esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Specific Application of Topic Identification Models and Algorithms
### Application of Citation Analysis and Its Associated Models and Algorithms  
Citation analysis is an activity and method used to analyze the relationship between literature citations and cited references. It quantitatively reflects the relationships between the inheritance and development of the literature through the network relationships between the literature references and cited references, and is also used in subject topic discovery.  
[PERSON] and [PERSON] selected the most highly cited papers in the field of \"carbon nanotube fiber\" as their research object, and used the C-value term extraction method for topic identification [61]. [PERSON] constructed an association matrix based on the paper-patent reference and text association, and identified the innovative topics in the field of Bruton tyrosine kinase inhibitors based on the method of science-technology reference and text associations [62]. [PERSON] et al. used backward patent references (where patents cite other patents) in combination with the Bass model to identify disruptive technologies in solar photovoltaic power generation [63]. [PERSON] et al. selected the most highly cited papers in the field of artificial intelligence for analysis, and identified the cited themes combined with K-means, LDA and GSDMM clustering methods and models [12]. Based on the change in the direction of patent references, [PERSON] et al. built a new method to identify disruptive technologies in the field of speech recognition [64]. [PERSON] et al. used patented citation networks and genetic backward-forward path methods to identify the main paths, thus enabling them to track technology evolution and identify disruptive technologies in the field of surgical robotics [65].
## 4 Physical Audio Features Extraction Techniques
### Image Domain Physical Features  
This approach to feature extraction is based on a joint two-dimensional image-based m of the audio signal. Typically, one of the dimensions corresponds to a frequency vision of the signal, while the other corresponds to a time view (as defined by [PERSON] [137]).  
* **Spectrogram image features:** or SIF for short, are features that comprise a set of techniques that focus on applying techniques from the image processing field to the time-frequency representations (using Fourier, cepstral, or other types of frequency mapping techniques) of the sound to be analyzed ([PERSON] _et al._[14], [PERSON] _et al._[138]). Spectrogram image features like subband power distribution (SPD), a two-dimensional representation of the distribution of normalized spectral power over time against frequency, have been shown to be useful for sound event recognition ([PERSON] [4]). The advantage of the SPD over the spectrogram is that the sparse, high-power elements of the sound event are transformed to a localized region of the SPD, unlike in the spectrogram where they may be scattered over time and frequency. Also, Local Spectrogram features (LS) are introduced by [PERSON] [4] with the ability to detect an arbitrary combination of overlapping sounds, including two or more different sounds or the same sound overlapping itself. LS features are used to detect keypoints in the spectrogram and then characterize the sound using the Generalized Hough Transform (GHT), a kind of universal transform that can be used to find arbitrarily complex shapes in grey level images, and that it can model the geometrical distribution of speech information over the wider temporal context ([PERSON] _et al._[139]).  
In Section 5.5 other approaches for image-based audio feature extraction which incorporate perceptual auditory models are reviewed.
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## Related Work  
### Existing Project in the Field  
In recent years, various studies have been conducted to attempt to change network systems by using SDN. For example, Cisco has been conducting research on location and identifier separation and research on SDN in line with the paradigm shift of the network for several years [14]. Recently, with the increase of IoT devices, Cisco is having been preparing for processing rapidly increasing network traffic. In addition, Cisco has been developing a network solution with SDN that can be applied to various places and industries where IoT devices are used, such as enterprises, factories, warehouses, and healthcare areas. Another company, Spinent communication, has also been developing an SDN network solution [15]. For the development of the SDN network solution, Spinent has been working to provide a more stable and efficient network solution by conducting various studies and tests through the development of network function virtualization (NFV), OpenFlow switched network and path computation element protocol.
## 5 The Main Results
Considering mean forecasting accuracy values, accuracy fluctuations, and communication network load with equal weights, the second scenario with filtering has better overall performance than the rest of the scenarios and the average overall improvement is about 27%. For both scenarios, an increase in reading time intervals leads to better accuracy, but the communication network load is increased. A trade-off between traffic forecasting accuracy and communication network load should consider determining the length of reading intervals.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.1 Sampling Plan  
Eleven test sites were identified with the assistance of ecoguards to conduct this study in the Bossematie classified forest (Figure 1). The flights were conducted during the dry season at these sites during three acquisition campaigns: in November 2018 and April 2019. These sites are characterized by their 2016 land-use type, allowing us to evaluate the area's level of deforestation and degradation, as well as its accessibility (Table 1). The level of deforestation and degradation of these sites varies from 2% to 41% [12].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site Name**} & \multirow{2}{*}{**Acquisition Dates**} & \multicolumn{3}{c}{**Types of Land Use and Land Cover in**} & \multirow{2}{*}{**Area (ha)**} & \multirow{2}{*}{**Level of**} \\  & & & & **2016 (ha)** & & \\ \cline{2-6}  & \multirow{2}{*}{**Acquisition 1**} & \multirow{2}{*}{**Acquisition 2**} & **Dense Forest** & **Degraded** & \multirow{2}{*}{**Crops**} & & **Degradation** \\  & & & **Formation** & & **Forest** & & \\ \hline Site 1 & 01-Nov-18 & 09-Apr-19 & 120.8 & 5.7 & 4.5 & 131 & 8\% \\ Site 2 & 01-Nov-18 & 09-Apr 19 & 117.3 & 10.5 & 3.2 & 131 & 10\% \\ Site 3 & 01-Nov-18 & 09-Apr-19 & 128.7 & 1.8 & 0.4 & 131 & 2\% \\ Site 4 & 02-Nov-18 & 10-Apr-19 & 122.2 & 4.8 & 4.9 & 132 & 7\% \\ Site 5 & 03-Nov-18 & 12-Apr-19 & 122.6 & 3.6 & 3.5 & 130 & 5\% \\ Site 6 & 03-Nov-18 & 12-Apr-19 & 117.2 & 2.3 & 11.6 & 131 & 11\% \\ Site 7 & 03-Nov-18 & 10-Apr-19 & 124.1 & 5.8 & 2.13 & 132 & 6\% \\ Site 8 & 03-Nov-18 & 10-Apr-19 & 94.9 & 4.7 & 31.4 & 131 & 28\% \\ Site 9 & 04-Nov-18 & 11-Apr-19 & 101.6 & 14.3 & 15.1 & 131 & 22\% \\ Site 10 & 04-Nov-18 & 11-Apr-19 & 76.99 & 20.8 & 31.9 & 130 & 41\% \\ Site 11 & 04-Nov-18 & 11-Apr-19 & 86.4 & 4.7 & 39.8 & 131 & 34\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of different study sites in the Bossématé classified forest.  
Figure 2: Protocol for acquisition, processing, and analysis of drone data for the early detection of tree loss.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 5 Simulation and Results
### Network Yield  
Figure 12 shows the network yield for varying the number of vehicles and CBR connections. The network yield represents the comprehensive performance of a network, which similar to the goodput. For example, when the packet loss rate increases, the number of packets that reach the destination becomes smaller, thus, the value of network yield is small. In the same condition of packet loss rate, a route with a large number of hops will have a negative impact on network yield. From Figure 12, it can be found that when the number of nodes increases, the performance of network yield is better (with exception of Figure 12d, where the number of CBR connections is 20, the results of MM-GPSR are similar). The main reason to limit the performance in a sparse network is that the network connection is not enough due to the small number of nodes. Actually, location-based greedy forwarding mode is very suitable for well-connected networks due to this scheme aims to obtain the minimum number of hops. From Figures 10 and 11, we can found that RETT has a lower packet loss rate and lower end-to-end delay. Obviously, our proposed RTEIT has a higher network yield in a sparse environment by comparing with GPSR, MOPR, and MM-GPSR in all set of CBR connections. These results can be interpreted as: (1) link utility considers both one-hop transmission distance and link stability, which increases the packet delivery ratio and reduces the number of hops; (2) effective information traffic leads nodes to find a correct path to a destination which can avoid path redundancy.  
Figure 12: Network Yield for varying the number of vehicles and CBR connections: (**a**) 5 CBR connections; (**b**) 10 CBR connections; (**c**) 15 CBR connections; (**d**) 20 CBR connections.
## 4 Experiment and Result Analysis
### Differential Displacement Measurement with Rotational Angle Compensation  
In this displacement measurement experiment, the measuring mirror M\({}_{2}\) of DPMI and the measuring reflector of the Renishaw interferometer were mounted back to back on the M531.DD1 stage. After the setup was constructed, the displacement compensation parameters in Equation (8) were obtained. The distances S\({}_{0}\) and S\({}_{1}\) were 236 mm and 195 mm, respectively, and the distances H\({}_{\mathrm{x}}\) and H\({}_{\mathrm{y}}\) were 90 mm and 12 mm. During the experiment, the M531.DD1 stage drove the two measuring reflectors to move from 0 mm to 300 mm, with a step increment of 2 mm, simultaneously measuring the displacement and rotation angle via DPMI and the displacement via the Renishaw interferometer, for comparison.  
The experimental results of displacement comparison are shown in Figure 6 and Table 2. As shown in Figure 6a, the maximum yaw and pitch angles of the M531.DD1 stage were \(-0.0048^{\circ}\) and \(-0.0095^{\circ}\), respectively. As shown in Figure 6b, the displacement measurement results of the Renishaw interferometer are shifted upward by 20 mm for clarity. The maximum deviation between the Renishaw interferometer and the stage positioning was \(5.448~{}\upmu\)m, with a standard deviation of \(1.225~{}\upmu\)m. Before rotational angle compensation, the maximum deviation between DPMI and the stage positioning was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Angle**} & **Maximum** & **Average** & **Standard** \\  & **Deviation (\({}^{\circ}\))** & **Deviation (\({}^{\circ}\))** & **Deviation (\({}^{\circ}\))** \\ \hline Pitch & \(-4.74\times 10^{-5}\) & \(-5.71\times 10^{-6}\) & \(1.68\times 10^{-5}\) \\ Yaw & \(4.96\times 10^{-5}\) & \(9.71\times 10^{-6}\) & \(1.86\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rotational angle: comparison of experimental results.  
Figure 5: Comparison of experimental results of angle measurement: (**a**) pitch angle; the blue dots are shifted by 0.001\({}^{\circ}\) to make the plots visible; (**b**) yaw angle; the blue dots are shifted by 0.001\({}^{\circ}\) to make the plots visible.  
5.207 \(\upmu\)m, with a standard deviation of 1.214 \(\upmu\)m. After rotational angle compensation, the maximum deviation between DPMI and the stage positioning was reduced to 0.719 \(\upmu\)m, and the standard deviation was reduced to 0.263 \(\upmu\)m. The experimental results showed that the proposed DPMI can effectively reduce the environmental disturbance in addition to reducing the rotation error in the displacement measurement and, therefore, can be applied for precise displacement measurement in the millimeter range.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 4 Submarine Amplifiers
### Differences between Terrestrials and Submarine Amplifiers  
The wet plant (compared to the dry plant) has higher demands and standards regarding the deployed undersea network infrastructure. This is partly due to the high cost of maintenance and failover for undersea components. Consequently, terrestrial and submarine network amplifiers are rather different. The key concept for a submarine amplifier is the high reliability. This is one of the most important differences between its terrestrial counterpart, as a point failure in a submarine amplifier means both long downtime in provided services and expensive repairs.  
A second difference is the amplifier gain. As initial and maintenance costs are high, the construction of submarine amplifiers should be efficiently designed. Submarine amplifiers should provide spectral and power-efficient amplification and at the same time keep the costs down. In the upcoming SDM era, features such as the repeater pumping schemes and the use of high count of FPs have to be implemented.  
Submarine amplifiers must also have low NF and high OSNR, due to the submarine systems' longest links and much more expensive infrastructure. In the next few years, we will have to face the capacity crunch, therefore, the capacity requirements for submarine transmission have to overcome the power efficiency limits which a submarine amplifier repeater can support at the present time.
# Radon Dynamics in Granite and Calcareous Soils: Long-Term Experiments in a Semi-Arid Context
## 2 Materials and Methods
### Monitoring System  
The experiments were performed in two experimental columns that contained the soils under study (Section 2.2). The columns were placed outdoors at the campus of the University of Alicante (Alicante province, SE Spain). This study was carried out in different periods depending on the soil: April 2021 to July 2022 for the granite soil, and July 2022 to February 2023 for the calcareous soil. Two different measurement periods were established because radon could only be measured continuously in one soil at a time.  
The experimental procedure was identical for both columns. The columns consisted of a 110 cm high corrugated polyethylene tube with a 50 cm diameter (Figure 1). Several sensors controlled by dataloggers were installed providing continuous data on soil parameters through the studied period. The monitoring system consisted of a set of dataloggers (HOBO H22 (Onset, Bourne, MA, USA) and CR-1000 Campbell Scientific(r)--Logan, UT, USA) connected to the power supply and to a backup battery to prevent data loss. In each column, there were two different measurement points (at different depths; top and bottom, Figure 1) to continuously register the following at hourly intervals: (i) dielectric permittivity (to later calculate soil water content (SWC); accuracy: \(\pm 0.03\) m\({}^{3}\) m\({}^{-3}\)[51]); (ii) soil temperature (accuracy: \(\pm 1\)\({}^{\circ}\)C) using a 5 TE automatic sensor (Decagon Devices, Pullman, WA, USA) connected to the CR-1000; and (iii) CO\({}_{2}\) soil concentration GMP252 Vaisala CAR-BOCAP sensor (reading accuracy: \(\pm 2\)%; Vaisala, Vantaa, Finland), with a 0-10,000 ppm measurement range, connected to the HOBO H22. Note that the operating range of the CO\({}_{2}\) sensor conditioned the measurement in the periods of highest soil CO\({}_{2}\) production, mainly spring, since the probe was saturated, and real values were not available.  
Figure 1: The general layout of the experimental setup of the sensors and dataloggers in the artificial soil columns.  
In addition, at the bottom of each column (Figure 1), radon activity was continuously measured at 30 min intervals with a Barasol MC2 silicon detector (accuracy: \(\pm\)50 Bq m\({}^{-3}\); Algade, Bessines-sur-Gartempe, France). This device was placed in a PVC-capped tube at the bottom of the column at the target soil depth of 90-100 cm, as radon and gas permeability measurements are usually performed at a 100 cm depth [50]. This is an adequate depth to avoid dilution of radon in soil due to mixing with atmospheric radon-poor air.
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 2 Site and Instrumentation
### Cup Anemometer and Wind Vane Observations  
The FINO3 platform is equipped with a 100 m lattice mast with booms for meteorological observations between 30 and 105 m above the mean sea level. In certain directions, however, the mast disturbs the flow, so three levels are equipped with cup anemometers on booms for each of the directions 105\({}^{\circ}\), 225\({}^{\circ}\), and 345\({}^{\circ}\) providing a less disturbed dataset of the 10 min averaged wind  
Figure 2: Monthly data coverage for Carrier-to-Noise-Ratio (CNR) >\(-\)35 dB (white plus grey) and CNR >\(-\)22 (grey only) at the 126 m level (**a**) and at all levels up to 626 m (**b**). 100 % availability corresponds to the number of 10-min periods in each month.  
speeds. The procedure for selecting the corrected observations is provided in [32]. Wind directions are measured at 28 m and 100 m with wind vanes every 10 min.
# Simulation and Design of Three 5G Antennas
## 2 Materials and Methods
### The Unique Performance Parameters of MIMO Terminal Antennas
#### 2.2.2 Average Effective Gain (AEG)  
The average effective gain (AEG) is defined as the ratio of the average received power to the average incident power on the antenna [19]. Unlike measuring the gain of an individual antenna unit in a microwave chamber, in real-world antenna applications, various factors, such as the antenna's environment and terminal structure need to be considered. In such cases, the average effective gain (AEG) is used to accurately represent the antenna's gain performance. The AEG of the antenna is typically obtained through simulation or measurement of the gain pattern. The calculation formula is given in Equation (15).  
\[\mathrm{MEG}=\frac{1}{2\pi}\int_{0}^{2\pi}\bigg{[}\frac{\Gamma}{1+\Gamma} \mathrm{G}\mathrm{\varphi}(\frac{\pi}{2},\varphi)+\frac{1}{1+\Gamma}\mathrm{G }\mathrm{\varphi}(\frac{\pi}{2},\varphi)\bigg{]}\mathrm{d}\varphi, \tag{15}\]In the equation, \(G_{\Theta}\) is the vertical component of the power gain pattern, and \(G_{\varphi}\) is the horizontal component of the power gain pattern. MEG is the parameter used to assess the antenna's average effective gain. To meet the design requirements of a multi-antenna system, the MEGi/ MEGj should be less than 3 dB.
## 6 Limitations and Future Research  
The described approach has some limitations that must be taken into account. First, a very simple model of attention was adopted, considering the eye's gaze leaving the reading area as the only symptom of distraction. As stated above, the readers might be distracted even though their eye gazes were still fixated on the reading area, such as wandering randomly on it or moving backward and forward. If accurate attention level measurement is required, more effective and reliable measures are needed, such as one based on an evaluation of the understanding of the text read. To better monitor the real visual attention of the reader, a more effective approach, currently under investigation as a future improvement to the MICA, is to extract some metadata regarding the structure of the document, such as the division into paragraphs, the structure of each page and the presence  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distracted** & **Focused** & **Indifferent** \\ \hline
0\% & 90\% & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Answers to the user experience questionnaire.  
of graphical elements on the current page to estimate an expected visual path in the current page and to evaluate the consistency of the tracked eye gaze path with the estimated visual path, which is roughly similar to some visual trajectory analysis methods [58].  
A second limitation of the MICA is the low resolution of its eye gaze tracker, which is currently barely capable of discriminating between inside and outside the reading area. However, recent deep convolutional neural networks (CNNs) have been shown to be very effective in face and head detection, face tracking and eye detection [45; 59]. As a consequence, more accurate eye tracking approaches will be investigated in the near future, taking advantage of the most recent lightweight implementations of deep learning platforms such as TensorFlow Lite [60], Pytorch [61] and OpenVino [62].  
Finally, the MICA is defined as an \"interactive\" agent because it actually observes the user, estimates his or her cognitive state and provides \"human-like\" feedback in a continuous loop throughout the reading task. More articulated interactions are, in fact, supported by the TFTH platform, which involves the interpretation and synthesis of natural spoken language, the interpretation and synthesis of facial expressions and other abilities inherited from the Red platform [12]. However, it has not yet been demonstrated that introducing all those communication channels actually improves user attention, and therefore we plan to gradually test them in the future.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 5 Methodologies for Membrane Fouling Prevention and Reduction
### Membrane Cleaning
The results showed that cleaning with distilled water had the lowest cleaning efficiency, whereas cleaning with distilled water followed by either NaOH/citric acid or Novadan agents was more efficient. Among the tested cleaning strategies, it was claimed that Novadan agent was the most successful in removing proteins and carbohydrates from PTFE membrane while it removed only proteins from PP membrane [103]. For cleaning biofouled membranes, the use of biocides [15] followed by rinsing with distilled water can be effective to recover the initial permeate flux. [PERSON], et al. [158] used NaOH at a pH value of about 12 and a temperature of 40 \({}^{\circ}\)C to hydrolyze organic/bacterial fouling, followed by distilled water and 70% ethanol for sterilizing the system, and finally distilled water to remove ethanol. It was observed that the initial permeability was recovered by removing the biofilm deposit. The use of the appropriate cleaning agent depends on the fouling and scaling nature, deposit location and the membrane resistance to chemical cleaning [2; 111]. Typical MD polymers (PP, PTFE, PVDF) offer high resistance to chemical cleaning. However, it must be noted that cleaning porous and hydrophobic membranes used in MD, OD and OMD is not an easy task because of the high risk of pore wetting. In fact, generally in MD process, cleaning of membranes was found to be insufficient provided that fouling is also associated with membrane wettability. For instance, pressure and temperature play an important role in membrane cleaning [156]. High temperatures improve cleaning by increasing transport and solubility of the fouling material as well as the reaction kinetics. Minimum pressure cannot force the fouling layer onto the membrane surface, making it less adhesive.