esa-sceva/satcom-chunk-collection huggingface dataset:
# Variations in the Equatorial Ionospheric F Region Current during the 2022 Tonga Volcanic Eruption
## 5 Conclusions  
Using the joint observations of the Swarm and ICON satellites, in this work, we report the drastic variations in the F-layer FRC almost 10 h following the 2022 Tonga volcanic eruption for the first time. The Swarm constellation observed that the radial current amplitude increased and its polarity reversed several times when the volcanic wave arrived. Using neutral wind observations obtained by the ICON satellite, the possible physical mechanisms responsible for these changes are disclosed in this work. Both the ICON and Swarm satellites were in nearly the same distance to the eruption, thus providing a good opportunity for analyzing coordinated observations. The F-layer neutral wind exhibited fluctuations in the eastward and westward directions; these fluctuations were basically consistent with the polarity variations in the FRC. Volcanic eruptions can trigger atmospheric gravity wave disturbances that can propagate upward, redistributing the E and F region wind pattern and inducing significant spatial variations in the FRC and electron density in F region. Since ionospheric current changes may damage electrical power grids, the results discussed above provide a novel perspective for understanding how the ionosphere is affected by natural disaster on the surface of the Earth and will eventually help us mitigate the impacts of these disasters on society.  
Investigation, H.X.; methodology, K.Z.; writing--original draft, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Nature Science Foundation of China (41974182), National Natural Science Foundation of China Basic Science Center (42188101), and Fundamental Research Funds for the Central Universities (2042021 kf0208).  
The Swarm magnetic field and electron density data product are available at the European Space Agency site [[https://swarm-diss.e.oe.esa.int](https://swarm-diss.e.oe.esa.int)]([https://swarm-diss.e.oe.esa.int](https://swarm-diss.e.oe.esa.int)) (accessed on 15 January 2022). The ICON data are available at [[https://icon.ssl.berkeley.edu/Data](https://icon.ssl.berkeley.edu/Data)]([https://icon.ssl.berkeley.edu/Data](https://icon.ssl.berkeley.edu/Data)) (accessed on 15 January 2022). The solar wind and interplanetary magnetic field and magnetic activity index data are from NASA/GSFC'S Space Physics Data Facility's OMNIWeb ([[https://omniweb.gsfc.nasa.gov](https://omniweb.gsfc.nasa.gov)]([https://omniweb.gsfc.nasa.gov](https://omniweb.gsfc.nasa.gov)), accessed on 15 January 2022). We appreciate the availability of the data in the website.  
The authors declare no conflict of interest.
## 6 Strengths, Limitations, and Future Research Directions
### Future Research Directions
#### 6.3.6 Architecture  
In future investigations, it will be necessary to study the impact of the MLP-Mixer deep learning architecture in image processing, what are the characteristics that allow it to have a performance superior to CNNs, but inferior to the performance obtained by the ViT architecture [5]. Future research should also focus on developing novel architectures that can achieve high performance with fewer parameters or that are more efficient in terms of computation and memory usage.
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
###### Abstract  
Standard single-mode telecommunication optical fiber is still one of the most popular in distributed acoustic sensing. Understanding the acoustic, mechanical and optical features of various fibers available currently can lead to a better optimization of distributed acoustic sensors, cost reduction and adaptation for specific needs. In this paper, a study of the performances of seven fibers with different coatings and production methods in a distributed acoustic sensor setup is presented. The main results include the amplitude-frequency characteristic for each of the investigated fibers in the range of acoustic frequencies from 100 to 7000 Hz. A single-mode fiber fabricated using the modified chemical vapor deposition technique together with a polyimide coating has shown the best sensitivity to acoustic events in the investigated range of frequencies. All of this allows us to both compare the studied specialty fibers with the standard single-mode fiber and choose the most suitable fiber for a specific application, providing an enhancement for the performance of distributed acoustic sensors and better adaptation for the newly aroused potential applications.  
Keywords:sensing fiber; distributed acoustic sensor; signal-to-noise ratio; specialty fibers +
Footnote †: journal: _Sci_
## 3 Results
### Metabolic Pathways of Lipid-Derived Flavors in Dry-Cured Mackerel
#### 3.6.1 C18:1n9c Metabolic Pathway  
Nonal, octanal, decal, heptanal, and decanol are the main oxidative products of C18:1n9c, of which nonal, octanal, decanal, and heptanol are the key flavors in dry-cured mackerel. Nonal, octanal, and heptanol were produced by mackerel LOX-induced C18:1n9c oxidation, and a significant increase was observed compared with autoxidation (\(p<0.05\)). This suggests that LOX-induced oxidation of C18:1n9c plays a bigger role compared with autoxidation. C18:1n9c mainly generates 10-hydroperoxide, 11-hydroperoxide,  
Figure 5: **(a)** The main metabolic pathways of unsaturated fatty acids. CoA: Coenzyme A; ACOT1\(2\)4: Acyl-CoA thioesterase; PLA2G: secretory phospholipase A2; PPOA: Linoleate 8R-lipoxygenase; PPOC: Linoleate 10R-lipoxygenase; LOX: lipoxygenase; LOX1_5: linoleate 9S-lipoxygenase; ALOX15B: 8-lipoxygenase (S-type); ALOX12: Arachidonic acid 12-lipoxygenase; ALOX12B: arachidonic acid 12-lipoxygenase (R type); HPL: hydrogen peroxide lyase; ADH: alcohol dehydrogenase class P; CHAT: 3-hexene-1-ol acetyltransferase; 9(S)-HPOT: (9S,10e,122,15Z)-9-hydroperoxy-10,12,15-octadecorbitenoate; 13(S)-hpot: (9Z,11E,15Z)-(13S)-hydroperoxide octadecane-9,11,15-trioleate; HPETE: hydrogen peroxide eicosaptanenoic acid; HETE: hydroxyl eicosaptanenoic acid; HPODE: hydrogen peroxide lipid. **(b)** Oleic acid metabolic pathway. **(c)** Linoleic acid and arachidonic acid metabolic pathways. **(d)** Linoleic acid, EPA, and DHA metabolic pathways.  
and 8-hydroperoxide through autoxidation and enzymatic oxidation. Under the catalysis of hydroperoxidase lyase (HPL), 10-hydroperoxide is degraded to nonal, 11-hydroperoxide is degraded to octanal, and 8-hydroperoxide is degraded to decal. It can be reduced to the corresponding alcohols by alcohol dehydrogenase (ADH) [35]. In conclusion, C18:1n9c is an important lipid-derived flavor precursor in dry-cured mackerel (Figure 5c).
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 1 Introduction  
The quest for environmentally sustainable production is increasingly focused on energy-efficient technological advances that aim to reduce energy consumption while maintaining or improving production levels [1]. The efficient use of energy not only reduces demand, but also increases energy security and economic competitiveness and mitigates environmental degradation. Although energy policies vary across regions, a significant disparity in energy efficiency exists between industrialised and developing countries [2]. Energy remains a key driver of economic growth, yet it is also the main source of greenhouse gas emissions, which contribute heavily to global environmental pollution. This imbalance has led to a surge in research on energy efficiency, positioning it as an essential component of sustainable development.  
In response to these global challenges, various strategies have been proposed to optimise energy consumption and increase the use of renewable energy [3]. The EU, where three quarters of energy consumption comes from non-renewable sources, has taken measures aimed at maintaining or improving processes while reducing energy consumption [4]. Over the last decade, the EU has taken legislative action to achieve itsclimate and energy targets for 2020, the so-called 20-20-20 targets, and with the adoption of the Paris Agreement, it has reaffirmed its commitment to limit global warming to no more than 1.5 \({}^{\circ}\)C. As a result, the EU member states have signed the Green Deal, which aims to make the EU climate-neutral by 2050. To achieve this goal, the Fit for 55 legislative package was adopted, which aims to reduce greenhouse gas emissions by 55 by 2030 compared to the 1990 levels. Two further targets are to increase energy efficiency in order to reduce final energy consumption by at least 11.7% (Directive (EU) 2023/1791) and to achieve a share of at least 42.5% of energy from renewable sources (Directive (EU) 2023/2413). This policy prioritises energy efficiency as the most cost-effective way to reduce emissions, improve energy security, and make energy more affordable [5]. The industrial sector, a major energy consumer, is particularly suited to energy efficiency improvements due to its potential to realise significant energy savings. To address these challenges, this paper focuses on optimising electric drive systems as an important step toward improving energy efficiency in industry.
## 3 Results and Discussion
### Meteorological Conditions
Error bars represent the standard deviation of the daily values for each month. was true for all years except 2022. The differences between tree age within the same location and that caused by tree density between Las Tiesas and Tarazona represent a common response regarding the higher photosynthetic potential due to tree size in older trees and the higher tree density in Tarazona [41; 94]. For the same tree age (4 and 5 years corresponding to 2018-2019 period in LTs and 2020-2021 period in TzM), lower NEE and GPP values were observed in Las Tiesas compared to Tarazona (Figures 3 and 4). In some cases, the Tarazona values doubled their counterparts in Las Tiesas on daily cumulative values (Figure 3), daily mean values (Figure 4) and average monthly daily values (Figure 5). In fact, for the six-season experiment, NDVI values ranged from 0.2 to 0.6 throughout the full growing season (see Figure 6). Figure 6 illustrates a dual effect pattern: as the trees mature, the canopy growth leads to an annual increase in NDVI, while a general trend of decreasing NDVI is observed from July onwards, except during the first two years in Las Tiesas. This positive relationship between higher GPP and lower NEE with increasing NDVI has been reported in previous studies and will be further discussed in the following sections. These results suggest that the growth of almond trees, as well as the increase in the canopy vegetation fractional cover, with denser canopies throughout their interannual development, may increase their carbon capture capacity. In terms of seasonal variability, late spring and summer months accounted for the highest GPP and ET, and the lowest (more negative) NEE (Figures 3-5). These carbon fixation seasonal patterns were statistically significant (\(p<0.001\)) when we analyzed average daily values for each month and year (Figure 4). The lowest NEE and highest GPP were found in late spring months (May-June) and match the period in which the almond growth rate is the highest in Spain [95; 96] (Figure 4). From July onwards, these values are dampened (see NDVI in Figure 6). This is mainly due to inherent phenological changes [97] but also because of harsh weather conditions. Very high temperatures and high evaporative demand occur in summer months in Spain (see July and August in Figure 2 and Table S1). Additionally, at the end of the almond fruit formation cycle and close to harvest (end of August), regulated deficit irrigation was applied, this being a common practice among almond growers [41; 97; 98]. This deficit was also crucial in diminishing photosynthetic capacity, while higher temperature could result in elevated respiration rates, leading to lower GPP and higher NEE at the end of summer.
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 4 Thermal-Structural Coupling Mathematical Model of the Subsea Connector  
The subsea connector will produce structural stress under the action of external load. When subjected to the temperature load, the components of the connector will expand with the increase in temperature and contract with the decrease in temperature. Due to the constraints of each component, thermal stress will be generated, so it is necessary to study  
Figure 8: Relationship between the composite thermal conductivity \(h_{e}\) and the temperature.  
the coupling of thermal stress and structural stress [21]. The stress on the sealing gasket of the subsea connector is divided into three parts: one is the thermal stress generated by the structure due to the combined action of internal and external temperature; the second is the structural stress caused by the internal oil and gas pressure acting on the lenticular sealing gasket and hub; the third is the structural stress generated by the axial preload in the contact area of the sealing gasket.
# Heterogeneous Distributed Big Data Clustering on Sparse Grids
## 4 Other Steps
### Pruning the \(k\)-Nearest-Neighbor Graph  
To prune the \(k\)-nearest-neighbor graph, we use two criteria. First, we remove data points and their corresponding edges in regions of low density. To this end, the density function is evaluated at the position \(\mathbf{x}_{i}\) that corresponds to the current graph node \(g_{i}\). The node and its edges are removed if the density is below a threshold \(t\). Second, we remove edges that pass through regions of low density to separate nodes that belong to different clusters. As it is impossible to test all points along the corresponding line segment, we heuristically test only its midpoint, which has the largest distance to the two nodes belonging to high-density regions. If it falls below the threshold \(t\), we remove the edge. Our pruning approach is shown in Algorithm 4.  
```
Input :\(k\)-nearest-neighbor graph \(g\) as neighborhood list, dataset \(T\),  density estimation \(\hat{f}(\mathbf{x})=\sum_{j=1}^{N}\alpha_{j}\phi_{j}(\mathbf{x})\), threshold \(t\) Output : pruned \(k\)-nearest-neighbor graph \(g\) for\(i=1\dots m\)do if\(\hat{f}(\mathbf{x}_{i})<t\)then \(\text{prune\_node}(g_{i})\) continue \(\text{p}_{1},\dots,\text{p}_{k}\leftarrow\text{load\_midpoints}(T,g_{i})\) for\(j=1\dots k\)do if\(\hat{f}(\mathbf{p}_{j})<t\)then \(\text{prune\_edge}(g_{i,j})\)
```  
**Algorithm 4**A streaming algorithm for pruning low-density nodes and edges of the \(k\)-nearest-neighbor graph. The density function is evaluated at the location of the nodes and at the midpoints of the edges.  
Similar to the other algorithms presented, iterations of the outer loop are independent and can therefore, be parallelized. The most expensive operations in this loop, multiple evaluations of the density function, are branch-free. Therefore, this algorithm is straightforward to vectorize as well. As there are only \(\mathcal{O}(m\cdot(k+1))\) conditionals compared to overall \(\mathcal{O}(m(k+1)N)\) operations, the conditionals do not significantly impact performance.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wired Technologies
#### 2.1.3 Digital Subscriber Line (DSL)  
DSL is another technology that can be used for smart metering applications. DSL makes use of the traditional telephone line for the data transmission. It provides a backhaul for the smart grid data from homes to the utilities. DSL is a reliable and also a low-cost solution, since the infrastructure exists in most cases. A disadvantage of the DSL technology is that its throughput depends on the distance of the subscriber from the serving company [6]. Another issue that arises with DSL when applied in Smart Grids is the possibility that the end-users get to be involved in smart grid access. This could give rise to the installation cost or create functional problems [25].
## 5 Massive MIMO
### Classification of Applications
#### 5.1.1 Mobile Phone  
Various mMIMO systems for mobile phone applications for 5G and beyond networks have been investigated that are categorized by a number of antenna elements such as 18, 12, 10, and 8. In [148], an 18-port 5G mMIMO antenna operating in the sub-6 GHz band is investigated. The 18-slot antennas were simply adjusted on the 6-inch board, performing as a radiator which is used for modern smartphones. The measured results of this mMIMO antenna have high isolation > 20 dB, and peak efficiency > 87% over the  
Figure 6: Classifications of applications for 5G massive-MIMO antennas.  
operating frequency. A 12-element MIMO antenna is presented in [149] for 45/5G mobile phones. As per the measured results, the ergodic channel capacity of 34 b/s/Hz and 26.5 b/s/Hz, were achieved in LTE bands 42/43 and 46 respectively. Although, this design achieved enhanced channel capacity, a trade-off between the isolation of \(<\)\(-\)12 dB, and total efficiency of \(>\)40% has been observed. Various 10-elements mMIMO antennas for the 5G smartphones have also been investigated [150; 151; 152; 153; 154]. All designs were proposed for 4G/5G smartphones operating in the sub-6 GHz band and prototypes were fabricated to evaluate isolation, efficiency, and MIMO diversity performances. It has been observed that a 10-element MIMO antenna array can achieve the maximum ergodic channel capacity of 51.4 bps/Hz.  
Similarly, various 8-elements mMIMO antennas systems for the 5G smartphones have been presented operating in sub-6 GHz frequency band [155; 156; 157; 158]. In order to consider the importance of backward compatibility, these designs are equally capable of working in two frequency bands including multi-and or wide-band structures. Although the antenna elements of mMIMO in the mobile phones must be in equal or greater number than eight antennas, various designs are not able to support the massive-MIMO system in the 5G mobile phones regardless of employing the same number of mMIMO antennas. Nevertheless, all such designs are observed to be suitable candidates for the mMIMO systems.
## 3 Application and Results
### Neutron and Gamma Preprocessing Data Construction  
Neutron and gamma data were adopted from waveform data generated by the experimental platform constructed based on the CLYC scintillator sensor [6]. In order to ensure the consistency of the amount of input data for the convolutional neural network, the input length of the data was all set to 128 and all normalized via the input data. Subsequently, data enhancement methods such as random cropping, noise addition, and downsampling were used to generate 18,000 training waveform data (9000 each for neutron and gamma), 3000 validation waveform data (1500 each for neutron and gamma), and 3000 test waveform data (1500 each for neutron and gamma), as shown in Figures 8-10.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 7 Evaluation and Discussion
### Proof of Concept Demonstration of Data Plane Forwarding
#### 7.1.4 Backup Tunnels and Resilience  
The architecture opens up the use of backup tunnels in order to enable fast reroutes and multi-path SFCs. Making backup paths of all possible combinations of SFCs when a VNF fails creates an exponential set of extra tunnels and additional computational overhead. However, a set of bypass backup tunnels are assumed to not extensively impound computational power when they are not in use.  
The size of the route tables depends on the SFC protection algorithm that is used. A simple link protection algorithm where only one VNF is allowed to fail linearly increases the SFC route table entries by the number of VNFs. A full mesh of redundancy link where many VNFs are allowed to fail increases SFC routes exponentially by the number of VNFs. Hence, link protection of SFC routes and SFC multi-pathing is suggested to be handled by the control plane. This is because a lower number of routes makes the control plane capable of scaling and to have low failover times. A full SFC protection is suggested to be handled by the orchestration plane by using re-instantiation of VNFs and redistribution of routes. This is a slow procedure, but it makes the solution scale better when all the possibilities of failures do not need any pre-calculation.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### O-eNB  
The O-eNB is defined as an eNB or ng-eNB that supports E2 interface [58]. In the nutshell, O-eNB provides the O-DU and O-RU functions in an integrated node while still keeping the O-FHI between them. The O-eNB will have the E2, NG, O1, S1, X2, and Xn interfaces.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 5 Performance Evaluation
### Dynamic Weak Signal Environment Simulation
This indicates that the proposed algorithm outperforms the conventional two-stage tracking algorithm under variable weak and dynamic conditions. The bottom panel of Figure 9 demonstrates the Doppler frequency estimates of the two algorithms. The Doppler frequency is proportional to the LOS velocity. When the receiver is static, the Doppler rate changes slightly. Note that the Doppler frequency linearly changes when the receiver is accelerating. For the algorithm, 2-stage KF1, the estimated Doppler frequency is consistent with the actual values, while the estimates from the 2-stage conv1 method deviate from the actual values at approximately 410 s and lose the lock at approximately 424 s, which is consistent with the results of the middle panel of Figure 9.  
In summary, under low dynamic weak signal conditions, the proposed method shows a superior performance and strong robustness relative to the conventional two-stage tracking approach. This dynamic tracking level of the proposed method is sufficient for pedestrians and low-speed vehicles in urban canyons. Figure 9: Tracking results for PRN 19 satellite signals under the dynamic weak signal condition. Top: C/\(N_{0}\) estimates. Middle: velocity estimates. Bottom: Doppler frequency estimates.
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### IoT Standardized Protocols  
The U. S. Defense Standards, also called Military Standards (MIL-STD), are used to help achieve standardization objectives. These documents are also used by other non-defense government organizations, technical organizations, and industry. The ASSIST database [122] gathers these documents and also includes international standardization agreements, such as NATO standards, ratified by the United States and International Test Operating Procedures. Furthermore, the DoD is starting to use civilian standards, since numerous contributions to the deployment and standardization of the IoT paradigm come from the scientific community. Among them, the most relevant are the ones provided by the European Commission and the European Standards Organisations (i.e., ETSI, CEN, CENELEC), by their international counterparts (i.e., ISO, ITU), and by other standards bodies and consortia (W3C, Institute of Electrical and Electronics Engineers (IEEE), EPCglobal). The M2M Workgroup of the European Telecommunications Standards Institute (ETSI) and some IETF Working Groups are particularly important.  
In this section we provide an overview of some of the standardized protocols that could be used for providing the IoT services described in the previous sections.