esa-sceva/satcom-chunk-collection huggingface dataset:
# GazeRayHand: Combining Gaze Ray and Hand Interaction for Distant Object Manipulation
## 5 Results
### User Feedback  
Three participants, P9 (the ninth participant; 'P' is the abbreviation for participant, and the number means the order of participation in the experiment), P14, and P23, reported arm fatigue when using two hands for object rotation with the GP condition. P11 noted the inconvenience of needing two hands for object rotation when using the GP condition (\"I felt it was unnatural to switch from one hand to two hands just for rotating the object, which made it difficult to get used to\"). P9 and P23 added, \"using two hands for GP was complicated.\" On the contrary, several participants (P1, P8, P15, and P22) reported that the GRH and GRH2 conditions reduced arm fatigue by requiring minimal movement and allowing one-handed operation.  
The majority of participants (15 out of 24) preferred the GRH condition with its intuitiveness and ease of use. P5, P12, and P19 commented that \"controlling direction simply by moving my hand relative to the ray was very easy.\" This intuitiveness and easiness might have contributed to precise and accurate manipulation. P2 commented, \"GRH allows for more precise control compared to other techniques.\" P16 noted, \"GRH has fewer errors and didn't require multiple steps like GB.\" Regarding the GRH2 condition, many participants (P1, P4, P6, P13, P18, P20, and P24) appreciated its convenience in translating an object with a simple gaze movement for a long distance: \"GRH2 allows for quick object movement by eye, especially over long distances.\" Additionally, in both the GRH and GRH2 conditions, the gaze ray serves as visual guidance, allowing users to verify the direction of manipulation. However, the GRH2 condition had an issue compared to the GRH condition. P17 reported \"GRH2 was more complicated than GRH. After selection, if I accidentally looked somewhere else, the object would suddenly teleport there.\" P7 mentioned that \"After sufficient training, GRH2 could be the fastest and most convenient technique, but in the first impression it was more difficult to control compared to hand movement techniques.\"  
The GB condition was the worst among the conditions because of its lack of flexibility due to its multi-step manipulation. P3, P10, P21, and P24 stated, \"GB's step-based approach was difficult.\" P20 added, \"GB was difficult because it did not allow me revise what I have done in previous step.\"
# A Quad-Band Highly Selective Frequency Selective Surface with Ultra-Wideband Rejection
## 3 Experimental Verification  
In order to further validate the feasibility of the designed FSS, a prototype with a size of 300 mm \(\times\) 300 mm, consisting of 50 \(\times\) 50 elements, was fabricated using three F4 BM-2 boards, as presented in Figure 7a.  
Figure 5: Surface current and electric field distributions at 6.54 GHz.  
Figure 6: Simulated transmission coefficients under different incidence angles and polarizations. (**a**) TE polarization. (**b**) TM polarization.  
The thickness of the F4 BM-2 boards was 1 mm. The FSS layers were connected by nylon studs and nylon nuts to ensure 2.6 mm high air cavities between the three substrates. The FSS prototype was measured in a microwave anechoic chamber using the free-space measurement method. Two standard gain horn antennas acted as the transmitting and receiving antennas. As shown in Figure 7b,c, the prototype was placed in the far-field range of the transmitting antenna and directly above the receiving antenna. The vector network analyzer (VNA) was used to measure the transmission coefficient of the prototype. To cover the wide measurement frequency range, three pairs of standard horn antennas operating at 1-20 GHz, 18-26 GHz, and 26-40 GHz were used.  
The comparison between the measured and simulated results under oblique incidences is shown in Figure 8. As observed, the measured result is similar to the simulated result. In actual measurement, the lower transmission coefficient curve is difficult to measure. Hence, there is a difference in transmission depth between the measured results and the simulated results. The deviations in measurement results can be attributed to the fix error when the height of the air cavity is inaccurate. Nevertheless, the four passbands and out-of-band rejection characteristics can still be observed.  
Subsequently, a comparison of the proposed structure with those from existing studies is presented in Table 1. It can be seen that the proposed FSS not only considers more passbands but also considers out-of-band suppression.  
Figure 8: Measured transmission coefficients of the structure under oblique angles and polarizations. (**a**) TE polarization. (**b**) TM polarization.  
Figure 7: (**a**) Photograph of the fabricated FSS structure. (**b**) Photograph of free-space measurement environment. (**c**) Photograph of measurement environment under oblique incidence.
# Current Practices of Railway Ballastless Track Design Methods in China
## 2 Ballastless Track Structure Design Method
### Allowable Stress Method
For the longitudinally connected track structure, it is necessary to consider the bulk temperature and the concrete shrinkage load because the temperature stress is difficult to release and has great influence on the structure. For the ASM, loads are classified into main force and additional force, and load combination includes two methods differentiated by considering additional force or not, each of which has a certain value of safety factor \(K\). The train load effect is the most important in the main force, which multiplies by a greater factor, and the details are shown in Figure 2.  
The reinforcement result is calculated according to the load combination effect. First, the cross-sectional area of the steel bar \(A_{s}\) is preliminary calculated using the force balance formula, and the stress of steel bar should be no more than the allowable stress [\(\sigma_{s}\)], which is determined by its tensile strength \(f_{yk}\) and the safety factor \(K\). According to the stress distribution characteristics, the force balance diagram is shown in Figure 3, and the relevant parameters are labeled in the diagram. Then, the preliminary calculation results should be checked by the normal stress of the structure. The tensile stress of the steel bar \(\sigma_{s}\) is less than the allowable [\(\sigma_{s}\)], and the compressive stress of concrete \(\sigma_{c}\) is less than the allowable [\(\sigma_{b}\)]. Formulas for stress checking are shown in Figure 2. Finally, the reinforcement result must ensure that the crack width \(\omega\) is not greater than the maximum value \(\omega_{\text{lim}}\). The final reinforcement scheme is obtained after meeting all checking conditions.
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 7 Clinical Applications and Challenges  
Although some papers have studied CVD detection using EEG signals, there has not been much improvement in this area. There are many challenges and areas for improvement that need to be considered in the future work in this field. Firstly, there is the data challenge. A significant amount of data is required for training to construct a good model. The unavailability of public datasets makes it important to have a large number of subjects in studies to construct a new dataset and obtain reliable results. Also, there is the emotions challenge, as EEG signals can be affected by emotions. Therefore, it is important to consider the individuals' emotional states when gathering data. Moreover, there is the demographic challenge. Demographic factors such as age, gender, and race may affect the EEG signals, so it is important to incorporate these factors in the analysis, which will require a lot of time and effort. Another challenge is the ethical challenge, which includes obtaining permission from specific authorities and ensuring the willingness of the subjects involved in the study [91]. Finally, there is the standardization challenge [97]. Many factors could affect EEG signal acquisition and analysis, such as electrode placement, signal processing techniques, and analysis methodologies. All of these factors make it difficult to standardize the procedures across different studies and research groups, while it is crucial for ensuring the consistency and comparability of results.  
Despite these challenges, using EEG signals to detect CVD can overcome many limitations of the conventional diagnostic approaches. The behavioral response, which is considered a limitation as it relies on communication and excludes individuals with communication disabilities, can be overcome with the new method that relies on the brain's response, eliminating the need for direct communication with the individual. Additionally, the subjective characteristics associated with conventional methods, which can be influenced by individual factors, can be overcome with the new objective method. The objective nature of the new method ensures that it remains unaffected by the individual's situation as it is based on observable data. Another advantage is that the new method does not require training, especially when compared to the anomaloscope, making it easier to use. Finally, while the conventional methods can be easily manipulated, it is impossible to control one's brain signals. This inability to manipulate brain signals enhances the accuracy and reliability of CVD diagnostic methods. Overall, the advantages and problems that can be eliminated by the new method outweigh the disadvantages or challenges that may be encountered.
## 4 Multiprocessing: Models and Architectures
### Communication, Synchronization, and Data Sharing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Host** & **Matrix** & **Data (Bytes)** & **N** & **\(\boldsymbol{\tau}\) (ms/msg)** & **BW (MB/s)** \\ \hline CFLX3-FF52-WB & 2 \(\times\) 2 & 81 & 50,000 & 0.37 & 0.21 \\ CFLX3-FF52-WB & 10 \(\times\) 10 & 1943 & 10,000 & 0.47 & 3.9 \\ CFLX3-FF52-WB & 100 \(\times\) 100 & 192,880 & 1000 & 8.3 & 22.2 \\ CFLX6-CR90-WB & 2 \(\times\) 2 & 81 & 50,000 & 0.01 & 7.0 \\ CFLX6-CR90-WB & 10 \(\times\) 10 & 1945 & 10,000 & 0.02 & 110 \\ CFLX6-CR90-WB & 100 \(\times\) 100 & 192,929 & 10,000 & 0.42 & 450 \\ CFLX3-WS & 2 \(\times\) 2 & 80 & 50,000 & 0.018 & 4.2 \\ CFLX3-WS & 10 \(\times\) 10 & 1950 & 10,000 & 0.08 & 23.5 \\ CFLX3-WS & 100 \(\times\) 100 & 192,919 & 1000 & 1.44 & 127.8 \\ HPG3-WS & 2 \(\times\) 2 & 81 & 1,000,000 & 0.005 & 15.4 \\ HPG3-WS & 10 \(\times\) 10 & 1945 & 1,000,000 & 0.024 & 78.1 \\ HPG3-WS & 100 \(\times\) 100 & 19,2864 & 10,000 & 1.3 & 141.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Communication latency \(\tau\) and bandwidth BW for local-worker communication using channels by sending serialized matrix data (initialized with random numbers) between a worker and the main process. send(mas) varmat=deserialize(await receive()) varresult=awaitrpc.foo(2); //Inparentprocess vardata=awaitworker.receive() varmatrix=deserialize(data) matrix.transpose() worker.send(serialize(matrix)) ```
Example 3. Master-worker communication using message channels or RPC. The interprocess communication shown above is mainly used by parent-child process groups. Totally independent worker processes can communicate via a proxy server, as discussed in Section 4.8, creating ad hoc groups.
## 4 Discussion
### Limitations  
This study has several limitations. First, to conduct 12,096 simulations considering various variables and for applying the variations, the MADYMO AHBM was used instead of an FE human model. The MADYMO AHBM has been primarily validated for upright postures and component-level blunt impact conditions. Consequently, its biofelity under reclined postures requires further validation. The interaction between the AHBM upper body and the seat belt in a reclined posture is particularly worthy of investigation. The risk of abdominal injuries was mostly insensitive to crash and restraint parameters. Thus, this model might require an improvement of its biofidelity around the abdominal and pelvic regions to be able to predict the submarining phenomenon. If the capability of the model in predicting submarining is improved, the pelvic forward excursion and abdominal compression amount would also increase with the occurrence of the submarining phenomenon during the frontal crashes [21]. The increased pelvic forward excursion would also increase the l-spine load due to the increase in the flexion of the l-spine. Although the biofidelity of the AHBM is limited, general trends in the sensitivity between crash and occupant parameters and the risk of injuries could still be observed.  
Second, the study considered only a limited range of crash conditions, including occupant anthropometries, vehicle interiors, restraint systems, and crash scenarios. The findings are applicable only to frontal crashes, as the two reconstructed autonomous vehicle cases involved oblique frontal impacts. Furthermore, the results are based on typical restraint systems, including a three-point seatbelt with a pretensioner and load limiter, a frontal airbag, and a knee bolster. Additionally, the exclusion of toe pan intrusion effects may have masked the potential protective benefits of rearward seating for foot and ankle injuries.  
Future research should explore a broader range of occupant anthropometries, pre-crash vehicle maneuvers (such as lateral avoidance), and varied vehicle interior and restraint system configurations.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 5 Results
### Fault Prediction
For example, by summarizing the prediction results from 29 s to 30 s before the fault is 0.1 s.  
Figure 7: Experimental results and comparison. (**a**) Fault prediction effect on test data by CIIPCA-BPNN. The “lower bond” is the result of lower bond classifier; “upper bond” is the result of upper bond based classifiers; “ensemble” is the result of ensemble classifier. (**b**) Comparison of fault prediction effect on test data between PCA-BPNN and CIIPCA-BPNN. “CIIPCA-BPNN” is the result of CIIPCA-BPNN’s ensemble classifier. (**c**) Comparison of ROC(Receiver operating characteristic) curves on test data between PCA-BPNN and CIIPCA-BPNN. “CIIPCA-BPNN” is the result of CIIPCA-BPNN’s ensemble classifier. fault occurs, we can know that the model can accurately predict the occurrence of the fault at 29 s.  
We can see from Figure 8 that CIPCA-BPNN can predict the occurrence of a fault before it occurs. When modeling with 30 or more hidden neurons, we had two time windows that could predict in advance and accurately when a fault would occur. The first window was 16 s before the fault occurred. At this time, the accuracy, precision, and recall of the model were all close to 1, indicating that if the result of the model is \"fault\" at a particular time, there was a nearly 100% possibility of a fault 16 s later. But the first time window was very short, only 1 s. The second window was 9 s to 7 s before the fault occurred. In that window, we had 2 s to react to the fault that would occur. In addition, between 28 s and 2 s before the fault, the recall of CIPCA-BPNN was above 90%, and the accuracy was close to 1, so CIPCA-BPNN could fully predict the fault within 30 s before the fault occurred.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach
### De-Noising Based on Wavelet Energy
#### 3.2.4 Threshold De-Noising  
When the location point required de-noising is selected, the detail coefficients of each layer before the location point are de-noised using threshold. Hard threshold and soft threshold are adopted in this paper. In hard threshold processing, when the absolute value of the wavelet coefficients is less than the given threshold, it will be zero; when the wavelet coefficients are larger than the given threshold, it will remain unchanged. That is as follows:  
\[\omega=\begin{cases}\omega&|\omega|\geqslant\lambda\\ 0&|\omega|<\lambda\end{cases} \tag{12}\]  
where \(\omega\) is a certain wavelet coefficient and \(\lambda\) is a given threshold.  
In soft threshold processing, when the absolute value of the wavelet coefficient is less than the given threshold value, set it as zero; If it is larger than the given threshold value, let the wavelet coefficient subtract the threshold value.That is as follows:  
\[\omega=\begin{cases}\left[sgn\left(\omega\right)\right]\left(\left|\omega\right| -\lambda\right)&\left|\omega\right|\geqslant\lambda\\ 0&\left|\omega\right|<\lambda\end{cases} \tag{13}\]  
Hard threshold is superior to soft threshold in mean square error, however, the signal will produce additional oscillation and jump point, which does not have the smoothness of the original signal. The wavelet coefficients obtained by soft threshold have good continuity and the tested signal will not produce additional oscillation. However, due to the compression of the signal, there will be a certain deviation, which directly affects the approximation level between the reconstructed signal and the real signal. We adopt a fixed threshold to signal de-noising, and the formula is as follows:  
\[\lambda=\sigma\sqrt{2 logN} \tag{14}\]  
where \(\lambda\) is the threshold, \(N\) is the number of samples, and \(\sigma\) is the noise standard deviation. The noise standard deviation is calculated separately according to each layer, and the values are different in the threshold processing of different layers.
## 4 Dynamic Analysis of the PIM System
### Sensitivity Analysis of the Boundary Conditions of System Parameters
#### 4.2.3 Total Length of the Cables  
The total length of the cables varies from 1500 to 1900 m. Figure 15 illustrate the variations in tension at the cable-structure connection point and the changes in the center of gravity coordinates of the structure over time under various cable lengths. From Figure 15a, it is observed that changes in cable length result in a delayed response of axial force at the connection point during the resonance and rising stages. Horizontal displacement (Figure 15b) and vertical displacement (Figure 15c), influenced by different cable lengths, exhibit linear changes and maintain consistency during the stable stage of the model. Figure 15d displays the change in horizontal velocity of the center of gravity over time, indicating that increasing cable length does not affect the magnitude and trend of velocity, but delays the response time. Similar phenomena are noted in the vertical velocity-time curve shown in Figure 15e, particularly during the rising stage. It is evident that changes in cable length affect the tension, displacement, and velocity of the structure, resulting in delayed responses.  
Figure 14: (**a**) Time history of axial force at the structural connection and horizontal displacement at the center of mass under different structural masses. (**b**) Vertical displacement at the center of mass. (**c**) Horizontal velocity at the center of mass. (**d**) Enlarged time history of horizontal velocity at the center of mass. (**e**) Vertical velocity at the center of mass.  
Adjusting the cable length significantly influences the dynamic characteristics of the cable system, particularly by enhancing the precision and stability during structure deployment. These findings offer vital insights for optimizing the design and operation of such systems.
## 3 Three-Port Electrical Converter Technologies  
### Traditional Configuration of BESS-Integrated PV Systems  
Conventionally, a BESS-integrated PV system consists of two individual two-port power electronic converters [58]. One of them is interposed between the PV module and the load, which is unidirectional, delivering the PV power to the load and/or battery with the implementation of MPPT. The other DC-DC converter, which is bidirectional, is connected to the BESS, and functions as a load power regulator.  
The configuration of a traditional PV system with a BESS is shown in Figure 5. With the employment of an additional two-port DC-DC converter connected to the BESS, more components and power conversion stages are used, leading to limited efficiency, low power density, and high costs.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 5 LoRaWAN Analysis
### Estimation of the Collision Rate
We also take these messages into account in the computation of the load, i.e., when the sum of the durations of all of the messages and their acknowledgments is equal to the duration of the simulation, the value of the load is one. The result is shown in Figure 11.  
Figure 11: Link capacity usage and packet collision rate for a LoRaWAN network when using confirmed messages. The load is as defined in Section 5.2. Figure 10: Link capacity usage and packet collision rate for a LoRaWAN network and compared to an ALOHA network. The load is as defined in Section 5.2. As expected, the success rate is significantly lower than without confirmed messages. However, this is a relatively efficient way of implementing this functionality, because two successful transmissions are necessary anyway. The results show that LoRaWAN is extremely sensitive to the channel load, similar to ALOHA. The solution implemented by usual network protocols, such as 802.11 or cellular networks, to help mitigate this problem is CSMA [25]. In order to ensure the scalability of LoRaWAN, it could be interesting to study the feasibility of the implementation of a CSMA mechanism into LoRaWAN. A possible issue is the duty cycle limitation that applies to the gateway, and that would prevent it from sending messages too often; another is the potential non-transitivity of the channel (i.e., an end-device may or may not be able to \"carrier sense\" if another end-device is transmitting to the same gateway). If the current architecture is kept, the CSMA mechanism would have to be controlled by the network server, which would put even more load on it. Alas, a CSMA mechanism could also remove the risk of collision of the acknowledgment for confirmed messages, by making it happen during a contention-free period. The current LoRaWAN specification does not have any means to enforce quality of service, and thus, it should not be used for critical applications or applications where the delay between the first time at which the device tries to send a message and the time at which it is received is important. Adjusting the number of times a device sends its packets may increase the chance of these packets going through, but it does so at the expense of more collisions with transmissions from other nodes and does not provide any hard guarantee. LoRaWAN currently uses ISM bands, which have the advantage of being free and not requiring a license. However, these bands are more and more used by LoRaWAN's competitors. Even if LoRa is very resistant to interferences, these bands have a finite capacity, and it is not guaranteed that the capacity of this band is sufficient. Moreover, it is perfectly legal for a malicious individual to emit random LoRa symbols, which will jam LoRa transmissions. Using a proprietary frequency band would have the advantage to remove most interferences, as well as remove the duty cycle cap, possibly making the implementation of a CSMA mechanism easier.