esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sink-oriented Dynamic Location Service
### Sink Mobility and Data Forwarding Maintenance
#### 3.5.2 Manual Operation  
The second rule is a manual operation. At that point, if the sink wants to move far-away, the sink informs its movement to the PA. Then, the PA notifies the source node to stop sending data. Nevertheless, the source node will continuously store the flowing data in its cache to prepare to resend the data. If the sink wants to renew the data flow, it sends the query through the new PA. The source node sends the data including the stored data in its cache. This approach is possible because the sink can quickly obtain the location of the source node whenever it wants. Let us consider an example in Figure 5. Assume that a mobile sink is located at points A, at time T1, and B, at time T2. The routing path for ALS and LSR at T1 is the same as P1. However, at T2, the routing path for ALS and LSR is P1 + P2 and P3, respectively. This is due to the LSR generating a new path based on the distance between the location of the source node with the current location of the sink and the last registered PA. Conversely, ALS simply forwards data without considering the distance between the location of the source node and the sink. The following algorithm 3-2 summaries the second rule.  
**Algorithm 3-2. Pseudo-code to manual operation.**  
**Input**: estimated distance between source node and sink  
**Output**: shortest path in manual operation  
**Parameter**:  
PA: Primary Agent, NPA: New Primary Agent  
**Procedure**:  
01: **if** sink wants to move far-away from PA /* close to source node */* 02: sink sends stop message to source node
* 03:if source node receives stop message
* 04:source node saves data in its cache
* 05:endif
* 06:endif
* 07:if sink wants to renew data
* 08:sink finds NPA and sends query message to source node
* 09:if source node receives query message
* 10:source node sends data including stored data
* 11:endif
* 12:endif
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 5 Conclusions  
This study proposes a robust image watermarking scheme that is based on a block-wise maximum singular value produced by the matrix 2-norm without an SVD transformation. These singular values are insensitive to ninety-degree rotations and row and column flips and are used to modify the pixel values in the spatial domain using a quantization parameter to incorporate the watermark's information. This study presents an alternate method for enhancing image watermarking schemes by using block-wise invariant singular values. The experimental results of the proposed watermarking system were analyzed using ten standard test images, four binary watermarks, and fifteen image distortion attacks. The examination of the numerical data and the quality of the recovered watermark images revealed that the proposed watermarking scheme worked extremely well against the majority of attacks considered in this study. The proposed scheme performed better on average than the other schemes tested in terms of imperceptibility and robustness. However, it did not operate as intended in some situations, indicating that additional research is needed in this domain. The complexity of the proposed scheme is slightly higher than that of the other schemes but comparable, so reducing this scheme's complexity may be the next step in this study. This approach may also be used for color images, medical images, and video and audio watermarking.  
This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. 3405].  
Not applicable.  
All the relevant data is contained within the article, further inquiries can be directed to the corresponding author.  
The author declares no conflict of interest.
# Person Re-Identification by Low-Dimensional Features and Metric Learning
## 4 Experiments and Discussion
### Comparisons with Traditional Methods  
Most traditional person re-identification studies try to learn the most distinctive and stable characteristics to describe the characteristics of each pedestrian. Usually, they use the color feature, texture feature, shape feature, and a fusion of the three features [21]. The comparison results with the traditional method are shown in Table 2.  
On the Market-1501 dataset, the experimental results show that the results of our model far exceed the traditional person re-identification methods in both evaluation indicators, where Rank-1 is 95.7% and mAP is 88.1%.
## 2 Materials and Methods
### Research Method and Process
#### 2.3.1 Normalized Difference Vegetation Index (NDVI)  
The normalized vegetation index (NDVI) was calculated using reflectance values in the near-infrared and red wavelength bands [42]. Additionally, the NDVI value, \(N\), was normalized using the following formula to facilitate subsequent data comparison and feature space construction.  
\[\mathrm{NDVI}=(\rho_{\mathrm{nir}}-\rho_{\mathrm{red}})/(\rho_{\mathrm{nir}}+ \rho_{\mathrm{red}}) \tag{1}\]  
\[N=(\mathrm{NDVI}-\mathrm{NDVI}_{\mathrm{min}})/(\mathrm{NDVI}_{\mathrm{max}}- \mathrm{NDVI}_{\mathrm{max}}) \tag{2}\]  
where \(\rho_{\mathrm{nir}}\) denotes the reflectance in the near-infrared band, \(\rho_{\mathrm{red}}\) indicates the reflectance in the red band, and \(\mathrm{NDVI}_{\mathrm{max}}\) and \(\mathrm{NDVI}_{\mathrm{min}}\) represent the maximum and minimum values of NDVI.
# Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
## 1 Introduction
[PERSON] [18] established an intelligent vehicle dynamics model considering the roadway inclination and constrained the safety of the vehicle's sideways inclination, which can effectively prevent the vehicle from rollover in mountainous environments, but the method did not consider the effect of the nonlinear side bias characteristics of tires on the control accuracy. [PERSON] [19] proposed a path-tracking controller with corner envelope constraints, which had better trajectory-tracking accuracy at different coefficients of road adhesion. However, it only took the lateral displacement as the evaluation index, which was prone to the risk of vehicle instability caused by the lateral stability of the vehicle exceeding the envelope under the premise of setting the relaxation factor to ensure a feasible solution. As the above methods all set the weight of each evaluation index as a fixed value, it became difficult to adaptively adjust the vehicle when the driving conditions changed, and thus it was difficult to ensure that the vehicle had good dynamic control performance [20]. When an unmanned vehicle is traveling in a complex mountain environment, the tire cornering stiffness is very easy to change in accordance with the driving status of the body and the road surface adhesion. [PERSON] [21] considered the effect of load transfer on tire cornering stiffness and obtained real-time tire cornering stiffness by fitting recursive least square to the current tire slip angle and load transfer, but the method failed to consider the effect of the coefficient of road adhesion on the tire sidewall bias characteristics. In view of this, in order to enhance the adaptive ability of unmanned vehicles in trajectory tracking under complex working conditions, such as the uncertainty of the road surface attachment coefficient, the large curvature of the reference path, and sideways road inclinations, it is possible to enhance the accuracy of trajectory tracking while ensuring the driving stability of the vehicle. We first establish a trajectory-tracking model of unmanned vehicles considering road lateral inclination angle in view of the road characteristics of complex working conditions. Secondly, the lateral forces of the front and rear tires are estimated based on the square root cubature Kalman filter (SRCKF), and the tire cornering stiffness is corrected in real time according to the lateral force of the tires so as to improve the adaptive ability of the model under complex working conditions. And an adaptive prediction time-domain method based on the Gaussian function is designed, which can adaptively optimize the size of the predicted time domain according to the road surface adhesion coefficient and path curvature. Finally, the proposed Adaptive Model PredictiveControl (AMPC) trajectory-tracking control method is validated by the joint simulation of Carsim and MATLAB/Simulink.
## 3 Results and Discussion  
### Morphology Analysis  
In order to verify that the fiber bleaching process was efficient to eliminate the lignin present in native nanofiber, the obtained samples were analyzed by SEM. Figure 1 shows SEM images of native (see PL-N in Figure 1a) and delignified-blanched nanofibers (see PL-DB in Figure 1b). As can be seen in Figure 1a, PL-N were non-homogeneous due to the presence of compounds (such as lignin, hemicellulose, and pectin, among others) which acted as binders, preventing the defibrillation of the cellulose chains [24]. In Figure 1b, on the other hand, a morphological change in fibrils with a defined and uniform diameter with a size of roughly 3.6 \(\upmu\)m was observed, indicating that the lignin and hemicellulose that enveloped the cellulose fibers had been eliminated by the effect of delignification and blanching [25].
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 2 Laser Crosslink Mission
### Orbit Scenarios
#### 2.2.4 Operation Modes  
Figure 4 shows that each phase consists of several modes, and it describes their flow. After the end of detumbling in the LEOP, both nanosatellites enter the standby mode by the ground telecommand. The standby, communication, and safe modes are autonomously exchanged and activated by monitoring the systems' statuses, such as battery capacity, temperature, telecommand schedule, etc. In standby mode, they are ready for receiving telecommands to change the mode and synchronize the time with ground station, periodically monitoring the status and schedule. The telemetry and mission data are downloaded during the communication mode. The safe mode, having the highest priority, handles the systems contingencies, such as solar panels sun-pointing to charge the batteries, detumbling to stabilize body spins, and autonomous execution of back-up operations to mitigate risks induced by a communication fail. The commissioning, maneuver, and mission modes can be entered by ground telecommands. In the NOP, the commissioning is relevant to formation flying for the PAT. Finally, the mission mode is defined as the entire sequence for the laser crosslink, and is described in the following subsection. When each mode operation is completed, they get back to the standby mode automatically.
## 5 Numerical Results and Discussion
### Variance Robustness  
Figures 10 and 11 show the performance of exact LLR given in (8) and (21) under different CSI estimation errors, where \(\Delta\sigma^{2}=\alpha\sigma^{2}\), and \(\rho=1\). It can be seen from Figures 10 and 11 that when the variance estimation error of the AWGN is large, the detection performance of the exact LLR scheme is seriously attenuated. It is caused by a large CSI estimation error. That is, the exact LLR scheme has poor robustness to CSI. However, our proposed the simplified LLR scheme using MS decoding does not need CSI, so it has better robustness.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 4 Experimental Results
### Dataset 2  
Dataset 2 is composed of three infrared image sequences and is briefly described in Table 4 (further details in [12]).  
The parameters we used for our experiments are the following:  
* _Wave Heuristic_ as Growing Heuristic;
* _OneRow+OneColumn_ as Dictionary Update Heuristic;
* _Freeze_ as Deletion Heuristic;
* _MSE-based_ as Match Heuristic (threshold value equal to 5.5).  
Finally, the dictionary is initialized with all pixels for continuous grayscale (IDH). Furthermore, we set the size of the dictionary (AVQ) as well as the shared dictionary (AVQ\({}_{\text{IS}}\)) as 4096 (\(2^{12}\)) and 8192 (\(2^{13}\)), respectively.  
In Figures 11 and 12, we graphically compare the experimental results, in terms of _Compression Ratio_ (C.R.), achieved by the AVQ algorithm (blue line) and the AVQ\({}_{\text{IS}}\) algorithm (red line), by considering the size of the dictionary/shared dictionary of 4096 (\(2^{12}\)) entries and of 8192 (\(2^{14}\)), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Resolution** & **\# of Frames** \\ \hline _Dune_ & \(360\times 270\) & 22 \\ _Trees_ & \(360\times 270\) & 18 \\ _UN camp_ & \(360\times 270\) & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset 2 description.  
Figure 11: Graphical comparison of the achieved C.R. between the AVQ\({}_{\text{IS}}\) algorithm (blue line) and the AVQ algorithm (red line), by using a dictionary of 4096 (\(2^{12}\)), for (**a**) the _Dune_ sequence; (**b**) the _Trees_ sequence; and (**c**) the _UN camp_ sequence.
## 3 Simulation Results
### Case II  
In this experiment, the stability of the system was studied using an auxiliary noise power scheduling strategy. Except for the variable power of the auxiliary noise, the other conditions were the same as those in Case I. The auxiliary noise is \(v^{\prime}(n)=G(n)v(n)\), where:  
\[G(n)=2\sqrt{\frac{P_{f}(n)}{P_{e}(n)}}, \tag{71}\]  
\(P_{e}(n)\), \(P_{f}(n)\) is the power of \(e(n)\), \(f(n)\), respectively. The calculation of \(P_{e}(n)\) can use the type of expression (41) and \(P_{e}(0)\) is initialized to 1. The simulation results are presented in Figure 4.  
For variable power auxiliary noise, the system remains stable. Figure 4a,b shows that the convergence speeds of the ANC and SPM filters were faster than those of Case I in stages I and II. It can be seen from Figure 4b,e that the time required for the SPM filter to reach a steady state is the same as in Case I, whereas the relative error of the SPM filter decreases to \(-10\)dB faster. Figure 4c,f suggests that the upper bound of the step size is larger than the upper bound in Case I at the beginning of stages I and II.  
Figure 4: _Cont._
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 2 Methodology
### Lstm  
In this study, after dividing driving behaviour semantics and similarity evaluation, acceleration is predicted in order to evaluate the effectiveness of the proposed personalized driving behaviour analysis method.  
Because the driving state is a continuous time-lapse dependent process, the driver's acceleration can be predicted using the complete time-series information. LSTM is a special type of Recurrent Neural Network (RNN) that can capture long-term temporal dependency. LSTM has a long-term memory function, which can deeply explore the long-term dependency and trend of limited data samples. Moreover, it can overcome the disadvantage of RNNs that they cannot perceive distance due to gradient disappearance during the training process [18].  
LSTM has two transmission states between nodes, i.e., \(\mathbf{c}_{t}\) and \(\mathbf{h}_{t}\). In the LSTM layer, the range of input information is controlled by input gates. The input from the previous node is selectively forgotten by forget gates. Finally, the recursion between the nodes is  
Figure 3: A graphical model of the MHMM.  
reached by outputting \(\mathbf{c}_{t}\) and \(\mathbf{h}_{t}\) through output gates. The mechanism of each stage is described as follows [19]:  
Input gate:  
\[i_{t}=\sigma_{g}(W_{t}\mathbf{x}_{t}+R_{t}\mathbf{h}_{t-1}+b_{t}) \tag{7}\]  
Forget gate:  
\[f_{t}=\sigma_{g}\Big{(}W_{f}\mathbf{x}_{t}+R_{f}\mathbf{h}_{t-1}+b_{f}\Big{)} \tag{8}\]  
Status update:  
\[g_{t}=\sigma_{c}\big{(}W_{g}\mathbf{x}_{t}+R_{g}\mathbf{h}_{t-1}+b_{g}\big{)} \tag{9}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot g_{t} \tag{10}\]  
Output gate:  
\[o_{t}=\sigma_{g}(W_{o}\mathbf{x}_{t}+R_{o}\mathbf{h}_{t-1}+b_{o}) \tag{11}\]  
\[\mathbf{h}_{t}=o_{t}\odot\sigma_{c}(c_{t}) \tag{12}\]  
where \(\mathbf{c}_{t}\) denotes the alternative values of the cell state at time \(t\); \(\mathbf{h}_{t}\) denotes the output value of the hidden state at time \(t\); \(\mathbf{x}_{t}\) denotes the input value of the memory cell at time \(t\); \(\sigma_{c}\) and \(\sigma_{g}\) represent the cell state activation function and the gating activation function, respectively; and \(\odot\) denotes the Hadamard product between the matrices.
## 3 Results and Discussion
### Selectivity, Repeatability, and Reproducibility Assay  
Before real sample detection, the selectivity and repeatability, as well as reproducibility were also evaluated. To assess the anti-interfering ability of the proposed MnO2 NFs/NrGO/GCE, the LSV responses of the DA and UA in the coexistence of common interfering substances (i.e., ascorbic acid, alanine, citric acid, glutamic acid, cysteine, and lysine) were compared. The relative errors (less than 5%) were accepted even in the presence of 100-fold the above interfering species, demonstrating good selectivity (Figure S1). It is noted that the 100-fold AA have no obvious interfering because of the well-separated peak potential between AA and DA (\(\Delta E_{\text{p}}=0.260\) V). To check the repeatability, eight successive determinations of 1 uM DA and UA (1:1) were also performed. The relative standard deviation (RSD) for DA and UA were 6.35% and 5.32%, respectively, indicating good repeatability. To examine electrode reproducibility, the anodic peak currents of 1 uM DA and UA were recorded at five MnO2 NFs/NrGO/GCEs, which were prepared by similar procedure. The RSD of the anodic peaks were 6.02% and 4.70% for DA and UA respectively, showing that the electrode preparation have excellent reproducibility.
# Numerical Study of an Ultra-Broadband All-Silicon Terahertz Absorber
## 3 Simulated Results and Discussion
To obtain the absorber with high-absorption performance, it is necessary to discuss the influences of the incident angles for both the TE and TM polarizations. At non-zero angles of incidences, we chose the plane wave related with the broadband fixed angle source technique (BFAST). Figure 6a,b show the contrast of light absorption at different incident angles. In the case of TE polarization, the first resonant peak maintains an almost perfect absorption response at 45\({}^{\circ}\), while the second resonant peak has a redshift as the incident angle increases. It is worth noting that the absorption bandwidth above 90% is maintained up to 70\({}^{\circ}\). This is owing to the fact that the magnetic field is maintained when the incident angle is varied. Accordingly, the magnetic resonant strength can be enhanced. In the case of TM polarization, the absorption efficiency is weakened when the incident angles are greater than 40\({}^{\circ}\). The second absorption peak is less than 90% when the incident angle is greater than 10\({}^{\circ}\). This is because that the direction of the magnetic field varies with the angle of incidence, which results in the decrease of magnetic resonant strength. This all-silicon terahertz absorber can maintain wide band absorption under large angle incidence, so it can have great application value. Figure 5: Absorption spectra of the diamond absorber in the case of TE polarization for five different thickness values \(t\) of the diamond metamaterial layer (**a**), for four different lengths values \(a\) of the diamond layer (**b**), for four different lengths values \(p\) of periodic (**c**), and for four different thickness values \(h\) of the substrate (**d**).
# High-Frequency and Spectrum-Clean Shear-Horizontal Acoustic Wave Resonators with AlN Overlay
## 4 Propagation Characteristics of SH Wave in AlN/LiNbO\({}_{3}\)/Si
### Frequency Response  
Combining the previous analysis toward a high-\(f\), large- \(k_{\text{eff}}^{2}\), and spurious-free response utilizing the nonleaky SH wave, a stack with optimized substrate values is achieved: \(5^{\circ}\)\(Y\)X-LiNbO\({}_{3}\), \(h_{\text{LiNbO}}\)/\(\lambda\) = 0.25, and \(h_{\text{AlN}}\)/\(\lambda\) = 0.2. Figure 12a,b plot the FEM-simulated narrow-band responses of the SH wave in the AlN/LiNbO\({}_{3}\)/Si layered stack compared to LiNbO\({}_{3}\)/Si and LiNbO\({}_{3}\) substrate, as well as wide-band response with periodic structure (1.5D model). Intriguingly, the AlN/LiNbO\({}_{3}\)/Si layered structure enables frequency as high as 3 GHz while the IDT pitch is as large as 1 \(\upmu\)m, ensuing good power handling. The \(f\) can further scale up if smaller \(\lambda\) is employed. The frequency or \(v_{\text{p}}\) has been increased by 18%, breaking the frequency limits for SAW resonators and filters and applicable to high-frequency bands in LTE-Advancement Pro and 5G NR.  
Comparing admittance curves of the SH modes in AlN/ LiNbO\({}_{3}\)/Si, LiNbO\({}_{3}\)/Si, and LiNbO\({}_{3}\) substrate in Figure 12a, it can be noted that the antiresonances for both AlN/LiNbO\({}_{3}\)/Si and LiNbO\({}_{3}\)/Si are very sharp, indicating a very high-quality factor at parallel-resonance (\(Q_{\text{p}}\)) due to the elimination of bulk leakage (quality factor at series resonance (\(Q_{\text{s}}\)) is usually dominated by the transducer resistance \(R_{\text{s}}\) and \(Q_{\text{p}}\) dominated by the acoustic propagation loss).  
In addition, the AlN/ LiNbO\({}_{3}\)/Si layered substrate with the optimized parameters shows an extremely clean response in both narrow and wide spectrums. The Rayleigh mode and Lamb modes are suppressed with \({k_{\text{eff}}}^{2}\) of near-zero. By the analysis and careful design, the novel stack provides high performance, the ability of high frequency, and an extremely clean spectrum from 0 to 6 GHz simultaneously.
## 4 Results and Discussion  
### Geographical Patterns of the Growth  
Total shell length (L), width (l), height (h), and tissue mass (M) measurements on mussel allow us to calculate allometric growth indices, that may vary depending on the _Mytilus_ species and growth conditions [29; 30]. In the St. Lawrence Estuary and Gulf, L, l, and h are linearly correlated, whereas L and M are better related by power functions [17]. These findings are confirmed by the present data (Table 1, Figure 2). In this study (2016-2019), allometric growth indices (h/L, l/L ratios, and the parameters of the L vs M power functions) were similar to the earlier sampling (1977-1979) (Table 1). This similarity in the mussel morphometry suggests that the possible environmental changes in the St Lawrence Estuary and Gulf [31] during the last decades, such as temperature, did not generate shell shape differences despite the high plasticity of the mussel shell [32]. Nevertheless, the parameters of the L vs M equations, derived for each of the six regions, display some variations (Figure 2, Table 1). Such slight regional morphological shell differences are consistent with the small regional differences in the absolute growth rate estimated in 1977-1979 [17]. We can infer from these allometric index comparisons that mussel growth conditions have not differed significantly for the last forty years, with slight regional differences in the L vs M index persisting (Table 1). This finding allows us to compare the Hg load of the mussel soft tissues between the two time periods without any significant bias affecting Hg bioaccumulation due to the change of mussel growth rates.  
Figure 1: Location of the sampling sites and hydro-ecological regions. LSLE: Lower St. Lawrence Estuary. P.-E.I. Prince Edward Island. Brown arrows indicate main surface water circulation.