esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Lateral Position  
Table 6 presents the results of the within-subjects repeated measures ANOVA, with Greenhouse-Geisser correction. It demonstrates that all factors, including curve direction (\(F\)(1.00, 46.00) = 15.89, \(p\) < 0.001, \(\eta^{2}\)\({}_{p}\) = 0.26), curve radius (\(F\)(1.00, 46.00) = 9.28, \(p\) = 0.004, \(\eta^{2}\)\({}_{p}\) = 0.17), condition (\(F\)(2.99, 137.96) = 16.80, \(p\) < 0.001, \(\eta^{2}\)\({}_{p}\) = 0.27), and measuring point (\(F\)(2.85, 130.92) = 5.35, \(p\) = 0.002, \(\eta^{2}\)\({}_{p}\) = 0.10), significantly influenced the lateral position. This indicates that the overall variations in lateral positioning are affected considerably, regardless of other factors. Figure 8: Average acc/dcc on measuring points based on (**a**) curve directions and (**b**) curve radii. Figure 9: Average acc/dcc across conditions and measuring points. Furthermore, the two-way interaction effects of curve direction \(\times\) curve radius (\(F\)(1.00, 46.00) = 57.41, \(p\) < 0.001, \(\eta^{2}p\) = 0.56), curve radius \(\times\) condition (\(F\)(3.56, 163.55) = 3.94, \(p\) = 0.006, \(\eta^{2}p\) = 0.08), curve direction \(\times\) measuring point (\(F\)(2.59, 118.93) = 62.93, \(p\) < 0.001, \(\eta^{2}p\) = 0.58), curve radius \(\times\) measuring point (\(F\)(3.63, 166.75) = 6.41, \(p\) < 0.001, \(\eta^{2}p\) = 0.12), and condition \(\times\) measuring point (\(F\)(10.52, 483.83) = 13.11, \(p\) < 0.001, \(\eta^{2}p\) = 0.22) were also significant. These findings revealed that lateral positioning varied significantly between the two curve radii, five road marking conditions, and seven measuring points. The three-way interaction effect for the factors curve direction \(\times\) curve radius \(\times\) condition (\(F\)(3.41, 156.77) = 2.80, \(p\) = 0.035, \(\eta^{2}p\) = 0.06), curve direction \(\times\) curve radius \(\times\) measuring point (\(F\)(2.39, 110.01) = 27.45, \(p\) < 0.001, \(\eta^{2}p\) = 0.37), and curve direction \(\times\) condition \(\times\) measuring point (\(F\)(12.21, 561.47) = 5.68, \(p\) < 0.001, \(\eta^{2}p\) = 0.11), are also significant. The descriptive statistics and pairwise comparisons of lateral positioning, adjusted using the Bonferroni correction, are shown in Table 7. The RWM condition exhibits a considerable difference in mean lateral positioning compared to the other conditions. Furthermore, the vehicle's position under the RE condition was, on average, closest to the centerline. Additionally, only RWE and RDT did not show a significant difference compared to the control condition, while other conditions demonstrated a significant difference. The average lateral position in measuring points based on curve directions and radii is shown in Figure 10. The horizontal axis displays the different meas
# Design, Fabrication, and Control Algorithm of Self-Reconfigurable Modular Intelligent Vehicles
## 2 Mechanical Design and Fabrication  
The Mecanum wheel is utilized as the driving mechanism for its unique motion flexibility, which makes it an ideal choice for movement in a narrow space [9]. In the reconfigurable vehicle system proposed in this paper, four Mecanum wheels are driven by four independent driving motors so that each Mecanum wheel has an independent speed and steering control mechanism. The control chip on the submodule vehicle first receives the target position signal sent by the main control vehicle, and then calculates the motion direction and speed of each Mecanum wheel according to kinematic equations. Finally, the motion data are converted into the actual motor drive signals [10,11].  
The Mecanum wheel is an omnidirectional wheel structure; it is mainly composed of a wheel hub and a circle of rollers whose axis is arranged at 45\({}^{\circ}\). When the Mecanum wheel rotates, the envelope of each roller is approximately circular. Through the speed vector synthesis between the four Mecanum wheels, as shown in Figure 6, motion in both the longitudinal and lateral directions can be realized. The arrangement of the four Mecanum wheels is shown in Figure 7. The axis of the rollers is end to end, and the contact points of the four wheels are in rectangular shape. In this arrangement pattern, the torque generated by the rollers has a long effective force arm, which guarantees the stability of the spin and trim. It is thus the most common pattern of Mecanum wheel arrangement.  
Figure 6: Model of a modular vehicle.  
Figure 7: Mecanum wheel and layout of wheels.  
A magnetic suction structure is mounted on each longitudinal and lateral face of the modular vehicle (Figure 8), which guarantees a stable connection between each modular vehicle. When the modular vehicle departs from each other, the driving force in both longitudinal and lateral directions will overcome the magnetic force so as to realize reformation.  
A camera is mounted on a frame in the central vehicle, and the fixation angle of the camera can be adjusted to make sure each modular vehicle is within the scope of the camera. After the image capture, the location and posture of each unit are calculated, and motion commands are made correspondingly.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 2 Challenges of Pulse Compression with High Time-Bandwidth Products
### Bistatic Geometry Relations on the Effectively Measurable Velocity  
An important movement consideration is that the target speed \(|\vec{v}_{\text{trg}}|\) will only be partly effective against a single sensor. This has to be considered in for example a typical passive radar (PR) scenario that is at least bistatic because it is based on the illumination by a third party transmitter in the radar scenery. Although most PR systems are multistatic, the fundamental issues can often be broken down to their bistatic relation. This has been illustrated in Figure 4.  
The general situation present in a bistatic passive radar is that it inherits a weak illumination of the target, a strong direct path interference (DPI) at the receiver node and challenging waveforms. A large instant dynamic range and high coherent integration gain are therefore required for target detection. The bistatic range towards the target can be defined as \(R_{\text{bi}}=(R_{1}+R_{2}-L)\) whereby the baseline \(L\) in each transmitter and receiver pair can easily exceed \(30\,\text{km}\). The effective velocity \(v_{\text{eff}}\) that is directly measurable at the receiver node will be smaller or equal to the target speed \(|\vec{v}_{\text{trg}}|\). This relates in a bistatic geometry to the bistatic angle \(\beta\) and velocity angle \(\delta\) relative to \(\beta\) bisector as [19]  
\[v_{\text{eff}}=v_{\text{trg}}\,\cos\delta\,\cos\frac{\beta}{2}. \tag{8}\]  
A time-dependent bistatic velocity \(v(t)=v_{\text{eff}}(t)\) can hereby include an acceleration of the effective velocity that is geometry-induced by the relative target position along its trajectory or due to an actual target acceleration. Common processing approaches assume a constant effective velocity. This case will be treated in more detail in the following and it will later be related to practical considerations.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
###### Abstract  
This paper presents a land area measuring instrument based on an STM32 microcontroller (STMicroelectronics, Paris, France) and a BeiDou positioning chip (Zhongke Microelectronics, Hangzhou, China). The system employs BeiDou navigation as the core positioning, leveraging its unique three-track satellite cooperative networking and BDSBAS satellite-based enhancement technology as the physical layer guarantee mechanism to maintain high-precision positioning in the weak signal area of the Asia-Pacific region. At the same time, this design integrates the FreeRTOS real-time operating system to implement a dynamic memory management strategy. By adopting the linear incremental memory allocation mechanism coupled with the emergency Kalman prediction algorithm, the software layer establishes a memory buffering and fault-tolerant processing framework. Test results demonstrate a relative error below 1% in open terrains, while errors remain below 2.72% under weak signal conditions and in complex terrain environments.  
BeiDou positioning chip; any area measurement; Green's formula; STM32 microcontroller; dynamic memory management; Kalman prediction 2025  
2
## 4 Discussion
### Data Integration for Improving Classification Accuracy  
As highlighted in previous PB LULC classifications [37; 38; 39], and also in this application, the integration of S1 data improved the accuracy results of the S2 dataset. This improvement is more marked in the PB approach than in the OB one. This evidence can be explained considering that the calculation of GLCM indices on S2 data provides textural information about the LULC patches that can be related to that provided by the S1 bands. The application of GLCM on S1 bands slightly improved the OB classification of the S2 and S1 composite datasets even though this improvement was statistically not significant. This evidence may appear to be in contrast with that noted in a recent study where the GLCM texture analysis on S1 images was a valuable source of information for obtaining excellent classification results of onion and sunflower in the Bonaerense Valley of the Colorado River, Argentina [92]. This difference may be related to the very different classes used in the classification. Thus, the efficacy of GLCM textural analysis on S1 data in land cover classification may strictly depend on the classes chosen for the classification and the formula used for obtaining the 8-bit grayscale image. Thus, in this regard, more investigation is necessary.  
In this research, the integration of the three datasets gave the best classification results, producing a LULC map at 4.77-m resolution. As shown in the results, the integration of PL data with S2 and S1 in the PB RF classification remarkably improved the accuracy results of the only PL dataset. Similarly, the application of the OB approach on the single-sensor PL dataset produced accuracy results, also in terms of UA and PA of the various LULC classes, comparable with those obtained with the PB approach on the three-sensor dataset. This outcome confirms previous studies that focused on crop classification [42; 43]. The proposed application of the OB approach to the three datasets is a further step forward considering other previous studies. The more accurate textural analysis and object identification obtained from PL data, combined with the spectral information provided by S2 and S1, enhanced the effectiveness of the three datasets' integration. The PL bands in the visible and NIR bands (OB_PLS2S1), although not confirmed by the McNemar test, seem to perform slightly better than the correspondent S2 bands (OB_S2S1 PL). This result is due mainly to a better classification of smaller features using PL higher-resolution multispectral data, as highlighted in previous studies [42; 43]. In this direction, more investigation is necessary.
## 4 Materials and Methods
### Sample Extraction and Wide-Target Metabolomics Analysis
#### 4.2.3 UPLC-MS/MS Conditions  
The metabolite data were obtained using the ultrahigh-performance liquid chromatography electrospray tandem triple quadrupole mass spectrometry (UPLC-ESI-MS/MS) system (UPLC, SHIMADZU Nexera X2; MS, Applied Biosystems 4500 Q TRAP, Waltham, MA, USA). The analysis conditions were as follows: the columns used in the UPLC were Agilent SB-C18 columns (1.8 \(\upmu\)m, 2.1 mm \(\times\) 100 mm); the mobile phase solvent A was composed of pure water containing 0.1% formic acid, and solvent B was acetonitrile containing 0.1% formic acid. The samples were detected using a gradient procedure with initial conditions of 95% A and 5% B. Within 9 min, the linear gradient was modified to 5% A and 95% B and a composition of 5% A and 95% B was maintained for 1 min. Subsequently, a concentration of 95% A and 5.0% B was adjusted to within 1.10 min and maintained for 2.9 min. The UPLC flow velocity was set as 0.35 mL/min, and the column oven was set to 40 \({}^{\circ}\)C; the injection volume was 4 \(\upmu\)L (repeated three times for each sample). The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 5 Experimental Results and Analysis
### Robustness against Common Image Attacks  
To prove that the algorithm also has excellent robustness against common image attacks without screen-cam attack, we performed corresponding experiments. The results are shown in Table 2, and the PSNR and mean structural similarity index (MSSIM) [54] values are also listed.  
The robustness primarily depends on whether feature points and watermarking information can be simultaneously detected. As shown in Table 2, the algorithm is robust to JPEG attacks, which can mostly survive at a JPEG of 20%. Because scale attacks cause the frame to shrink, we restore the scaled images before detection. The algorithm works when under a scaling 0.5 attack and basically works when under a scaling 0.4 attack. For cropping-off attacks, which refer to a continuous crop from the right in this section, assuming more than one relatively complete embedded LSFR exists, the detection can be successful in theory. Due to the fact that the watermark is repeatedly embedded in each LSFR, we can detect the watermark information at a cropping-off 50% attack in the experiments. The rotation attack may cause the loss of feature points since we only need at least one successful detection, and the algorithm is also effective. The algorithm also works at a median filter \(3\times 3\) attack. Thus, our watermarking scheme has excellent robustness to common image attacks.  
Figure 14: Watermarked images.
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 5 Results and Evaluation  
Some prediction results are shown in Figure 10. In the figure, image A, B and C are corrosion image and D, E and F are from rubber bearing data set. These images prove that the VGG-Unet model trained from squashed images could generally identify the location of damage on the rubber bearing, not only for corrosions, but also for rubber bearing cracks, as shown in (c). However, the VGG-Unet model may ignore some pixels of damage because some features of damages are lost during the process of compressing, especially minor damages.  
On the contrary, the results trained by Cropping Segmentation have a large gap in the ability to identify corrosions and rubber bearing cracks. The model for corrosion prediction has a good performance on detection corrosions from steel structure, while only a few crack pixels on the rubber bearings are predicted, even though the two VGG-Unet models are trained from the same methodology and hyperparameters. From the experiment of other researchers, the larger data set should help Deep Learning models to reach higher accuracy. The volume of the data set of rubber bearing is several times larger than that of the corrosion data set and the number of damage pixels is not much different, while the detection results have such a large difference. The largest possibility is the differences on proportion of the target object in the whole data set. Due to the damage pixels from the data set don't have enough percentage during training process, the VGG-Unet model is not so sensitive to them.  
The accuracy results are calculated and shown in Table 3. From the table, it shows that Cropping Segmentation has a better capability on predicting corroisons than Squashing Segmentation, while the results of these two methods are similar when it comes to rubber bearing detection. And the results corrosion prediction from two evaluation methods which don't enlarge the weight of damage, don't have a large gap, compared with other two methods of increasing the weight of damages. Nevertheless, if only focus on the rubber bearing images with damages, the evaluation method of enlarging or not enlarging the weight of damage will produce large difference in results. This is because that the TN pixels are accounted for a large proportion and seriously affect the calculation of accuracy under each method. In this case, RWIoU is an alternative method to balance the huge disparity between the evaluation results.The table proves that the balance of the number of pixels in each category in the data set can affect not only the ability of prediction results, but also the accuracy of evaluation results.  
Figure 10: Prediction results: (**a**) original images, (**b**) ground truth, (**c**) Squashing Segmentation results, (**d**) Cropping Segmentation results.
## 4 Experiments and Discussion
### Experimental Setup  
All experiments were implemented on an GeForce RTX 4090 GPU, NVIDIA, Macao SAR, China, using the PyTorch framework with 24 GB RAM. The specific setup is as follows: We used the AdamW optimizer with an initial learning rate of 0.0001, momentum of 0.9, a decay factor of 0.0005, and a sliding window size of 256 \(\times\) 256 for training. During testing, the sliding window size was 32. Data augmentation methods like random rotation, horizontal flipping, and vertical flipping were employed. The training duration was set to 100 epochs.  
The AdamW optimizer performed excellently in image segmentation tasks, particularly during deep network training. To avoid overfitting, L2 regularization (weight decay) was added during training with a weight decay coefficient of 1 \(\times\) 10\({}^{-4}\). The learning rate was dynamically adjusted using the CosineAnnealingLR scheduler to help the model con -verge more effectively. The batch size was set to four, and we also implemented dropout and early stopping mechanisms to prevent overfitting. Validation metrics like IoU and F1-Score were monitored, and training was stopped early when no improvement was observed.  
We utilized custom loss functions such as multi-scale boundary loss and adaptive focal loss. The multi-scale boundary loss helped the model better handle boundary regions, while the adaptive focal loss addressed class imbalance by adjusting the weight of each class dynamically.
# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 5 Case Study
### Informer PV Prediction
Overall, the MVMD-Informer model strikes a balance between accuracy and computational complexity, making it a robust and efficient method for short-term photovoltaic power generation forecasting. Moreover, to investigate the impact of different seasons on PV power and the superiority of MVMD over VMD, the model was trained and predicted quarterly. Example plots of days to be predicted for different seasons are shown in Figure 7. The MAPE error results are shown in Figure 8.  
According to Figures 7 and 8, the MVMD-Informer model consistently exhibited the lowest MAPE values across all quarters: 4.46% (January-March), 4.27% (April-June), 4.22% (July-September), and 4.39% (October-December), demonstrating its superiority in handling multivariate information. In contrast, the VMD-Informer model had higher MAPE values, at 5.21%, 4.91%, 4.89%, and 5.11%, respectively. Although the Informer model had its highest MAPE value in the first quarter (6.01%), it gradually decreased over subsequent quarters to 5.66%, 5.51%, and 5.76%. \begin{table}
\begin{tabular}{c c c c c} \hline  & **RMSE (MW)** & **MAE (MW)** & **MAPE (\%)** & **Training Duration (s)** \\ \hline MVMD-Informer & 0.939 & 0.758 & 4.31 & 1369 \\ Informer & 2.50 & 1.59 & 5.45 & 623 \\ MVMD-CNN-LSTM & 3.24 & 1.91 & 6.31 & 1820 \\ CNN-LSTM & 5.88 & 3.71 & 7.57 & 826 \\ MVMD-WaveNet & 1.88 & 1.11 & 5.01 & 1582 \\ WaveNet & 3.24 & 1.85 & 5.92 & 733 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics for prediction results. While VMD can reduce the volatility and nonlinearity of PV power and meteorological sequences, MVMD achieves synchronized time-frequency analysis of these sequences and completes multivariate decomposition. This aligns better with the actual operation of distributed PV power stations and highlights the algorithm's advantages. Based on its robustness and accuracy, the proposed short-term PV power forecasting method using MVMD and the Informer model shows extensive application prospects. It can be employed in PV power station monitoring systems for accurate short-term forecasts  
Figure 8: MAPE error plot for different quarters. Figure 7: Example plots of PV power prediction for different seasons. and optimizing operation and maintenance. Grid dispatch centers can use it for better power generation forecasts, enabling rational load allocation, reducing power curtailment, and optimizing grid operations. Additionally, integrating this method into distributed energy management systems can enhance energy utilization efficiency, reduce costs, and promote renewable energy adoption. In smart grids, accurate forecasts aid in demand response and load balancing, improving grid resilience and reliability. Therefore, this method has significant potential in monitoring systems, grid dispatch, energy management, and smart grids.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 6 Removing Context-Dependent Cumulate Prediction Error
### Fourth Type of Context  
The number 4 of the context type is determined as a number that is a composite of three values. The first two are determine as six-state quantizer with thresholds {-100, -10, 0, 10, 100} for value \(d_{1}\) and \(d_{2}\), respectively, where \(d_{i}=x(n-i)-x(n-i-1)\), for \(i\) = {1, 2}. The third value of the context number is the six-bit \(\kappa_{5}\kappa_{4}\kappa_{3}\kappa_{2}\kappa_{1}\kappa_{0}\) character value, where \(\kappa_{i\ -1}=\alpha_{i\ +8}\) for \(i\) = {1, 2}, \(\kappa_{2}=\alpha_{2}\), whereas three bits \(\kappa_{5}\kappa_{4}\kappa_{3}\) are determined using an eight-state quantizer of the sum of \(S_{2}\) with seven thresholds {40, 80, 180, 400, 1000, 2000, 4000}, totally obtaining 6-6-26 = 2304 contexts of type number 4.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Bits** & **Condition** \\ \hline \(\alpha_{i}\) & \(2\cdot x(n-i)-x(n-i-1)>\ \hat{x}(n)\), for \(i\) = {1, 2, 3} \\ \hline \(\alpha_{i\ +3}\) & \(x(n-i>\ \hat{x}(n)\), for \(i\) = {1, 2, 3} \\ \hline \(\alpha_{i\ +6}\) & \(e(n-i)>0\), for \(i\) = {1, 2} \\ \hline \(\alpha_{i\ +8}\) & \(e(n-i)>e(n-i-1)\), for \(i\) = {1, 2, 3} \\ \hline \(\alpha_{i\ +11}\) & \(\bigl{|}x(n-i)-\hat{x}(n)\bigr{|}>T_{h}(i)\), for \(i\) = {1, 2, 3, 4}, \(T_{h}(i)\) = {250, 100, 1500, 1500} \\ \hline \(\alpha_{i\ +15}\) & \(\bigl{|}e(n-i)\bigr{|}>T_{h}(i)\), for \(i\) = {1, 2}, \(T_{h}(i)\) = {50, 150} \\ \hline \(\alpha_{i\ +17}\) & \(\bigl{|}e(n-i)\bigr{|}>T_{h}(i)\), for \(i\) = {1, 2}, \(T_{h}(i)\) = {750, 900} \\ \hline \(\alpha_{i\ +19}\) & \(x(n-i)>x(n-i-1)\), for \(i\) = {1, 2} \\ \hline \(\alpha_{22}\) & \(1.75\cdot x(n-1)-0.75\cdot x(n-2)>\hat{x}(n)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: A set of decision rules for determining number of contexts.