esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approach  
In this paper, we propose an interference-aware CAF PAPR reduction scheme for LS-MU-MIMO-OFDM systems. The PAPR reduction approach from LS-MU-MIMO-OFDM should be different from that of traditional MIMO-OFDM. The distinctive characteristic of LS-MU-MIMO-OFDM is an excessive number of TX antennas. This characteristic requires a simple and effective PAPR reduction technique rather than a performance-centric technique. The CAF technique can be a good solution for such a purpose [22; 23]. Even though CAF is also applied to each antenna, the complexity and price are much lower than DPD. Simply applying the CAF to each TX antenna separately is not so meaningful. Instead, the CAF inevitably accompanies clipping distortion noise. Since the LS-MU-MIMO-OFDM transmits dozens of streams with an excessive number of TX antennas, we can successfully use the inter-user interference (IUI) to suppress the clipping distortion effect. In the proposed scheme, the clipping level is adaptively adjusted based on the IUI information, and the distortion is suppressed under IUI by adaptively adjusting the CAF level. Comparing with other techniques that require high complexity and price, the proposed scheme can simply increase the EE.  
Numerical analysis shows that the proposed scheme provides a very high PAPR performance with little spectral efficiency (SE) loss.  
In what follows, the system model is described in Section 3. Section 4 presents the proposed interference aware clipping scheme to suppress the clipping distortion effect. Section 5 provides the spectral efficiency, bit error rate (BER), and PAPR performance analysis based on the proposed scheme. Finally, the conclusions are given in Section 6.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 2 Theoretical Analysis and Methods
### Methods  
The trailing edge of the delay waveform is divided into two parts, named the low energy zone and the high energy zone, respectively. Figure 2 illustrates these two energy zones at a wind speed of 2 m/s.  
As shown in Figure 2, the trailing edge is divided into two energy zones by two thresholds. We set the \(Threshold1=P_{noise}\), where \(P_{noise}\) is the noise power. \(Threshold2\) is the boundary value between the high energy zone and low energy zone: \(Threshold1<Threshold2<1\). A detailed discussion on threshold is presented in Section 4.3. The delay range of the high energy zone is \((\tau_{0},\tau_{1})\), \(\tau_{0}\) and \(\tau_{1}\) are the delay values when the correlation amplitudes equal maximum and \(Threshold2\), respectively. And the delay range of the low energy zone is \((\tau_{1},\tau_{2})\), where \(\tau_{2}\) is the delay value when the amplitude equals \(Threshold1\). The description of the new observables that are used to derive wind speed is provided in the following section.  
Figure 1: Simulation of the 1-D delay waveform for different wind conditions: (**a**) Wind speed from 2 m/s to 20 m/s, wind direction: 20\({}^{\circ}\). (**b**) Wind directions from 20\({}^{\circ}\) to 80\({}^{\circ}\), wind speed: 6 m/s.  
Figure 2: Definition of the low energy zone and the high energy zone from the normalized delay waveform.
###### Abstract  
We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.  
citations context classification; citation network analysis; deep learning Article  
A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations  
1 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia, [EMAIL_ADDRESS]  
2 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6 BH, UK, [EMAIL_ADDRESS]
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 3 Design of Cooperative Search Strategy
### Establishment of Q-Value Table  
Since the table method is vulnerable to the problem of \"dimension disaster\", here, the state and control input of UAVs are simplified as much as possible when the table is designed. The possible location of a UAV is determined by the total number of grids. There are \(n\) yaw angles at each grid; thus, the number of rows in the Q-table is \(L_{x}\times L_{y}\times n\), which is the number of UAV states. There are \(m\) optional control inputs for each UAV; thus, the number of columns in the Q table is \(m\), which is the number of decisions contained in decision set \(A\). Variable \(Q(\mathbf{s}_{i}(k),u_{i}(k))\) is defined as the value that \(V_{i}\) selects the decision \(u_{i}(k)\) in the state \(\mathbf{s}_{i}(k)\).  
In the initial stage of learning, because there is no prior information, the decision in the Q-value table is made randomly; thus, Q-learning should have more opportunities to explore the unknown decision space. If only the maximum Q-value is used, the algorithm converges quickly to a poor \(\mathbf{u}(k)\). If the random selection strategy is used, although the environmental state information can be fully explored and the optimal strategy can be found, the algorithm converges too slowly. Therefore, designing a reasonable decision selection mechanism to achieve balance between exploring information and Q-value can ensure the fast convergence of the algorithm to a better strategy.  
In this paper, the Boltzmann distribution mechanism is used to select the decision in the Q-learning process. That is, the probability that the policy set \(\mathbf{u}(k)\) is selected in the state \(\mathbf{s}(k)\) is determined as follows:  
\[P(\mathbf{u}(k))=\frac{e^{Q(\mathbf{s}(k),\mathbf{u}(k))/T}}{\sum\limits_{ \mathbf{u}\in A}e^{Q(\mathbf{s}(k),\mathbf{u})/T}}, \tag{17}\]  
where \(\mathbf{u}\in A\) represents that strategy \(\mathbf{u}\) is an enforceable strategy in decision set \(A\). The value of \(T\) determines the ability of learning to explore unknown spaces, and as \(T\) increases, the ability to explore new decision spaces is improved (if \(T\) is infinite, it is a random decision because \(P(\mathbf{u}(k))\)\(=1/m\)). \(T\) is defined as  
\[T=T_{0}\cdot M^{-1/\lambda}, \tag{18}\]  
where \(\lambda>1\), \(T_{0}>0\), and \(M\) is the number of iterations of the algorithm.  
In the initial stage of learning, \(T\) is set to be large in order to explore more decision space \((T=T_{0})\). Upon increasing the number of learning times, \(T\) gradually decreases in Equation (18), increasing the empirical effect of the Q-value and speeding up the convergence of the algorithm.
# A Multimode Fusion-Based Aviation Communication System
## 3 Hardware Design
### RTK High-Precision Positioning Module  
RTK carrier phase difference technology is a real-time differential method used to process carrier phase observations from two measurement stations. The reference station collects the carrier phase, which is then sent to the user receiver for differential calculation of the coordinates. This results in the accurate positioning information are shown in Figure 11. This module provides precise positioning services, achieving centimeter-level accuracy. It can also provide timing, speed measurement, and direction finding for aircraft.  
The RTK high-precision positioning module, power module, and fusion control module are integrated on one board, and the circuit composition diagram is shown in Figure 12.  
After receiving the RTK signal, it is amplified and smoothed. The RF RFIC then converts this signal down to an intermediate frequency signal, which is sent to the processor for capturing satellite signals, tracking, navigation message demodulation, decoding, raw observation extraction, PVT calculation, and protocol conversion. The module provides support for various differential modes like RTK, DGNSS, and ground enhancement. This allows the module to provide centimeter-level precision positioning services and high-precision orientation services. The module also has anti-multipath, anti-occlusion, and anti-interference technologies, which enable it to achieve continuous and effective localization, even in complex environments. The module has a power consumption of approximately 3 W.  
Figure 11: RTK high-precision positioning navigation schematic diagram.
# Data Downloaded via Parachute from a NASA Super-Pressure Balloon
## 2 Upgraded Hardware Design
### Data Storage  
Each DRS was fitted with \(5\times 1\) TB MicroSDXC cards: one in the internal slot that includes the operating system and four in USN card readers. We set these up as independent drives (rather than RAID to further reduce thermal load) and copy data to one of the SD cards at a time, beginning with those mounted in a USB socket. An rsync script was not sufficient because the SD cards were independent. Instead, whenever the Pi booted, a custom script compared files on the payload with those on the DRS (matching name and size because some files, such as instrument timestreams, continually grow) and copied new data to an SD card with sufficient space. SUPERBIT's rate of data acquisition was sufficiently slow that we could have duplicated all the data to second SD cards on every DRS but, with so many backups already, we chose not to.  
We tested MicroSDXC cards from several manufacturers before the flight. Resilience is more important than speed in this context, and we found considerable variation in cards' abilities to withstand a harsh environment. SanDisk Ultra (A1 UHS-I Class 10 U1) and SanDisk Extreme (A2 UHS-I Class 10 U3 V30) cards worked reliably throughout our tests and the flight. Two different Integral (A2 UHS-I Class 10 U3 V30) cards showed intermittent faults during testing: when used to house the operating system, the Pi would occasionally require power cycling to boot. After a few early problems, this was not reproducible, so we flew two DRS with SanDisk Ultra SD cards and two with Integral SD cards. All our Lexar (LMSPLAY001T-BNNAG) cards failed during testing, so these were discarded.
## 3 Results and Discussion
### Dispersion Curves of Receiver Spacing  
The purpose of this part of the study was to find out the influence of different receiver spacings on the efficiency of detecting the existence of a weak layer underneath the concrete using the SASW method. The layered model 3 (concrete-weak layer) was used in this analysis. In addition to the 40 cm spacing discussed above, extra 20 cm, 60 cm, and 80 cm receiver spacings were also introduced. The corresponding nearest offsets were set to be double those of those spacings, while the impact duration was still kept at 250 us.  
The analysis results for those three scenarios are shown in Figures 10-12. At a receiver spacing of 20 cm, a noticeable disturbance occurs in the first cycle of the phase spectrum, which leads to a  
Figure 8: Phase spectrum and dispersion curve of the layered model 2 (concrete-weak interlayer-bedrock).  
Figure 6: Phase spectrum and dispersion curve of the uniform model.  
Figure 7: Phase spectrum and dispersion curve of the layered model 1 (concrete-bedrock).  
Figure 9: Phase spectrum and dispersion curve of the layered model 3 (concrete-weak layer).  
discontinuity on the dispersion curve at an R-wave length of about 20 cm, but this is not related to the weak layer underneath, since at this wave length, the R-wave does not penetrate through the 40 cm-thick concrete layer above. In fact, according to the basic rules of SASW, the maximum R-wave length of the dispersion curve is limited to two times that of the receiver spacing. This dispersion curve discontinuity may be caused by the element size effect, as discussed above in Section 3.1.  
The phase spectrum and dispersion curve at a receiver spacing of 60 cm in Figure 11 are quite similar to those at a receiver spacing of 40 cm in Figure 9, except that the discontinuity on the phase spectrum moves towards the end of the first phase cycle. However, as shown in Figure 12, at a receiver spacing of 80 cm, the phase spectrum is distinct from those of 40 cm and 60 cm receiver spacings. The phase cycles are seemingly smooth and no apparent discontinuity or interference occurs, and the shape of the dispersion curse is also a little dissimilar. Although there is an interruption on the dispersion curve at the wave length near 40 cm, the R-wave velocity seems more likely to decease gradually after that point, rather than exhibiting an abrupt drop, as shown in Figures 9 and 12.  
It is found from the results above that in order to clearly identify the weak layer underneath the concrete slab using the SASW method, the receiver spacing should be set to within the range of 1 to 1.5 times of the slab thickness.
# Review of Code Similarity and Plagiarism Detection Research Studies
## 2 Code Clones: Definition and Types  
In this section, the concept of \"code clone\", which is a general term for similar code, is defined, and its types are explained. Additionally, background theories and terminologies related to code similarity and plagiarism detection techniques are presented to provide foundational knowledge for understanding the various techniques explained in later sections.  
Generally, the term _code clones_ refers to two identical code fragments. However, the adjective \"same\" is not a technical term, and sometimes similar code with some differences, rather than exact identicalness, is considered a code clone. Depending on how the similarity of two codes is defined, the method for detecting code clones may differ, and the meaning of the detected code clones may vary.  
Here, we explain the definitions that distinguish code clones from types I to IV, in accordance with the methods by [PERSON] et al. [36] and [PERSON] et al. [37], which are widely used to describe the types of code clones. Table 1 shows brief information about these different types.  
If the types of code clones are broadly classified, they can be grouped into type I, II, and III code clones, which are based on text similarity, and type IV code clones, which consider functional similarity.  
Of all of the four types, type I is the case with the highest textual similarity, in which code parts are completely identical except for comments and spaces. In types II and III, the actual textual similarity becomes progressively lower. Finally, in type IV, even if there is no similarity based on text, functionally similar cases are judged and considered as code clones. In the following sections, each type of code clone is described in more detail, and examples and additional methods for classifying code clones besides types I-IV are introduced.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 3 Experiments and Result Analysis
### Performance Analysis of IPSO-AR
#### 3.2.3 Scheme #3 Experiments  
The DD ambiguity float resolutions selected in Scheme #3 are obtained from observations with one epoch. The corresponding ADOP value varies from 0.5 to 2 and indicates the poor precision of the algorithms. The average efficiency and correct rate of IPSO/SPSO-AR in Scheme #3 are shown in Figures 9 and 10.  
Figure 8: Correct rates of IPSO/SPSO–AR under various DD ambiguity dimensions in Scheme #2.  
Comparing Figures 5 and 9, Figures 7 and 10, and Figures 6 and 8, shows that under the same parameter setting, the average efficiency of IPSO/SPSO-AR in Scheme #3 is lower than that in Scheme #1/#2. Although the precision of the DD ambiguity float resolution is low, the correct rate of IPSO-AR in Scheme #3 remains high under the appropriate parameter setting, such as \(n=60\) under low-dimensional conditions, and \(n=90\) under median-dimensional conditions. Meanwhile,  
Figure 10: Correct rates of IPSO/SPSO–AR under various DD ambiguity dimensions in Scheme #3.  
Figure 9: Average number of iterations and search time of IPSO/SPSO–AR under various DD ambiguity dimensions in Scheme #3.  
as the number of dimensions continues to increase, the correct rate gradually decreases. Given the IPSO-AR regularities explained in Scheme #1 and Scheme #2, the corresponding correct rate can still be improved even if we increase the value of parameter \(n\). We will not conduct the validation test given the limitations of this study.
## 4 Results and Discussion
### Mechanisms of the Effect of Inclination Angle on Heat Pipe Performance
#### 4.4.1 Liquid Film Profile of the MF-AHPAs
As reported by [PERSON] (1998), a heat transfer coefficient ranging from 4000 W/m\({}^{2}\cdot\)K to 28,000 W/m\({}^{2}\cdot\)K in the evaporation section is at least four times larger than that of condensation ranging from 1000 W/m\({}^{2}\cdot\)K to 4000 W/m\({}^{2}\cdot\)K in grooved heat pipes. Thinner liquid film in the condensation section can greatly reduce the R\({}_{\rm c}\), and hence, the total thermal resistance. The minimum thermal resistance could be always achieved at a certain profile of liquid film by changing the inclination angle. This criterion could be extended to predict the optimal inclination angle of other grooved heat pipes. Note that the optimal volumetric fraction should be different for grooves with various dimensions. Figure 13 shows the optimal volumetric fractions of heat pipes reported in the literature, which were plotted as a function of volume of wick defined as the groove volume in a given area (mL/m\({}^{2}\)). The volumetric fractions (\(\alpha_{\rm c,a}\)) were calculated by the specific parameters of heat pipes working in the optimal inclination angle in some given input powers, as shown in Table 5.  
Figure 12: The volumetric fraction \(\alpha_{\rm c,a}\) in condensation and adiabatic wick as a function of inclination angle for (**a**) different working fluids at the heat load of 90 W; and (**b**) different heat loads of the acetone MF-AHPA. Figure 13 illustrates that the optimal volumetric fractions were well correlated with different types of grooves with various shapes or dimensions. It appears that the optimal volumetric fraction decreases as the volume of wick increases. For example, in the study of [PERSON] et al. [24], the optimal volumetric fractions are as small as 13.6-17.7% with a large wick volume of 336 mL/m\({}^{2}\). However, in the study of [PERSON] et al. [23], the optimal volumetric fractions in this case were larger, ranging from 51.4% to 65.2% with a small volume of wick of 111 mL/m\({}^{2}\). It is reasonable for a deep groove to have a smaller volumetric fraction. Otherwise, the thick liquid film in the condensation section will lead to a large thermal resistance.
## 2 Results  
### Chemical Analysis of Rosmarinus Officialis Extract (Roex)  
Extraction Yield and Chemical Characterization of Tuscan _R. officialis_ Extract  
The solvent utilized for the production of _Roex_ was a hydro-alcoholic mixture with 60% \(v/v\) ethanol. As reported in Table 1, the amount of dry extract obtained from each gram of dried rosemy subjected to extraction was 112 mg, and 1 mL of suspension contained 24.3 mg of dry material corresponding to a percentage yield of 11.2%.  
In order to quantify total polyphenols and total flavonoids, the extract was analyzed by means of Folin-Ciocalteu colorimetric assay and direct spectrophotometry, respectively. Total sulfuric-vanillin assay-reactive triterpenoids were also quantified. Rosmarinic acid content was evaluated by means of high-performance liquid chromatography-diode array detection (HPLC-DAD) analysis, that was also used in order to identify other phenolic compounds in Roex.  
The quantifications of total phenolic content, expressed as gallic acid equivalents, of total flavonoids expressed as hyperoside, and of total triterpenoids, expressed as \(\beta\)-sitosterol, were reported in Table 1. The amount of each class of compounds was expressed as mg per gram of dry extract (d.e.).  
In order to identify its major components, the extract was analyzed by HPLC-DAD which recorded three main constituents at the following retention time (RT): 6.40 min, 6.95 min and 16.20 min. (Figure 1). By comparing RT and UV spectra of reference standards, it was possible to assign the peak at 16.20 min to rosmarinic acid (Figure 1), while the peak at 6.40 and 6.95 min were assigned to two other hydroxycinnamic derivatives, identified by characteristic UV spectra with \(\lambda\)max at 198-200, 288-298 and 322-332 nm (see Supplementary Materials). Other hydroxycinnamic derivatives identified in _R_oex were chlorogenic and caffeic acid, at RT = 7.80 and 10.00 min, respectively. The content of rosmarinic acid, chlorogenic acid, caffeic acid and total hydroxycinnamic derivatives (as the sum of rosmarinic, chlorogenic and caffeic acid and undefined hydroxycinnamic derivatives), expressed as rosmarinic acid, are reported in Table 2. Beside hydroxycinnamic acids, the chromatogram showed two main flavonoids at RT = 18.58 min and 20.55 min, identified by the characteristic UV profile of this class of metabolites with \(\lambda\)max at 194-200, the highest absorbance at 265-290 and a wide shoulder at 330-350 nm (see Supplementary Materials). These flavonoids could not be unambiguously identified, since RT and UV spectrum did not match any used reference standard. Nevertheless, by comparison with published literature on rosmary flavonoids [22; 32] and polarity order of identified compounds, the main flavonoids in _R_oex could be likely related to luteolin glycosides.