esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Toxicity Evaluation of Cr(VI) Reduction Products in Batch Treatment
Although the removal efficiency of the biosystem achieved 99.6% \(\pm\) 0.08% at 40 \({}^{\circ}\)C, the outlet Cr(VI) concentration in the effluent was 0.8 \(\pm\) 0.16 mg L\({}^{-1}\)--meeting the current industrial discharge standards for Cr(VI) was difficult. With a further increase of the operating temperature, Cr(VI) removal was decreased to 96.2% \(\pm\) 0.15%, presumably due to the loss of viability or metabolic activity of the CUST210-1 strain. Thus, the operating temperature of the following experiment was controlled at 35 \({}^{\circ}\)C. Figure 3D represents the effect of the inlet Cr(VI) concentration on the continuous treatment of synthetic Cr(VI)-containing wastewater. The removal capacity increased linearly with the inlet Cr(VI) concentration, indicating that the system processed a higher removal capacity, with no chromium toxicity to the CUST210-1 biosystem [36]. Moreover, the removal efficiency maintained a high efficiency until 350 mg L\({}^{-1}\). _Vigribacillus_ sp. was reported to completely reduce Cr(VI) at 150 mg L\({}^{-1}\) after 100 h in a batch operation [38]. Acinetobacter junii reduced 54-mg L\({}^{-1}\) Cr(VI) with 99.95% removal efficiency after 2 h in a batch operation [43]. C. funkei removed 250-mg L\({}^{-1}\) Cr(VI) with 80.43% removal efficiency after 120 h of exposure in a batch operation [35], whereas S. maltophilia could treat 500-mg L\({}^{-1}\) Cr(VI) with 92% removal efficiency after 120 h of exposure in a batch operation [14]. By comparison, _Ochroductrum_ sp. CUST210-1 achieved a competitive efficiency in removing Cr(VI) in a continuous operation. Although the efficiency was high at 99.5% \(\pm\) 0.13% when feeding with 350-mg Cr(VI) L\({}^{-1}\), the outlet Cr(VI) concentration was 1.75 \(\pm\) 0.46 mg L\({}^{-1}\), exceeding the current discharge limit of 0.5 mg L\({}^{-1}\). To extend the treatment range of the Cr(VI) concentration, more details (e.g., DO control and turbidity pretreatment) for better performance were investigated by feeding with actual wastewater.
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 4 Discussion
### Experimental Results  
The prediction performances of LSTM and GRU for three different driver subgroups are shown in Table 5. As shown in Table 5, the prediction accuracy of GRU is slightly better than LSTM in all three driver subgroups. This is because GRU can capture the nonlinear relationship of driver's acceleration data and fits better than LSTM. The prediction of the subgroup of 10 drivers who have high similarity in driving behavior performs best. This is because the driving behaviors of these 10 drivers are semantically similar, and the computational difficulty and error of the model are greatly reduced compared to 30 drivers with different driving behaviors. In particular, for the comparison results of GRU, the _MAE_ is reduced by 38.8%. As shown in Figure 7, the peak of the _MSE_ and _MAE_ of GRU appears earlier than that of LSTM, which also means that the GRU outperforms the LSTM [12]. Because the internal storage unit structure of GRU is simpler than LSTM, GRU is much faster than LSTM when training the model, and the prediction effect is slightly better than that of LSTM, and it is better than LSTM in processing car-following data [32]. In addition,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Activation** & **Optimizer** &
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **Batch Size** & **Epoch** \\ \hline
180 & Sigmoid & Adam & 0.001 & 1 & 331 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Super parameter values of the GRU model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Activation** & **Optimizer** &
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **Batch Size** & **Epoch** \\ \hline
128 & tanh & Adam & 0.001 & 16 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Super parameter values of the LSTM model.  
the effectiveness of MHMM in the division of behavior semantics and the performance of similarity evaluation for building a better car-following prediction model was also verified.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 6 Models of Communication in the Internet of Vehicles
### Vehicle-to-Everything (V2X)  
Vehicle-to-everything communication forms a key part of the IoV, which covers the V2V and V2I interactions. ITS is a step forward in technology development to support road safety, optimally manage traffic, and provide a better driving experience. V2V communication in V2X allows direct interaction between vehicles on the road and facilitates making collaborative decisions as well as several safety applications. This information is critical to avoiding accidents, improving traffic flow, and detailing the speed, position, and planned schedule of the moving vehicles, unlike V2I communication, which allows vehicles to intercommunicate with roadside infrastructure like traffic lights, road signs, smart intersections, etc. In LTE-based V2X services, vehicles involve cellular networks to transmit data to other V2I [86]. This integration expands the reach and reliability of V2X communication, allowing for seamless information sharing even when traditional V2V or V2I communication may not be available.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 2 Method of Gaze Guided System
### Interval Type2 Fuzzy Control System
#### 2.3.1 Fuzzifier  
In this case, the inputs of the fuzzy sets are described. There are two inputs parameter used in the proposed system. These are named robot direction and robot speed respectively. The width (X) and height (Y) of the eye was taken into account for the input membership functions' values range (see Figure 9). As mentioned before, X-axis is representing the robot direction and Y-axis is representing the robot speed which is used to determine the two crisp inputs variables. Table 1 shows the width and height information of the eye. These data are taken into account for the input function. In this table, it is illustrated that the eyes' start and end coordinate space in 2D is used for arranging the membership function. The membership functions consist of one or several type-2 fuzzy sets. A numerical vector x of fuzzifier maps into a type-2 set \(\widetilde{A}\). Type-2 fuzzy singleton is considered. In a singleton fuzzification, the inputs are crisp values on nonzero membership. We contemplate three fuzzy membership functions for the robot direction with labels \(\widetilde{\text{L}}\), \(\widetilde{\text{S}}\), \(\widetilde{\text{R}}\). These are indicating left, straight, and right respectively as illustrated in Figure 10. We worked using Gaussian and sigmoidal membership functions. A Gaussian type-2 fuzzy set is one in which the membership grade of every domain point is a Gaussian type-1 set contained in [0,1]. These functions are unable to specify asymmetric and archive smoothness membership function which are important in certain applications. The sigmoidal membership function, which is either open left, right asymmetric closed, it is appropriate for representing concepts such as very large\" or very negative\". In the same method, we contemplate three membership functions for robot speed with labels \(\widetilde{\text{N}}\), \(\widetilde{\text{M}}\), \(\widetilde{\text{F}}\), these are indicating near, medium, far respectively as illustrated in Figure 10. The variable range of functions is not infinite (see Figure 10).  
Figure 8: Structure of a type-2 fuzzy logic system.  
Figure 9: Eye tracking region variable.  
The output fuzzy controllers are the left and right velocities of the wheel speed. The linguistic variables are implemented with tree membership function. For both right and left wheel speed, these are labeled as \(\overline{\text{S}}\), \(\widetilde{\text{M}}\), \(\overline{\text{F}}\)--slow, medium and fast respectively. It is illustrated in Figure 11.
## 4 Results and Observations
### RUL Prediction of Test Bearings under Condition C1
(**d**) State prediction at the 55 th minute, with \(\overset{\sim}{\mathit{rul}}_{55}=56\) minutes. Figure 11: Bearing 2 degradation state prediction based on the EKF: (**a**) State prediction at the 66 th minute. The corresponding estimated RUL is \(\overset{\sim}{\mathit{rul}}_{66}=\overset{\sim}{\mathit{t}}_{EOL}-t_{PT}=80\) minutes. (**b**) State prediction at the 69 th minute, with \(\overset{\sim}{\mathit{rul}}_{69}=82\) minutes. (**c**) State prediction at the 71 st minute, with \(\overset{\sim}{\mathit{rul}}_{71}=78\) minutes. (**d**) State prediction at the 74 th minute, with \(\overset{\sim}{\mathit{rul}}_{74}=76\) minutes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bearing** & **Baseline** & **Baseline** & **RUL PT * & **Estimated** & **Baseline RUL** & **Estimated RUL** & **RUL 95\%** \\
**ID** & **FDT *** & **EOLT *** & **RUL PT/minute** & **End-of-Life Time** & **/minute** & **(Residual)** & **CI** \\ \cline{3-8}  & & & & \(\overset{\sim}{\mathit{t}}_{EOL}\)/minute** & **/minute** & **/minute** & **/minute** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{35} & \multirow{4}{*}{126} & 40 & 127 & 86 & 87 (+1) & [84,89] \\ \cline{3-8}  & & & & 45 & 126 & 81 & **81 (0)** & [79,83] \\ \cline{3-8}  & & & 50 & 134 & 76 & 84 (+8) & [83,87] \\ \cline{3-8}  & & & 55 & 111 & 71 & 56 (\(-15\)) & [56,63] \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{59} & \multirow{4}{*}{151} & 66 & 146 & 85 & 80 (\(-5\)) & [70,88] \\ \cline{3-8}  & & & 69 & 151 & 82 & **82 (0)** & [74,91] \\ \cline{3-8}  & & & 71 & 149 & 80 & 78 (\(-2\)) & [70,85] \\ \cline{3-8}  & & & 74 & 150 & 77 & 76 (\(-1\)) & [69,8
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 2 Problem Formulation  
### Problem Statement  
In this paper, the main problem we address is the effect of the Doppler on the carrier signal. In a communication system, the carrier frequency of the signal may change due to the relative motion between the transmitter and the receiver. The carrier frequency with Doppler effect is  
\[f^{\prime}=(\frac{v\pm v_{0}}{v\mp v_{s}})f_{c} \tag{1}\]where \(f^{\prime}\) denotes the observed signal frequency, \(v\) denotes the wave propagation speed, \(v_{0}\) is the movement speed of the receiver, and \(v_{s}\) denotes the movement speed of the transmitter source, \(f_{c}\) is the original frequency of the signal. The ratio of \(f^{\prime}\) and \(f_{c}\) is defined as  
\[\lambda=f^{\prime}/f_{c}=(f_{c}+f_{\triangle})/f_{c}=1+\delta \tag{2}\]  
where \(f_{\triangle}\) denotes the absolute Doppler shift, and the Doppler factor \(\delta=f_{\triangle}/f_{c}\) denotes the magnitude of the Doppler shift with respect to the carrier frequency.  
In the time domain, the Doppler effect causes the length of the received signal to be compressed or broadened, resulting a length ratio of \(1/\lambda\) between the received signal and the original signal. This length change corresponds to a change in the sampling rate at the receiver. If the original sampling rate is \(F_{s}\), then the actual sampling rate is  
\[F_{s}^{\prime}=F_{s}/\lambda \tag{3}\]  
Thus, in the time domain, the effect of Doppler is equivalent to signal resampling. The frequency shift caused by Doppler prevents the receiver from performing accurate carrier synchronization. The change in sampling rate also affects bit synchronization and thus degrades the decoding performance. Considering a single-carrier communication system with BPSK modulation, if the transmitted symbol is \(x_{n}\), the transmitted signal can be expressed as  
\[s_{\text{BPSK}}(t)=\left\{\begin{array}{c}A\cos(w_{c}t)\,x_{n}=0\\ -A\cos(w_{c}t)\,x_{n}=1\end{array}\right. \tag{4}\]  
The CB-Gardner algorithm proposed in this paper processes the passband signal in (4). This method can also be extended to higher-order modulations.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 5 Experimental Evaluation
### Test Area  
The NVIS transmitter was located in La Salle Campus Barcelona (Spain). It has a horizontal dipole set at 5.4 MHz. The receiver, which consists of an inverted Vee, was located approximately 100 km away (Cambrils, Tarragona). Between these locations, there was no Line Of Sight (LOS) considering the elevation of the profile, which is more than 500 m high. The NVIS link is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **SC-FDE Values** & **OFDM Values** \\ \hline Bandwidth & 3 kHz & 3 kHz \\ Useful symbol length (TS) & 0.33 ms & 9.33 ms \\ Prefix cyclic length (TCP) & 3 ms & 3 ms \\ Number of subcarriers (NSC) & 28 & 28 \\ Number of symbols & 7 & 7 \\ (Nsymbol) & 1 & 1 \\ Number of pilot/symbol & 6 & 6 \\ Packet duration & 87.64 ms & 86.31 ms \\ Bits in packet & 336 bits & 324 bits \\ Modulation & QPSK & QPSK \\ Equalization & MMSE & ZF \\ Bitrate of signal frame & 3.833 kbps & 3.753 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: SC-FDE parameters.
## 3 Discussion
Moreover, utilising both real and synthetic data addresses the common challenge of unbalanced datasets, improving the accuracy and robustness of defect detection algorithms. The GUI and all the developed libraries will also be open-source and available through GitHub. Finally, the sophisticated machine learning techniques will be more accessible to the research and aviation community, encouraging further innovation. Automated Non-Destructive Testing (NDT) methods, such as Infrared Thermography (IRT) and Photogrammetry, offer advantages. However, implementing these technologies in real-world applications comes with challenges. One major obstacle is cost, as high-resolution IRT cameras, photogrammetry tools, and advanced processing software require a significant initial investment. Integrating automated NDT systems into existing maintenance workflows can also be complex due to compatibility issues with industry standards and regulatory requirements. Another challenge is training personnel since shifting from traditional NDT methods to automated systems requires expertise in image analysis, defect identification, and software operation. Without proper training, inspection accuracy could suffer. Regular maintenance, including sensor calibration, software updates, and system adjustments, is also necessary to keep the equipment running reliably. Several strategies can help address these challenges. A cost-benefit analysis can demonstrate how automated NDT systems save money over time by speeding up inspections and minimising aircraft downtime. Modular system designs could make implementation easier by allowing gradual upgrades instead of requiring a significant upfrontinvestment. To bridge the skills gap, collaboration between industry and academia can provide training programmes and certification courses for NDT professionals. Online learning platforms and augmented reality (AR) tools can further support remote learning. Lastly, predictive maintenance strategies, such as AI-based condition monitoring, can enhance system reliability and reduce unexpected failures. These solutions can make automated NDT technologies more practical, improving efficiency and safety in aviation. Overall, this work advances current NDT practices and sets new standards that can be applied to improve industrial inspection processes.
## 3 Results and Discussion
### Unconfined Compressive Strength
#### 3.3.2 Effect of Variation in Fly Ash-Stabilized Loess on Strain  
Figure 2 shows the influence of the variation in FASL specimen on strain at different curing times under non-submergence and submergence conditions. The addition of FA to loess influenced the UCS strain. For non-submergence samples, the UCS strain decreased by 30.27%-54.48%, 13.33%-33.33%, and 11.69%-30.77% at 0, 14, and 28 days of curing time (Figure 2), respectively, compared with that of untreated loess especially for the high FA ratio (30%). A similar trend was also observed for submergence samples; in particular, the UCS strains of specimens at 0, 14, and 28 days of curing time  
decreased by 6.86%-42.86%, 41.31%-46.95%, and 6.01%-43.51%, respectively, compared with that of untreated loess (Figure 2). These results suggested that the addition of FA significantly reduced the UCS strain of loess; therefore, FA-treated soils exhibited a more brittle behavior than untreated soils. This finding agreed with the previous conclusion [66; 83; 89]. However, this result was in contrast to that of [PERSON] (2009), who concluded that failure strains increased by 50% when the FA ratio was increased from 0% to 25% [51]. This difference might be explained by the difference in properties of clay used in the investigation [51]. However, the UCS strain under submergence condition was more decreased than that under non-submergence condition. Thus, the submergence condition played an important role in decreasing the UCS strain on FASL.  
The decreasing trend of UCS strain differed with the change in curing time under dissimilar FA ratios. The UCS strain gradually increased with the increase in FA ratio under non-submergence and submergence conditions at zero and 28 days of curing time. The highest UCS strain was obtained with 0% FA, and the lowest UCS strain was observed with 30% FA. Surprisingly, a completely opposite finding was obtained at 14 days of curing time. Under non-submergence condition, the highest UCS strain was observed with 0% FA, whereas the lowest UCS strain was obtained with 10% FA. Under submergence condition, the highest UCS stress was found with 0% FA, whereas the lowest UCS strain was derived with 10% and 30% FA.
## 3 Methodology  
### Grid-Based Method  
The grid cell-based method divides a geographical area into a grid of uniformly sized and shaped cells. Each cell represents an independent spatial unit used to collect, analyze, and display data. It has been used in various past studies [34; 35; 36]. This cell size was chosen because it closely aligns with the standard block width in Manhattan (264 ft) and the block length (900 ft) is divisible by 300 ft. By using cells of 300 ft in length, location-specific features can be captured more accurately, allowing for a detailed street-by-street resolution that enhances risk analysis. After cleaning the cells that do not contain any data points (e.g., around lakes), the grid map with complete data is shown in Figure 1.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 4 PEID: LOS/NLOS Identification Based on Peak Energy Index Distribution
### Handling the Mobility  
PEiD adopts a sliding window to handle the LOS/NLOS identification in mobile scenarios. Its main idea is to gradually replace the existing packets with newly arrived ones and perform the identification using the method discussed above. Such a process can be realized by window sliding. Figure 6 illustrates the operation of our proposed approach.  
The detailed process is listed in Algorithm 1, which is composed of four steps. The first step is to build a window that contains \(\omega\) packets received continuously. Since there is no packet in the window when the algorithm begins to work, the user has to wait until \(\omega\) packets are collected. Once the window is loaded, the second step is to extract ten features by observing the distribution of packets within the window. The third step is to call the trained model to complete the binary classification task of LOS/NLOS identification. In  
Figure 5: The feature importance estimated by Random Forest.  
the fourth step, the algorithm will slide the window by \(\delta\) packets to update observation, taking newly received packets into consideration. Then, the algorithm repeats steps two and three to identify the channel state at the a new time point. It continues this process iteratively until it has processed all the packets up to time \(T\). Each window corresponds to an identification result.  
It is worth noting that \(\omega\) is a critical parameter that could severely impact the performance. On the one hand, increasing the window length could improve the identification accuracy. It works because a longer window length means a larger sample size, which can reduce the random errors of peak energy index distribution. However, a larger sample size also means a longer time of packet collection and data processing, which may cause the identification delay to grow. It affects not only the waiting time in the initial step but also the transition time caused by the channel state change. Since real-time identification is also a necessary criterion, we will further discuss it in Section 5.  
```
0: The received packets up to time \(T\), \(\{pkt_{1},pkt_{2},\ldots,pkt_{T}\}\);
0: The channel state (LOS or NLOS) at time \(t\leq T\);
1: Build a window containing \(\omega\) packets, \(\{pkt_{1},pkt_{2},\ldots,pkt_{T}\}\);
2: Calculate the 10 features listed in Section 4;
3: Use trained classifiers to identify the channel state the current path;
4: Slide the window by \(\delta\) packets and build a new window containing \(\omega\) packets. Then, repeat Step 2 to Step 4.
```  
**Algorithm 1** LOS/NLOS identification with a sliding window
## 3 Results and Discussions
### Microbiological Results  
Figure 1 shows the data corresponding to the growth of TVC, _Pseudomonas_ spp., and H\({}_{2}\)S-producing bacteria of HTS fillets during refrigerated storage. The initial TVC was 2.34 log10 CFU/g, indicating quite a good quality of the fresh HTS fillets considering the proposed upper limit for aerobic plate count of \(5\times 10^{5}\) CFU/g for fresh fish [20]. The microbial count increased during storage and the mesophile number of the C2 sample was significantly lower than others at any of the storage time points (Figure 1a). At day 18, CK1 and CK2 had to be removed due to reaching the maximum limit allowed of 7.0 log CFU/g. [PERSON] et al. [21] demonstrated that RA had a poor antibacterial activity on retarding growth of gram-negative bacteria, and similar results are also shown in the current experiment regarding the increase of _Pseudomonas_ spp. and H\({}_{2}\)S-producing bacteria. The growth pattern for _Pseudomonas_ spp. (Figure 1b) was similar to that of TVC, suggesting that aerobic spoilage bacteria dominated the HTS fillets storage. An initial count of H\({}_{2}\)S-producing bacteria in HTS fillets was approximately 1.6 log CFU/g and no significant differences were observed during the first six days (\(p>0.05\)) in all samples (Figure 1c). At the end of storage, samples treated with \(\varepsilon\)-PL and RA presented lower counts between 4.81-5.73 log CFU/g. \(\varepsilon\)-PL could suppress the growth of gram-negative bacteria in HTS fillets ascribed to its ability to interact with the microbial cell surface, strip the outer membrane, and cause abnormal distribution of the cytoplasm [7].