esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings and Discussion
### Web Graph Analysis
#### 4.1.2 Web Graph Analysis for Lower Star-Rated Hotels
\begin{table}
\begin{tabular}{l l l l} \hline \hline Strong links & Links & Web of 10 fields: Overall \% & \\ \hline ID & & Field 1 & Field 2 \\ \hline
1 & 99.08\% & Value for Money = “Yes” & Safety = “No” \\
2 & 99.07\% & Location Accessibility = “No” & Room Facility = “Yes” \\
3 & 99.07\% & Safety = “No” & Room Facility = “Yes” \\
4 & 99.03\% & Safety = “No” & F \& Issue = “Yes” \\
5 & 98.90\% & Location Accessibility = “No” & Cleanliness = “Yes” \\
6 & 98.48\% & Location Accessibility = “No” & Room Space = “Yes” \\
7 & 98.48\% & Safety = “No” & Room Space = “Yes” \\
8 & 98.44\% & Location Accessibility = “No” & Hotel Facility = “Yes” \\
9 & 98.22\% & Safety = “No” & Customer Service = “Yes” \\
10 & 98.01\% & Location Accessibility = “No” & Miscellaneous Issue = “Yes” \\ \hline Medium links & & Web of 10 fields: Overall \% & \\ \hline ID & & Field 1 & Field 2 \\ \hline
1 & 33.94\% & Value for Money = “Yes” & Miscellaneous Issue = “Yes” \\
2 & 33.33\% & Location Accessibility = “Yes” & Miscellaneous Issue = “Yes” \\
3 & 33.33\% & Safety = “Yes” & Miscellaneous Issue = “Yes” \\
4 & 32.41\% & Room Facility = “Yes” & Cleanliness = “Yes” \\
5 & 32.04\% & Miscellaneous Issue = “Yes” & F \& Issue = “Yes” \\
6 & 28.79\% & Room Space = “Yes” & Cleanliness = “Yes” \\
7 & 23.81\% & Location Accessibility = “Yes” & Value for Money = “Yes” \\
8 & 23.49\% & Miscellaneous Issue = “Yes” & Customer Service = “Yes” \\
9 & 23.33\% & Safety = “Yes” & Cleanliness = “Yes” \\
10 & 23.30\% & F \& Issue = “Yes” \\ \hline Low links & & Web of 10 fields: Overall \% & \\ \hline ID & & Field 1 & Field 2 \\ \hline
1 & 14.68\% & Value for Money = “Yes” & Hotel Facility = “Yes” \\
2 & 14.29\% & Location Accessibility = “Yes” & Cleanliness = “Yes” \\
3 & 12.04\% & Value for Money = “Yes” & Room Facility = “Yes” \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top 10 links for lower star-rated hotel. Figure 6: Web graph analysis of online complaint attributes for lower start-rated hotels. The Web graph depicts information related to the pair-wise associations [21]. However, it is important to decompose the network for the purpose of interpreting the sets of frequent associations. The processes can provide greater insight on the associations that exist between the online complaining attributes. To develop a more in-depth understanding of the sets of frequent associations, this study employed Apriori rule-based machine learning.
## 2 Results and Discussion
### Identification of Shared Proteins and Interactions of PP1c and I2 in Schizonts and Gametocytes  
The assembly of PP1c with a given regulator is expected to select appropriate substrates to target and/or to regulate the dephosphorylation process by controlling access to the catalytic site. It has been shown that _Plasmodium_ PP1c interacts not only with I2 in vitro but also in the parasite by IP of PP1c and reciprocal IP of I2 (this study) [12]. We therefore searched for possible common proteins among the two interactomes. This allowed the identification of 14 and 18 common proteins in schizonts and gametocytes, respectively (I2 and PP1c included) (Figure 6). GO enrichment and STRING analyses of these sets of common proteins did not reveal significant scores which could be due to the low number of shared proteins. Further analysis of shared proteins allowed us to identify five common proteins regardless of the parasite stage examined (Figure 6). This included PP1, I2, nuclear export mediator factor (NEMF), peptidyl-prolyl cis-trans isomerase, and PBANKA_0621300 with unknown function, designating these proteins as potential substrates for the regulation of their phosphorylation status by the PP1-I2 holoenzyme. The detection of these common proteins in PP1c and I2 interactomes is likely specific and independent of the mCherry tag, as these proteins were not detected in the interactome of mCherry-PbGEXP15 in schizonts and gametocytes that we recently reported as a partner of PP1c [14]. When the common partners were examined according to the parasite stage (schizonts versus gametocytes), our findings are supportive of the capacity of the I2/PP1c complex to form distinct co-complexes. Moreover, they suggest that the proteins of these co-complexes could not only be potential substrates but also play the role of transporters and/or adaptors of the I2/PP1c complex. Of note, among the common proteins of both interactomes in gametocytes, a direct and specific partner of PP1c that we previously reported and designated RCC-PIP was detected [18]. This partner that binds PP1c via its RVxF binding motif has also been shown to interact with CDFK7 kinase through its RCC domain, suggesting a subtle control of phosphorylation levels of partners to accurately coordinate the binding of the PP1c.
## 3 Performance Analysis
Therefore, obtaining the best beam divergence angle by HQAM modulation is easier. However, compared with the quasi-stationary aircraft system, the BER of the G-S system is higher. For example, when u = 1, the BERs of the G-H-S and G-S systems at the optimal beam divergence angle are \(3.9\times 10^{-5}\) and \(1.6\times 10^{-4}\), respectively. When u = 3, the BERs of G-H-S and G-S systems at the optimal beam divergence angle are \(4.54\times 10^{-6}\) and \(2.1\times 10^{-5}\), respectively. So, the performance of the G-H-S system is the best. In communication systems, the beam divergence angle is challenging to compress, and a large beam divergence angle is easier to obtain. Figure 7a,b show the variation in the scintillation index of HQAM, XQAM, RQAM, and QAM with BER at different anisotropy factors of u = 1 and u = 3, respectively. The results indicate that the bit error rate of the system not only increases with the increase in scintillation index but also decreases with the increase in anisotropy factor. From Figure 7a,b, it can be seen that the error rate of the four modulation schemes increases with the increase in the scintillation index, and the scintillation index decreases with the increase in the anisotropy factor. When u = 1 and the scintillation index is 0.2, the BERs of HQAM, XQAM, RQAM, and QAM are \(2.57\times 10^{-4}\), \(9.84\times 10^{-4}\), \(1.52\times 10^{-3}\), and \(3.17\times 10^{-3}\). When u = 3 and the scintillation index is 0.2, the BER of HQAM, XQAM, RQAM, and QAM are \(2.18\times 10^{-5}\), \(6.33\times 10^{-5}\), \(2\times 10^{-4}\), and \(3.86\times 10^{-4}\). Therefore,  
Figure 5: Relationship between the transmission radius and BER of HQAM, XQAM, RQAM, and QAM under different anisotropy factors in the ground–satellite uplink. (**a**) u = 1; (**b**) u = 3.  
when the scintillation index is the same, the BER using HQAM is relatively small. At the same time, the bit error rates of the G-H-S and G-S systems increase monotonically with the increase in the scintillation index. Regarding trends, the initial BER difference between the two is relatively large, but as the scintillation index increases, the laser beam is more affected by the intensity scintillation. The BER of the two gradually increases, and the difference in error rate gradually decreases. However, the BER of the G-H-S system is always lower than that of the G-S system. When u = 3 and the flicker index is 0.2, the bit error rate of the G-H-S system under QAM modulation is 3.86 \(\times\) 10\({}^{-4}\); the bit error rate of G-S system is 1.16 \(\times\) 10\({}^{-3}\). Obviously, the communication performance of the G-H-S system is more superior. In the uplink, the light intensity scintillation has a significant impact on the propagation of laser beams.
# Designing an Adaptive Underwater Visible Light Communication System +
## 5 Transmitter Design
### Signal Generation
#### 5.1.2 Modulating Signal  
Baseband PPM signal transmission can be performed in two ways [4].  
(1) Modulating the signal with a carrier whose sampling frequency aligns with the IQ rate of the USRP.  
(2) Initializing the carrier frequency of the software-defined radio (SDR) object and transmitting the baseband PPM signal directly.  
The second method results in signal demodulation with an unknown offset, which is difficult to calculate. Hence, the first method was chosen [4]. The choice of carrier frequency for the modulating signal is crucial since, if not chosen within the operating range, it can result in affecting the USRP configuration and Li-Fi operation. After several experimental trials, 2.4 MHz was chosen as the carrier frequency. It not only addresses the underrun issue but also helps the optical transceiver to accurately deliver the signal at the receiving end. The interpolation factor of 16, which corresponds to the sampling frequency of 6.25 MHz, satisfies the Nyquist criterion for the tone signal [4].
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 2 Synthesis and Characterization of DWNTs  
DWNTs are mainly produced by three different methods, such as arc discharge, catalytic chemical vapor deposition and the thermal treatment of pea pods (_i.e._, SWNTs encapsulating fullerenes). For the arc discharge method and the catalytic chemical vapor deposition (CCVD) method, the concepts of the synthesis are similar. By either carbon evaporation or using a carbon precursor with the decomposition method, carbon sources, such as graphite- or carbon-containing molecules, are decomposed into small fragments, and this is followed by the formation of an sp\({}^{2}\) carbon fibrous material due to the catalytic effect of metal nanoparticles. The last method listed above is based on the chemical reaction of confined molecules within the confined hollow space of the SWNTs.
## 4 Formal Chaos Existing Conditions of 1-D Maps
### Formal Chaos Existing Conditions of a General 1-D Map  
In this subsection, in order to verify _formal chaos_ existence for 1-D map easily, we provide Theorem 3 to guarantee the _formal chaos_ existence in Definition 1 in the case of \(m=2\). Although a similar 1-D map to the map in Theorem 3 appears in [PERSON] or [PERSON]'s mathematical proof process for \"\(\varphi\)_has a homoclinic point_\" implying Definition 1 in [15; 21], the existence of the similar 1-D map has not been considered as an easy-to-use sufficient condition implying Definition 1.  
Therefore, the authors propose to adopt the existence of the similar 1-D map as the sufficient condition implying Definition 1 and to summarize this condition as Theorem 3. This theorem can be regarded as 1-D map version of [PERSON]'s theorems [14; 19] for two dimensional maps.  
**Theorem 3**.: _Let \(I\) be a closed interval of real numbers. Let \(f\) be a continuous piecewise differentiable mapping from \(I\) to itself and \(Df\) be a derivative of \(f\)._  
1. _There exist_ \(I_{0}\) _and_ \(I_{1}\) _disjoint closed subintervals in_ \(I\) _such that_ \(I_{0}\cup I_{1}\subset f(I_{0})\) _and_ \(I_{0}\cup I_{1}\subset f(I_{1})\)_._
2. \(|Df(x)|>1\) _holds for any_ \(x\) _in_ \(I_{0}\cup I_{1}\)_. If_ \(f\) _satisfies_ \(A1\) _and_ \(A2\)_, then the invariant set_ \(\Lambda=\cap_{0\leq\leq\infty}(f)^{n}(I_{0}\cup I_{1})\subset I\) _exists, and the_ \(1\)_-D map_ \(f\) _on_ \(\Lambda\) _is topologically conjugate to the shift dynamics_ \((\sigma,\Sigma_{2})\) _with 2 symbols. The one-dimensional map_ \(f\) _on_ \(\Lambda\) _is mathematically chaotic._  
Proof in Appendix A.
# Methane Gas Density Monitoring and Predicting Based on RFID Sensor Tag and CNN Algorithm
## 3 Proposed Algorithm
### Least Squares Support Vector Regression
Figure 10a shows the measured SNR under different distances; it can be seen that within the communication distance of 10.3 m, the measured SNR is higher than 26 dB. Figure 10b shows the measured BER under different SNR. As seen in the figure, when the SNR is higher than 25 dB, the measured BER is less than 0.001. Therefore, the proposed wireless sensor works well in the test scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data Location** & **Operation** & **Time (ms)** \\ \hline Traditional & NVM & Inventory and Read & 7.45 \(\pm\) 3.92 \\ Proposed & ID & Inventory & 22.13 \(\pm\) 3.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Time delay comparison. Figure 8: Comparison of the power consumption under two different sensor data storage mechanisms. In order to verify the estimated performances of the proposed method, we tested the wireless sensor at the underground mine site. Meanwhile, a commercial gas concentration detector was put in the same spot as a reference. The proposed approach was employed to predict the methane gas density, and the measurement results are shown in Figure 11. As can be seen, the predicted results are consistent with the measured results and the maximum error is 0.42 ppm within 100 min. In general, the available historical data is also one of the factors that affect the prediction accuracy. Therefore, we measured the prediction error under different available historical data and the measured results are shown in Figure 12. At the beginning of prediction, the LS-SVR model is trained by little historical data, which leads to big prediction error. With the increase of available historical data, the error is quickly decreased to 0.00012. Thus, it can be deduced that the proposed method can achieve high prediction accuracy with sufficient historical data. Figure 10: (**a**) Measured signal to noise ratio (SNR) under different distances; (**b**) measured bit error rate (BER) under different SNRs. Figure 9: (**a**) Received signal strength indication (RSSI) measurement environment; (**b**) measurement results. To better verify the priorities of the proposed method, its performance is compared with several wireless sensors which are introduced for methane gas density measurement. Table 2 shows the comparison results. As can be seen, the exploited wireless sensor shows the advantages including low cost, additional ID information, additional predicting ability, longer communication distance, and higher accuracy compared with the other reported wireless sensors.
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 3 Experimental Data and Evaluation Indicators  
The data source selected for the experiment and analysis is first introduced in detail. Furthermore, evaluation indicators used to evaluate the prediction performance of these models are then presented.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 3 Results
### Geodetic Mass Balance Entire SPI 2000-2015/16  
Results of the geodetic glacier mass balance calculations are given in Table 2. Averaged over the entire SPI the volume loss dominates over the 16-year period. With a negative mean elevation change, the total SPI shows also a negative mass balance for all the scenarios calculated. With lower densities assumed for the accumulation area (Scenario 3), the balance becomes significantly less negative, since the area above the assumed Equilibrium Line Altitude of 1050 m is large (7258 km\({}^{2}\)) compared to the ablation area (3514 km\({}^{2}\)).
## 1 Introduction
[PERSON] [29] estimated the PV potential of a vehicle with a module attached to the roof while moving on an expressway section 120 km long, considering the changes in solar radiation, shaded area based on time and location, and tunnel sections. [PERSON] et al. [30] developed a power output prediction model for a solar bus with a solar roof attached and conducted a validation experiment to compare the predicted and observed values. [PERSON] et al. [31] assumed that the module was attached to the roof of a solar train and developed an algorithm for predicting the PV potential considering solar irradiation and shadow effects that vary with time and location during operation. However, these studies have limitations in that they evaluated the PV potential by assuming that the module was attached only to the roof of the mobile device (Table 1). Furthermore, no study has evaluated the potential by considering the following four sides: Bonnet, rear window, left door, right door, and roof of the vehicle. First-generation PV-powered EV have only one module on the roof; however, next-generation models are expected to adopt translucent solar cells on glass and lightweight solar cells on the bonnet, roof, and steel plates on both sides of the vehicle, such as a VIPV. Therefore, considering various vehicle spaces to maximize the power output of future PV-powered EVs is essential. The aim of this study is to analyze the potential and change patterns of four modules (roof, rear window, left door, and right door) based on the time and driving route of a PV-powered EV. To this end, this study investigates the effects of change on (1) solar irradiation, (2) solar incidence angle for each module, defined by the following three factors: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Type** & **Module Attachment** &
\begin{tabular}{c} **Slope/Aspect** \\ **of Pathway** \\ \end{tabular} & **Temporal Unit** & **Validation** \\ \hline [PERSON] [29] & Solar car & Roof & Not considered & Hourly & No \\ [PERSON] et al. [30] & Solar bus & Roof & Not considered & Minutely & Yes \\ [PERSON] et al. [31] & Solar train & Roof & Not considered & Minutely & No \\ This study & Solar car & Roof, rear window, side doors & Considered & ¡Minutely & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous studies on the PV potential assessment of solar mobility. Sun position, module position (inclination and azimuth), and road section inclination and azimuth and (3) shaded area. Additionally, we discuss the effectiveness of the rear window module or door modules on both sides compared to the roof module. To this end, this study compares the simulated PV potential with the observed values obtained via field experiments.
## 2 Related Research
### Employable Sensors in Finger Movement Impairment-Level Recognition of the Fugl-Meyer Assessment  
Several approaches have been applied to address inherent subjectivity in the FMA. Depending on the task of the FMA, appropriate modalities should be utilized. The assessment of non-hand tasks is simple, as they only comprise a single and long extremity part, such as an arm. Thus, any sensor has the potential to record the FMA task activity from this part of the body. Several studies have introduced the idea that the movement activity of non-handFMA tasks could be captured using proprioceptive and camera-based modalities such as accelerometer, Force Sensing Resisto (FSR), and Kinect sensors [10; 12; 13].  
Nevertheless, employing the same sensors in the hand tasks of the FMA is less appropriate, as the corresponding tasks involve the fingers, which are small and short extremities. Therefore, a specific or customized sensor is needed to capture the movement activity of the FMA's hand tasks. For instance, two studies constructed grasping tools embedded with FSR sensors to capture the activity of the fingers, especially in pincher grasping [11; 13]. The studies indicated that a finger requires a customized sensor to capture its complex movement mechanism due to its small size. However, a customized grasping object embedded with a sensor is limited in terms of the type of movement and the size of the finger.  
Another utilizable sensor in the FMA hand tasks is EMG sensors. EMG enables the recording of the complex movement of fingers by capturing the electrical activity of muscles that control the fingers [8; 12]. Furthermore, EMG can capture several channels, facilitating the recording of muscle activities of individual finger movements, regardless of the various sizes of fingers and the types of movements. Accordingly, EMG has prominent potential to capture finger activity of the FMA's hand task, as it addresses the limitation of the aforementioned customized grasping object embedded with sensors.
# Shearer-Positioning Method Based on Non-Holonomic Constraints
## 2 Positioning Algorithm of Shearer
### Kalman-Filter Algorithm  
Kalman-filter algorithm is a kind of algorithm that can fuse prediction information and observation information to produce the best estimation of the system state at the next time. The Kalman-filter algorithm is divided into two parts, prediction and update, which can be expressed by the following five iterative equations.  
(1) Prediction algorithm  
\[X_{k+1,k}=F_{k+1,k}X_{k} \tag{12}\]  
\[P_{k+1,k}=F_{k+1,k}P_{k}F_{k+1,k}{}^{T}+Q_{k} \tag{13}\]  
(2) Update algorithm  
\[K_{k+1}=P_{k+1,k}H_{k}{}^{T}\Big{(}H_{k}P_{k+1,k}H_{k}{}^{T}+R_{k}\Big{)}^{-1} \tag{14}\]  
\[X_{k+1}=X_{k+1,k}+K_{k+1}(Z_{k+1}-H_{k+1}X_{k+1,k}) \tag{15}\]  
\[P_{k+1}=[I-K_{k}H_{k}]P_{k+1,k}[I-K_{k}H_{k}]^{T}+K_{k}R_{k}{}^{T} \tag{16}\]  
In the formula, \(P,\ Q,\) and \(R\) form the covariance matrix; \(P\) represents the prediction error, \(Q\) represents the system noise in the state equation, and \(R\) represents the observation noise. \(K\) is the filter-gain matrix.  
With this algorithm, optimal estimation of the shearer's posture, speed, and position information can be calculated.
# Power Control and Channel Allocation Algorithm for Energy Harvesting D2D Communications
## 5 Conclusions  
In this paper, we investigate the problem of assigning optimal transmission power, energy harvesting time and channels to D2D users under the scenario where multiple D2D users reuse the same channel  
Figure 4: Total capacity of D2D users with different number of D2D users.  
Figure 5: Total capacity of D2D users with different number of cellular users.  
and energy harvesting technology is adopted by the D2D transmitter. An optimal joint power control and channel allocation algorithm is proposed to maximize the total capacity of D2D users while ensuring the QoS of cellular users and considering the power limitation of D2D users. The proposed algorithm firstly divides D2D users into several clusters by constructing the interference weight graph based on the channel gain between D2D users, and then D2D users in the same cluster are modeled as a non-cooperative game; the optimal transmission power and energy harvesting time are obtained through joint iteration optimization method for each D2D cluster reuse of each channel; and finally, according to the maximum cluster capacity of each cluster when it reuses every channel, the KM algorithm is used to allocate channels for D2D clusters to maximize the total capacity of D2D users. Simulation results show that the proposed algorithm outperforms the algorithm in [13] and the equal time allocation method in terms of total capacity of D2D users. In the next step, ways of harvesting energy from the environment will be considered, such as solar energy and wind energy, and other methods can be learned to study resource allocation in hybrid networks with D2D communication, such as random geometry and Q learning methods.