esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.5 HF Radar  
As discussed, HF radars measure ocean surface parameters, such as OSW speed, in a timely fashion over relatively larger areas and at a reasonable cost compared to in situ measurements. OSW speed can be extracted from either the first-order radar backscatter or the second-order sea echo. [PERSON] and [PERSON] [105] revealed that the 10-dB width of the first-order resonant scattering could be used for the estimation of the local OSW speed. However, this method was not a robust solution since it depended on different ocean parameters, such as OSC [106]. The second-order scattering is also employed to calculate the OSW speed because the amplitude of the second-order radar backscatter and its location in Range-Doppler is greatly dependent on the local OSW speed [107]. In other words, by increasing the wind speed, the second-order peaks increase and move to the first-order peaks. Moreover, [PERSON] et al. [108] postulated that the ratio of the second-order peak amplitudes to the first-order ones could be used for measuring the local OSW. [PERSON] et al. [109] also suggested that the ratio of the second-order continuum closer to zero Doppler to the amplitude of the first-order resonant peak can be employed for OSW  
Figure 2: The spatial distribution of the global mean (**a**) OSW speeds and (**b**) wind power densities at 10 m above SL derived by a combination of QuikSCAT, Windsat, and ASCAT datasets from 1999 to 2015. The figure is directly adopted from Reference [103].  
speed measurement because short waves represented in this region reflect the OSW speed. Additionally, [PERSON] and [PERSON] [110] proposed a method to calculate OSW speed from the significant OWH and period. Subsequently, [PERSON] et al. [111] applied this method to an HF radar installed in the Eastern China Sea.
## 3 System Design
### Data Repository
#### 3.1.1 Simulation and Observation Data  
Data sources include both model simulation data and metadata of observation data. Climate model simulation data are usually stored in the formats of HDF, HDF-EOS and NetCDF [26]. We use NetCDF as an example in this paper. When a simulation is finished on a distributed computing node, the outputs are uploaded into the repository server for centralized management. The metadata entry recording basic information of outputs such as spatiotemporal coverage, variables, and computing node is inserted into the database. We do not host observation data provided by the other vendors in the repository but only manage the metadata of observation in the database. Data analysts need to acquire observation data from original providers when observation data are required by analysis. The system will assist in automatic data preprocessing.
## 3 New Techniques Used in Detection of Forest Fire in China
### Establishment of a New Fire Detection Channel Selection from Fire Experiment  
For the purpose of selecting the most effective spectral channels for identifying forest fires, a controlled experiment was carried out at a designated place in a pine-dominated forest site in Wuming County, Guangxi Province, China in October 2005 [65]. The infrared and visible spectral radiances and BT were measured synchronously at the time of satellite overpass using a medium and near-infrared MOMEM MR154 FT-Spectroradiometer, an infrared thermal imager, and a visible and near-infrared spectroradiometer (ASD FR). The results showed that two radiance peaks in the MIR band corresponded to the fire burning strength. One peak at 4.17 \(\upmu\)m is located at the CO emission peak [66]. The other peak spans through the wavelengths of 4.34-4.76 \(\upmu\)m, which exhibited a strongerresponse to the fire than the commonly used wavelengths of 3.5-4.0 \(\upmu\)m, and seem to be more sensitive band for the fire detection using remote sensing techniques [65].
###### Abstract  
Physical layer security is an attractive security mechanism, which exploits the randomness characteristics of wireless transmission channel to achieve security. However, it is hampered by the limitation of the channel condition that the main channel must be better than the eavesdropper channel. To alleviate the limitation, cooperative communication is introduced. Few studies have investigated the physical layer security of the relay transmission model. In this paper, we performed some experiments to evaluate the physical layer security of a cooperative communication system, with a relay operating in decode-and-forward (DF) cooperative mode, selfish and malicious behavior in real non-ideal transmission environment. Security performance is evaluated in terms of the probability of non-zero secrecy capacity. Experiments showed some different results compared to theoretical simulation: (1) to achieve the maximum secrecy capacity, the optimal relay power according to the experiments result is larger than that of ideal theoretical results under both cooperative and selfish behavior relay; (2) the relay in malicious behavior who forwards noise to deteriorate the main channel may deteriorate the eavesdropper channel more seriously than the main channel; (3) the optimal relay positions under cooperative and selfish behavior relay cases are both located near the destination because of non-ideal transmission.  
physical layer security; different relay behavior; experiment +
Footnote †: journal: Sensors
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 3 The Joint Receiving Scheme of Correlator
### Correlator Joint Receiving Algorithm
\begin{cases}\alpha\left|E_{d}\cdot e^{i\frac{\pi}{2}} \right|+\beta\left|E_{p}\right|&for:B1C,L1C(\text{QZS1})\\ \alpha\left|E_{d}\cdot e^{i\frac{\pi}{2}}\right|+\beta\cdot\left|E_{p}\right|& for:L1C(\text{QZS}(2\sim 4),\text{GPS})\end{cases}  \tag{18}\]  
\[P_{J}= \begin{cases}\alpha sign(P_{d,l})\cdot sign(P_{p,l})\cdot P_{d}\cdot e^ {i\frac{\pi}{2}}+\beta\cdot P_{p}&for:B1C,L1C(\text{QZS1})\\ \alpha sign(P_{d,l})\cdot sign(P_{p,l})\cdot P_{d}+\beta\cdot P_{p}&for:L1C( \text{QZS}(2\sim 4),\text{GPS})\end{cases}  \tag{19}\]  
\[\left|L_{J}\right|= \begin{cases}\alpha\left|L_{d}\cdot e^{-j\frac{\pi}{2}} \right|+\beta\left|L_{p}\right|&for:B1C,L1C(\text{QZS1})\\ \alpha\left|E_{d}\right|+\beta\left|L_{p}\right|&for:L1C(\text{QZS}(2\sim 4), \text{GPS})\end{cases}  \tag{20}\]  
where \(E_{J}\) and \(L_{J}\) represent the non-coherently combined early and late correlation, which are used in the joint code loop used for code phase error estimation. \(P_{J}\) represents the coherently combined punctual correlation, which is used in the joint carrier loop used to estimate the carrier phase error. \(\alpha\) and \(\beta\) are the combination coefficients. Weight combination based on the correlators output variances helps to exclude noisy measurements. Since the data and pilot powers are different, their weights in the joint correlation are also different. To obtain the best combination performance, the optimal coefficients need to be estimated. However, the optimal coefficient is related to the discriminator structure used in the tracking loop. In this paper, the noncoherent delay-locked code loop (DLL) envelope discriminator is used in the code loop, and the two-quadrant arctangent function discriminator is used in the carrier loop. The mathematical model is shown in Equations (18) and (19) as follows:  
\[d\tau_{s}=(1-\frac{d}{2})\frac{\left|E_{J}\right|-\left|L_{J}\right|}{\left|E _{J}\right|+\left|L_{J}\right|} \tag{21}\]  
\[d\theta_{s}=\text{arctan}\frac{imag(P_{J})}{real(P_{J})} \tag{22}\]  
The positioning solution module is shown in the lower part of the structure in Figure 3. \(P_{d}\) is used to calculation message information. The user position of the satellite participating in the positioning solution is estimated after the navigation message is translated into the broadcast ephemeris. \(P_{p}\) is used for the frame synchronization. Observations used to calculate the pseud-orange are as follows: carrier frequency \(\hat{f}_{s,J}\), carrier phase \(\hat{\theta}_{J}\), code frequency \(\hat{f}_{c,J}\) and code phase \(\hat{\tau}_{J}\) are the combined estimated observations output by the phase discriminator. Finally, the satellite position and the pseudo-orange are combined to obtain the combined positioning result using the least square method.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance
### CRISPR-like Region  
Previous studies explored the association between CRISPR-like regions and biofilm formation [91]. Among the isolates, 23 were CRISPR-like positive, with 19 capable of forming biofilms. However, contrary to previous studies, statistical analysis did not reveal a significant relationship between CRISPR-like regions and biofilm formation. The study findings diverged from the existing literature, highlighting the role of CRISPR systems in biofilm regulation in other bacterial species. For instance, CRISPR targeting the _luxS_ gene inhibited biofilm formation in _E. coli_, while Cas genes in _P. aeruginosa_ and _Salmonella_ were implicated in biofilm production [21; 102]. Additionally, a recent study reported that CRISPR deficiency in _S. agalactiae_ led to decreased biofilm formation. These results shed light on the complex interplay between CRISPR-like regions and biofilm formation in _H. pylori_, providing valuable insights into the mechanisms underlying biofilm development in this pathogen.
## 2 Methods  
This study investigates the impact of EEG NFB on improving motor deficits in patients with PD and identifies the key factors for successful implementation and broader adoption of this method.  
To formulate research questions and establish inclusion and exclusion criteria, we utilized the PICO method [31,32].  
Our main research questions were as follows:  
1. Can EEG NFB influence motor deficits in patients with PD, and if so, how?
2. What key factors for the successful implementation and broader adoption of the EEG NFB method can be identified in the literature?  
Based on existing evidence that suggests improvements in attention and concentration through EEG NFB, we hypothesize that improvements in motor symptoms of PD patients could also be observed, more specifically, enhancing movement (gait), tremor control and balance issues. We also anticipate identifying general brainwave patterns or regions that respond best to the applied method.  
To address the research questions, we conducted a targeted literature review following the PRISMA-S strategy [33].  
The inclusion criteria for study selection were developed using the PICO method:  
P (Population): We included studies involving participants diagnosed with Parkinson's disease who used experimental EEG NFB to improve motor abilities. We included randomized control studies, clinical trials, pilot and sham-controlled studies, age-matched studies and case studies. We excluded studies that did not utilize the EEG NFB method or did not apply it to alleviate motor deficits. Additionally, studies not written in English or Slovenian, animal studies, unfinished studies and observational and narrative studies were excluded.  
I (Intervention): We analyzed studies where the EEG NFB method was used to alleviate motor deficits in PD patients.  
C (Comparison): In this targeted literature review, we compared studies that employed the EEG NFB method.  
O (Outcome): We specifically analyzed tests that were used to assess motor abilities.  
We retrieved studies published up to October 2023 from PubMed, Scopus, Web of Science and Europe PMC, as these databases provide open access to a wide range of research relevant to our interests. We included randomized control studies, clinical trials, pilot studies, sham-controlled studies, age-matched controlled studies and case studies. All included studies were peer-reviewed and written in English. Studies in foreign languages, those conducted on animals, observational studies and narrative studies were excluded. We also reviewed the reference lists of the retrieved studies for a broader literature overview. Participants in these studies, except for control groups, were individuals diagnosed with Parkinson's disease who had difficulty performing motor movements. A detailed overview of the study selection process is presented in the PRISMA-S flow diagram (see Figure 1). A more detailed search string is explained in Appendix A.
## 7 Conclusions  
Streaming video queries have been one of the most challenging research areas in computer vision, software engineering, and big data. In recent years, several approaches to video queries have emerged. However, none have become popular due to many limitations related to the high latency between the user's query and information. These approaches also require intensive CPU and memory usage, several database operations per second, complex data structures, and high network usage. In addition, the related solutions are usually created as monolithic structures. Although GPU processing enables fast computing, existing video query approaches are still expensive for real-time processing. Considering these aspects, this work presents the following contributions.  
First, a new lean query processing method called NOP Query (Notification-oriented Querying Method) was presented as a straightforward and intuitive approach to dealing with streaming video queries. As far as we know, this is the pioneering work based on NOP for this context. NOP Query inherits important features from NOP, such as formalism and tools that are constantly being improved. Additionally, this work has demonstrated the viability of NOP Query for applications that require resource optimization and fast processing, especially in the context of video queries and event matching.  
Next, a new dataset based on the popular Noscope has been developed by adding two more fields (color and ID), making it more suitable for this type of experiment. The NS100 KCOLORID dataset is available to the community to promote further research and comparisons.  
Then, we carried out systematic experiments and compared the results with the literature. In this way, we evaluated the suitability of the NOP Query method and its potential for processing large amounts of data and different types of queries. These experiments can be useful for future work and comparisons with other emerging technologies.  
Finally, NOP Query is a prominent application that creates and manages thousands of _Rules_, simultaneously demonstrating the NOP Framework C++ 4.0's ability to process complex queries.
## 2 Spectrum Sharing Processes
### Distribution of Rules  
The output of the negotiation process, _i.e._, the rules and policies that control spectrum access, needs to be distributed to all interested nodes. In this _update_ phase, competing nodes are informed of their usage rights for each spectrum portion under negotiation, the duration of the particular spectrum allocation, and, importantly, the node configuration necessitated by these rules, putting constraints to node operation (e.g., maximum transmission power, operating frequency, timeslot size and timeslot allocation map, _etc._).  
There are also cases when new nodes emerge and are not aware of current spectrum allocation status in their vicinity. This phase thus also aims at informing newcomers of current access rules. Also, new nodes may lead to spectrum re-allocation or modification of current access rules, which existing nodes need to become aware of.  
Potential attackers may be tempted to tamper with the rule distribution process [24], thus giving their victims a false view of current spectrum allocations in an attempt to increase their spectrum access opportunities or to deliberately reduce the performance of the underlying network and the QoS attained by other nodes.
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 5 Some Applications
### Steel Surfaces
#### 5.1.3 Anion Effects  
That, beyond the cation feature, the anion can also make a real difference towards the tribological activity of ILs has been confirmed by a study by [PERSON] et al. in which the authors use three ILs as neat lubricant for steel-steel contact [102]. More specifically,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load/N**} & \multicolumn{4}{c}{**Friction Coefficient**} \\ \cline{2-5}  & **IL 1** & **IL 2** & **IL 3** & **PFPE** \\ \hline
100 & 0.086 & 0.094 & 0.095 & 0.131 \\
200 & 0.077 & 0.081 & 0.085 & 0.138 \\
400 & 0.076 & 0.082 & 0.077 & 0.122 \\
600 & 0.082 & 0.085 & 0.074 & - 2 \\
800 & 0.079 & 0.082 & 0.075 & - 2 \\ \hline \hline \end{tabular}  
* Friction coefficient measured at 25 \({}^{\circ}\)C; b No lubrication.  
\end{table}
Table 3: Friction coefficients measured for steel/steel frictional pairs by ionic liquids and PFPE 2.  
Figure 9: Synthesis of imidazolium ILs.  
the tested ILs were: (1) [(NEMM)MOE][FAP] ethyl-dimethyl-2-methoxyethylammonium tris(pentafluoroethyl)trifluoro-phosphate, (2) [BMP][FAP] 1-butyl-1-methyl-pyrrolidinium tris(pentafluoroethyl)trifluoro-phosphate, and (3) [BMP][NTF2] 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide. The investigations were conducted at 40 and 100 \({}^{\circ}\)C on steel discs, and the [BMP][NTF2] showed the better antifriction coefficient at 40 \({}^{\circ}\)C, though at 100 \({}^{\circ}\)C, its tribological behaviour as a friction inhibitor was considerably decreased due to its lower prevalent viscosity than the corresponding IL with a FAP anion (Figure 10).  
In our opinion, this confirmation is somehow expected, since anions and cations are specular actors in the framework of electrostatic interactions, although, in practise, they have different sizes in ionic liquids. In fact, in this case, it is not so strange to think that the distinct chemical nature of the perfluoroalkylphosphate (FAP) and the bis(trifluoromethylsulfonyl)imide (NTF2) groups allows interactions with probable different strengths between ILs and the contacting surface to be invoked.
## 2 Data and Methods
### Data Source and Preprocessing
#### 2.2.2 Meteorological Data Acquisition and Preprocessing  
The month-by-month meteorological data were obtained from the China National Meteorological Science Data Sharing Service Platform ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))) (accessed on 13 January 2022), including monthly average temperature, monthly maximum temperature, monthly minimum temperature, wind speed, sunshine hours, relative humidity, barometric pressure data, and other elements. The station meteorological data were interpolated into raster data with the same resolution and coordinate system as the vegetation index product NDVI by using the kriging interpolation tool in ArcGIS spatial analysis tools. The kriging interpolation used was ordinary kriging.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Network Administration
#### 4.4.4 Fault Administration  
Network fault administration involves the detection of network faults and taking precautionary measures to reduce the impact of the faults.  
In hybrid micro-grids, a technique for Fault Identification and Relay Protection (FIRP) was developed by combining blockchain with machine learning, where blockchain has been applied to create a layered framework for related elements and machine learning is utilized to detect defects [308]. Alternatively, in heterogeneous smart grid neighboring area networks, Privacy Reinforcement Learning with Blockchain (PRLB) is used to match anomalies in the energy to detect faults in the network [309].
## 3 Literature Review and Related Work
### Fingerprinting-Based Positioning Implementing Fingerprint Images Processed Using CNNs
The reference positions are either manually or randomly selected in the whole area in which the positioning is intended. The reference positions define the mapping between the fingerprints and the ground truth positions in the training phase. The reference positions are, in some works, set at uniform distances from each other. In some works, the whole positioning area is discretized into small _discretized positioning areas_ or grids, which are treated as reference positions. Discretized positioning areas are explicitly defined as such in the scenario considered or simply defined in general terms as _areas_, with associated inter-area distances. In some articles, the reference points are provided in the selected dataset. Some examples of the datasets referred to in the surveyed articles are [87; 88].
## 1 Introduction
### Recovery Mechanisms of Polymer Flooding  
A significant amount of remaining oil saturation is left in the reservoir due to the poor sweep efficiency of brine flooding [26; 27; 28]. Hence, polymer flooding is performed to recover the unswept and distributed oil saturation remaining. The literature [7; 29] has reported many recovery mechanisms of polymer flooding, for instance, viscous fingering reduction [30], relative permeability reduction [31], pull-out in dead-end pores [32], stripping from oil-wet surfaces [33], enhanced flow between heterogeneous layers [26; 31], shear thickening [7; 11], and elastic turbulence [34]. However, all of the mentioned recovery mechanisms are associated with polymer's improved mobility ratio and possibly its viscoelastic properties.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 5 Performance Evaluation
### Simulation Settings  
In this simulation, we configure a randomly generated MECC system where the computing power and link bandwidth of each ES are randomly determined within specified parameter ranges. The system supports five types of applications, with parameters for each also randomly generated. The distance from the user to the RAN nodes is fixed at 10 m. Users can randomly select from one of the five application types to generate tasks for offloading. The wireless communication bandwidth between the user and the RANs is set to \(B=10\) MHz, the transmission power to \(p_{t}^{j}=10\) dBm, the channel power at the reference distance of 1 m to \(-50\) dB, and the Gaussian noise power spectral density is established at \(N_{0}=-170\) dBm/Hz.  
In our simulations, all deep neural networks (DNNs) are structured as four-layer fully connected networks, consisting of an input layer, two hidden layers, and an output layer. Each hidden layer in both the actor and critic networks contains 256 neurons. The learning rate for the networks is set at 0.001. As the objective is to minimize the average response delay across all tasks over a prolonged period, we set the discount factor for reinforcement learning to 0.9. Additional details on the experimental simulation parameters are provided in Table 2.