esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results  
In this section, a series of experiments are conducted to investigate the advantages of the proposed imaging mechanism. We first compare the performance of conventional imaging correlography with ptychographic imaging correlography, and then analyze the effects of the number of speckle patterns on the proposed imaging method. Finally, we compare the performance of different reconstruction algorithms, including error reduction (ER), Wirtinger flow (WF), amplitude flow (AF), and amplitude flow with RED (AF-RED).
# Value of Information Based Data Retrieval in UWSNs
## 5 Performance Measures, Simulation Setup and Results
### Results
#### 5.3.2 Justification of Heuristics  
The algorithms proposed in this paper are based on different heuristics. In this section, we verify whether our intuitions behind those heuristics are valid or not. We use \(\Upsilon_{ACC}^{n}\) (values normalized with respect to RPP) and study the performance of the path planners for the case where the number of hot-spots \(N_{HS}=\{0,1,10\}\).  
Our first hypothesis was that using VoI aware algorithms may help in accumulating a higher amount of VoI. From the results in Figure 4 we can see that all path planners have a value greater than 1.0, i.e., they are all better than RPP, and hence, suggesting that the hypothesis is correct.  
Our second hypothesis was that greedy path planners amass a higher VoI when there are hot-spots. However, when there are no hot-spots, a time minimization planner like the Lawn-Mower accumulates a higher VoI. This hypothesis is validated through the results in Figure 4a. When \(N_{HS}=0\) LPP performs best, but in the case of hot-spots, the greedy approach accumulates more VoI.  
Our third hypothesis was that inter-node traversal helps in minimizing time while still maintaining its VoI greedy character, thereby, improving the VoI accumulated. GIPP, for instance, visits hot-spots on priority but also visits other nodes that lie along the path. Figure 4b confirms this intuition where GIPP accumulates more VoI than GPP.  
Our fourth hypothesis was that hybrid path planners can provide the best of both worlds, i.e., when \(N_{HS}=0\) they behave like time minimization path planners and when there are hot-spots they use greedy techniques for scheduling purposes (until all hot-spots have been visited). Figure 4c,d are a proof for this. When there are no hot-spots HPP performs as good as the LPP (because their algorithmic construction, in this case, is the same) and in case of hot-spots, they accumulate more VoI than their greedy counterparts, i.e., GPP and GIPP. Figure 4c shows that the hybrid algorithm is better  
Figure 4: Bar graphs for justifying the use of various heuristics: (**a**) Greedy vs. Lawn-Mower; (**b**) Inter-Node Traversal Advantage; (**c**) Hybrid Advantage; (**d**) Hybrid vs. Greedy counterparts.  
than both LPP and GPP while Figure 4d shows that hybrid algorithms are better than their greedy counterparts in both cases, i.e., with or without intermediate node visitation.
## 9 Identification of Flutter Derivatives from SDOF Tests  
The flutter derivatives for vertical (\(H_{1}^{*}\) and \(H_{4}^{*}\)) and torsional (\(A_{2}^{*}\) and \(A_{3}^{*}\)) DOFs were extracted from the SDOF heave and pitch vibration responses. The basic experimental parameters used for SDOF testing and the calculation of flutter derivatives are listed in Table 2. The initial displacements given in the vertical and torsional DOFs were \(h_{0}=4\) cm and \(\alpha_{0}=5^{\circ}\), respectively. The required mechanical and effective system parameters were obtained from wind-off and wind-on responses. The obtained direct flutter derivatives are plotted in Figures 23 and 24. Additionally, these derivatives are compared with the flat plate derivatives and the derivatives obtained from the selected wind tunnel studies [2,42] on the Great Belt section. It is important to mention that the derivates obtained from the literature are manually digitized and, therefore, subject to uncertainties.  
Flutter Derivatives Using the Hilbert Transform Method, Logarithmic Decrement Method, and Exponential Decay Curve Method  
The derivatives obtained using Hilbert transform and logarithmic decrement methods and by using the polynomial fit of exponential decay curve are shown in Figure 23. The \(H_{1}^{*}\) derivative does not show good agreement with the reference experimental results but shows quite good agreement with the flat plate at low reduced wind speeds, whereas \(A_{2}^{*}\) illustrates a good correspondence with the reference experimental results. Overall, both derivatives \((H_{1}^{*},A_{2}^{*})\), computed from different methods, demonstrate a similar trend. Additionally, \(A_{3}^{*}\) and \(H_{4}^{*}\) derivatives obtained from HTM show close correspondence with the reference experimental values. The \(A_{2}^{*}\) and \(A_{3}^{*}\) have similar results in all three methods but have a shift in values when compared with the flat plate by [PERSON].
# Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition
## 4 Proposed Variable Rate IndRNN
### Variable Rate Sampling Strategy and Network Architecture  
To make the network more robust to various sampling rates and performing speeds of different sequences, inputs of different sampling rates and performing speeds need to be provided for training. With respect to the performing speeds (per frame) of the captured data, it can be changed in the same way by changing the sampling rates used to generate the input to the network. Therefore, in the proposed method, variable sampling rates are used to provide inputs of both different sampling rates and performing speeds to the network. In the following, the variable rate sampling strategy based on the equal-length sampling used in the skeleton-based action recognition task is explained, and it can be easily extended to other tasks.  
For one skeleton sequence, the inputs extracted with different sampling rates would be of different lengths. Figure 2 illustrates the sampling strategy with different sampling rates. First, the conventional sampling method is shown in Figure 2a, where N is the length of the sampled input. Assume there are L frames in the sequence, then the sampling rate is L/N. In the training process, the sequence is separated into N segments, and each frame is sampled from one segment to compose the final input. Although for different inputs, frames sampled from one segment may be different, the overall sampling rate and speed are the same. The proposed variable rate sampling strategy uses different sampling rates to generate inputs of different rates and speeds. Figure 2b,c illustrates two examples with higher (2\(\times\)) and lower (1/2\(\times\)) sampling rates, respectively. It is obvious that with higher sampling rates, more frames are contained in the input, and vice versa.  
Figure 1: Illustration of the difference between RNN and IndRNN. IndRNN removes the crossed recurrent connections among neurons (in dotted lines) to simplify the recurrent computation and, thus, the gradient backpropagation.  
In the proposed method, IndRNN is used as the classification network. The architecture of the IndRNN network with variable sampling rates is shown in Figure 3. It is composed of several (denoted by \(L\)) IndRNN layers (with batch normalization layers inserted after each IndRNN layer). A fully connected (FC) layer with the softmax function is used for the output. With the inputs of variable lengths, the RNN network also processes dynamic time steps. As shown in Figure 3, when the sequence is longer, IndRNN will keep processing more inputs, and the fully connected layer will be added at the end of the time steps for classification. The same applies to shorter sequences, where fewer time steps are processed. Since the network processes inputs of different lengths, the statistics of the batch normalization layers at each time step are kept individual, while the weight parameters are shared over time.
# The Effect of the Water Table on the Bearing Capacity of a Shallow Foundation
## 1 Introduction
### Influence of Saturation on Mechanical Property of Building Foundations
At the same time, the cohesion and internal friction angle gradually decrease with the increase in saturation. The degree of influence of saturation on the compressive index and compressive modulus was almost the same, and the degree of influence on the cohesion was larger than that of the internal friction angle. It is generally believed that the properties of the soil itself, the structure and composition of the soil, and the bonding between the fine particles affect its shear strength index. The cohesion of soil [35; 36; 37; 38] is the result of the interaction of attraction and repulsion between soil particles, which mainly comes from the mutual attraction between soil particles, the cementation between particles, the mutual connection of water films, etc., while the friction angle [39; 40] mainly includes sliding friction and bite friction between solid particles. For low liquid limit clay, with the increase of water content, the lubrication effect of water between particles gradually increases, the bonding force of water film gradually decreases, and the relative motion between soil particles becomes larger. The friction resistance of the clay decreases gradually, and its ability to resist deformation gradually decreases so that the cohesion, internal friction angle, and compressive modulus of the low liquid limit clay gradually decrease, and the compressibility coefficient increases gradually. This will closely relate to the bearing capacity and burial depth of the buildings in the immersion area of the reservoir.
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 5 Conclusions  
In the context of difficulties in obtaining data and high verification costs for the PMSM servo system of spacecraft in real-world scenarios, a theoretical model of the spacecraft PMSM servo system was established based on the characteristics of elasticity, backlash, and friction. Based on the control principle of PMSM and mechanical transmission, the mechanism of three nonlinear factors in servo systems is described in detail, which can comprehensively characterize the impact of various nonlinear factors on the system. Based on the actual data, the use of sinusoidal sweep frequency and the least squares method for parameter identification greatly improves the accuracy of the model, making the output more closely related to the real situation. This can provide reliable and effective theoretical and technical support for the research on high-performance control methods and system optimization design.  
Conceptualization, Z.S., H.X. and X.X.; methodology, Z.S., H.X. and X.X.; validation, H.X., Z.S. and Y.M.; investigation, X.X.; resources, Y.M.; data curation, H.X., P.W. and W.Z.; writing--original draft preparation, Z.S., W.Z. and P.W.; writing--review and editing, H.X.; supervision, Z.S.; project administration, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2020 YFE0205400), the National Natural Science Foundation of China (52337002 and 52305541).  
Not applicable.  
The data are not publicly available due to copyright issues with co-developers. They are restricted to the experimental results.  
The authors declare no conflicts of interest.  
Figure 15: Simulation and experimental position waveforms under sinusoidal wave position instruction.
# Pile Base and Shaft Capacity under Various Types of Loading
## 5 Discussion  
The main purpose of the measurements was to compare the results of the bearing capacity of the pile base and pile shaft in different load conditions. For the pile base, slight differences were observed between the results from the static load test and the bidirectional static load test. For laboratory tests, the ratio \(R_{b,s}/R_{b}\) is in the range of 0.91-1.03, and 0.88-1.11 for numerical tests. Therefore, the maximum difference between different types of tests reaches 12%. This shows that the load-applying method has little effect on the value of the pile base bearing capacity.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Soil** & **Interface** & **Rb (kN)** & **Rc (kN)** & **Rs (kN)** & **Rb,s (kN)** & **Rs,s (kN)** & **Rb,s/Rb (-)** & **Rs,s/Rg (-)** \\ \hline \multirow{3}{*}{FSa} & 0.666 & 136 & 298 & 218 & 129 & 176 & 0.95 & 0.81 \\  & 0.750 & 136 & 368 & 239 & 133 & 189 & 0.98 & 0.79 \\  & 0.900 & 136 & 423 & 353 & 130 & 248 & 0.96 & 0.70 \\ \hline \multirow{3}{*}{MSa} & 0.666 & 358 & 534 & 174 & 333 & 173 & 0.93 & 0.99 \\  & 0.750 & 358 & 575 & 224 & 346 & 201 & 0.97 & 0.90 \\  & 0.900 & 358 & 740 & 379 & 353 & 263 & 0.99 & 0.69 \\ \hline \multirow{3}{*}{CSa} & 0.666 & 573 & 716 & 222 & 634 & 188 & 1.11 & 0.85 \\  & 0.750 & 573 & 723 & 279 & 596 & 214 & 1.04 & 0.77 \\ \cline{1-1}  & 0.900 & 573 & 1061 & 482 & 505 & 316 & 0.88 & 0.66 \\ \hline \end{tabular}
\end{table}
Table 4: Bearing capacity obtained from the numerical simulations.  
Figure 11: Results of bidirectional and traditional static load test simulations for coarse sand and different interfaces: (a) R = 0.900, (b) R = 0.750, (c) R = 0.666.  
However, comparing the shaft bearing capacity reveals larger differences between loading methods. For laboratory tests, the ratio \(R_{s,s}/R_{s}\) is in the range of 0.51-0.89, and for numerical tests in the range of 0.66-0.99. Therefore, the differences reach 49% in the laboratory tests and 34% in the case of numerical simulations. The differences are slightly larger than those reported in the literature [19]. From the numerical simulations, it was observed that larger differences occur for higher values of the \(R\) coefficient of the interface layer. This shows that the rougher the pile surface is, the greater the \(R_{s,s}/R_{s}\) ratio can be expected.  
However, it should be mentioned that the measured values of shaft bearing capacity are different from the normal compression and uplift conditions. The uplift capacity is based on bidirectional tests, while the compression capacity is constructed from the full static test results of the whole pile and pile base. This may cause slightly different results to what the literature indicates. The analysis did not also include the residual values, which, when taken into account, could result in smaller differences.
## 2 Materials and Methods
### Single Event Effect (SEE) Mitigation  
Two kinds of radiation phenomenons are typically considered in ASIC design: cumulative (TID) and temporary (SEE) effects. The former affects mainly analogue circuitry changing transistor parameters, while the latter mostly affects digital blocks generating parasitic pulses. If the radiation induced pulse is created and propagated in combinational logic it is called Single Event Transient (SET) [28], but when it changes a memory element state, for example, a flip-flop (directly or indirectly by latched SET), it becomes a Single Event Upset (SEU) [29].  
The 130 nm CMOS processes are considered as radiation resistant for moderate radiation doses. Therefore, no special provisions were applied against TID effects in the SALT analogue part, except for the 8-bit baseline trim DACs, drawing very small currents (\(<\)1 mA), where NMOS enclosed layout transistors [30] are used.  
There are many different SEE mitigation techniques [31]. Most of them utilise some kind of redundancy to be able to recover the correct information in a radiation affected node or bit. For the digital circuitry in SALT, a partial Triple Modular Redundancy (TMR) [32] was chosen as more complicated approaches are impractical or impossible to implement due to the available power budget and limitations on the amount of material in the active detector volume. The mitigation of the effects of SEE was restricted to flip-flops and two critical global nets: clock and asynchronous reset, as shown in Figure 7. The TMR mitigates SEUs, which was recognised as the predominant source of radiation errors taking into account the technology and the main clock frequency [33; 34]. Protection of global nets against SEU is crucial. Without it, a single SET in a clock or reset net can affect many flip-flops in the digital part.  
Figure 6: PLL block diagram: PFD—Phase and Frequency Detector, CP—Charge Pump, LPF—Low Pass Filter, VCO—Voltage Controlled Oscillator.  
The selected TMR architecture has some consequences. Functional blocks, such as the reset synchroniser or PLL, located inside the reset or clock trees, have to be triplicated with great care. To ensure the correct operation of SerDes block, each PLL output can be used as a fast clock only after its internal synchronisation (see _pll_connect_ signal in Figure 6). Moreover, clock frequency and phase manipulation divide the chip in several clock domains, using triplicated synchronisers in several places (for details see [35]).  
Configuration registers, in addition to the normal triplication, have also protection against SEUs, as they must keep their values for a long time. Each register has an error detection circuit, which refreshes it automatically when an error is detected. Registers are organised in blocks and each block is connected to the SEU counter, which is incremented by one when an error is corrected. The counters allow for the calculation of the SEU cross-section in real experimental conditions.
## 1 Introduction
Inspired by Swin-Transformer, we aim to propose a Swin-Transformer-based network framework for segmenting the liver and tumor in CE-MRI images, addressing the needs of clinical practice and surgical procedures. As illustrated in Figure 1, the exact location and shape of the liver and tumor are distinctly outlined. Following the administration of a contrast agent, the blood flow dynamics of the tumor and normal liver differ, leading to relatively clear boundaries on CE-MRI images. However, the tumor's boundary can be either smooth or irregular and may vary in size. Thus, accurately locating the tumor within the liver and delineating its boundaries is essential. To capture the global spatial relationships between the liver and tumor, we have designed a Swin-Transformer-based U-Net framework that extracts global features from the CE-MRI. Furthermore, we integrated attention gate and SDM regression tasks into the Swin-Transformer-based encoder-decoder architecture, providing additional shape and global constraints for the segmentation task. These enhancements significantly improve the model's ability to capture local details of small targets, such as tumors. Deep-learning methods typically require large amounts of annotated data, but in medical imaging, only a small portion is labeled, leaving much data unannotated. Self-supervised learning is an innovative approach that enables the model to learn relevant visual features for segmentation tasks through pretraining on pretext tasks. Compared to CNN-based architectures, ViT-based frameworks can learn more stable visual representations during pretraining on these tasks [22]. Recent studies have developed creative pretext tasks, which yield promising results when fine-tuned for downstream tasks [23; 24; 25; 26; 27; 28]. In our approach, we apply contrastive learning to differentiate between various image regions, enabling the model to capture more meaningful data representations. We pretrain the encoder using contrastive learning to acquire valuable visual features, which are then refined through supervised learning in the subsequent training phase. The main contributions of our work are threefold:  
1. We propose a deep-learning architecture based on the Swin-Transformer structure, which is employed to capture the spatial relationships and global information for liver and tumor segmentation. 2. To enhance the Swin-Transformer framework's ability to capture local details of small targets (such as tumors), we incorporate attention gate and SDM regression tasks into the Swin-Transformer-based encoder-decoder architecture, providing additional shape and global constraints for the segmentation task. 3. We design an upstream pretext task to pretrain the proposed network, enabling it to learn effective visual features from a larger amount of unlabeled data, thereby enhancing the model's performance. Our model is evaluated on a dataset of 192 CE-MRI from liver cancer patients, with performance compared against other segmentation approaches. Additionally, we conduct several ablation studies to demonstrate the significance of our key design choices.
## 3 Results and Discussion
### TPPE in the Down1, Up2, and Down2 Scan Processes and Activation Energy  
The TPPE plots during all of the temperature scan processes for sample A and sample C are shown in Figures 3 and 4, respectively. The values of \(\Delta\)E\({}_{\text{down1}}\), \(\Delta\)E\({}_{\text{up2}}\), and \(\Delta\)E\({}_{\text{down2}}\) for sample A, and \(\Delta\)E\({}_{\text{CDown1}}\), \(\Delta\)E\({}_{\text{Up2}}\), and \(\Delta\)E\({}_{\text{CDown2}}\) for sample C are given in Tables 1 and 2, respectively. It is seen that these values are negative and almost independent of the liquids, and further, the absolute values of \(\Delta\)E\({}_{\text{down1}}\) were considerably smaller than those of \(\Delta\)E\({}_{\text{uUp1}}\). Regarding the electrical resistance (\(\rho\), ohm-cm) of Cu2O (p-type semiconductor), it was reported that the electrical resistance increases with decreasing temperature [41]. From the data of the electrical resistance in Figure 13.3 of Ref. [41], we obtained the activation energy of \(-0.774\) eV by applying an Arrhenius plot. On the other hand, the conductivity (\(\sigma\)) of Cu2O was investigated by [PERSON] and [PERSON] [42]. These authors found that the conductivity increases with the increasing oxygen content of Cu2O. It was found that the conductivity of Cu2O can be represented by Formula (5):  
\[\sigma=A_{1}\text{e}^{-\epsilon 1}/kT+A_{2}\text{e}^{-\epsilon 2}/kT \tag{5}\]  
where \(\epsilon_{1}\) = 0.7 eV, \(A_{1}\) \(\sim\) 100 cm\({}^{-1}\cdot\)ohm\({}^{-1}\), both being independent of the oxygen content, and \(\epsilon_{2}\) varies between 0.129 and 0.134 eV, while \(A_{2}\) depends strongly on the oxygen content. For specimens with about 0.1% of weight of excess oxygen, \(A_{2}\) \(\sim\) 0.3 cm\({}^{-1}\cdot\)ohm\({}^{-1}\). These authors suggested that the first term represents the conductivity of pure Cu2O, and that the second term represents the conductivity due to excess oxygen [42; 43]. In the present TPPE experiment, we think that the adsorbates on the sample surface were fully desorbed by the temperature increase scan to 350 \({}^{\circ}\)C, leaving adsorbed oxygen. Compared to the activation energies of the electrical resistance of \(-0.774\) eV in Ref. [41] and of the conductivity of \(\epsilon_{1}\) = 0.7 eV [42; 43], it is seen that the experimental values of TPPE obtained in the present experiment are remarkably small: for sample A, \(\Delta\)E\({}_{\text{down1}}\) varies between \(-0.131\) and \(-0.161\) eV, \(\Delta\)E\({}_{\text{Up2}}\) = \(-0.131\) eV, and \(\Delta\)E\({}_{\text{down2}}\) = \(-0.214\) eV; for sample C, \(\Delta\)E\({}_{\text{CDown1}}\) varies between \(-0.101\) and \(-0.175\) eV, \(\Delta\)E\({}_{\text{CUp2}}\) = \(-0.158\) eV, and \(\Delta\)E\({}_{\text{CDown2}}\) = \(-0.222\) eV. We consider that the PE in the Down1, Up2, and Down2 scan processes can be controlled by the electrical resistance or the conductivity of the overlayer. Therefore, it is thought that the small activation energy values may be due to excess oxygen content in the Cu2O formed on the copper surfaces.