esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Method 1: The Crisp Boundary Approach  
The output values, 'z' with decimals generated by ANFIS, are rounded up to discrete ranks, providing crisp boundaries between the regions. This results in six discrete values; thus, six distinct regions, from Region 5 to Region 0, as shown in Figure 8. Any surge in power generated, due to an unsteady state is classified as 'undefined'. ANFIS is trained with the training data, and its ability to estimate the output based on the power generated is evaluated using the testing data, where it produced a root mean square error of 0.31.  
The trained ANFIS function is programmed to detect any presence of faults in the EPS after every simulation run. For demonstration purposes, the ANFIS monitor screens the steady-state simulation run time every 0.25 s to check the state of total power generated by the EPS and in this paper, two cases are tested.
# Pseudorange Measurement Method Based on AIS Signals
## 4 Accuracy Analysis of Single Timestamp Detection
The simulation results are shown in Figure 8.  
Figure 7: Timestamp detection method. (**a**) Repeated GMSK baseband waveform; (**b**) Zero-crossing detection method; (**c**) Differential peak detection method; and (**d**) Detection error distribution. It can be seen in Figure 8 that the zero-crossing detection method is better than the differential peak detection method under the same conditions. The mean of the measurement error is defined as \(\mu\), and the standard deviation is defined as \(\sigma\). Assuming that the measurement results are normal distribution, according to statistics, we can get that the probability of detection is 68.4% in the range of \([\mu-\sigma,\mu+\sigma]\). When the SNR is greater than 10 dB, the average value of the transmission delay measurement error of the zero-crossing detection method is less than 1.8 \(\times\) 10\({}^{-9}\) s, which is equivalent to the pseudorange measurement error better than 54 cm; however, if the standard deviation of the transmission delay measurement error sample is 3.7 \(\times\) 10\({}^{-6}\) s, it means that the single point detection of pseudorange measurement accuracy is 1.1 km (8). When the SNR is greater than 40 dB, the average value of the transmission delay measurement error of the zero-crossing detection method is less than 1 \(\times\) 10\({}^{-10}\) s, which is equivalent to the pseudorange measurement error better than 3 cm; when the standard deviation of the transmission delay measurement error sample is 4.3 \(\times\) 10\({}^{-7}\) s, it means that single point detection of the pseudorange measurement accuracy is 129 m (8). From the simulation results, we can see that the measurement precision of the zero-crossing detection method for the Gaussian baseband waveform can theoretically reach the meter level, but the reliability of single-point measurement is not ideal.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## Appendix A  
(i) The derivation of stator flux linkage  
From Equation (1), ignore \(R_{s}\), there is  
\[\overrightarrow{v}_{s}=D\overrightarrow{v}_{s}.\] (A1)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & \multicolumn{2}{c}{**The Error of Fault Distance Measurement Result**} \\ \cline{2-4}  & **5 km** & **10 km** & **15 km** \\ \hline Single-phase ground & 3.67\% & 3.38\% & 3.11\% \\ Two-phase ground & 3.27\% & 3.13\% & 2.94\% \\ Phase-to-phase short circuit & 7.24\% & 7.64\% & 7.29\% \\ Three-phase short circuit & 26.39\% & 26.07\% & 25.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the Fourier method when faults occur.  
The solution of Equation (A1) can be written as:  
\[\overset{\rightarrow}{\psi}_{s}=\overset{\rightarrow}{\psi}_{sn}+\overset{ \rightarrow}{\psi}_{sf}.\] (A2)  
It can be found that the response of the stator flux after the fault consists of two components: natural flux linkage \(\overset{\rightarrow}{\psi}_{sf}\) and forced flux linkage \(\overset{\rightarrow}{\psi}_{sf}\). \(\overset{\rightarrow}{\psi}_{sf}\) can be determined by the post-fault terminal voltage:  
\[\left\{\begin{array}{c}\overset{\rightarrow}{\psi}_{sf1}=\int\left(1-A \right)V_{pre}e^{j(\omega_{s}l+\theta)}=\frac{\left(1-A\right)V_{pre}}{\mu_{ \omega_{s}}}e^{j(\omega_{s}l+\theta)}\\ \overset{\rightarrow}{\psi}_{sf2}=\int V_{2}e^{j(-\omega_{s}l+\theta^{\prime} )}=-\frac{V_{2}}{j\omega_{s}}e^{j(-\omega_{s}l+\theta^{\prime})}\end{array} \right..\] (A3)  
It is well known that the flux can only change continuously. Thus, there is  
\[\overset{\rightarrow}{\psi}_{s}(0_{-})=\overset{\rightarrow}{\psi}_{s}(0_ {+}),\] (A4)  
and pre-fault stator flux linkage is  
\[\overset{\rightarrow}{\psi}_{s0}=\frac{V_{pre}}{j\omega_{s}}e^{j(\omega_{s}l +\theta)}.\] (A5)  
Therefore, the stator natural flux linkage is obtained as:  
\[\left\{\begin{array}{c}\overset{\rightarrow}{\psi}_{s1}=\frac{AV_{pre}}{j \omega_{s}}e^{-t/\,T_{s}^{\prime}+j\theta}\\ \overset{\rightarrow}{\psi}_{sn2}=\frac{V_{2}}{j\omega_{s}}e^{-t/\,T_{s}^{ \prime}+j\theta^{\prime}}\end{array}\right.,\] (A6)  
(ii) The derivation of rotor flux linkage.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 3 System Model
### Network Model Using IEEE 802.11 ah  
All drone UAV nodes including CS and drones communicate using IEEE 802.11 ah as medium access control protocol (MAC). Sensing nodes in the cluster comprise the second networking tier. Relay  
Figure 1: System model.  
nodes can directly communicate with CS and they comprise first communication tier. Cluster head nodes then only keep administrative data while each sensing drone can communicate with the relay drone. Each sensing (non-relaying) drone is associated with single relay node. All packets received from non-relay nodes are forwarded to CS. Similarly, the response message transmitted by CS are received by the relay node and forwarded to the non-relay nodes.  
In our framework, the UAV network uses Restricted Access Window (RAW) scheme where beacon interval is divided into separate RAW slots to implement the uplink channel access to a relay and non-relay nodes. Relay nodes use a dedicated RAW slot to communicate with CS. Additional isolation among different clusters can be also achieved using RAW slots. During network initialization, the initial relay nodes in tier one are configured first. In addition, in initialization, each non-relay drone is first associated with CS. As the non-relay drones fly away from the transmission range of CS, the non-relay nodes get re-associated with a relay node in order to achieve two hop communication with CS. In steady state operation, tier two drones form a cluster where each node works as an administrative cluster head and thus increase the scalability and lifetime of the network. When tier two node's energy level drops below a certain threshold, this node moves towards CS and takes on the relay function. We assume that the re-charging process on the ground ensures a steady supply of the nodes in tier two and tier one. The selection of new cluster head node from the cluster of non-relay nodes may be based on available energy level of the non-relay nodes.  
CS will allocate a RAW slot to a relay node and broadcast the allocation using an S1G beacon (DTIM) frame. The DTIM beacon frame contains the allocation of which slot is allocated to which relay node and the start time of each RAW slot. At the beginning of an allocated RAW slot, a relay node will broadcast a TIM beacon frame which contains the bitmap for the nodes that have packets for downlink direction. The TIM beacon also carries the information of the duration for which the non-relay nodes are allowed to contend for medium access. At the end of transmission or the expiry of RAW duration, non-relay nodes go to sleep mode and wake up at the start of the next TIM beacon transmission time. However, the relay node goes to sleep mode only at the expiry of RAW duration and wakes up at the next beacon transmit time.
## 4 Results and Discussion
### Component Identification of Wellhead Blockage
#### 4.1.1 Identification of Inorganic Phases
The energy spectrum identification results show that the unknown minerals mainly comprised four elements: Fe, O, Na, and Cl (Figure 5c,d). The four elements accounted for more than 95% of the total elemental amount. Among them,  
Figure 5: Scanning electron micrograph and energy spectrum analysis of unknown mixture (x. spot: sampling points for energy spectrum analysis). (**a**) Scanning electron micrograph of 180\(\times\); (**b**) Scanning electron micrograph of 540\(\times\); (**c**) Scanning electron micrograph of 4700\(\times\); (**d**) energy spectrum analysis of sampling points. Fe and O elements accounted for 80%. According to the k\(\alpha\) line of element oxygen and iron obtained from the energy spectrum, the atomic ratio of the two elements was calculated to be about 2:3. This suggested that the unknown mineral attached to the surface of the crystal was iron oxide. Undoubtedly, it is impossible to contain a large quantity of iron oxide in the formation [19]. A plausible explanation is that the iron oxide in the blockage may have come from rust on the metal parts of the inner wall of the wellbore. The crystal was mainly composed of two elements: Na and Cl. According to the K\(\alpha\) spectral lines of the elements Na and Cl obtained from the energy spectrum, the atomic ratio of the two elements was calculated to be about 1:1. Combined with the morphological characteristics of the mineral, the large minerals observed under the scanning electron microscope were considered to be high-purity sodium chloride crystals [20]. Sodium chloride mainly derives from seawater or deep geological fluids [21]. The formation of the wellhead blockage can be thought to be influenced by geological fluid action during the formation process.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 2 Experimental Method
### Data Acquisition  
To evaluate the output characteristics of the FSR sensors over time for each load value, a data acquisition (DAQ) program was developed (Figure 4). This program collected 1500 data points at a frequency of 0.1 Hz. The load data were measured using an Arduino Mega2560 equipped with a 10-bit analog-to-digital converter (ADC), and the raw data were converted to output voltages corresponding to the applied loads. Figure 5 shows our experimental setup for the static load characteristic test of the FSR sensor using a weight. The Arduino board, in particular, is known for its low cost and relatively high ADC output linearity. With a 10-bit resolution (output values ranging from 0 to 1024), it  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & & & & & & & & & & \\ \hline \multicolumn{12}{c}{**Weight Load (g) \(\pm\) Tolerance (mg)**} \\ \hline
20 \(\pm\) 2.5 & 50 \(\pm\) 3 & 100 \(\pm\) 5 & 200 \(\pm\) 10 & 300 \(\pm\) 15 & & \\ \hline \multicolumn{12}{c}{**Test load (g)**} \\ \hline
20 & 50 & 100 & 200 & 300 & 500 & 600 & 700 & 800 & 820 & 850 & 870 \\ \hline \end{tabular}
\end{table}
Table 2: Weights for the static load test (M1 class).  
Figure 3: Voltage divider.  
Figure 2: Puck for load testing.  
is deemed sufficient for measuring resistance changes in this test. While it is possible to use a MATLAB program with an Arduino board for data acquisition, the interface between MATLAB and the Arduino board can sometimes be unstable. Therefore, a separate data logging program was created using C#, and the data files were saved in CSV format. A program was then written to analyze the collected CSV files using MATLAB for data analysis in each experiment.  
The Arduino system provided a consistent 5 V input voltage, limiting the measurable output voltage to 5 V or below. MATLAB software was used to process the collected data, calculating the average output voltage for each applied load and fixed resistance. The resistance and conductivity of the FSR sensors were then computed from these voltages to analyze the relationships among the applied load, sensor resistance, and conductivity.  
By implementing this detailed experimental methodology, we ensured precise and repeatable measurements, enabling a thorough analysis of the linearity and repeatability of the selected FSR sensors. This rigorous approach allowed us to derive accurate calibration equations, enhancing the reliability of FSR sensors for various practical applications.
## 3 Results and Discussion  
### Hyperparameter Tuning and Experimental Setup  
To optimize the model performance, hyperparameter tuning was conducted for PSPNet, U-Net, and DeepLab V3+. Three key hyperparameters were tested: learning rate, batch size, and training iteration count. For the learning rate, values of \(1\times 10^{-4}\), \(5\times 10^{-5}\), and \(1\times 10^{-3}\) were evaluated. The learning rate of \(1\times 10^{-4}\) was selected for both PSPNet and U-Net, while DeepLab V3+ performed optimally with a learning rate of \(5\times 10^{-5}\). The batch sizes tested were 16, 32, and 64. PSPNet and U-Net achieved the best performance with a batch size of 32, while DeepLab V3+ showed the best results with a batch size of 64. The final hyperparameter configurations are summarized in Table 1.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 2 Related Works
### Self-Supervised Learning  
Recently, as an effective way to alleviate the data sparsity problem, self-supervised learning (SSL) has been widely used in computer vision [16], natural language processing [17] and graph-based tasks, including various GNN-based recommendation tasks [12; 13; 14; 15]. For product recommendation, SGL [12] applies SSL in recommendation tasks by changing the graph structure through a dropout and random walk strategy, as well as by maximizing the mutual information (MI) between multiple embeddings of the same node while minimizing the MI between the embeddings of different nodes. NCL [13] proposed structure-based and prototype-based contrastive learning objectives, which were used to improve graph collaborative filtering methods. For social recommendation, MHCN [14] designed a hypergraph convolutional network based on social relations and used hierarchical mutual information maximization to recover connectivity information in the hypergraph convolutional network. For session-based recommendation, S\({}^{2}\)-[PERSON] [15] proposed a two-channel hypergraph convolutional network and maximized the mutual information between the learned session representations of both channels. However, these methods are not designed for POI recommendation, and they lack the use of spatio-temporal information, thus making them not applicable to spatio-temporal graphs.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Orbital Angular Momentum Antennas
#### 3.4.2 Requirements of OAM Systems  
To achieve the widespread usage of OAM waves, it is required to develop new technologies to accommodate them. Such technologies could be antenna arrays that are reconfigurable. They will provide more degrees of freedom in wireless communications, mainly in urban areas where the telecommunication load is massive. Additionally, feeding mechanisms of reduced complexity have to be used in the majority of applications (refs. [85; 86; 83]). Moreover, it is required to maintain the orthogonality of OAM modes in order to guarantee their purity. To be more precise, during the excitation of a single mode, it is possible that some neighboring modes become excited too, which leads to less purity and results in the system's instability (ref. [87]). Thus, the achieved orthogonality should be as clear as possible. A really concerning factor is that the transmission distance has a negative influence on purity, as shown by simulations (ref. [88]).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Near/Far Field** & **Application** & **Configuration** & **Algorithm/Technique** \\ \hline
[74] & Far Field & High-definition & RHS & Amplitude-controlled beamforming algorithm \\ \hline
[70] & Near Field & HMIMO communications & SIM & Phase shifts and covariance optimization \\ \hline
[75] & Far Field & Wireless communications & RHS & Amplitude-controlled beamforming algorithm \\ \hline
[76] & Near Field & Handling spatial-wideband & HMA & Spherical wave propagation, iterative beamforming \\ \hline
[77] & Far Field & Dual beam generation, polarization control & Tensor metasurfaces & - \\ \hline
[78] & Far Field & Channel modeling & Dense antenna arrays & Deterministic and stochastic modeling \\ \hline
[79] & Far Field & Energy efficiency & RRS & - \\ \hline
[80] & Near and & Spectral and energy & SIM & Dynamic waveform shaping, gradient-based algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 2 Data and Methodology
### Numerical Model
#### 2.3.3 Validation of the Numerical Model  
The computing time for model is from 00 a.m. 11 February 2002 to 00 a.m. 13 March 2002, which lasted for a month. The validation time of the model is from 00 a.m., 1 March 2002 to 00 a.m. 10 March 2002. In this paper, four tidal station data of Chongmingzhou (Cmz), Lianxinggang (Lxg), Nannen (Nm), and Hengsha (Hs) are selected for tide level verification (Figure 5).  
Figure 5: _Cont._  
Figure 5: Verification of tidal level.  
Figure 6: Verification of salinity levels.  
Model results are quantitatively assessed against the observed values using the skill model [28]. The skill model is as below:  
\[skill=1-\frac{\Sigma_{i=1}^{N}|M-D|^{2}}{\Sigma_{i=1}^{N}(|M-\overset{-}{D}|+|D- \overset{-}{D}|)^{2}} \tag{5}\]  
Symbol list: \(M\) is the measured values; \(D\) is the mean value of in situ observed data; a skill value of 1.0 indicates perfect agreement between the model results and observed data, excellent for skill between 0.65 and 1, very good for skill in the range of 0.5-0.65, good for skill in the range of 0.2-0.5, and poor for skill less than 0.2.  
The numerical model was validated at 4 tide stations (Cmz, Lxg, Nm, and Hs) and 4 salinity stations (Z4, Z7, Y4 and Y8) around the YRE spanning the period from March 1, 2002 to March 10, 2002 (for positions see Figure 1). The time series of the hourly measured data at 8 stations are collected to validate the simulations. The simulation results at Cmz, Lxg, Nm, Hs, Z4, Z7, Y4 and Y8 are in excellent agreement with the observations (Figures 5 and 6). The skill values are 0.94, 0.92, 0.95, 0.91, 0.71, 0.81, 0.79 and 0.77 at eight stations (Table 2).  
From the verification results, the skill of tidal level simulation is above 0.9, and the fitting effect is very good. The skill of salinity level simulation is 0.71-0.81, which is slightly lower than the fitting result of the tide level. This is because, when at low tide, some stations have extremely low salinity and high sensitivity to water flow, so some of the measurements have deviations from the measured salinity.
## 3 Results and Discussion
### Noise Level  
The SPL was measures at the real power \(P=5;\,6;\,8;\,10\) and12 W, and it was presented in Figure 7. The noise level was in the range of \(49\leq\mathrm{SPL}\leq 58\) dB. Additionally, the logarithmic approximation based on all measurements point was presented in Figure 7 with a solid line, and the field of the measurement uncertainty was restricted with the dashed line. The differences between the SPL generated by the actuators with the chevron orifices and the circular orifice were presented in Figure 8.  
The SPL differences presented in Figure 8 are small, and most of them are smaller than the measurement uncertainty (see Table 2). However, their distribution is not random, and the SPL generated by SJA with the chevron orifice is smaller than the SPL generated by SJA with the circular orifice at all cases, except cases 1a, 1b, 5, and 6. Additionally, in cases 1, 2, and 3, the smaller SPL was obtained at rounded cutouts than at classic, sharp. The SPL generated in case 4a was smaller than in case 4b only at power \(P=8\) W.  
The SPL generated in case 1 was higher than in that of case 0. Based on the presented results, it is impossible to decide how the number of chevrons impacts the SPL, but a too-small number of chevrons increased the SPL. The number of chevrons should be dependent on the orifice diameter and not considered as an absolute number.  
The obtained SPL reduction was similar to in the other papers. [PERSON] et al. [50] obtained a higher SPL reduction of about 2.5 dB, at a Mach number equal to 0.3. [PERSON] and [PERSON] [51] received approximately 4 dB reduction but at a Mach number range of 0.6-1.6. [PERSON] and [PERSON] [52] showed that chevron orifice use can increase the SPL. Against this background, the presented results are correct and do not differ from the  
Figure 8: Differences between SPL generated by actuator with chevron orifice and circular orifice.  
Figure 7: Sound pressure level (SPL) vs. real power.  
others. The presented noise reduction is smaller than the reduction obtained by the use of the other shape of the orifice. [PERSON] and [PERSON] [30] obtained the maximal SPL reduction about of 13 dB for the oval orifice, and [PERSON] et al. [31] about of 5.7 dB for the square office.
## 1 Introduction
Through appropriate external cavity feedback methods, the coherent performance of lasers has been significantly improved, leading to the widespread application of semiconductor light sources [14; 15; 16]. As the research and development technology of narrow linewidth semiconductor lasers has become increasingly mature, their performance has improved significantly, making them a focal point of recent studies. In order to explore their application prospects in the field of inter-satellite laser communication and achieve high-quality communication while compensating for the Doppler frequency shift caused by relative motion, this study investigates a novel low-noise narrow linewidth diode laser with an external cavity structure. By introducing external cavity optical feedback and extending the cavity length, the laser achieves reduced linewidth and noise suppression. This is achieved by utilizing F-P etalons in combination with ultra-narrow bandpass filters for laser frequency selection. Simultaneously, it enables the generation of a high-power single-frequency laser output. Furthermore, based on the standard wavelength table specified by the International Telecommunication Union (ITU), a bidirectional temperature-scanning laser method is employed to select the optimal operating point, thereby achieving wavelength-hop-free tuning within a certain range.