esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 5 Conclusions  
The implications of this work to the PNT domain are clear and concise in that we have demonstrated that different network infrastructures can exhibit a wide range of variable performance when used for the purposes of SoP navigation, particularly regarding velocity constraints or time holdover purposes. The question of which infrastructure should be used and even at what time scale the measurements should be taken for optimal performance varies with tower-specific implementations, and this fact should be taken into account when the signals are passively exploited for PNT purposes. A weak inference based on several differential pairs tested is that a measurement update rate of approximately 100 Hz might be selected as a default measurement rate in the absence of other information about the tower or tower pairs when one is using it for SoP navigation (as longer observation intervals are past the knee of the Allan deviation curve). Further testing is needed to determine if this is generally true, as well as to what extent lower cost local oscillators might contribute to in masking this effect.  
Most of the work was performed by [PERSON] under the guidance and supervision of [PERSON]. This includes all work related to the experimental setup, as well as data analysis and software development. N.S. provided conceptual guidance and discussion, as well as editorial review. All authors have read and agreed to the published version of the manuscript.  
This study was conducted in cooperation with NTNU and SINTEF Digital, and it was funded by SINTEF Digital. It received no external funding.  
Not applicable.  
Data available on request due to size considerations.  
All Authors are employed by SINTEF Digital. This work was created in a close and established research cooperation between NTNU and SINTEF Digital, using internal project funds. The authors declare no relationship that could be constructed as potential conflict of interest.  
Figure A4: (**a**) Phase comparison between the two cells from two operators, similar toFigure 2(b). (**b**) The corresponding phase differences.  
Figure A5: (**a**) Phase comparison for the two cells from the same operator, similar to Figure 2(a). (**b**) The corresponding phase differentials.
## 3 Results
### Spatiotemporal Dynamics of Territorial Space
#### 3.1.1 Spatiotemporal Evolution of Territorial Space
Figure 3: Spatiotemporal distribution patterns of territorial space in the Dongting Lake region from 2005 to 2020 (**Left**: overall spatiotemporal distribution of primary land-use categories; **Right**: magnified spatiotemporal distribution of secondary land-use categories). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Production Space**} & \multicolumn{2}{c}{**Living Space**} & \multicolumn{4}{c}{**Ecological Space**} \\ \cline{2-10}
**Year (km2)** & **Agricultural** & **Industrial-** & **Agricultural** & **Urban** & **Forest** & **Grassland** & **Water** & **Potential** \\
**Period (\%)** & **Production** & **Mining** & **Living** & **Living** & **Ecological** & **Ecological** & **Ecological** & **Ecological** \\  & **Space** & **Production Space** & **Space** & **Space** & **Space** & **Space** & **Space** & **Space** \\ \hline
2005 & 30266.89 & 177.09 & 1270.95 & 643.76 & 29126.38 & 1036.56 & 7873.81 & 848.46 \\
2010 & 29527.30 & 389.29 & 1259.85 & 958.77 & 29065.38 & 980.12 & 7919.43 & 1143.94 \\
2015 & 29312.07 & 687.47 & 1259.92 & 984.43 & 28961.47 & 975.65 & 7919.17 & 1143.36 \\
2020 & 29320.22 & 1034.54 & 1289.27 & 994.39 & 28793.33 & 974.60 & 7711.35 & 1120.79 \\
2005–2010 & \(-\)0.49 & 23.97 & \(-\)0.17 & 9.79 & \(-\)0.04 & \(-\)1.09 & 0.12 & 6.97 \\
2010–2015 & \(-\)0.15 & 15.32 & 0.00 & 0.54 & \(-\)0.07 & \(-\)0.09 & 0.00 & \(-\)0.01 \\
2015–2020 & 0.01 & 10.10 & 0.47 & 0.20 & \(-\)0.12 & \(-\)0.02 & \(-\)0.52 & \(-\)0.39 \\
2005–2020 & \(-\)0.21 & 32.28 & 0.10 & 3.63 & \(-\)0.08 & \(-\)0.40 & \(-\)0.14 & 2.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Numerical development trends of territorial space from 2005 to 2020.
## 3 Testing Results and Analysis
### Swelling-Shrinkage Testing  
The swelling-shrinkage curve of phosphogypsum-modified lime-ash-stabilized soil is presented in Figure 10, and various shrinkage indicators are summarized in Table 4. The test results reveal that the addition of phosphogypsum to the lime-ash stabilized soil significantly exacerbates its shrinkage and swelling characteristics, which intensify progressively with increasing phosphogypsum content. Specifically, as observed from Table 4, soils with higher initial moisture content exhibit greater linear shrinkage. In the early stages of the experiment, moisture loss from the specimen's surface, resulting in the thinning of the water film between the particles, leads to increased matrix suction between the particles, causing specimen shrinkage [16]. However, specimens with higher initial moisture content experience a more significant reduction in the water film as they lose moisture, leading to greater matrix suction and exacerbating internal pore and crack shrinkage, ultimately resulting in increased specimen shrinkage deformation [24]. Furthermore, under the alkaline conditions created by lime, the active silicon and aluminum in the soil react chemically with water, generating cementitious substances like hydrated calcium silicate and hydrated calcium aluminate. The addition of phosphogypsum continues these chemical reactions, leading to the formation of ettrinigite. Although the formation of ettrinigite enhances the soil's strength, its multilayered structure and large specific surface area make it highly adsorbent, causing the soil to absorb more water during wetting. Consequently, the soil undergoes significant expansion upon wetting. Additionally, with increasing immersion time, the large volume of ettrinigite results in more pronounced water absorption and expansion, damaging the specimen's structure and causing significant expansion. This phenomenon further exacerbates the soil's shrinkage and swelling characteristics.  
In summary, the addition of phosphogypsum does impact the swelling and shrinkage properties of lime-ash-stabilized soil. However, in moderate amounts, it significantly enhances the soil's performance and durability. When phosphogypsum is added to lime-stabilized soil and subsequently subjected to prolonged curing and saturation, its expansion can be significantly reduced or even eliminated [25]. Furthermore, although the inclusion of 4% phosphogypsum does lead to increased expansion and shrinkage deformation, the magnitude of this change is not substantial. In contrast, the improved engineering  
Figure 9: Relationships between moisture stability coefficient and phosphogypsum content.  
properties of the soil after the addition of phosphogypsum are more significant. Therefore, it is possible to further control these adverse effects by incorporating other additives and extending the curing duration.
## 6 Novel mHealth Predictive Outbreak for COVID-19 (mPOC) Algorithm and IoMT Protocol
### Pandemic Response as a Service (PRaaS) Stage  
During the final stage of the mPOC system, it establishes real-time bidirectional connections with other smartphones within the D2D wireless range (within 500 m), as well as with Public Health Broadcast Agencies (PHBA) and the Outbreak Tracking Center (OTC) through various network channels, such as mobile, internet, and cloud. Through these connections, the mPOC system collects anonymous location data from users within the wireless range and exchanges relevant information with the OTC. The PEI value, which reflects the environmental risk based on the user's location, activity type, and movement direction, and the DRI value, indicating the physiological risk factors if exposed to the virus, play a crucial role in this stage.  
The concept of Pandemic Response as a Service (PRaaS) is rooted in the utilization of cloud technology to provide an effective pandemic response. First introduced in 2014 within the framework of Healthcare as a Service (HaaS) [41], PRaaS shares several common features with HaaS, including scalability based on regional and hierarchical structures,seamless and on-demand operations, virtually infinite computation through fog and edge computing, big data analytics, online storage, and resource sharing [42; 43]. However, it is important to highlight that the key differentiating factor of PRAaS lies in the integration of end-user smartphones equipped with Bio-Watch and running the mPOC platform, which form the backbone of the system's functionality.
# An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information
## 1 Introduction  
Open-domain event extraction tasks refer to extracting event information without pre-defined event types. Currently, the methods used in event extraction are based on semantic features [1], dependency syntactic structures [2], joint methods, knowledge graphs, etc. Although the semantic fusion method can capture semantic-rich features of the text sequence, the news cluster contains multiple events, so it is difficult for the model to fully and accurately identify all the events in the sentence. This is because the existing methods only use the sequence to represent the sentences, ignoring the syntactic structure, long-distance dependency, and global information of the text. It is difficult to capture long-distance information; in other words, the relevance of multiple events is lost.  
Dependency syntax information has been widely used in natural language processing tasks. A large number of models and experiments have proven that the dependency relationship between words can effectively improve the performance of multiple tasks under various modeling methods, including event extraction tasks. The main method uses graphical convolutional networks (GCNs) to integrate dependency syntactic information into the deep neural network model, but this method has some noise, such as the influence of secondary dependency information, that is, no dependency relationship but with important features. A dependency attention-aware graphical convolutional network (DAGCN) [3], based on the dependency tree of dependency parsing, further uses the attention mechanism to increase the nodes' attention to key information and enhance the cohesion of the same parameter features, effectively using the main dependency relationships between nodes to learn syntactic structural information.  
To address this challenge, we proposed using bidirectional long- and short-term memory (Bi-LSTM) to encode sentences to further enhance the semantic understanding of sentences, which have been proven to be suitable for capturing context-dependent semantic information. At the same time, we fused dependency syntactic information to obtain syntactic-rich structural information. Our work contributes in the following ways:1. Semantic information is introduced based on BERT. We selected BERT end-layer features to obtain rich semantic features through Bi-LSTM and conducted experiments on the dataset to verify the validity of the semantic information;
2. Dependency syntactic information is further integrated. We analyzed the dependency syntactic information based on BERT middle layer features using the Stanford CoreNLP tool, further increasing the attention of nodes to the information in the graph through a DAGCN;
3. Semantic information and semantically enhanced dependency syntactic information are dynamically fused. We introduced a gating mechanism to reasonably control the information flow, extract rich and accurate feature representations, and again verify the feasibility of combining semantics and dependent syntax.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 4 Experimental Verification
### Lateral Shift Experiment  
When the receiving coil RX is shifted laterally from the top of the transmitting coil TX(2, 2) to the top of the TX(2, 3), the experimental results of the reflection impedance of the receiving end and the transmission efficiency of the system are shown in Figure 14.  
The efficiency was calculated as \(\eta\) = \(P_{\text{out}}/P_{\text{in}}\)\(\times\) 100%, where \(P_{\text{out}}\) (load power) and \(P_{\text{in}}\) (input power) were measured using a power analyzer.  
It can be seen from Figure 14 that the transmitting coil TX(2, 2) is powered at the beginning, and \(Z_{\text{max}}\) = \(Z_{(2,\,2)}\) at this time, and the system transmission efficiency is the highest. As the transverse displacement distance \(dx\) increases, \(Z_{(2,\,2)}\) gradually decreases, and the system transmission efficiency gradually decreases. When the transverse displacement distance \(dx\) = 100 mm, \(Z_{\text{max}}\) = \(Z_{(2,\,2)}\), and the system transmission efficiency is the smallest. As the transverse displacement distance \(dx\) continues to increase, \(Z_{(2,\,3)}\) > \(Z_{(2,\,2)}\). At this time, \(Z_{\text{max}}\) = \(Z_{(2,\,3)}\), the receiving coil is offset to the top of the transmitting coil TX(2, 3), and the transmitting array is switched to the transmitting coil TX(2, 3) for power supply.  
After the coil switching is completed, as the transverse displacement distance \(dx\) continues to increase, \(Z_{\text{max}}\) = \(Z_{(2,\,3)}\), the system transmission efficiency increases, and there is no need to switch the power supply coil. The experimental results show that when the transverse displacement of the receiving coil RX occurs, the transmitting array switching control strategy based on the position estimation of the receiving coil proposed in this paper  
Figure 14: Experiment results of transverse displacement. (**a**) Receiver reflection impedance curve. (**b**) Transmission efficiency curve.  
can achieve the optimal transmitting coil switching control, and the system transmission efficiency is maintained above 80%.
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 3 Molecular Mechanisms of T3 SS Deployment in Response to Environmental Stimuli
### Negative Regulation of the T3 SS by the GacSA Global Regulatory System
In contrast to T3 SS deployment, GacA was required for the cellular motility of DC3000, suggesting that the T3 SS and motility may be subject to inverse regulation in _P. syringae_[63; 95; 140]. The requirement of GacA for motility may explain why various _P. syringae_ gacS- and _gacA_- mutant isolates are unable to effectively colonize host plants, as bacterial motility is often required for epiphytic _P. syringae_ to gain access to the foliar apoplast through stomata or other openings in the leaf surface [127; 140]. Together, these results support a model in which GacA is activated on the leaf surface, where it suppresses the T3 SS and induces cellular motility, potentially promoting _P. syringae_ epiphytic fitness and ingress into the leaf interior. GacA is then deactivated within the apoplast, permitting de-repression of the T3 SS (Figure 3). Future characterization of where and when during plant infection the GacA system is activated, as well as the identification of the GacA inducing signal(s), is necessary to further evaluate this model. The mechanism(s) by which GacA affects T3 SS deployment is also not clear, though GacSA is known to modulate the activity of the RsmA regulatory protein family reviewed in Section 4.4. Figure 3: Hypothetical model of GacSA-mediated repression of the T3 SS in _P. syringae_ infection. Depicted is a leaf cross-section infected with _P. syringae_. Cells with an activated GacSA system are in blue (lower inset; GacA-P\({}^{+}\)), while cells with an inactive GacSA system are in red (upper inset; GacA-P\({}^{+}\)). On the leaf surface (lower leaflet), the GacSA system is activated by an unknown stimulus (lightning bolt), leading to repression of the T3 SS and promotion of flagellar motility. GacA-P\({}^{+}\) cells use flagellar motility to infiltrate gaps in the leaf surface, gaining access to the leaf interior or apoplast. GacSA is deactivated within the apoplast, leading to de-repression of the T3 SS. GacA-P\({}^{+}\) cells deploy the T3 SS in order to disarm host immune defenses and establish _P. syringae_ growth in the apoplast. Once _P. syringae_ has grown to a high density within the apoplast in the advanced stages of infection, GacSA may be re-activated to downregulate T3 SS expression in order to conserve cellular energy. This model of GacSA regulation is based solely on phenotypes of _gacA_’ loss-of-function mutants and is not yet supported by biochemical evidence. Figure was made using BioRender ([[https://app.biorender.com](https://app.biorender.com)]([https://app.biorender.com](https://app.biorender.com)), accessed on 30 April2021).
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 4 Waveform-Dependent Performance Optimization Methods
### Approach II: Coexistence Waveform Design Approach by Multiobjective Optimization
The MSE is obtained by implementing the Slepian-Bangs formulation normally used to compute Cramer-Rao lower bounds on the estimation variance; the complexity is in the order of magnitude \(O(N_{\mathrm{s}}^{2})\), where \(N_{\mathrm{s}}\) in Equation (9) is the number of discrete-time samples of the radar waveform, which depends on the prescribed sampling rate.
## 3 Results and Discussion
### COD, Chromium Removal, and Coulombic Efficiency
#### 3.4.2 Chromium Removal  
Student \(t\)-tests underlined that for each anode the differences in the chromium removal rate were significant (\(p<0.001\)). High chromium removal values (\(94.6\pm 3.6\)% and \(97.5\pm 2.2\)%) were found using LTA zeolite and bentonite, respectively (Table 1). Meanwhile, a lower chromium removal value was found using the unmodified carbon felt (\(72.4\pm 3.1\)%). These findings could be explained by the adsorption properties of zeolite and bentonite [19; 20]. Also, the microorganisms adhering to LTA zeolite and bentonite were proven to be highly efficient in biological heavy metal removal from wastewater, especially of chromium [21; 22]. There are several mechanisms that might contribute to chromium removal in the BESs. The chromium removal in the unmodified carbon felt with acclimated activated sludge suggests that biosorption is an important mechanism for the heavy metals. The components participating in biosorption are principally the functional groups (carboxylate, hydroxyl, phosphate, amine, and sulfate) of polysaccharides, proteins, and lipids on the microbial cell walls [23]. Metal precipitation due to the presence of bicarbonate anions in the solution may also contribute to the metal removal in this study, since bicarbonate is created as a byproduct of acetate oxidation at the bioanode. The non-inoculated reactors with activated sludge showed electrodeposition of low concentrations of chromium on the surface of the carbon felt anode, which means that chromium reduction was implicated in the chromium removal process. Besides the mechanisms described above, the LTA zeolite and bentonite particles adsorbed chromium in modified anodes.
## 2 Description
It will be shown below that this value is approximately 10% of the air flow rate generated by the piston effect. On average, the ND-induced air flow rate is not higher than 4% of the air flow rate caused by the piston effect. For this reason, the effect of ND on the air exchange in the tunnel is insignificant and negligible. For subway tunnels and railway tunnels, the parameters of the aerodynamic elements used in the piston effect models differ considerably. One such design variable is the ratio of tunnel and train car section areas. In subway tunnels, a car passes over half of the tunnel area, whereas in railway tunnels on the Baikal-Amur Mainline, this overlap is only 18%. The second variable is the freight train length, which is 10 times longer than in subways. Meanwhile, these differences have an essential influence on the draught loss in the tunnel clearance. Moreover, the value of the diffusion resistance under such parameters of railway tunnels is unknown. A computation experiment with ANSYS is implemented to determine the piston effect model parameters for the railway tunnel. The input data are determined from the analysis of statistics on the existing extra-long tunnels. The average train velocity in the Baikal-Amur Mainline tunnels is 37-41.6 km/h [1; 20; 21]. The freight tonnage should not exceed 75 cars (on average 57 cars without regard to locomotive), an empty train should be 105 cars and a passenger train should be 24 cars. Cars for freight and passengers will have a length of 16.7 and 23.6 m, respectively. Similarly, the section area of the model tunnel was taken from BAM tunnels with similar structures, lengths and loads (Baikal, Kuznetsovsky, etc.). Based on these data, air distribution is modeled for tunnels 3000 and 6000 m long, with a hydraulic radius. To solve the plane axially symmetric problem, real dimensions have been converted into equivalent values. The atmospheric pressure is assumed to be 101,325 Pa, and the ambient air temperature is 25 \({}^{\circ}\)C. At the boundaries of the computational domain, unbounded with solid surfaces, the atmospheric pressure is accepted. The air velocity at the initial time is zero. Such a formulation of the problem sufficiently and adequately describes the geometry and physical processes in a tunnel with a moving train. At the same time, the finite-element mesh and the computation time are essentially reduced.
# Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm
## 2 Proposed ANN Based MPPT and Hybrid Boost Converter Model  
Figure 1 explains the HBC and ANN based MPPT presented model for minimizing the solar power system critical caring and balancing output power issues. The output of PV is attained by ANN based MPPT in order to improve the performance of solar power system. The neural network technique consist of major amount of interconnected processors called neurons. Each neuron includes a huge number of weighted links for transforming signals. Thus, it has potential to manage the difficult task of data processing and interpretation. In this proposed model, the feed forward back propagation ANN is installed which contains logsig purelin and purelin activation functions based hidden layers, as depicted in Figure 2. In case of offline step the ANN training is performed in terms of activation function structure and training algorithm. On the other side for online step the trained ANN based MPPT is applied to track the MPP. The output of PV array voltage derivation (dv) and power derivation (dp) is given to ANN based MPPT which depends on solar radiation and temperature conditions. Table 1 summarizes the basic principle of ANN based MPPT which output is corresponding normalized increasing or decreasing duty cycle (+1, 0, \(-\)1).  
Table 1 explains the basic mechanism of ANN based MPPT controller.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**dv** & **dp** & **dp/dv** & **Duty Cycle** \\ \hline \(-1\) & \(-1\) & \(-1\) & D(n) = D(n \(-1\)) \\ \hline +1 & \(-1\) & +1 & D(n) = D(n \(-1\)) \\ \hline \(-1\) & +1 & +1 & D(n) = D(n \(-1\)) \\ \hline +1 & +1 & \(-1\) & D(n) = D(n \(-1\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ANN based MPPT controller basic principle.  
Figure 1: Proposed framework using ANN MPPT and enhanced hybrid boost converter.  
Figure 2: Internal structure of ANN based MPPT.