow statistics collection, entropy-based disorder, and Bayes network filtering to classify illegitimate flow measurement, has been utilized [360]. Similarly, research in [361] proposes using Permissioned Blockchain-based Federated Learning for Anomaly Detection (PBFLAD), where changes to the AI framework are linked utilizing a shared ledger, allowing auditing of the machine learning models. Furthermore, in [362], IoMT Blockchain network Anomaly Detection (IoMTBC-AD) is employed to prevent insider attacks in blockchain networks utilized in IoMT by combining the network with deep learning to detect network anomalies. Moreover, Hybrid Deep Learning (HDL) making use of LSTM and convolutional neural networks for evaluating traffic flow anomalies by assisting blockchain in resolving gaps in the datasets has been studied in [363].
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 3 Method
### Multi-BEV Feature-Fusion Module  
When multiple feature maps are fused, useful features among them can be aggregated to complement each other's advantages [26; 27]. Therefore, We developed a **multi-BEV feature-fusion module** (refer to Figure 5) to adaptively fuse BEV features from pseudo-image-based and voxel-based feature-extraction branches. First, we use two layers of convolution for spatial modeling and one layer \(1\times 1\) for channel modeling. Second, we use a \(1\times 1\) convolution layer to compress the number of channels in the feature map to two channels. We divide the feature map cuts of the two channels, use them as the BEV feature map weights from the two branches and multiply them, and then add the elements to fuse the weighted features. The multi-BEV feature fusion module effectively fuses the features extracted by different feature-extraction methods and adds richer geometric and semantic information to the BEV features to predict higher-quality 3D object proposals.  
Figure 5: Multi-BEV feature-fusion module. This module accepts BEV feature maps output by two feature extraction branches and predicts the weights of the two parts. The sum of the product of the weight and its corresponding BEV feature map serves as the input of the detection head. (The green feature map comes from the feature extraction branch based on voxel, and the yellow feature map comes from the feature extraction branch based on pseudo-image).
## 2 Overview
### General Sensor Node  
Figure 2 shows an architecture of a general sensor node in a marine environment monitoring system. It usually includes a buoy device in order to protect electronic devices of nodes against water. A marine monitoring sensor node normally consists of the following four main modules [29]:  
(1) A sensing module for data acquisition;  
(2) A central processing module for local data processing and storage;  
(3) A wireless transceiver module for wireless data communication;  
(4) A power supply module for energy supply.  
A sensing module is usually composed of several probes and sensors (with associated amplifiers and A/D converters) to sense and monitor the physicochemical parameters of marine environment as mentioned above. A central processing module normally includes a CPU and memory to process and store the collected data. A wireless transceiver module mainly consists of a RF transceiver and an antenna to send the collected data and receive instructions from the sink node. A power supply module usually contains energy storage devices (rechargeable batteries), power management system and energy harvesting devices (solar panel, wind energy, tidal power, seawater generator, _etc._). Finally the buoy has an anchor device in order to prevent it from moving (due to waves, marine currents, wind, tide, _etc._).  
Figure 2: General architecture of an oceanographic sensor node.  
The energy options for sensor nodes usually include batteries, capacitors, heat engines, fuel cells, and energy harvesting. Sensor nodes are normally battery powered in most application systems. However, the use of a battery in sensor nodes has a number of disadvantages [30]:  
1. As sensor nodes increase in number and size, the replacement of depleted batteries is wasteful and time-consuming.
2. A battery has limited energy that cannot last a long life for sensor nodes.
3. Batteries have environmental contamination and disposal issues since the chemical composition of a battery often involves toxic heavy metals.  
It is therefore necessary to explore an alternative power supply for sensor nodes. Harvesting energy from their ambient environment is a promising power supply for sensor networks with lower cost and long life. Energy harvesting methods include photovoltaics, fluid flow, temperature gradients, pressure variations and vibration harvesting. In terms of their efficiencies and realisability, the most outstanding energy harvesting at the moment is photovoltaics [30]. This issue will be further explored in Section 4.3.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Network Infrastructure  
#### 3.1.1 Fault-Tolerant Service Communication  
A resilient system should offer fault-tolerant communication mechanisms with the goal of minimizing the impact on the quality of service. To this end, considerations such as limiting the number of requests that can be made during failure periods, time limits on responses to requests, avoiding making requests to services that are not responding for some reason, and limiting the number of concurrent requests to a service should be taken into account.  
Replication is also essential for obtaining fault-tolerant services. In active replication, each client request is processed by all the servers. Client requests are assigned to non-faulty servers using a mechanism for coordinating the replies. In passive replication, there is only one server (called the primary) that processes client requests. After processing a request, the primary server updates the state on the other (backup) servers and sends back the response to the client. If the primary server fails, one of the backup servers takes its place. It is a less costly scheme in terms of redundant processing and communication and is, therefore, more prevalent in practice [36].
_Article_  
**Grid Deformation Real-Time Measurement System**  
**of Ion Thruster Based on Videometrics**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,3,*\({}^{\text{\textregistered}}\), [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (S.X.); [EMAIL_ADDRESS] (X.L.)  
National Key Laboratory of Vacuum and Cryogenic Technology on Physics, Lanzhou Institute of Physics, Lanzhou 730030, Gansu, China; [EMAIL_ADDRESS]  
No 92330 Unit of People's Liberation Army of China, Qingdao 266100, China  
Correspondence: [EMAIL_ADDRESS]  
Received: 16 March 2019; Accepted: 22 April 2019; Published: 28 April 2019  
**Featured Application: This work is suitable for the thermal deformation measurement of two- and three-grid ion optics. Our system satisfies the requirement of real-time detection in a vacuum, high-temperature, and plasma environment. It can also be used for off-line detection based on videos.**  
**Abstract:** In order to conduct high-precision measurement of the LIPS-300 ion thruster grid deformation in a vacuum, high-temperature, and plasma environment, a noncontact videometrics system using a telemicroscope was designed. Based on the captured image, the interactive partitioning edge detection method (IPEDM) was used to obtain stable and clear edges of multiple circular cooperative targets. Meanwhile, magnification factor calibration, rotation angle correction, and subpixel-level grid deformation measurement were performed with cooperative targets. The measurement results show that under the power of 750 W in the discharge chamber, the maximum thermal deformation of the screen grid is 1120 \(\upmu\)m, and the gap between the screen grid and the accelerator grid is reduced by 420 \(\upmu\)m. An accuracy assessment of the system shows that the grid deformation measurement accuracy is better than 12 \(\upmu\)m, and the system satisfies the requirement of high-precision real-time measurements of the grid thermal deformation of the ion thruster under the discharge-chamber-running condition and the plasma-beam-extraction condition.  
**Keywords:** ion thruster; ion optics; thermal deformation; grid gap; videometrics; telemicroscope; subpixel positioning; cooperative target
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 2 Concept and Taxonomy of IDS  
For an IDS, an intrusion means an attempt to access information about computer systems or to damage system operation in an illegal or unauthorized manner. An IDS is a computer-security application that aims to detect a wide range of security violations, ranging from attempted break-ins by outsiders to system penetrations and abuses by insiders [6]. The main functions of IDSs are to monitor hosts and networks, analyze the behaviors of computer systems, generate alerts, and respond to suspicious behaviors. Because they monitor related hosts and networks, IDSs are typically deployed near the protected network nodes (e.g., the switches in major network segments).  
There are two types of IDS classification methods: detection-based method and data source-based methods. Among the detection-based methods, IDSs can be divided into misuse detection and anomaly detection. Among the data source-based methods, IDSs can be divided into host-based and network-based methods [7]. This survey combines these two types of IDS classification methods, taking the data source as the main classification consideration and treating the detection method as a secondary classification element. The proposed taxonomy is shown in Figure 1. Regarding detection methods, the survey concentrates on machine learning methods. We introduce how to apply machine learning to IDS using different types of data in detail in Section 4.
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting
### Numerical Simulation Model  
#### 4.2.1 Numerical Model  
When carrying out numerical simulations, according to the scale and size of on-site bench blasting, a numerical model is established and appropriately simplified to shorten the calculation time. The simplified model is presented in Figure 9. The length, width, and height of the block were 700 cm, 300 cm, and 400 cm, respectively. The blast hole was 100 mm in diameter. Also, nonreflective boundary conditions were adopted at the left, right, bottom, and rear edges of the model, with free boundary conditions (reflective boundaries) considered for the remaining boundaries. The model was then meshed, considering all these inputs and specifications. For rock materials, the Lagrangian mesh method was used, while the Eulerian mesh method was used for explosives and the air. The model mesh grid is shown in Figure 10. The total number of unit divisions in the mesh was 770520, including the rock, explosive, and air units, with nodes shared by the air and explosive units. By defining the keyword *CONSTrained_LAGRANGE_IN_SOLID [28], fluidsolid coupling was achieved for the explosives, air, and rocks. The cm-g-us unit system was adopted in the whole modeling process.
## 6 Conclusions  
The clock and data recovery (CDR) design of the 112 Gb/s duo-binary four-level pulse amplitude modulation (DB PAM-4) receiver adopts the new Mueller-Muller Phase Detector (MMPD) combined with the digital signal processing (DSP) algorithm to complete the multi-stage phase-detection task with a shorter locking time, and high phase-detection density, and low phase space complexity detection. An MMPD algorithm based on DB PAM-4 is proposed, which reduces the space complexity of all-digital PD by the 7-level. The DB PAM-4 CDR algorithm based on PAM-4 CDR design shortens the design cycle, and analog to digital converter (ADC) + DSP architecture is compatible with low-order modulation methods such as PAM-4, which can realize the multi-mode isomorphic design of the receiver.  
Conceptualization, J.Z., F.L. and J.S.; methodology, J.Z. and J.S.; software, J.Z.; validation, Z.T., D.L. and J.Z.; formal analysis, J.Z. and F.L.; investigation, J.Z.; resources, J.Z. and F.L.; data curation, Z.T. and D.L.; writing--original draft preparation, J.Z.; writing--review and editing, F.L., D.L., J.Z. and Z.T.; visualization, J.Z.; supervision, F.L.; project administration, F.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China of Project 6204263.  
The authors declare no conflict of interest.
## 4 Discussion
In the Gram matrix, this results in a gradual growth of high-intensity subclusters, indicating that functional connectivity patterns are becoming increasingly similar over time. Seizures (ictal period) are characterised by hyper-synchronisation, where large portions of the brain become abnormally coordinated. This manifests as large, dense, high-intensity subclusters in the Gram matrix, showing that EEG graphs during the seizure period are highly similar across time. **B. Cognitive workload and mental state transitions--**When performing simple tasks or resting, inter-channel synchronisation is relatively low, leading to sparse or fragmented high-intensity subclusters in the Gram matrix. Engaging in complex cognitive tasks often requires enhanced communication between brain regions, resulting in stronger and more persistent subclusters, which reflect stable, synchronised connectivity patterns. **4. Clinical and Research Implications--*** **Seizure prediction:** The appearance of expanding high-intensity subclusters in preictal states provides early indicators of impending seizures. * **Real-time EEG monitoring:** Tracking changes in Gram matrix patterns can enable adaptive brain-computer interfaces (BCIs) to respond dynamically to user cognitive states. * **Cognitive workload analysis:** Monitoring synchronisation dynamics can inform fatigue detection systems in high-performance work environments.
## 3 Review of Indoor Navigation System Using Unity and a Smartphone (INSUS)
### SEMAR Server  
The _SEMAR_ server functions as a database provider that manages communication through the HTTP protocol [45]. It is responsible for storing and retrieving various types of user data, including the user identity for login and authentication processes, the user's initial position after scanning a _QR_ code, the target position inputted by the user through the destination input interface, and the room label associated with the target position. The _SEMAR_ server ensures reliable communication between _INSUS_ and the server by handling HTTP requests and responses using REST API services. The REST API service handles GET requests when users log into the application and POST requests for transmitting user data to the database.
# Structural Evolution of Polyimide-Derived Carbon during Phosphoric Acid Activation
## 3 Results and Discussion
### Acid-Base Properties  
All phosphorus-containing surface compounds discovered by XPS and \({}^{31}\)P MAS NMR techniques, including phosphates, pyrophosphates, and phosphonates, are acidic, and their presence in the carbon structure imparts an acidic character. Proton-binding isotherms show that the carbon obtained without phosphoric acid is amphoteric, that is, it is capable of both adsorption of protons in an acidic solution and dissociation of protons in an alkaline medium (Figure 9a). The point of zero charge (PZC), where the sorption of protons is zero, for this carbon is 8.6. On the contrary, the proton-binding isotherm for carbon obtained with phosphoric acid is located entirely in the negative region (Figure 9a). The point of zero charge for this carbon has not even been reached, but extrapolation gives a value of 2.3, which is close to the pK\({}_{\text{a}}\) of phosphoric acid.  
Figure 8: Chemical shift (**a**) and relative area (**b**) of deconvoluted components of \({}^{31}\)P MAS NMR spectra of polyimide-derived carbons obtained with phosphoric acid (H\({}_{3}\)PO\({}_{4}\)-series) at different temperatures.  
Figure 9: Proton-binding isotherms (**a**) and proton affinity distributions (**b**) for polyimide-derived carbons obtained with (H\({}_{3}\)PO\({}_{4}\) series) and without (Thermal series) phosphoric acid at 800 \({}^{\circ}\)C.  
The shape of the proton-binding isotherm for phosphorus-containing carbon is characteristic of multifunctional cation exchangers containing functional groups of different acid strengths. Proton affinity distributions of acid groups calculated by the CONTIN method show two types of acid surface groups for carbon obtained without phosphoric acid (Figure 9b). They can be classed as end or lactone groups (0.090 mmol/g, pK\({}_{4}\) 9.7) and phenolic groups (0.266 mmol/g, pK\({}_{4}\) 11.7). Proton affinity distribution for carbon obtained with phosphoric acid shows six types of surface groups that may be classed as phosphates (0.799 mmol/g, pK\({}_{4}\) 2.97, 0.515 mmol/g, pK\({}_{4}\)2 6.69), carboxylic (0.226 mmol/g, pK\({}_{4}\) 5.16), and two types of end and/or lactone (0.107 mmol/g, pK\({}_{4}\) 8.21 and 0.494 mmol/g, pK\({}_{4}\) 9.33) and phenolic groups (1.118 mmol/g, pK\({}_{4}\) 11.16) (Figure 9b). This result shows radically different surface chemistry of carbon obtained with phosphoric acid compared to carbon prepared without acid.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Identification of Crop Emergence and Sowing Periods
The daily GDU were calculated as:  
\[GDU_{i}=\frac{T_{max,i}+T_{min,i}}{2}-T_{base} \tag{6}\]  
where \(GDU_{i}\) are the growing degree units for the \(i\)-th day [\({}^{\circ}\)C], \(T_{max}\) and \(T_{min}\) are the maximum and minimum air temperatures for the \(i\)-th day and \(T_{base}\) is the low threshold temperature required for maize growth. According to the guidelines from the University of Wisconsin [53], \(T_{base}\) was assumed equal to 10 \({}^{\circ}\)C and the upper and lower limits for daily \(T_{max}\) and \(T_{min}\) were fixed to 30 \({}^{\circ}\)C and 10 \({}^{\circ}\)C respectively. Daily temperature data provided by Arpa Piemonte [54] were used to determine the daily GDU for each field. The daily GDU were cumulated backwards from the emergence days to reach the required amount of Growing Degree Units for the crop emergence: for each field, the day on which the cumulative Growing Degree Units reached 62.9 \({}^{\circ}\)C was chosen as the sowing date. According to [PERSON] and [PERSON] [55], soil moisture (SM) can affect the total GDU required by maize to reach emergence: for moisture values below the optimum in the seeding-zone, 16.7 \({}^{\circ}\)C should be added to the amount to be reached. Even if the impact of soil moisture occurs only in very dry periods, the optimum condition of soil water content is an important factor for the choice of sowing dates by farmers: for this reason, the impact of soil moisture on the length of emerging periods was considered in this study. The gridded moisture data described in Section 2.2.4 were used to identify moistures below optimum conditions. The upper limit of soil moisture was set for each field considering the maximum value from January 2018 to October 2021: these values were assumed as the field capacity of each field. According to the methodology described by FAO [56], each crop reach water stress conditions when soil moisture reaches values lower than specific fractions of field capacity: the FAO guidelines suggest that maize reaches stress conditions when soil moisture is lower than 45% of field capacity. The mean soil moisture during the 20 days before the plant's emergence was calculated over each field: for the fields where the mean SM was found lower than 45% of field capacity, the GDU requirement of 62.9 \({}^{\circ}\)C was increased by 16.7 \({}^{\circ}\)C.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Methods for Fungal Identification and Characterization in Food Samples
#### 2.2.3 Molecular Methods  
Molecular methods have revolutionized fungal identification and characterization in food samples, offering unparalleled specificity, sensitivity, and accuracy [80]. Polymerase chain reaction (PCR) techniques target conserved regions of fungal DNA, such as the internal transcribed spacer (ITS) region, allowing the rapid amplification and detection of fungal DNA in food samples [81]. Sequencing of PCR products, followed by comparison with reference databases, enables precise species identification. Next-generation sequencing (NGS) technologies, such as amplicon sequencing and metagenomic sequencing, provide comprehensive insights into the fungal diversity [82] and community structure in complex food matrices. Additionally, quantitative PCR (qPCR) allows the quantification of fungal species in food samples, providing valuable information for risk assessment and monitoring purposes [83]. While molecular methods offer high specificity and sensitivity, they require specialized equipment, technical expertise, and may be cost-prohibitive for routine analysis in some settings.