esa-sceva/satcom-chunk-collection huggingface dataset:
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 4 SKVOS Model
### Sketch-Anchored Temporal Contrastive Learning  
Although the methods described in the previous sections ensure a certain level of segmentation quality, the rich semantic information inherent in the sketches can be further utilized to match targets at the instance level. To this end, we propose a Sketch-Anchored Temporal Contrastive Learning strategy, aiming to establish correspondences between sketches and instances in video sequences, thereby addressing semantic inconsistencies caused by frequent appearances of multiple objects in complex video scenes. Unlike traditional contrastive learning methods that typically rely on positive and negative sample pairs, our approach focuses on constructing training sample pairs from multiple frames in videos containing multiple objects, enhancing the model's adaptability to complex scenes.  
Specifically, we select an anchor frame \(I_{0}\) and two consecutive frames \(I_{t-1}\) and \(I_{t}\) from a video clip, and treat the \(N\) annotated instances in each video as a potential sample set (as shown by the solid colored boxes in \(I_{a}\) in Figure 7). The training sample pairs consist of an anchor, a positive sample set \(\mathcal{P}_{+}\), and a negative sample set \(\mathcal{P}_{-}\). Due to their concise visual structure, sketches can effectively convey strong semantic and detailed information and have been proven to guide tasks such as image detection and segmentation [28; 56]. Therefore, we use the sketch \(S^{i}\) and the annotated object in the first frame as the anchor. First, we generate the anchor feature \(F_{s}^{+}\) by leveraging the latent feature \(F_{s}\) of the sketch \(S^{i}\) obtained in Section 4.3 and the anchor feature \(F_{a}\), implemented through a lightweight convolutional block \(CNN_{fuse}\):  
\[F_{s}^{+}=CNN_{fuse}(F_{s},F_{a}), \tag{14}\]  
Furthermore, we leverage annotated video frames from the VOS training set, along with annotated instances and their corresponding masks, to create candidate proposals. Specifically, \(\mathcal{P}_{+}\) contains the target object \(O^{i}\) from the selected training video frames (e.g., \(O^{1}\) in Figure 7), while \(\mathcal{P}_{-}\) includes negative samples of other objects in the set \(\{O^{i}\}_{j=1}^{B}\) (e.g., \(O^{2}\) and \(O^{3}\) in Figure 7), where \(|B|\leq|N|\). The visual encoder then extracts features \(F_{i}\) and \(F_{j}\) (omitting time \(t\) here) from the frames for contrastive training. In this way, we effectively utilize the semantic information from sketches to enhance the model's ability to distinguish between multiple object instances in complex scenes.  
Inspired by the InfoNCE loss [74], our loss is defined as follows:  
\[\mathcal{L}_{\text{OCL}}=-\frac{1}{B}\log\sum_{F_{i}\in\mathcal{P}_{+}}\frac{ \exp(\langle F_{s}^{+},F_{i}\rangle)}{\exp(\langle F_{s}^{+},F_{i}\rangle)+ \sum_{j\in\mathcal{P}_{-}}\exp(\langle F_{s}^{+},F_{j}\rangle)}. \tag{15}\]
## 3 Numerical Experiments  
In this section, we verify and test the effectiveness, feasibility and adaptability of our developed method using a simple isotropic elastic model (Section 3.1) and the Hess isotropic elastic model (Section 3.2). During the experiments of different isotropic elastic models, we compare our developed method with two conventional methods, including the Helmholtz decomposition algorithm and the vector decomposition algorithm in the wavenumber domain.
###### Abstract  
The multi-agile Earth observation satellite scheduling problem is challenging because of its large solution space and substantial task volume. This study generates observation schemes for static tasks over an execution period. To balance solution quality and computational efficiency, a deep reinforcement learning (DRL)-based algorithmic framework is proposed. A Markov decision process (MDP) is formulated as the foundational model for the DRL architecture. To mitigate problem complexity, the action space is decomposed into two interdependent decision layers: task sequencing and resource allocation. Given the resource occupation constraints during action execution, a novel reward function is designed by integrating resource occupation utility into the immediate reward mechanism. Corresponding to these dual decision layers, a Two-Phase Hybrid Optimization (TPHO) framework is developed. The task sequencing subproblem is addressed through an encoder-decoder architecture based on sequence-to-sequence learning. To preserve resource diversity throughout the scheduling horizon, a maximum residual capacity (MRC) heuristic is introduced. A comprehensive experimental suite is constructed, incorporating multi-satellite scheduling scenarios with capacity and temporal constraints. The experimental results demonstrate that the TPHO framework with MRC rules achieves superior performance, yielding a total reward improvement exceeding 16% compared with the A-ALNS algorithm in the most complex scenario involving 1200 tasks, yet requiring less than 3% of the computational duration of A-ALNS.  
eurm observation; scheduling; Markov decision process; deep reinforcement learning; heuristic rule Article
## 7 Conclusions  
According to the dynamic analyses of the bedded slope, the seismic response characteristics of the slope were investigated. Some conclusions can be drawn:  
1. Structural planes have a significant effect on the wave propagation characteristics through the slope. Irregular wave propagation characteristics can be identified through structural planes. To analyze the MPGA, the slope has obvious elevation and slope surface amplification effects. Structural planes have an impact on the slope dynamic response. The MPGA increases gradually with the slope elevation and reaches the maximum at the slope crest. The MPGA shows obvious nonlinear variation characteristics in the slope; in particular, a rapid increase of MPGA above the topmost structural plane can be found. The seismic direction of ground motion is closely related to the magnification effect of the slope. The MPGA under horizontal seismic load is approximately 1.10-1.20 times as much as the MPGA under vertical seismic force.
2. According to the frequency domain analysis, the first four natural frequencies of the bedding slope are approximately 2.53 Hz, 7.21 Hz, 10.64 Hz, and 17.47 Hz, respectively. The Fourier spectrum and modal analyses show that the slope owns the elevation, slope surface, and structural plane amplification effects. The natural frequency is closely related to the dynamic response characteristics of the slope. The low-order and high-order natural frequencies mainly cause the overall sliding deformation and the local deformation of the top slope.
3. Structural planes have an important influence on the sliding development process and mode. With the increase in seismic force, the sliding surface extends from the upper structural plane to the lower structural plane. The dynamic failure mechanism of the bedding slope is as follows: a crack first appears in the topmost structural plane, and the primary slip mass (surface slope) gradually forms. Cracks gradually extend in structural planes and promote the formation of the sliding surface; then, the surface slope produces shear failure along the topmost structural plane. With the increase in seismic force, the sliding body gradually extends to the lower slope body, and the structural plane of the lower part continues to form the sliding plane, which promotes the sliding body to slide on a larger scale.  
Methodology, L.D. and W.Z.; writing--original draft, D.S.; writing--review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the China Postdoctoral Science Foundation (2020M680583), the National Postdoctoral Program for Innovative Talent of China (BX20200191), the Excellent Sino-foreign Youth Exchange Program of China Association for Science and Technology in 2020 (No. 58), and the Shuimu Tsinghua Scholar Program (2019 SM058).  
Not applicable.
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
###### Abstract  
With the large-scale use of BeiDou navigation and 5G technology worldwide, integrating BeiDou navigation and communication has become a hot research topic in navigation and positioning technology. Low-cost, miniaturized, and susceptible mixed-signal receivers will become the future receiver technology development trend. However, the current receiver technology still faces the challenge of further improving the positioning service capability and communication quality, which includes the lack of practical analysis of the compatibility between signals and the lack of mixed-signal processing capability of the receiver baseband key technology. To address these problems, we start by analyzing the signal part of 5G out-of-band signals falling into the BD B1 signal band, conduct a detailed analysis of the mixed signal regime and frequency planning, and design a hybrid receiver architecture compatible with both signals, and propose an SC-PMF-FFT fast capture algorithm based on strong correlation, which takes advantage of the strong correlation of signals broadcast on the BD B1 frequency point from B1I to B1C, and reuses the structure of the CDMA system signal capture algorithm to complete the fast capture of 5G signals using an OFDM system. The experiments show that the method can capture the BeiDou B1 signal with a sensitivity of \(-154\) dBm and a whole constellation capture time of no more than \(40\) ms with the inlet power of the 5G signal not exceeding \(-45\) dBw.  
BeiDou; 5G; acquisition algorithm; compatibility analysis; SC-PMF-FFT +
Footnote †: journal: Applied Sciences
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 3 Experimental Setup and Conditions  
### Embedded Based Monitoring System Fabrication  
The sensor specifications shown in Table 1 were used to capture the process states of the mixing head and the mold by the developed embedded monitoring system. A pressure sensor (S-11, WIKA Alexander Wiegand SE & Co., Klingenberg am Main, Germany) and a temperature sensor (TM4101,IFM electronic gmbh, Essen, Germany) on the mixing head and cavity pressure sensors (6161 AA, Kistler Instrumente GmbH, Sindelfingen, Germany), temperature sensors (6194B, Kistler Instrumente GmbH, Sindelfingen, Germany), and LVDTs (GT2, Keyence corporation., Osaka, Japan) in the mold were installed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monitoring Parts** & **Sensors** & \multicolumn{2}{c}{**Specifications**} & \multicolumn{2}{c}{**Number of Sensors**} \\ \hline \multirow{4}{*}{Mixing head} & \multirow{2}{*}{Pressure (S-\(11\), WIKA)} & Range & bar & 0-250 & \\ \cline{3-5}  & & Accuracy & \% & 0.5 & 2 ea \\ \cline{3-5}  & & Analog output & mA & 4-20 & \\ \cline{2-5}  & \multirow{2}{*}{Temperature (TM4101, IFM)} & Type & - & Pt 100 & \\ \cline{3-5}  & & Range & \({}^{\circ}\)C & 0-150 & \\ \hline \multirow{4}{*}{Mold} & \multirow{2}{*}{Cavity pressure (6161A, Kistler)} & Range & bar & 0-200 & \\ \cline{3-5}  & & Sensitivity & pC/bar & 18.5 & 4 ea \\ \cline{3-5}  & & Analog output & V & 0-10 & \\ \cline{3-5}  & & Type & - & K & \\ \cline{3-5}  & & Range & \({}^{\circ}\)C & 0-200 & 4 ea \\ \cline{3-5}  & & Analog output & V & 0-10 & \\ \cline{3-5}  & & Range & mm & 0-12 & \\ \cline{3-5}  & \multirow{2}{*}{LVDT (GT2-Keyence)} & Accuracy & \(\mu\)m & 1 & 4 ea \\ \cline{3-5}  & & Analog output & mA & 4-20 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor specification.  
Figure 5: Schematic representation of the monitoring system.  
Figure 6 shows the HMI programmed with LabVIEW 2017 (National Instruments, Austin, TX, USA ) for data acquisition. HMI is a Beckhoff panel touch screen (CP6902) connected to the embedded computer that provides all monitoring operator input/output including, particularly data saving. The following signal processing was applied to detect the process conditions of HP-RTM.  
The raw signals of the sensors acquired during the operation of the HP-RTM equipment and utilities were analyzed using Fast Fourier transform (FFT) and the filters were designed to eliminate noise components other than signals. The noise-removed signals were sampled at 1 kHz by a trigger signal and processed at high speed using an FPGA.  
The digitally processed signals were displayed on the HMI through signal processing analysis such as average, moving average, root mean square (RMS), and peak value. Figure 7 shows the embedded monitoring system that can be operated alone without connecting to another computer.
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 1 Introduction
One of the potential reasons is the contention-based random access, in which a huge number of devices may attempt several times to access the network, resulting in a high power consumption. Therefore, in the case of cellular MTCs, it is beneficial to model the DRX mechanism of LTE-A networks by considering the maximum possible parameters in order to estimate the device power consumption of random MTC traffic more precisely. The remainder of the paper is organized as follows. Section 2 presents an overview of the DRX mechanism in an LTE-A network in order to introduce how it works and the related parameters. Section 3 covers the existing efforts made for the modeling and the analysis of the DRX mechanism. In addition, a detailed description of the proposed model for analyzing the DRX mechanism for MTC devices is given in Section 4. The descriptions of the simulation setup and parameters followed by the results are provided in Section 6. In the end, conclusions are drawn in Section 7.
## 3 Results
### Further Analysis of New Model
#### 3.2.2 Application with the MODIS Data to Estimate \(R_{nd}\)  
Since \(C_{d}\) could be easily estimated from New Model at any time during 9:30-14:30 hours, it was thus used to obtain \(R_{nd\_cl\_NM}\) from the \(R_{nii}\) estimated from the MODIS TOA data by using [PERSON]'s model. Meanwhile, the \(R_{nd\_SIN}\) was also calculated from the same \(R_{nii}\) with the sinusoidal model (see Section 2.2.1) for comparison. Note that the average would be taken as the final \(R_{nd\_cl\_NM}\) or \(R_{nd\_SIN}\) estimate if more than one group of MODIS TOA data were available in one day. Afterwards, against the ground measurements, the two results (Figure 13) were compared and further analyzed by the available MODIS data in one day.  
Overall, the \(R_{nd\_cl\_NM}\) estimates (Figure 13b) almost distributed near to the 1:1 line and were closer to the ground measurements than that of the \(R_{nd\_SIN}\) estimates (Figure 13a), with overall validated R\({}^{2}\) of 0.88 and 0.84, RMSE values of 19.60 and 27.70 Wm\({}^{-2}\), and Biases of \(-0.76\) and 8.88 Wm\({}^{-2}\), respectively. The significant overestimation tendency was observed for the \(R_{nd\_SIN}\) when its value was less than 150 Wm\({}^{-2}\), and vice versa for the other values. Combined with the used MODIS TOA data, it can be seen that more \(R_{nii}\) in one day would help in obtaining a more accurate \(R_{nd}\) when either using the \(C_{d}\) or sinusoidal model, particularly for the \(R_{nd\_cl\_NM}\) when its RMSE decreased by 4.03 Wm\({}^{-2}\). If only one \(R_{nii}\) was available in one day, then the application of \(C_{d}\) could improve the  
Figure 12: The spatial distribution of the validation accuracy of the estimated \(R_{nd\_cl\_NM}\) at site scale: (**a**) R\({}^{2}\), (**b**) Bias (Wm\({}^{-2}\)), (**c**) RMSE (Wm\({}^{-2}\)), and (**d**) rRMSE.  
Figure 13: The scatter plots between the ground measurements and the (**a**) \(R_{nd\_SIN}\) and (**b**) \(R_{nd\_cl\_NM}\) estimates. The red and blue points represent the final estimates calculated from one group of MODIS TOA data or more, respectively.  
accuracy of the estimated \(R_{nd}\) (red points in Figure 13a) significantly compared with that from the sinusoidal model (red points in Figure 13b), yielding RMSE values of 20.56 and 28.14 Wm\({}^{-2}\) and Biases of 0.30 and 8.63 Wm\({}^{-2}\), respectively. Furthermore, in this case, the estimated \(R_{nd\_cd\_NM}\) were even more accurate than the \(R_{nd\_5\_IN}\) from multiple \(R_{ni}\) (blue points in Figure 13a) by decreasing the rRMSE and the magnitude of Bias by 5.11% and 9.29 Wm\({}^{-2}\), respectively.  
Therefore, the \(C_{d}\) obtained from New Model has strong potential to be widely used in \(R_{nd}\) estimation from the instantaneous satellite data in the future.
## 3 Proposed Method
### Multilevel Shared Decoder
#### 3.2.1. Upsampling by SimpleUp  
For applications in low-resource apparatuses and mobiles, the design of the upsampling block must be very lightweight to fit the restrictions of the target hardware. Therefore, we suggest a simple high-efficiency module for the upsampling block called SimpleUp. As shown in Figure 4, the encoding results \(r_{n}\)_and_\(d_{n}\) are blended with the last-level decoding result \(D_{n-1}\) together in the \(n\)th upsampling block (UP\({}_{n}\)) by element-wise addition. We adopt element-wise addition rather than concatenation, because concatenation will increase the channels of features and consume additional resources for the subsequent convolution operation. SimpleUp consists of a \(5\times 5\) convolution kernel to reduce the channel number by half followed by bilinear interpolation to double the spatial resolution at each layer. In the convolution layers of SimpleUp and UpCSPN-k, depthwise separable convolution is adopted over standard convolutions to further reduce the parameters of the decoding layers. At the beginning of SimpleUp and UpCSPN-k, element-wise addition is applied to blend the RGB features and depth features. In Figures 3 and 4, the first decoding result \(D_{0}\) of the multilevel shared decoder has 512 channels, i.e., \(C=512\).
# Progress of the Computer-Generated Holography Based on Deep Learning
## 4 The Learning-Based CGH
### Loss Function, Dataset, and GPU or CPU  
Loss function is a significant part of the training process that governs the network performance. Different loss functions lead to significant performance differences. The popular loss functions used in the learning-based CGH are mean square error (MSE) or root mean square error (RMSE). The MSE function measures the reconstruction quality by calculating the square of the error between the predicted value and the ground truth. The closer the predicted value and the ground truth are, the smaller the mean square is. The MSE can be defined as  
\[MSE(y,y^{\prime})=\frac{\Sigma_{i=1}^{n}\big{(}y_{i}-y_{i}^{\prime}\big{)}^{2} }{n} \tag{9}\]  
For the GAN network, its generator and discriminator need two loss functions. The GAN uses a binary cross entropy loss function to train D and G. The D and G are opposed to each other till the convergence. Combined loss functions were also proposed for the comprehensive constraints. For example, Holo-Encoder uses the NPCC and perceptual loss function. The training results are constrained from both the pixel-by-pixel aspect and the overall aspect [10]. The MDHGN uses the MSE and L1 loss to compare the  
Figure 8: (**a**) Experimental setup of the Holo–Encoder. (**b**) Evaluation of the algorithm running time and reconstruction quality. (**c**) POH predicted by the Holo–Encoder. (**d**) Experimental reconstruction of (**c**). (Adapted from [10]).  
target and reconstruction images [29]. Moreover, tensor holography introduces two wave-based loss functions to train the CNN for an accurate hologram. The first loss function measures the data fidelity, and the second loss function measures the perceptual quality of the reconstructed 3D scene [50]. The definitions of different loss functions are presented in Table 2 and the loss functions used in the CGH generation tasks are summarized in Table 3, respectively.  
Moreover, it is important to have a good dataset and a suitable GPU for network training. The DIV2K dataset is a popular training dataset in the above learning-based CGH tasks [10; 26]. The DIV2K dataset is a super-resolution reconstruction dataset, which is large enough to avoid the simple overfit. There are 1000 high-definition 2K images, including 800 for training, 100 for verification, and 100 for testing. A CPU running @ 3.20 GHz with 128 GB of RAM and NVIDIA Tesla V100 (32 Gb) are expected for the aforementioned algorithms [10; 25; 26; 50].
## 2 Materials and Methods
### HyPlant Processing Chain
#### 2.2.1 HyPlant Data Streams  
Processing Cluster I  
The first cluster is illustrated in Figure 2, which provides an overview of the data stream of both sensors and the navigation data of the Oxford RT3052 GPS/INS unit to the subsequent processing clusters. Each sensor's data stream consists of the raw image cube in the ENVI 'band interleaved by line' (BIL) format and the associated header file containing information on the data format, and the GPS start and stop time of the recorded dataset. Besides this, corresponding navigation information from the GPS/INS unit (including time, latitude, longitude, altitude and sensor orientation) is transferred to the second and third processing cluster. Furthermore, important sensor calibration information, determined in the calibration facilities of the sensor manufacturer (e.g., information on central wavelength position, FWHM, radiometric calibration coefficients and point-spread function (PSF) kernel (FLUO only)), are forwarded for further processing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & \multicolumn{2}{c}{**DUAL Module**} & **FLUO Module** \\  & **VNIR Sensor** & **SWIR Sensor** & \\ \hline _Spectral Performance_ & & & \\ Wavelength range [nm] & 373.6-975.3 & 980.49–2504.64 & 669.50–781.91 \\ Number of bands & 352 & 274 & 1024 \\ Spectral sampling interval (SSI) [nm] & 1.71 & 5.58 & 0.11 \\ Full width at half maximum (FWHM) [nm] & 3.65 & 10.55 & 0.28 at O\({}_{2}\)-A/ \\ Spectral shift [nm] & 0.05 & 0.24 & \(<\)0.01 \\ Smile [nm] & 0.4 & 1.2 & \(<\)0.01 at O\({}_{2}\)-A/0.01 at O\({}_{2}\)-B \\ _Radiometric Performance_ & & & \\ SNR with full-scale signal & (510) & (1100) & (296) O\({}_{2}\)-A/ \\  & & & (442) at O\({}_{2}\)-B \\ Stray light and pixel cross talk [\%] & & & \(<\)0.5 \\ _Spatial Performance_ & & & \\ Spatial pixels & 384 & 384 & 384 \\ Field of view [deg] & 32.16 & 32.16 & 32.02 \\ Instantaneous field of view [deg] & 0.084 & 0.084 & 0.084 \\  & 392 at 680 m alg1  & 392 at 680 m algl \\ Swath [m] & 1176 at 2040 m algl & 1176 at 2040 m algl & 1171 at 2040 m algl \\  & 1.02 at 680 m algl & 1.02 at 680 m algl & 1.02 at 680 m algl \\ Spatial sampling interval (across-track) [m] & 3.06 at 2040 m algl & 3.06 at 2040 m algl & 3.05 at 2040 m algl \\ _Sensor Type_ & & & \\ Type & CMOS & MCT & sCMOS 2  \\ Dynamic range [bit] & 12 & 14 & 16 \\ \hline \hline \end{tabular}  
* agl: above ground level; 2 SCMOS: ‘scientific CMOS’ chip–new charge-coupled device (CCD) chip technology that combines different enhancements to achieve high light sensitivity with linear sensitivity.  
\end{table}
Table 1: Technical characteristics of the HyPlant 3 airborne imaging spectrometer.