esa-sceva/satcom-chunk-collection huggingface dataset:
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Predictor Settings  
#### 4.2.1 Prediction Mode, Local Sum Type, Number of Prediction Bands  
Figure 9a illustrates the relationship between the compression ratio and the number of prediction bands, \(P\), showcasing the effect of different options for the prediction mode and local sum type. The figure indicates that utilizing the reduced prediction mode in conjunction with the neighbor-oriented local sum type yields the best performance. Not using any prediction band (i.e., \(P=0\)) results in the worst performance, as expected. The compression ratio increases along with the increment of \(P\) because the adjacent OPD bands have relatively few detail changes and strong inter-frame correlations, leading to good prediction performance. However, when the number of bands used for prediction reaches a certain value, the inter-band correlation decreases, and the improvement in prediction performance becomes less significant or even results in a decrease in prediction accuracy due to the marginal diminishing effect. We can see that the compression ratio curve tends to flatten or even decrease when the number of prediction bands is larger than five.  
Figure 8: Compression ratio for different choices of rearrange methods under different prediction mode and local sum type combinations.  
Figure 7: Correlation coefficients between each band of the LASIS, LAMIS, and LADIS.
## 4 Results and Discussion
### Reliability Assessment of Heat Transfer Models
This coefficient value leads to a lower temperature prediction of 511.8 \({}^{\circ}\)C, which is 7 \({}^{\circ}\)C lower than the CFD result of 519 \({}^{\circ}\)C. The lack of higher convective coefficients using the Woschni heat transfer correlation led to lower heat dissipation and increased fluid temperature inside the cylinder. These deviations in temperature prediction using the Woschni heat transfer coefficient are even higher at 210 and 255 CAD, where a deviation of 72.3 \({}^{\circ}\)C and 84.4 \({}^{\circ}\)C, respectively, can be observed. Due to higher temperature predictions, lower trapped mass can be observed for Woschni heat transfer calculations. About 7% lower trapped mass can be observed at 210 CAD, while a 12.5% lower trapped mass can be seen at 255 CAD. All these deviations are attributed to a lack of proper heat transfer coefficients. On the other hand, the temperature and trapped mass errors are lower using the proposed heat transfer model, which accounts for the high flow velocity inside the cylinder during the scavenging process and better approaches the CFD results. Due to this, the fluid inside the cylinder is adequately cooled, and lower fluid temperatures are seen. The proposed heat transfer model predicts about 64 \({}^{\circ}\)C lower temperature while comparing results with Woschni (at 210 CAD). This led to an accurate prediction of trapped mass at 210 CAD, although, at 255 CAD, CFD results depict about 201 \({}^{\circ}\)C, and the proposed model provides a 25 \({}^{\circ}\)C higher temperature but accurately predicts the trapped mass. Finally, observing trends in Figure 5B,C and Table 4, it can be said that the proposed heat transfer model accurately predicts the temperature and trapped mass by employing a higher convective coefficient compared with state-of-the-art models.
## 4 Experimental and Results Analysis
### Experimental Conditions and Experimental Design  
The experimental framework utilized in this study for deep learning models comprises PyTorch 2.2.2 and torchvision 0.17.2. The computational infrastructure consists of two Intel (R) Xeon (R) Gold 6326 CPU @ 2.90 GHz processors (Intel, Santa Clara, CA, USA) and four NVIDIA A100-PCIE-40 GB graphics cards (NVIDIA, Santa Clara, CA, USA). The experimental models include LSTM_TSC, THF_TSC, UniTS, UniTS_SAF, UniTS_SAM, and UniTS-SinSpec, as delineated in Table 4. The datasets employed are A1, A2, A3, B1, B2, and B3, as illustrated in Table 3. Distributed training is executed using PyTorch's distributed data parallel (DDP) tool, while hyperparameter optimization (HPO) is conducted utilizing Optuna. The initial parameter configurations for the models are detailed in Table 5.  
To assess the efficacy of the enhanced model and investigate the factors influencing its performance, this study designed three experiments, each comprising multiple groups aligned with specific experimental objectives. Each group underwent 20 epochs of training.  
* Experiment 1: assessed the performance of the UniTS baseline model and examined the impacts of the temporal span and sequence length on model training. The LSTM_TSC, THF_TSC, and UniTS models were trained using preprocessed datasets A1, A2, A3, B1, B2, and B3 to establish a total of six comparative experiments.
* Experiment 2: validated the effectiveness of the improved UniTS-SinSpec model through three ablation experiments. The first group utilized UniTS_SAF, the second group used UniTS_SAM, and the third group employed UniTS-SinSpec. The dataset used in this experiment was consistent with that used in Experiment 1, where optimal training results were achieved by the UniTS baseline model.
* Experiment 3: Optuna was employed to search for hyperparameters for optimizing the performance of the UniTS-SinSpec model. The original parameters are detailed in Table 5. This was similar to Experiment 1's dataset usage comparison against benchmark models, including UniTS_SAF and UniTS_SAM.
## 3 Results and Discussion
### Characteristics of the Amine Immobilized GMA-g-NWF and IRP
#### 3.2.3 Water Absorptive Capacity Analysis
The selectivity of the EDA-IRP sample unable to reach the plateau even after 480 min of the adsorption process. This may due to the multilayer adsorption process, which requires a longer time to reach the equilibrium. This may further be confirmed by sorption isotherm data [38] in a future study. This observation is complimentary with the adsorption capacity of EDA-IRP towards Cu. DETA-GMA-\(g\)-NWF, TETA-GMA-\(g\)-NWF, and TETA-GMA-\(g\)-NWF samples showed similar outcomes as well. The IRP samples own some cavities which are complimentary to imprinted Cu ion in terms of size and coordination geometry. Meanwhile, amine immobilized GMA-\(g\)-NWF shows a random distribution of functionalities in the polymeric network without any rebinding affinities specificity [39]. Therefore, amine immobilized GMA-\(g\)-NWF randomly adsorb any metal ions which preset in the absorption system without being selective towards Cu. The improvement on selectivity is limited as the equilibrium state was not achieved within the experimental duration. However, the selectivity still shows improvement almost 100 times by ratio. This is considered to be very good which align with previous findings [37]. The selectivity of IRP samples decreases as the aliphatic amines chain grows greater. The selectivity towards Cu ion reduces slightly with a trend of EDA-GMA-\(g\)-NWF\(<\) DETA-GMA-\(g\)-NWF\(<\) TETA-GMA-\(g\)-NWF. The chain length and size of aliphatic amine plays important factor in this. Complex structural coordination with longer aliphatic amine chains may inhibit diffusion of Cu ions into template cavities.
## 2 Method
### Controller  
In the developed multi-DOF myoelectric prosthetic hand, a motor is mounted inside the PP of all five fingers, and each finger performs flexion motion when the motor is driven. In this study, a controller with a six-axis motor driver was designed to independently perform the flexion motion of each finger. The designed controller consists of an EMG sensor interface for measuring EMG signals, a micro controller unit (MCU), motor driver, a current feedback sensor, a battery, and a Bluetooth communication module for the debugger (see Figure 3a). The EMG sensor interface is designed to detect two EMG signals from the residual muscles of the upper limb amputee, and the MCU was designed using a 32-bit CPU. The motor driver is designed with six axes to control the fingers independently, and the current feedback sensor is designed to measure the generated motor load when the finger is actuated. The battery voltage is 7.4 V; the input voltages of the MCU and Bluetooth communication module are 5 and 3.3 V, respectively, from the battery.  
In this study, we developed a controller based on the block diagram for the multi-DOF myoelectric prosthetic hand shown in Figure 3b. The developed controller uses the cortex-M4 micro-controller and can independently control six motors according to the EMG signals. In addition, the hand gestures and grip motions of the developed hand can be controlled through the feedback of motor load during finger actuation. The controller has dimensions of 38 \(\times\) 25 mm (W \(\times\) L).  
\begin{table}
\begin{tabular}{|c|c|c|} \hline \begin{tabular}{c} **Hand Prosthesis** \\ \end{tabular} & \begin{tabular}{c} **Weight** \\ **(W \(\times\), L) (mm)** \\ \end{tabular} & \begin{tabular}{c} **Weight** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **(g)** \\ **(\(\times\)/s)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Developed \\ hand prosthesis \\ (Figure 2) \\ \end{tabular} & 73.6 \(\times\), 174 & 120 or more & 37.5 & 60/0.58 & 503 \\ \hline \begin{tabular}{c} i-LIMB hand [27] \\ \end{tabular} & \begin{tabular}{c} 74.5 \(\times\), 182.5 \\ (small size) \\ \end{tabular} & 136 & 10.8 & 81.8/1 & 504 \\ \hline \begin{tabular}{c} Bebionic hand [2] \\ \end{tabular} & \
\begin{tabular}{c} 72 \(\times\), 165 \\ (medium size) \\ \end{tabular} & 140 & 34 & 96.4/1 & 591 \\ \hline
\begin{tabular}{c} Vincent hand [3] \\ \end{tabular} & 75 \(\times\), 160 & & 103.3/1 & \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of specifications with commercial multi-DOF myoelectric hand prostheses.  
Figure 2: Photograph of the developed multi-DOF hand prosthesis.
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 3 A Condition with Which to Compare Risk Evaluation Formulas
### Five Scenarios for One Formula Better Than Another  
In a previous study [14], formula \(R_{1}\) is said to be better than \(R_{2}\) with the condition \"\(S_{B}^{R_{1}}\subseteq S_{B}^{R_{2}}\) and \(S_{A}^{R_{1}}\supseteq S_{A}^{R_{2}}\), which means that the number of statements in \(S_{B}^{R_{1}}\) is smaller than that in \(S_{B}^{R_{2}}\), and the number of statements in \(S_{A}^{R_{1}}\) is larger than that in \(S_{A}^{R_{2}}\). However, our experimental results show that this is not the only scenario for one formula better than another. In the following, we present five scenarios when a risk evaluation formula \(R_{1}\) performs better than \(R_{2}\). We denote them as \(B_{(<)}A_{(>)}\), \(B_{(<)}A_{(=)}\), \(B_{(<)}A_{(<)}\), \(B_{(=)}A_{(>)}\), and \(B_{(>)}A_{(>)}\), where (1) \(B\) indicates the comparison pair between the number of statements in the subsets \(S_{B}^{R_{1}}\) and \(S_{B}^{R_{2}}\); (2) \(A\) represents the comparison pair between the  
Figure 2: Criteria of \(R_{1}\) performs better than \(R_{2}\) in theoretical study (e.g., [14]).  
number of statements in the subsets \(S_{A}^{R_{1}}\) and \(S_{A}^{R_{2}}\); and (3) \(<,=\), and \(>\) respectively mean the former number is smaller than, equal to, and larger than the latter one. They are  
1. \(B_{(<)}A_{(>)}\): When \(n_{B}^{R_{1}}<n_{B}^{R_{2}}\) and \(n_{A}^{R_{1}}>n_{A}^{R_{2}}\), we have \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_ {2}}\).
2. \(B_{(<)}A_{(-)}\): When \(n_{B}^{R_{1}}<n_{B}^{R_{2}}\) and \(n_{A}^{R_{1}}=n_{A}^{R_{2}}\), we have \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{1}}=n_{B}^{R_{2}}-n_{A}^{R_ {2}}\).
3. \(B_{(<)}A_{(<)}\): When \(n_{B}^{R_{1}}<n_{B}^{R_{2}}\), \(n_{A}^{R_{1}}<n_{A}^{R_{2}}\) and \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{2}}\), it is obvious that \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{2}}\).
4. \(B_{(=)}A_{(>)}\): When \(n_{B}^{R_{1}}=n_{B}^{R_{2}}\) and \(n_{A}^{R_{1}}>n_{A}^{R_{2}}\), we have \(n_{B}^{R_{1}}-n_{A}^{R_{1}}=n_{B}^{R_{2}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_ {2}}\).
5. \(B_{(>)}A_{(>)}\): When \(n_{B}^{R_{1}}>n_{B}^{R_{2}}\), \(n_{A}^{R_{1}}>n_{A}^{R_{2}}\) and \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{2}}\), it is obvious that \(n_{B}^{R_{1}}-n_{A}^{R_{1}}<n_{B}^{R_{2}}-n_{A}^{R_{2}}\).  
Figure 3 depicts these five different scenarios, which cover all possible cases when one risk evaluation formula performs better than another one.
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
###### Abstract  
Rice seedling segmentation is a fundamental process of extracting the guidance line for automated rice transplanters with a visual navigation system, which can provide crop row information to ensure the transplanter plants seedlings along the crop row without damaging seedlings. However, obtaining accurate rice seedling segmentation in paddy fields is still a challenging task. In this paper, a rice seedling segmentation method in paddy fields is proposed. The method mainly consists of two steps: image graying and threshold segmentation. In the procedure of image graying, the RGB (Red Green Blue) seedling image is first converted into the YCrCb color space and a Cg component is constructed. A color-index 2 Cg-Cb-Cr is then constructed for image graying based on the excess green index (2G-R-B), which can reduce the influence of illumination variation on the equality of image graying. For the second step, an improved Otsu method is proposed to segment rice seedlings. With respect to the improved Otsu method in this research, the background variance of within class variance is weighted by a probability parameter to ensure that the method works well for both bimodal and near-unimodal histogram images, and the search range of gray levels is constrained to reduce the time to search the segmentation threshold. Experimental results indicate that the proposed method achieves better segmentation results and reduces the computational cost compared with the traditional Otsu method and other improved Otsu methods.  
automated rice transplanter; rice seedling segmentation; YCrCb color space; Otsu method; excess green index +
Footnote †: journal:
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 3 Method
### Detail-Preserving Gradient-Guided Strategy (GGS)  
Considering that the gradient magnitude is able to effectively reflect the frequency of the image signal, in other words, the gradient magnitude is higher in the edge and texture regions corresponding to the high-frequency regions, and lower in the smooth regions corresponding to the low-frequency regions, the gradient information is able to highlight the high-frequency components beneficial for the SR task and filter out redundant low-frequency components. Consequently, a detail-preserving Gradient-Guided Strategy (GGS) is proposed to take advantage of the gradient prior from the LR images. Specifically, we design a novel gradient branch to adaptively strengthen high-frequency details and to provide gradient prior for the SR task. At the same time, we propose a compact Gradient-Guided (GG) loss that leads GPSR to infer appropriate gradient information at the suitable positions by learning the gradient space to avoid blindly recovering details.
## 3 Results
### In Vitro MRI  
To demonstrate the T1 and T2 effects of MNPs and LHRH-MNPs in vitro, MR images of the aqueous solutions, with different nanoparticle concentrations, were obtained (Figure 4). Figure 4 shows the T1- and T2-weighted MR images of the as-prepared MNPs and conjugated LHRH-MNPs, in which the Fe concentrations were 0.025, 0.05, 0.1, 0.2, 0.4, 0.8 mg (Fe)\(\cdot\)mL\({}^{-1}\), from left to right. Solutions of both types of nanoparticles exhibited concentration dependent hyperintensities and hypointensities in T1- and T2- weighted maps, respectively, as measured using a 1 T magnetic coil system. In comparison with DI water, the signal intensity (SI) of T2-weighted phantom images decreased for both types of nanoparticles (Figure 4C,E). In contrast, the brightness of the T1-weighted MR images first increased with increasing Fe concentration up to approximately 0.1 mg (Fe)\(\cdot\)mL\({}^{-1}\) then decreased, as revealed by the trend of SNR (Figure 4B,D). These results suggest that both the MNPs and the LHRH-MNPs have the potential to serve as T2-based contrast agents, given the darkening consistency in T2 compared to T1. Thus, the in vitro study provided insights into the effects of nanoparticles on MRI signals, which are critical to understand before proceeding to in vivo studies.  
Figure 3: (**A**) UV-Vis spectra of MNPs at different concentrations, showing peak absorbance around 480 nm. (**B**) Absorbance versus concentration calibration curve for MNPs, with correlation coefficient (r\({}^{2}\)) of 0.99.
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 4 Conclusions  
The in situ studies of marine particles during the Arctic expedition using a submersible digital holographic camera (DHC) made it possible to draw the following conclusions on the features and possibilities of the DHC and the DHC technology:  
1. The DHC technology in the described configuration has the following performance characteristics (normalized per 1 m of the measured profile in depth): * Holographic sampling and recording time-3.5 min/m; * Required memory-400 MB/m; * Data processing-1 h/m.
2. The DHC technology can be used for noninvasive automatic evaluation of spatial and temporal distributions of plankton concentrations. However, the competent accounting of zooplankton taxonomy at the level of the main systematic orders requires that the signatures serving as the basis for the decision be significantly complicated (compared to a rectangle) and supplemented to achieve errors lower than 30%;
3. The DHC technology can be used to obtain additional information on the medium, namely: * Water turbidity estimated according to the radiation shielding factor (degree) by particles of the _Suspension_ taxon. Turbidity data obtained using the DHC technology is compared with that measured using a turbidimeter. The correlation coefficient between turbidity measurements using the turbidimeter and the DHC was 75.5%;
4. The DHC technology has certain prospects for its use in the biogeochemical contrast conditions of the East Siberian Arctic Seas. This requires revising the methodology of data selection in the following areas: * Holographic survey data shall be averaged along a large area-1 m\({}^{2}\) of water surface along the path of the vessel; * Holographic data shall be strictly bound to an acoustic survey; * In-lab DHC vs. generated bubble flux studies.  
These conclusions constitute a sufficient basis for the development of a towed holographic probe, which will make it possible to raise complex biogeochemical and methane studies in the East Siberian Arctic Seas to a new level.  
Conceptualization, V.D., I.S. and I.P.; data curation, I.P.; formal analysis, D.C., O.K. and A.O. ([PERSON]); funding acquisition, V.D.; investigation, I.S., D.C., E.C., O.K., A.O. ([PERSON]) and A.O. ([PERSON]); methodology, V.D., I.S. and I.P.; project administration, A.O. ([PERSON]); resources, V.D., I.S. and A.O. ([PERSON]); software, A.D. and N.K.; supervision, V.D. and I.P.; validation, D.C., E.C., O.K. and A.O. ([PERSON]); visualization, D.C. and A.D.; writing--original draft, A.D. and I.P.; writing--review and editing, V.D., I.S., D.C., O.K. and A.O. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The preparation and conduct of the expedition (Sections 2.1 and 2.2) were supported by the Tomsk State University Development Program (Priority-2030). Data processing and analysis (Section 2.3) were supported by the grant of the Russian Science Foundation (project 20-17-00185).  
Not applicable.
# High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Using Multi-Source Observations
## 2 Materials and Methods
### Two-Way Superimposition Strategy
#### 2.1.2 Problems  
Taking the IGSO-1 and MEO-1 as examples, the detrended 24 h two-way clock offsets calculated by the two-way superimposition strategy are plotted in Figure 3.  
In Figure 3. the red curves are the clock offsets only calculated by L-band two-way clock. The orange curves are the clock offsets calculated by C-band inter-station two-way clock offsets and L-band two-way clock offsets. The blue curves are the clock offsets through Ka-band two-way inter-satellite clock offsets, and the blue curves with different depths represent the clock offsets calculated through different intraregional satellites. Figure 3 demonstrates that whatever satellite is or is not over China. The two-way superimposition strategy can always measure their clock offsets. However, this figure also presents its shortcoming and problems:  
* Because there is little difference between the L-band uplinks and the L-band downlinks, Equation (1) cannot effectively eliminate the ionosphere delay and multipath effects. When the elevation of the L-band antenna is not low enough, the above residual errors will increase rapidly and decrease the accuracy of L-band two-way clock offsets (1 in Figure 3).
* Because the noise of C-band measurements is far from other bands' measurements. The noise of clock offsets will become large when the satellite clock offsets are calculated through C-band two-way inter-stations clock offsets (2 in Figure 3).
* Affected by temperature, antenna attitude, illumination, etc., the time delay of antennas has fluctuations, which are difficult to predict. Therefore, once the tracking antenna changes, the \"step errors\" will appear in the clock offsets (3 in Figure 3).
* The BDS-3 follows a time-division multiple access (TDMA) structure. One satellite connects with different visible satellites at different time in one connectivity cycle. Because an overseas satellite establishes links with different satellites frequently, the different \"step errors\" will be reflected in the clock offsets of overseas satellites, which are like the noise (4 in Figure 3).  
The above reasons lead to poor accuracy of BDS-3 clock offsets, which is the main bottleneck limiting BDS-3 from improving itself signal-in-space accuracy. Therefore, based  
Figure 3: The typical one-day detrended two-way clock offsets of IGSO and MEO measured by BDS-3 two-way superimposition strategy. 11�  
on the BDS-3 multi-source measurements, BDS-3 needs to urgently design a new strategy to measure the satellite clock offsets.