esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Previous earthquake polarization (as the ratio of vertical and horizontal components) studies using geomagnetic data were all performed with ground data. The advantage of satellite data is that it is not limited by geography. Therefore, in this work, we tried to select 12 typical earthquakes in Northeast Asia with Ms \(>\) 5.0 and an epicent depth \(\leq\) 40 km within the longitude 105\({}^{\circ}\) E-145\({}^{\circ}\) E and latitude 38\({}^{\circ}\) N-58\({}^{\circ}\) N ranges from December 2018 to January 2023 for analysis by using the satellite data of the high-precision magnetometer (HPM) payload onboard the China Seismo-Electromagnetic Satellite (CSES) for the first time in a quiet magnetic environment. The geomagnetic three-component vector data were investigated, and the minimum study period was divided into 10 s intervals. Fourier transform was performed to obtain 0.01-0.2 Hz geomagnetic three-component dynamic spectra, and the time series of the polarization (as the ratio of vertical and horizontal components) data was then obtained. The average value of the polarization data over four years was used to obtain the time series of the polarization perturbation amplitude, after which joint research was conducted. The results showed that (1) earthquakes with larger magnitudes are more likely to exhibit anomaly perturbations recorded by satellites; (2) among all earthquakes with anomalies, the horizontal east-west component perturbation is the largest, the vertical component perturbation is the smallest, and the east-west component may be the dominant component in seismic anomaly observations; (3) the applicability of the polarization method to space-based earthquake-related data is limited; (4) the perturbation amplitude of polarization data can be used as a reference for extracting seismic anomalies; and (5) ion velocity Vx data from the plasma analyzer package (PAP) can be considered to approximately verify the physical mechanism of the anomaly perturbation of the horizontal component in the ionospheric magnetic field, and the two kinds of data (PAP and HPM) can be combined in seismic prediction research.  
CSES; high-precision magnetometer; geomagnetic three-component; anomaly perturbation; polarization +
Footnote †: journal: _atmosphere_  
_Atmosphere_**2023**, _14_, 1555. [[https://doi.org/10.3390/atmos14101555](https://doi.org/10.3390/atmos14101555)]([https://doi.org/10.3390/atmos14101555](https://doi.org/10.3390/atmos14101555))
# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 3 LoED: LoRaWAN Dataset  
The analysis of the behaviour of LoRaWAN has been of carried out in many papers. Some of them leverage powerful simulation tools to observe the performance under different parameter settings [11] while others analyse real datasets by also employing classification methods to represent the behaviour [12].  
In this work, we operate under the second approach. To this aim we used, as case study, the dataset \"LoRaWAN at the Edge Dataset\" (LoED), an open dataset of LoRaWAN packets collected at gateways during a period of time of 4 months, generated by smart city application and research deployment [2]. We selected the LoED dataset since, after a deep analysis of the literature it is one of the largest (1,263,001 entries) and most complete. This unique dataset contains traffic generated by several distinct IoT applications. Indeed, packet collection was completed at gateway-level, passively listening for LoRa frames in the air. This means that all packets observed by the gateways were captured, regardless of what device generated them or which application they belong to. The traffic was collected by 9 gateways in London, covering different city areas. Five gateways were deployed outdoors, on the rooftops of buildings, with clear line of sight to the devices. Four indoor gateways instead had limited line of sight, one of them was placed on the ground floor with no line of sight to any devices. The collected packets use the explicit header mode that allows to extract metadata information, such as:  
* _time_: Time at which the packet was received by the gateway;
* _physical payload_: Raw payload contained in the received packet;
* _gateway_: Identifier of the gateway that has received the packet;
* _crc status_: Physical layer CRC;
* _frequency_: Transmission frequency;
* _Spreading Factor_: Transmission \(SF\) of the packet;* _bandwidth_: Bandwidth used by the received packet;
* _code-rate_: LoRa coding-rate of the packet;
* _RSSI_: Sampled _RSSI_ value of packet reception;
* _SNR_: Sampled SNR value of packet reception;
* _device-address_: Device-address of the device that has sent the packet;
* _mtype_: mtype bit fields of the packet;
* _fcnt_: Counter value of the packet;
* _fport_: Port of the packet.  
The gateway numbers are hexadecimal string of 16 characters; for sake of simplicity we assign to each gateway an ID in the form of \(GWX\) where \(X\) is a number from 1 to 9. The matching of the IDs and the gateway numbers is in Table 1.  
During the acquisition time were collected 11,263,001 packets from devices presenting 145,023 different device addresses.  
We remark that the methodology here proposed can be applied to any other dataset if sufficiently rich. We suggest that experiments can be also carried out by deploying LoRaWAN gateways and using the The Things Network NS [13] to collect the relevant data and apply the proposed approach to test and measure the network behaviour.
## 3 Results and Discussions
### Simulation Results of Micro Reacting Pipe and Selection of Model Parameters
#### 3.2.2 Simulation Results of Micro Reacting Pipe
The convergent fluids are divided into four strands after reaching the next set of obstacles which are orthogonal to the former, and then move towards the circle frame in Figure 10e, converge into two strands. In this periodic fluid movement, the fluid is constantly divided and converged, and the fluid microspheres from all directions collide violently at the confluence, resulting in the continuous deformation and fragmentation of the fluid microspheres and the formation of smaller microspheres. Thus, it is more favorable for the uniform distribution of the components in the fluid and the enhancement of the mixing efficiency. Figure 10: Velocity vector diagram of different conditions: (**a**) velocity vector diagram of the free channel (axial); (**b**) watershed model of the unobstructed micro reacting pipe; (**c**) velocity vector diagram with obstruction (axial), (**d,e**) velocity vector diagram of two successive obstacles (vertical axis). Figure 11 shows the comparison of mass fraction distribution of axial components and kinetic energy distribution of axial turbulence between the micro reacting pipe without obstacles and with obstacles designed in this paper. The distribution of mass fraction of the component ethanol (left in Figure 11) demonstrates that the mass fraction of the ethanol in the micro reacting pipe with obstacles converged to the ideal point 0.4473625 earlier than the unobstructed pipe. Therefore, the obstacles designed in this paper can enhance the mixing efficiency of the micro reacting pipe. The turbulent kinetic energy of the micro reacting pipe with obstacles in the mixing region is nearly an order of magnitude higher than that of the unobstructed micro reacting pipe as shown in the comparison of axial turbulent kinetic energy (right). Therefore, the fluid motion is more intense in the micro reacting pipe with obstacles. The mass and momentum exchange between the various fluid micro-clusters more frequently, which enhances the convection and diffusion of the micro reacting pipe and is beneficial to the improvement of the mixing efficiency.
## 2 Local Energy Markets
### Design of LEM
#### 2.1.2 Non-Auction-Based Design  
For non-auction-based designs, direct (i.e., P2P) and indirect (central coordinator) market access methods are possible.  
Direct market access: In direct market access designs, the buyers and sellers directly negotiate and finalize the quantity and price. The bilateral negotiation and contract approach offers extended degrees of freedom, as market participants can set their price range, which can significantly differ from market-based equilibrium prices. In this case, the LEM coordinator only provides the necessary digital infrastructure to carry out the negotiation process and acts as a regulating institution.  
Indirect market access: In this case, the central coordinator takes over all tasks, i.e., optimal resource allocation, determines the participant's schedule within the LEM to maximize social welfare, and finds the equilibrium prices. Market participants are required to provide all the necessary information regarding their assets, quantity and price valuation, and behavioral flexibility to the coordinator. Although social welfare maximization is possible, in this case, a player's active engagement in energy trading activities cannot be guaranteed.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 4 Differential Diagnosis and Disease Phenotyping and Staging in Breath Analysis
### The Case for Lung Diseases
In another recent study, the same e-Nose was used for the effective discrimination of HC and asymptomatic-controlled asthmatic children from the symptomatic partly-controlled and uncontrolled asthmatic children, after assessing the discriminatory ability of subsets of the 32 sensors of Cyranose 320 for the six different possible combinations of the 4 studied groups; increased feasibility and modest to good diagnostic accuracy values were obtained [119]. Cyranose 320 has been also used for COPD phenotyping permitting (especially in the case of GOLD stage I) the detection of activation of inflammatory cells, indicating increased inflammatory activity in mild rather than severe COPD [120]. Figure 15: DFA plots representing the discrimination of **(a)** LC patients from HC; **(b)** SCLC from NSCLC patients; and **(c)** SCC from ADC patients, using a 6-sensor array of UV-irradiated (394 nm) pristine or metal-doped WO\({}_{3}\)NWs. The arrays achieved the detection of lung cancer but also the prediction of LC histological subtypes. Reprinted with permission from Ref. [246]. Copyright © 2020 Published by Elsevier B.V.
# Optimizing Geospatial Data for ML/CV Applications:
## 1 Introduction
Studies have demonstrated the effectiveness of combining satellite images with Geographic Information System (GIS) data for land use and land cover classification, infrastructure monitoring and environmental assessment [36; 37; 38; 39]. For example, integrating high-resolution imagery with cadastral maps improves the precision of urban and rural land use classification, providing detailed insights into spatial patterns and changes [40; 41]. Figure 1: An aerial road image capturing surroundings beyond the road boundaries. Source: Orthophoto image obtained from Geoportal [27].
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
## 4 Application and Results
### Dynamic Simulation Results and Analysis
It, with the above results of preserving critical poles and adding some extra pole, thus illustrates that the reduced model is able to keep the more energetic poles, through achieving the target, (51), of minimising the energy error for each the simulated event input to the observed variables output. Three main conclusions can be drawn from the above dynamic simulation results. Firstly, the most appropriate reduced model depends on the location and nature of the simulated event. Secondly, by making the model order the same, state residualisation can capture event transients with better accuracy than phasor approximation models, particularly for short-circuit events where converter overcurrents are of most concern. Finally, for short-circuits, the phasor approximation model does not achieve good accuracy, and it is particularly concerning that the fast initial transients (very important for power converters) may be missed.
## 2 Related Works
### Conventional Authentication Techniques  
A Network Slice Access Authentication and Service Authorization method was developed in [11] to securely access data in satellite-terrestrial networks with minimal computational and communication overhead. However, the complexity involved during the authentication process was not reduced. A broad examination of state-of-the-art authentication techniques designed for satellite communication networks was presented in [12].  
A robust ECC-based authentication procedure was utilized in [13] for reliable satellite-to-satellite communication networks. However, the error rate measured during the authentication was higher. A High-Performance Identity-Based Quantum Signature system was implemented in [14] to attain strong security. However, the authentication efficiency was unsatisfactory. A provably secure and efficient emergency message verification scheme was presented in [15] to achieve privacy and accountability and support confidential transmission. However, the data loss rate was higher than others.  
A lightweight authentication and key agreement method was implemented in [16] for S2S communication with the application of a symmetric cryptographic system. However, the data integrity rate was poor. An access authentication protocol was developed in [17] with user anonymity and traceability, with the objective of decreasing the communication delay and signaling cost of access authentication. However, the true-positive rate of authentication was not sufficient.  
A secure three-factor anonymous-roaming authentication procedure was introduced in [18] with the assistance of ECC for space information networks, but the authentication failure was worse. However, the ratio of the number of users that are correctly authenticated as detector or attacker satellites was not higher. LSTM Networks were introduced in [19] with the aim of achieving better anomaly detection accuracy in satellite communications systems. However, it was computationally costly and training time and memory consumption was higher. A literature review of different security threats, solutions, and challenges faced during the process of secure satellite communication was presented in [20].
# Simplified Integrity Checking for an Expressive Class of Denial Constraints
## 4 A Simplification Procedure for Obtaining a Pre-Test
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Explanation** \\ \hline \(S^{\prime}=\mathsf{After}^{U}(S)\) & Schema \(S^{\prime}\) is a WP of schema \(S\) wrt update \(U\) (Definition 7) \\ \hline \(\mathcal{L}_{\mathrm{s}}\) & A class of schemata that can be unfolded as a set of denials (Definition 6) \\ \hline \(\Gamma=\mathsf{Unfold}_{\mathcal{L}_{\mathrm{s}}}(S)\) & For a schema \(S=\langle IDB,IC\rangle\in\mathcal{L}_{\mathrm{s}}\), \(\Gamma\) is the unfolding of \(IC\) wrt \(IDB\) (Definition 8) \\ \hline \(\Gamma=\mathsf{After}^{U}_{\mathcal{L}_{\mathrm{s}}}(S)\) & The set of denials \(\Gamma\) is a WP of schema \(S\in\mathcal{L}_{\mathrm{s}}\) wrt update \(U\), and is defined as \(\Gamma=\mathsf{Unfold}_{\mathcal{L}_{\mathrm{s}}}(\mathsf{After}^{U}(S))\) \\ \hline \(\phi_{1}\sqsubseteq\phi_{2}\) & Denial \(\phi_{1}\) subsumes denial \(\phi_{2}\) (Definition 9) \\ \hline \(\phi_{1}\sqsubseteq\phi_{2}\) & Denial \(\phi_{1}\) strictly subsumes denial \(\phi_{2}\) (Definition 9) \\ \hline \(\phi^{-}\) & Reduction: same as denial \(\phi\) but without redundancies (Definition 10) \\ \hline \(\phi^{+}\), \(\phi^{+}\) & Expansion: same as denial \(\phi\) (or set of denials \(\Phi\)) with constants and repeated variables replaced by new variables and equalities; see [8] \\ \hline \(\Gamma\vdash_{R}\phi\) & Denial \(\phi\) is derived from the set of denials \(\Gamma\). In particular, there is a resolution derivation of a denial \(\psi\) from \(\Gamma^{+}\) such that \(\psi^{-}\sqsubseteq\phi\) (Definition 11) \\ \hline \(\Gamma^{\prime}=\mathsf{Optimize}^{\Delta}_{\mathcal{L}_{\mathrm{s}}}(\Gamma)\) & \(\Gamma^{\prime}\) is the result of applying denial elimination and literal elimination from the set of denials \(\Gamma\in\mathcal{L}_{\mathrm{s}}\) as long as possible by trusting the set of denials \(\Delta\in\mathcal{L}_{\mathrm{s}}\) (Definition 12) \\ \hline \(\Phi=\mathsf{Simp}^{U}_{\mathcal{L}_{\mathrm{s}}}(S)\) & The set of denials \(\Phi\) is a CWP of schema \(S\in\mathcal{L}_{\mathrm{s}}\) wrt update \(U\) computed through \(\mathsf{Optimize}_{\mathcal{L}_{\mathrm{s}}}\) and \(\mathsf{After}_{\mathcal{L}_{\mathrm{s}}}\) (Definition 13) \\ \hline \end{tabular}
\end{table} 
Table 1: Operators used for the simplification procedure, as defined in Section 4. Figure 1: Sketch of the main blocks in the simplification procedure \(\mathsf{Simp}\). **Example 2**. : _Consider the update and IDB from Example 1 and let \(\Gamma_{1}\) be the constraint theory \(\{\gets p(X)\wedge q(X)\}\) stating that \(p\) and \(q\) are mutually exclusive. Then, \(\{\gets q(a)\}\) is a CWP (but not a WP) of \(\Gamma_{1}\) with respect to \(U_{1}\)._
## 4 Results and Discussion
#### 4.1.1 Accuracy  
Random Forest with extra tree regressor demonstrated the highest accuracy with the lowest MSE (10.23), RMSE (3.66), and MAE (1.99), along with the highest R-squared value (0.99). This performance indicates its exceptional ability to predict RUL with precision. In contrast, Long Short-Term Memory (LSTM) significantly underperformed with high error metrics (MSE: 1858.31) and R-squared value (0.96).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ML** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **Training** & **Prediction** \\
**Model** & & & & & **Time (s)** & **Time (s)** \\ \hline
**ANN** & 1858.31 & 43.11 & 36.06 & 0.96 & 276.80 & 1.61 \\
**LSTM** & 824.15 & 28.71 & 12.04 & 0.97 & 532.63 & 2.37 \\ \hline
**LR** & 51.86 & 7.20 & 4.54 & 0.98 & 2.35 & 0.01 \\
**RF with** & 10.23 & 3.66 & 1.99 & 0.99 & 7.34 & 0.05 \\
**ETR** & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Metrics of models with cycle count as a feature.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 4 Results and Discussions
### Weighted Fusion Correction
This study primarily used a weighted estimation method to achieve the optimal weighted fusion of the EMI series TOCs. Figure 9a shows the fusion results for the November 2023 EMI-DQ01, EMI-GF5(01A), and EMI-GF5(02) datasets. The spatial distribution was more similar to the spatial distribution of the TROPOMI data shown in Figure 4d, and it also has been gridded at the spatial resolution of 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) (latitude \(\times\) longitude). Figure 9b shows the correlation between the weighted fusion of the EMI-GF5(01A) and  
Figure 8: Comparison of time series of EMI-GF5 (01A), EMI-GF5 (02), and EMI-DQ01 and ground-based TOCs for several selected stations worldwide. EMI-DQ01 TOCs and the TROPOMI TOC results; the R value increased to 0.99 and the random and systematic errors decreased. As shown in Figure 9c, the differences between the EMI fusion TOC results and the TROPOMI TOC results (EMI minus TROPOMI) were relatively significant in the Antarctic near the coastline; the differences were within 3 DU and mostly between 1 and 2 DU. Thus, weighted data fusion improved the spatial resolution and consistency of the EMI TOCs product. TROPOMI TOC results are generally considered high-quality satellite measurements and are often used as a reference standard in atmospheric studies [34]. And because of the ice and snow coverage, the changes in the surface albedo were more significant. The surface albedos of the EMI series instruments were determined using the Ozone Monitoring Lambert Equivalent Reflectance (OMLER) climatological albedo [52], whereas the original OFFL data utilized the effective scene albedo. In most cases, the disparity between climatological albedo and effective scene albedo was minimal (typically less than 0.1). However, during spring in the Antarctic [49], notable differences were observed near coastlines, correlating with substantial deviations. Therefore, we attributed the significant bias (up to \(\pm\)3 DU) in Antarctic regions near coastlines to variations in the surface albedo algorithms. Figure 9: (a) Spatial distributions of monthly average TOC fusion results for the EMI series datasets for November 2023; (b) linear fit between TOC fusion results for the EMI datasets and TROPOMI TOCs for November 2023; (c) differences between the TOC fusion results for the EMI datasets and TROPOMI TOCs for November 2023.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 4 Interfaces and Protocols in QKD Networks Enabled by SDN
### Southbound Interface Protocol  
Southbound interface is used for the management from controller layer to QKD layer to enable some basic control functions like QKD light-path establishment, deletion, and adjustment, etc. Moreover, the types of information should include device configuration, light-path status and network alarm at least. In addition, current SDN version needs to obtain the types of QKD devices, because hybrid connection  
Figure 4: Related interfaces in QKD network enabled by SDN.  
between different QKD systems will cause failure links to interrupt the generation of secret keys. Considering information came from different devices, this interface should allow multiple protocol selections, and the alternatives are listed as following:  
(a) OpenFlow protocol: It can be extended in QKD networks to support resource reporting, link configuration, and other functions of QKD devices.  
(b) OpenFlow-Config protocol: It can be used as a supplement for OpenFlow protocol to configure network devices to support OpenFlow. It can use NETconf as the transport protocol.  
(c) Traditional management protocols: such as SNMP, TL1, and other protocols.