esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Lizard-Inspired Robot
## 2 Kinematic Analysis
### Stride-Length Analysis  
The second analysis was with respect to the stride length. The stride length in this paper was defined as the traveling distance of the robot coordinates with respect to each half period. The robot walks with different stride lengths between the first and second half  
Figure 4: Variations of the robot’s posture. (**a**) First half period. (**b**) Second half period. The black line with a gray axis shows both the initial posture and the posture after one period of the robot, and the purple line with a pale purple shows the posture after half of a period of the robot. Meanwhile, the green arrows define the angles of each state, and the red lines show the stride length of the robot and an angle for realizing the stride. It is shown that \(\psi(i)\) equals the difference between \(\theta_{b}(i)\) and \(\theta_{b}(i-1)\). In addition, it is shown that the relationships between \(\textbf{P}_{1}\), \(O_{r}(0)\), and \(O_{r}(1)\) and \(\textbf{P}_{2}\), \(O_{r}(1)\), and \(O_{r}(2)\) configure isosceles triangles.  
periods as long as \(\beta\
eq 0\) (see Figure 4). Hence, the stride lengths with respect to each half period were formulated.  
First, the stride length in the first half period was formulated. From Figure 4, when the robot walks, the relation of \(\mathbf{P_{1}}\) and the robot coordinates \(O_{r}(0)\), \(O_{r}(1)\) configure an isosceles triangle because lengths from \(\mathbf{P_{1}}\) or \(\mathbf{P_{2}}\) to \(O_{r}\) equal \(l/2\). By utilizing the relationship of the isosceles triangle, the stride length in the first half period, say \(lw(1)\), can be formulated as follows:  
\[l_{w}(1) =2\frac{l}{2}\sin\biggl{(}\frac{2\alpha-\psi(1)}{2}\biggr{)},\] \[=l\sin\biggl{(}\alpha-\frac{\psi(1)}{2}\biggr{)}. \tag{8}\]  
Next, the stride length in the second half period was formulated. From Figure 4, the stride length in the second half period, say \(lw(2)\), can be formulated as follows:  
\[l_{w}(2)=l\sin\biggl{(}\alpha+\frac{\psi(2)}{2}\biggr{)}. \tag{9}\]  
From (8) and (9), the stride length depends on the parameters of the input angle function, as well as the link length, which is a physical parameter of the robot. This means that the stride length can be tuned by the input angle function's parameters so that the actuator's specification satisfies a required specification. This also means that the robot's design influences the maximum limit of the feasible stride length; that is, (8) and (9) become an important index for locomotion control, as well as for the mechanical design.
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 4 Discussion  
To assess the accuracy of the tropospheric delay from the NWP and define the prior variance of the NWP ZTD, we compute the RMS of NWP ZTD residuals for eight stations. Experiments show that RMS of the tropospheric delay residuals derived from the medium-range NWP model from the SMB are between 24.02 and 32.62 mm, which is much better than that of the ECMWF and NCEP shown in the research of [PERSON] et al. (2018). However, it should be mentioned that it is not completely fair to compare the ZTD residual of them as the spatial resolutions of them are different. Research has shown that the RMS values of the tropospheric delay residual from the NWP revealed strong dependence on geographical latitudes: smaller in high-latitude and larger in low latitude areas ([PERSON] et al., 2018). In this condition, the prior variance of the NWP ZTD error is set considering the latitude of the stations. However, the ZTD from the medium-range NWP model in this study doesn't show this characteristic due to the limited area size. As a result, we set the mean value of the RMS of these eight stations as the prior variance of tropospheric delay error for the stations in this area.  
Experimental results (Figures 7 and 8 and Table 3) in our study show that the initialization speed improvement of NWP-constrained RTK for the long-range baseline is larger than that of the medium-range baseline. The possible reason would be that the tropospheric delay is one kind of distanced based error, which would be canceled through double difference over the medium-range baseline. However, the effectiveness of double difference will be weakened substantially for the long-range baseline due to the weak spatial correlation, and the residuals of the tropospheric delay after double difference over long-range baseline are declined by using the NWP model significantly.  
In our study, although the positioning precise of the GPS/BDS RTK constrained with NWP is satisfactory, the positioning precise of the long-range baseline is worse than that of the medium-range baseline. Larger error also results in a longer time needed for the AR over the long-range baseline shown in Table 3. The possible reasons would be: (1) most of the errors (solid earth tides, polar tides, ocean loading tides, etc.), which can be ignored in the medium-range baseline, should be estimated in the long-range baselines. However, the residuals of these errors after modeling or estimation will definitely affect the positioning precision; (2) the accuracy of the ZTD from the NWP model and the prior variance information for the ZTD residuals is not high enough. Both of the possible reasons remain to be further studied.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 3 Hardware Structures for Nonlinear Operation
### Nonlinear Operation Based on Special Material  
The main special materials used to realize nonlinear operation are a saturated absorber and two-dimensional graphene materials. Nonlinearity can be realized by placing the special material in the dotted line of Figure 7.  
The saturated absorber mainly uses its transmission characteristics. The absorption coefficient has a reverse relationship with the intensity of the incident light. The transmission coefficient increases with the growth of the optical power. Thus, the Relu function can be realized [21]. Two-dimensional graphene materials work analogous to the saturated absorber. However, compared with the saturated absorber, graphene has the advantages of low threshold and that it is easy to be excited in nonlinear effects.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 4 Results, Analysis and Discussion
### Discussion
#### 4.3.2 Coherent Integration Time Effects  
As the scattered signals are contaminated by additive thermal noise and speckle noise, the coherent integration was employed over the complex waveforms to suppress the effect. We tested different lengths of coherent integration time to study their effect on wind speed retrieval and to achieve an optimum value of coherent integration time. Figure 11 presents the results for different coherent integration time. Both methods show degradations in the wind speed retrieval performances with increasing coherent integration time.  
In order to keep the correlation of the waveform, the coherent integration time should be smaller than the coherent time of the scattered signal [18]. In our coastal experiment scenario, the coherent time ranged from 50 ms to 150 ms, depending on the sea state. Therefore, the best performances were obtained as expected for a coherent integration time of 50 ms, in agreement with the principle that the coherent integration time should be smaller than the correlation time.
## 3 Results and Discussion
### Analysis of Field Tests Results  
#### 3.2.1 Effect of Sowing Depth Control  
Figure 15 shows the sowing depth measurement data of three vehicle speed levels under three types of downforce regulation. In order to reduce the error, the mid-range measurement data of each vehicle speed were selected. The one with the best sowing depth was selected as the comparison data among the two sets of mechanical spring adjustment data. As shown in Figure 15, the black line is the sowing depth data, and the stacked area graph is the soil compaction data after sowing. Although the test machine has high profiling accuracy for homogeneous profiling, there will be profiling advance and lag due to the terrain, so a small amount of measured sowing depth data will slightly exceed the set sowing depth \(h\).  
By comparing the sowing depth data of three kinds of pressure adjustment methods, it can be seen that the sowing depth under electro-hydraulic adjustment is the closest to the set value on the whole, and the sowing depth stability from high to low is electro-hydraulic adjustment, mechanical spring adjustment and self-weight adjustment; by comparing three kinds of vehicle speed levels, it can be seen that with the increase in the operating speed, electro-hydraulic adjustment has the least influence on the sowing depth effect, mechanical adjustment is second and self-weight adjustment is the most influenced by vehicle speed.
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
## 3 Distributed GNSS Receiver Concept with Two 2 \(\times\) 1 Antenna Sub-Arrays  
### Receiver Design  
Our approach of splitting one 2 \(\times\) 2 antenna array into two sub-arrays resulted in 50% reduction of the array footprint per sub-array. Additionally, we reduced the complexity of the front-end design by processing the RHCP signals only at both frequencies, f\({}_{1}\) and f\({}_{5}\). This led to four channels per sub-array, namely two per frequency band. A dedicated application-specific integrated circuit (ASIC) was designed for the receiver in 180 nm United Microelectronics Corporation (UMC) technology. The ASIC contains four complete receiver chains, each with two RF low-noise amplifiers (LNA), a down-converting mixer, and an IF amplifier. An external bandpass filter can be connected between the LNAs for the pre-selection of the chosen frequency band. The ASIC also contains two phase-locked loop (PLL) synthesizers to generate the LO signals at the two operational frequencies. Alternatively, external LO signals can be applied to the ASIC.  
Each sub-array module incorporates one ASIC and provides four receiver channels, two for each frequency, fed by two commercially available dual-band ceramic patch antennas APAKM2507 [15], as shown in Figure 2.The complete GNSS receiver consists of two sub-arrays. The common LO and calibration signals for both frequency bands, provided by the central base-band module, have to be split and fed to the sub-arrays  
Figure 1: Simplified block diagram of the 2 \(\times\) 2 multi-antenna dual-band dual-polarized GNSS array receiver with calibration (CAL) and local oscillator (LO) signals, leading to a total number of 16 channel outputs.  
by cable connections, as illustrated in Figure 3. Due to such a distributed arrangement of the two sub-arrays, the provision and distribution of identical and phase-coherent CAL and LO signals is challenging and requires dedicated considerations of the various trade-offs involved.
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
## 2 The Review
### Audio-Based Pseudo-Haptics
Overall, 100 participants were tasked with matching sound effects to pictures of real materials based on the perceived sensation. A 53% accuracy and a 78% comprehension rate were observed, leaving room for improvement. Focusing on the influence of sound on visual cues using head-mounted displays (HMDs), [PERSON] et al. [56] were interested in material perception in virtual reality, with a particular focus on how sound might impact the perception of virtual material textures. In one experiment, they sought to determine if the presence of a collision sound could alter the perceived appearance of a material in virtual environments, focusing on what they called low-level perceptual traits (soft/hard, glossy/matte, and rough/smooth), and high-level descriptors such as \"plastic-like\" or \"fabric-like\"). Their results suggested that the effect of sound in material identification tasks was more relevant when using low-fidelity visual stimuli. Driven by the COVID-19 pandemic and the resulting move to remote learning, our own earlier sound-haptic interaction work served as a motivator to shift our research focus on audio-based pseudo-haptics as a cost-effective method to facilitate psychomotor skills development without the use of any haptic devices. Our first study, conducted entirely online during the COVID-19 lockdowns, examined whether a virtual drilling task can be appropriately simulated using combinations of sound and kinesthetic stimuli obtained with movement of a basic 2D mouse in the absence of a haptic device [57]. Drilling is a common task used across many domains, including medicine (e.g., dentistry and orthopedic surgery). Participants were asked to drill to a pre-defined depth in a virtual block of wood under various auditory conditions using a standard computer mouse or keyboard (e.g., using commonly available computer hardware and devices). Figure 2 provides a view of the visual scene presented to the participants. Although only a pilot study and based on the results of just 13 participants with low statistical power, results showed that sound feedback alone is not enough to provide adequate drilling feedback. However, when coupled with kinesthetic feedback using a simple computer mouse, sound can convey pseudo-haptic cues and simulate a simple virtual drilling task [57].
# Cooperative Control of Interconnected Air Suspension Based on Model Predictive Control
## 6 Conclusions  
To improve ride comfort in a bus with interconnected air suspension by a minimal energy cost, this paper focuses on cooperative control considering energy consumption between OMA and IMA. Both simulated and experimental results show that proposed cooperative control strategies can significantly improve the performance of ride comfort:  
1. A model predictive controller is designed to obtain best suspension force. Simultaneously, the rule is defined to distribute the best suspension force between IMA and OMA.
2. The simulation results show that the indexes of ride comfort including sprung mass acceleration and tire dynamic load in the cooperative controller are significantly improved compared with non-cooperative controller under different road levels.
3. The vehicle on-road test verified that the designed cooperative controller improved the ride comfort, especially, the RMS of the sprung mass acceleration of cooperative control reduced by 24.2% compared with the traditional control (0.4661 m/s\({}^{2}\) and 0.7086 m/s\({}^{2}\), respectively), and the RMS of the unsprung mass acceleration reduced by 14.5% (1.8928 m/s\({}^{2}\) and 2.2127 m/s\({}^{2}\), respectively) under the straight working condition. Under the bump condition, the RMS of the sprung (unsprung) mass acceleration are 1.1694 m/s\({}^{2}\) (3.9133 m/s\({}^{2}\)) and 1.4311 m/s\({}^{2}\) (4.6275 m/s\({}^{2}\)), respectively, in the cooperative controller and traditional controller. The indexes reduced by 22.38% and 15.43%, respectively. The experimental results show that the proposed control strategy not only makes the IMA and OMA work effectively, but also the ride comfort under different working conditions has significantly improved.  
The simulation and vehicle on-road test results show that the proposed cooperative control is feasible. Therefore, it is believed that the proposed cooperative control has a wonderful application in the bus to improve vehicle ride comfort. In following works, the cooperative control used in controllable structures will be explored further.  
Funding acquisition, Z.L.; Methodology, W.Y.; Project administration, Y.Y.; Software, Y.Z.; Validation, W.Y.; Writing--original draft, Y.Z.; Writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (grant number 51975254).  
Figure 11: Comparison of cooperative and traditional control under bump condition. (**a**) Front-left sprung mass vertical acceleration. (**b**) Front-left unsprung mass vertical acceleration.
## 2 Method  
The proposed WPD-Net, illustrated in Figure 1a, is designed to effectively remove noise while preserving structural details in wrapped-phase images. The network consists of three main stages: a shallow feature extraction (SFE) module, residual dense attention blocks (RDABs), and a dense feature fusion (DFF) module. These components work together to capture both local and global features. The input to the network is a noisy wrapped-phase image of size 256 \(\times\) 256 pixels, and the output is a denoised image of the same resolution.  
The input noisy wrapped-phase image, denoted as \(\rm I_{N}\), is first processed through the SFE module, consisting of two convolutional layers. This stage enhances primary structural features while reducing noise. The shallow feature extraction is defined as:  
\[\rm I_{-1}=H_{SFE1}(I_{N}), \tag{1}\]  
\[\rm I_{0}=H_{SFE2}(I_{-1}). \tag{2}\]  
where \(\rm H_{SFE1}\) and \(\rm H_{SFE2}\) represent convolution operations in the SFE module. The feature map \(\rm I_{-1}\) is the output of the first convolutional layer in the SFE module. The feature map \(\rm I_{0}\), output from the SFE module, serves as the input to the following RDAB. Each RDAB consists of multiple convolutional layers with ReLU activation [36], followed by attention mechanisms [37] to enhance feature attention in both spatial and channel dimensions. Given D RDABs, the output of the d-th RDAB, denoted by \(\rm I_{d}\), is recursively computed as:  
\[\rm I_{d}=H_{RDAB,d}(I_{d-1}) \tag{3}\]  
where \(\rm H_{RDAB,d}\) denotes the function of the d-th RDAB.  
After passing through all RDABs, the DFF module integrates features from all preceding layers. The feature map \(\rm I_{DF}\) produced by the DFF module is given by:  
\[\rm I_{DF}=H_{DFF}(I_{-1},I_{0},I_{1},\ldots,I_{D}). \tag{4}\]  
Figure 1: (**a**) Architecture of the proposed WPD-Net, which comprises a shallow feature extraction (SFE) module, residual dense attention Blocks (RDABs), and dense feature fusion (DFF) module with global residual learning. (**b**) Detailed structure of RDAB, including local residual learning and both channel and spatial attention mechanisms.  
Finally, the denoised wrapped-phase image I\({}_{\text{DN}}\) is obtained as:  
\[\text{I}_{\text{DN}}=\text{H}_{\text{Final}}(\text{I}_{\text{DF}}). \tag{5}\]  
Here, H\({}_{\text{Final}}\) denotes the final convolution layer that maps the refined feature map I\({}_{\text{DF}}\) to the denoised wrapped-phase output I\({}_{\text{DN}}\).
## 2 Materials and Methods
### Research Method  
#### 2.3.1 Dimidiate Pixel Model  
Fraction Vegetation Coverage (FVC) is an important index to measure vegetation quality and ecosystem change [4]. NDVI is the normalized vegetation index, which quantifies the condition of vegetation by calculating the difference between the near-infrared light reflected by vegetation and the red light absorbed by it. It is an important reference indicator for monitoring vegetation growth and cover changes. Existing studies have shown that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
** Landsat** & **Sensor** & **Red Band** & **Near-Infrared Band** & **Image Dataset ID** & **Dataset Availability** \\ \hline Landsat 5 & ETM & B3 & B4 & LANDSAT/LT05/C01/T1\_SR & 1990-03 to 1998-10 \\ Landsat 7 & ETM+ & B3 & B4 & LANDSAT/LE07/C01/T1\_SR & 1999-03 to 2012-10 \\ Landsat 8 & OLI/TIRS & B4 & B5 & LANDSAT/LC08/C01/T1\_SR & 2013-03 to 2020-10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions about Landsat images in GEE.  
Figure 1: Geographical location of the study area.  
there is a linear regression relationship between NDVI and FVC, so pixel dichotomy can be used to calculate vegetation cover changes in the study area. The commonly used methods for calculating FVC also include empirical research and spectral mixture analysis (SMA) methods [16; 17]. In this research, the pixel dichotomy method is used to estimate FVC. This method is the most commonly used and simplest linear model among all methods. The principle is based on the fact that the picture element is composed of a vegetation-covered area and a non-vegetation covered area, and the spectral information is composed of a linear mixture of the two parts [18]. The ratio of each part of the image element to its area is its weight, and the percentage of vegetation area is the FVC of the picture element. The FVC is calculated as follows:  
\[\text{FVC}=\frac{\text{NDVI}-\text{NDVI}_{\text{soil}}}{\text{NDVI}_{\text{ veg}}-\text{NDVI}_{\text{soil}}}\times 100\% \tag{1}\]  
In the formula, NDVI is the vegetation index value in the mixed image element. In the pixel dichotomy model, the vegetation cover structure is divided into two categories: pure pixels and mixed pixels. Pure pixel coverage refers to the complete range with a vegetation coverage value of 1, while mixed pixels refer to the composition of vegetation and non-vegetation types. \(\text{NDVI}_{\text{soil}}\) is the vegetation index value of the pure soil image element, which is theoretically close to 0; \(\text{NDVI}_{\text{veg}}\) is the vegetation index value of the pure vegetation image element, which is theoretically close to 1. To prevent noise points and grayscale interference of NDVI on the image, 5% and 95% of NDVI were taken to represent \(\text{NDVI}_{\text{soil}}\) and \(\text{NDVI}_{\text{veg}}\), respectively, in this research. Based on the vegetation cover thresholds and previous classification studies from [18] and in the context of the study area, the final FVC values were classified into five classes (Table 2).
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 5 Data Integrity Algorithms
### ECDSA-P25-Sha256  
ECDSA is an asymmetric digital signature generation and verification algorithm. Digital signature algorithms use a pair of public and private keys to ensure data authenticity and integrity. A digital signature is generated by using the private key to encrypt a message digest code, which is usually typically created by hashing the data to be signed. The signature can then be verified by decrypting the data using the public key, extracting the message digest code from the decrypted signature, recalculating the data message digest code, and comparing the two digest codes. ECDSA generates and verifies digital signatures by using elliptic curve mathematics, modular arithmetic, a hash function, and a random number [48; 49]. The random number, which is generated during the signature generation phase, ensures that a different signature is produced each time for the same data and key.  
ECDSA-P256 utilizes the P256 elliptic curve over prime fields [48] to generate a 512-bit signature using a 256-bit private key. The signature can then be verified using a 512-bit public key. Typically, ECDSA and other asymmetric digital signature schemes provide a greater level of security for secure boot processes compared to symmetric MAC algorithms and hash functions. However, this comes at the cost of processing speed, as asymmetric schemes consume more time than their symmetric counterparts.  
Digital signatures are the digital correspondence of handwritten signatures. Therefore, the ECDSA algorithm is used in online banking services to avoid identity forging and guarantee non-repudiation. ECDSA is also used in a variety of different applications, such as communication of wireless sensor networks, authentication of smart cards, handling of cryptocurrencies such as bitcoins, and the validation process of radio frequency identifiers [50].