esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Sensor Architecture
### Data Logging and Storage  
Data from the sensors are recorded every five minutes in the internal database (RRD4) of OpenHAB. The data are also copied through the built-in tools to a remote Internet MariaDB (open-source MySQL) server installed on the top of Raspberry Pi 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Air Component** & **Sensor** & **Operating** & **Sensitivity** & **Analog Output** & **Calibration** \\ \hline Carbon dioxide (CO\({}_{2}\)) & CCS811 & Measurement Range: CO\({}_{2}\): 400 ppm to 8192 ppm, TVOC: 0 ppb to 1187 ppb. \\ and TVOC & CCS811 & Interface: I2C (Inter-Integrated Circuit). \\  & Digital sensor with a built-in algorithm for equivalent CO\({}_{2}\) (eCO\({}_{2}\)) and TVOC. \\ \hline Humidity and & Si7021 & Measurement Range: Humidity: 0\% to 100\% RH, Temperature: \(-\)10 \({}^{\circ}\)C to 85 \({}^{\circ}\)C. \\ Temperature Sensor & Si7021 & Interface: I2C, digital output. \\  & Provides calibrated, linearized sensor data. \\ \hline Barometric Pressure & BMP280 & Measurement Range: Pressure: 300 hPa to 1100 hPa, Temperature: \(-\)40 \({}^{\circ}\)C to 85 \({}^{\circ}\)C. \\ and Temperature Sensor & BMP280 & Interface: I2C and SPI (Serial Peripheral Interface). \\  & & Low power consumption. \\ \hline PM2.5 & SDS011 & Measures particulate matter in two size ranges: PM2.5 (particles with a diameter of 2.5 \(\upmu\)m or smaller) and PM10 (particles with a diameter of 10 \(\upmu\)m or smaller). \\  & & Utilizes laser scattering to determine the concentration of particles in the air. \\  & & Measurement Range: PM2.5: 0 to 999.9 \(\upmu\)g/m\({}^{3}\), PM10: 0 to 999.9 \(\upmu\)g/m\({}^{3}\). \\  & & Provides a digital output of particle concentration in micrograms per cubic meter (ug/m\({}^{3}\)). \\  & & Communication Interface: UART (Universal Asynchronous Receiver-Transmitter) serial communication protocol. \\  & & Compact and lightweight design. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 4 Key Application Areas for Passive Wake-Up Radio
### Smart Grid  
Smart grid is a new sensor technology which adds communication, monitoring, control, and analysis capabilities to the electricity grid to improve the electricity supply chain. It is an extension from the power generation station to the user's home. The smart grid enables high system throughput as well as low energy consumption by allowing facilities to transfer the electricity from place to place in the system as economically and efficiently as possible. It also lets business and homeowners utilize electricity economically. Sustaining efficiency in communication is one of the major challenges in this application.  
Power line communication (PLC) is a method by which smart grids achieve efficient point to point communication. However, the energy efficiency of this method is reduced in periodic communication scenarios such as smart street lighting. Passive wake-up protocols proposed in [118; 119] eliminate this energy inefficiency by removing idle listening when the devices are on stand-by. Figure 10 shows a graphical representation of smart grid application in passive street light PLC. The PLC modem is attached to the passive wake-up radio, which allows the modem and the connected load (in this case street lights) to remain in a deep sleep and only wakes up after receiving a signal from the control center through the gateway. Data aggregation in smart grids is either on demand or periodic and hence, multi-hop capability, reduced latency, and high energy efficiency should be prioritized in the protocol design [120; 121]. Protocols such as those proposed in [108] are suitable for the smart grid application.
## 3 Results and Discussion  
Once all the cubes had been scanned, an initial processing of the information was carried out. The commercial software AVIZO (FEI Visualization Sciences Group, Hillsboro, OR, USA) was used for this purpose.  
First, we proceeded to extract all the voxels belonging to the pores, which were defined on the basis of a certain gray level. In this case, an upper gray threshold of 5500 was considered appropriate. Therefore, all voxels whose gray level was between 0 and 5500 were considered as pores. As a general criterion, the gray threshold is taken in a valley of the gray distribution histogram, since the valleys correspond to the boundaries between the different components of the material.  
Next, all the voxels that were in contact were added. Subsequently, we proceeded to separate all those pores that had been joined by small necks. In these cases, the voxel aggregation subroutine results in one pore when in fact there are two pores very close to each other. An algorithm based on watershed segmentation [33] was applied. The final result is shown in Figure 5.  
At this point, the aforementioned postprocessing software provides the following information for each pore: X, Y, Z coordinates of its center of gravity; pore length, defined as the longest distance that can be established between two voxels; width, defined as the shortest distance between two contour voxels; pore volume; outer surface area; and orientation. Elements with a length of less than 100 \(\upmu\)m were discarded, which corresponds to approximately three times the voxel size.  
Regarding pore orientation, it is obtained from AVIZO in Spherical coordinates; that is, the angular information it provides are the values of \(\theta\) (theta) and \(\phi\) (phi). Theta is the polar angle, i.e., the angle between the principal axis of the pore (the one containing  
Figure 4: Example of 3D reconstruction.  
Figure 5: Pores identified and isolated.  
its length) and the vertical \(Z\) axis. It should be noted that the vertical axis corresponds to the concreting direction and also to the loading direction. Phi is the azimuthal angle, i.e., the angle between the projection of the principal axis on the XY plane and the X axis. Theta varies from \(0^{\circ}\) to \(90^{\circ}\) while phi varies from \(-90^{\circ}\) to \(90^{\circ}\).  
Using this information, the pore orientation can be obtained according to the Cartesian angles, \(\alpha\) (alpha), \(\beta\) (beta) and \(\gamma\) (gamma) (Figure 6b).  
From the basic data provided by the software it is possible to obtain much more information to understand the morphological characteristics of the pores and their spatial distribution. Some of the main techniques and procedures to obtain such information are described in detail below.
## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Blockchain as a Solution for Traceability and Product Identity
Figure 2 illustrates the interaction among the four architectural layers: physical (producers, logistics providers, IoT devices), digital (digital twins, DIDs), blockchain (smart contracts), and application (interfaces for regulators, retailers, consumers. Data are captured from the physical world, represented through digital twins and Decentralised Identifiers, secured on the blockchain via smart contracts, and accessed by stakeholders through application interfaces. This layered approach ensures end-to-end product identity verification, traceability, and transparency across the supply chain. At the base is the physical layer, which includes vineyards, farms, storage facilities, and transport mechanisms. This layer is equipped with sensors and IoT devices that capture real-time environmental and operational data such as temperature, humidity, and location. Above this is the data storage layer, which separates information into on-chain and off-chain categories. On-chain data include critical identity and certification records that require immutability, while off-chain data, such as large sensor logs or quality reports, are stored using decentralised solutions such as IPFS or Arweave. Next is the digital twin layer, which represents physical product batches with virtual counterparts. These digital twins track the movement and transformation of goods through the supply chain. This layer also supports the tokenisation of product units for secure digital identification. The blockchain layer functions as the core of the system. It leverages DIDs to assign unique, verifiable identities to products, stakeholders, and transactions. Smart contracts are used to automate workflows and enforce predefined rules across the supply chain. At the top of the architecture is the application layer, where stakeholders such as regulators, consumers, and analytics platforms interact with the system. Regulators can verify compliance data, consumers can scan QR codes to trace product provenance, and analysts can generate insights from aggregated, tamper-proof data. Each layer is vertically connected by arrows, showing the flow of data from the field to the blockchain and eventually to end-users. The system ensures transparency, accountability, and traceability throughout the agricultural supply chain. Figure 3 illustrates four thematic drivers--hybrid blockchain models, AI and analytics integration, IoT-enabled automation, and policy and regulatory developments--and their interconnected roles in enhancing traceability, establishing robust product identity, and fostering trust across agricultural supply chains. Each component contributes uniquely to overcoming current limitations and advancing blockchain adoption at scale. Figure 3: Key enablers for improving traceability and product identity in agricultural supply chains using blockchain. Figure 2: Data flow in blockchain-based product identity architecture for agricultural supply chains.
# An Artificial Neural Network Autoencoder for Insider Cyber Security Threat Detection
## 5 Discussion
### Impact of Tuning Parameters on Threat Detection
#### 5.1.1 The Impact of the Combination of the Loss Function and Optimizer Type  
The four combinations of the impact of loss functions and optimizer types on model performance were analyzed. In the analyses, the sent bytes (normalized with values in the range between 0 and 1) have been used as parameters for the reconstruction of anomaly and normal data patterns.  
The first combination analyzed is the impact of the combination of using MSE (mean square error) as a loss function and the Adaptive Moment Estimation (ADAM) optimizer. The obtained results for the reconstruction of normal and anomaly data are presented in Figures 14 and 15, respectively.  
Figure 14: Reconstruction of normal data with MSE and ADAM.  
According to the presented results, it can be seen that the loss function and optimizer combination show good performance for both normal and anomaly data, since these analyzed parameters fit well.  
The second combination analyzed is the combination of using MSE (mean square error) as a loss function and the stochastic gradient descent (SGD) optimizer. The obtained results for the reconstruction of normal and anomaly data are presented in Figures 16 and 17, respectively.  
Figure 16: Reconstruction of normal data with MSE and SGD.  
Figure 17: Reconstruction of anomaly data with MSE and SGD.  
Figure 15: Reconstruction of anomaly data with MSE and ADAM.  
This combination of loss function and optimizer leads to the worst results compared with the other tuning combinations, and the performance was especially poor in the case of reconstructing the normal data. This is due to the gradient being updated after every training sample rather than updating after every epoch. Also, the amount of data used in the analyses is also not large. All these factors make the optimization process unstable in the case of MAE as the loss function and ADAM as the optimizer.
## 8 Implementation Challenges and Solutions
### Interoperability and Standardization  
Maritime systems are complex ecosystems involving diverse actors--such as ship operators, port authorities, traffic control systems, and regulatory bodies--often using non-compatible technologies. Without interoperability, AI agents risk operating in informational silos [150].  
The following challenges are identified:  
1. Lack of universal data formats, communication protocols, or safety standards for AI-driven systems;
2. Difficulty integrating with legacy vessel systems and port infrastructures;
3. Inconsistent implementation of IMO and EU guidance across jurisdictions [23]. The following solutions are proposed:
2. Promote global adoption of open communication standards, standardized APIs, and modular data schemas [35,151];
3. Design AI agents to adhere to common operational protocols, ensuring they function safely alongside manned ships and under VTS (Vessel Traffic Services) [152,153];
4. Encourage IMO, IALA, EMSA, and industry stakeholders to co-develop reference architectures for maritime autonomy integration. Standardization is not only a technical goal; it is the foundation for a global scale-up of safe and coordinated maritime AI adoption [154, 155, 156, 157, 158].
## 8 Conclusions and Discussion
### Conclusions
By establishing a spatial-temporal coordinated integration mechanism, the paper studies the optimized configuration of control parameters such as phases, phase sequences, green ratios, and green intervals, as well as coordinated control strategies with related intersections. Empirical research proves that coordinated optimization can significantly enhance intersection operational efficiency while ensuring safety. The above research purposefully focuses on key scientific issues and technical bottlenecks in the optimization design and refined management of non-conventional lanes. It is expected to not only consolidate the theoretical foundation but also promote the refinement and precision of urban traffic governance systems and governance capabilities with innovative solutions, providing technological support for alleviating \"urban traffic diseases\" and promoting urban traffic development. Y.W. : conceptualization, methodology, writing--original draft, investigation, writing--review and editing. X.Y. : conceptualization, methodology, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This study is supported by the National Natural Science Foundation of China (General Program), Research on Basic Problem of Vehicle-infrastructure Cooperative Traffic Control for Special Vehicles (52472350). China Postdoctoral Science Foundation, Cooperative Optimization on Right-of-Way at Signalized Intersections in Mixed Traffic Environment (2022M712410). Guangxi Major Science and Technology Special Subproject, Reutilization of Pinglu Canal Cross-Line Bridges and Optimization of Traffic Organization (2023 AA14006). **Data Availability Statement:** Not applicable. **Acknowledgments:** All authors are grateful for the resources provided by the Intelligent Transportation System Research Center of Tongji University. **Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.
## 5 Conclusions and Implications  
A statistical-mechanistic model driven by remote-sensing data was formulated for simulating the spatial and temporal variability of _RE_. The effect of plant productivity, temperature, and moisture were comprehensively considered using the publicly available MODIS products _EVI_, _LST_, and _LSWI_, thereby yielding a better performance for the _RE_ simulation across seven vegetation types in the Tibetan alpine grasslands and Inner Mongolian temperate grasslands. The temporal variations as well as the spatial patterns of _RE_ in northern China's grasslands were well reproduced by the model. The inclusion of _LSWI_ in the _Reref_ and the seasonal dynamics of _RE_ significantly improved the model performance in arid and semi-arid ecosystems. Our results suggest that a good representation of the spatial process and moisture effect on _RE_ should be considered in the next-generation satellite-based _RE_ models.  
Biotic and climatic control over _RE_ is different for temporal and spatial processes. Plant productivity and moisture mainly contribute to the spatial variation of _RE_ in northern China's grasslands. The influence of moisture on the spatial pattern of _RE_ is stronger in the temperate grasslands than in the alpine grasslands. Temperature plays a minor role in regulating the spatial pattern of _RE_. In contrast, temperature tends to be more important in controlling the seasonal patterns of _RE_ in northern China's grasslands, whereas in the temperate grasslands of Inner Mongolia, moisture exerts strong impact on the seasonal variation of _RE_, almost equally important as temperature. These conclusions drawn from the Tibetan and Inner Mongolian grasslands, on behalf of the alpine grasslands and Eurasian temperate grasslands across the word, can provide valuable information for large-scale estimates of _RE_ and better understanding the response of _RE_ to climate change in grassland ecosystems.  
We thank the staff of ChinaFLUX and COIAS for their dedication in observation to data processing. This study was supported by the National Natural Science Foundation of China (grant number: 41571424) and the National Key R&D Program of China (grant number: 2016 YFC0500204).  
[PERSON], [PERSON] and [PERSON] conceived the idea; [PERSON], [PERSON] and [PERSON] provided the design of total content; [PERSON], [PERSON], [PERSON], and [PERSON] processed the data; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conducted the collection of flux-tower data; [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the writing and revisions; All of the authors contributed to the result discussion.  
The authors declare no conflict of interest.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 2 The Proposed Method of Spoofing Detections' Fusion  
A detailed discussion of the proposed method is provided in this section. First, a simplified frame of discernment is given. The simplification is for the purpose of the quantization for detection results. Then, the valuation of the belief function, which is the key of information fusion, is given and discussed. Finally, the performance of detections' fusion is analyzed briefly.
## 3 System Architecture
### Strategy of Each Player
#### 3.3.2 Legacy Telecom Operator  
The existing Legacy Telecom Operator has been providing mobile communication services to end users using its infrastructure equipment and spectrum resources assigned by the government. However, in the B5G era, they will not always be able to survive due to the exhausting spectrum resources, part of which are released for regional operators, e.g., private 4G/5G and MVNOs. Therefore, it is necessary to provide new operators with development assets and infrastructure such as equipment and RAN/Core software to expand their service areas. Legacy carriers need mobile infrastructure such as RAN and Core using dedicated servers, while the development/innovation of RAN using virtualization technology through NFV and open interfaces empowered by O-RAN alliance supports the above framework. As described above, various technologies and existing infrastructure (mobile backhaul, macro-coverage, etc.) can be provided to private/local carriers. Therefore, by anticipating the technological background and future growth, it is possible to provide support from the viewpoint of operation.
## 3 RX Phased Array Design
### Dual-Polarized Antenna  
As is illustrated in Figure 10, the proposed dual-polarized antenna was simulated as an infinite array using HFSS, with the antenna ports starting from the beamformer ones. The antenna element contained four substrate layers, with layers 1 and 2 using Rogers RO3003 substrate (\(\varepsilon_{r}=3\), tan\(\delta=0.001\)) at thicknesses of 0.508 and 1.58 mm, respectively; while layers 3 and 4 were 0.254 mm thick and fabricated from Rogers TC600 substrate (\(\varepsilon_{r}=6.15\), tan\(\delta=0.002\)). The two patches of the antenna were arranged at the top of layers 1 and 2, and were excited by the feeding striplines at the bottom of layer 3 through coupling slots at its top portion. Two H\(-\)shaped coupling slots were positioned vertically to one another, allowing the antenna element to form two orthogonal line polarizations. The use of two dual-offset feedlines as the feeding striplines also represented an effective way to increase the impedance matching of the proposed antenna [25]. This is because the offset feed can reduce the coupling level, thus adjusting the impedance matching between the feeding stripline and antenna. Two symmetrical offset feedlines were combined into one, using a T-junction combiner, allowing the antenna to achieve high cross-polarization levels. The microstrip lines are designed on a single Rogers RO3003 substrate with a thickness of 0.127 mm (Layer 7).  
To ensure the proposed array was free of grating lobes over a scanning range of \(\pm 70^{\circ}\) in the operating band, the antenna's elements were arranged in a rectangular grid with a spacing of 12 mm, corresponding to about 0.5\(\lambda\) at the highest operating frequency of 12.7 GHz. The directivity of the antenna element \(D_{\text{element}}\) was determined to be 4.4 dBi at the central frequency of 11.7 GHz, through the equation:  
\[D_{\text{element}}=10\log_{10}\frac{4\pi D_{x}D_{y}}{\lambda^{2}}\Big{|}_{ \lambda=11.7\text{GHz}} \tag{14}\]  
Figure 10: Structure of the proposed dual–polarized antenna element. Units: mm.  
Figure 9: Multilayer PCB stackup of the subarray. Units: mm.  
where \(D_{x}\) and \(D_{y}\) represent the dimensions of the rectangular grid in the \(x\) and \(y\) directions.  
As can be seen in Figure 11, the simulated return losses of the antenna from the chip ports in both azimuth (Phi = 0\({}^{\circ}\)) and elevation (Phi = 90\({}^{\circ}\)) plane are below \(-\)8 dB at 10.7-12.7 GHz in the range of \(\pm\)60\({}^{\circ}\) scan angles. Figure 12 shows the antenna loss is between 0.3-0.4 dB at 11.7 GHz at boresight, including the dielectric loss, the ohmic loss, and the matching loss. The antenna loss increases to about 1 dB at \(\pm\)60\({}^{\circ}\) scan angles in the operating band due to the deterioration of \(S_{11}\). The cross-polarization level of the antenna is superior to \(-\)27 dB at boresight, and \(-\)20 dB at \(\pm\)60\({}^{\circ}\) scan angle.  
Figure 11: Simulated return losses of the antenna from the chip ports for the V– and H–polarization. (a) Azimuth plane. (b) Elevation plane.  
Figure 12: _Cont._