esa-sceva/satcom-chunk-collection huggingface dataset:
Thus, for example, for a central detector setting (sample-detector distance equals the collimation length), an ideal mapping of the sample aperture would lead to a pixel size identical to that of the generally used sample aperture (\(10\times 10\) mm\({}^{2}\)), while the chosen pixel resolution is \(6\times 6\) mm\({}^{2}\). On the other hand, for the shorter detector position of the front detector, this still improves the resolution. The wavelength resolution of the instrument is mainly determined by the rejection of unwanted neutrons in a selector or, as is the case with SKADI, by the time discrimination of arriving neutron pulses with a spread of kinetic energy and a time-of-flight readout of the detector. In order to transfer the highest amount of neutrons per second, solid angle, angular divergence, and time, i.e., brilliance, to the sample the complete instrument has to be considered, since any component that is influencing the flow of neutrons can also absorb or deflect them. Special focus, in this case, has to be given to the extraction of neutrons from the moderator in order to maximize the brilliance transfer while avoiding a direct line of sight between sample and moderator to minimize unwanted background, for example, from \(\gamma\)-radiation. The general feasibility for different experiments is again a holistic issue concerning all components of the instrument. The instrument needs to facilitate large, unconventional, or custom made sample environments, delivering the correct choice of resolution for the experiment at hand, while performing it in a timely manner. The control software needs to allow to adjust the experimentally relevant parameters, while allowing a good overview of everything that is happening at the same time and not distract the user from the experiment at hand. For SKADI, in fact, the whole instrument suite of ESS, NICOS [8] as a control software was chosen, and the appropriate requirements are being implemented. The data will be recorded in event-mode, i.e.,with a time-stamp for each single neutron event on the detector. This will allow for cross-referencing additional settings of motor positions or sample environments to a specific neutron event. Finally, the data that will be provided to the user need to be in a format that facilitates the work flow of data analysis. The description will treat the components following the path of the neutrons from the moderator to the detector in order to give a full overview of SKADI in a comprehensive manner. All of the simulations shown here have been carried out using the McStas framework [9].
## 3 The Proposed Imputation Method  
As shown in Figure 2, our proposed imputation method consists of two main steps. Given data that include missing values, our method first applies two main preprocessing steps to prepare the data for the subsequent stages by removing features and data points that lack sufficient information. Next, for each feature, our method iteratively selects the most highly correlated features to build a regression model for estimating the missing values. The main notations used in this paper are listed in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Notation** & **Meaning** & **Notation** & **Meaning** \\ \hline \(N\) & No. of solutions & \(\mathbf{x}_{i}\) & The position of the \(i\)th particle \\ \(\mathbf{v}_{i}\) & Velocity of the \(i\)-th particle & \(c_{i}\)-\(c_{2}\) & Acceleration coefficients \\ \(A,C\) & Coefficients of WOA and GWO algorithms & \(\omega\) & Inertia weight \\ \(t\) & Iteration no. & \(\mathbf{Max}_{init}\) & Maximum no. of iterations \\ \(r_{i}\) & Random variable & \(\mathbf{p}_{i}\) & The previous best position of the particle \(i\) \\ \(\mathbf{G}\) & The position of the best solution & Constant for defining the shapefibe (SCA) \\ \(D\) & Original dataset & \(F=\{f_{i},f_{i},\ldots,f_{n}\}\) & Set of features \\ \(a_{X}\) & Threshold value used in the PF process & \(\mathbf{\hat{m}}\) & The original number of features are FF \\ \(x_{r}\) & Training set & \(M_{f_{i}}\) & the number of missing values in the feature \(f_{i}\) \\ \(X_{u}\) & Test set & \(M_{u_{i}}\) & the number of missing values in the instance \(\mathbf{x}_{i}\) \\ \(D_{d}\) & Dataset after applying the data filtering process & \(D_{d}\) & Dataset after applying the feature filtering process \\ \(\mathcal{F}^{\prime}\) & Fitness function & \(S\) & The solutions \(\{\mathbf{x}_{i},\mathbf{x}_{i},\mathbf{x}_{i},\mathbf{x}_{i}\}\) \\ \(O\) & Fitness value for \(\mathbf{x}_{i}\) & \(N_{init}\) & The number of samples in the test set \\ \(\overline{y}_{i}\) & The observed values of the \(j\)-th instance in \(X_{u}\) & \(\hat{y}_{j}\) & The predicted values indicated by the solution \(\mathbf{x}_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main notations and descriptions used in this paper.
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages
### Wheeler Graphs
#### 3.7.3 Sorting and Recognizing Wheeler Graphs  
Perhaps not surprisingly, not all labeled graphs admit a Wheeler order of their nodes. Intuitively, this happens because of conflicting predecessors: if \(u\) and \(v\) have a pair of predecessors ordered as \(u^{\prime}<v^{\prime}\) and another pair ordered as \(v^{\prime\prime}<u^{\prime\prime}\), then a Wheeler order cannot exist. As an example, consider any automaton recognizing the language \(\mathcal{L}^{\prime}=(ax^{*}b)|(cx^{*}d)\) (original example by [PERSON] et al. [50]). Since any string beginning with letter 'a' must necessarily end with letter 'b' and, similarly, any string beginning with letter 'c' must necessarily end with letter 'd', the paths spelling \(ax^{k}b\) and \(ax^{k^{\prime}}d\) must be disjoint for all \(k,k^{\prime}\geq 0\). Said otherwise, the nodes reached by label 'x' and leading to 'b' must be disjoint from those reached by label 'x' and leading to 'd'. Denote with \(u_{\alpha}\) a state reached by string \(\alpha\) (read from the source). Then, it must be \(u_{ax}<u_{bx}\): from the above observation, those two states must be distinct, and, from the Wheeler properties, they must be in this precise order. For the same reason, it must be the case that \(u_{bx}<u_{axx}<u_{bxx}<\ldots\). This is an infinite sequence of distinct states: no finite Wheeler automaton can recognize \(\mathcal{L}^{\prime}\).  
This motivates the following natural questions: given an automaton \(\mathcal{A}\), is it Wheeler? if yes, can we efficiently find a corresponding Wheeler order? It turns out that these are hard problems. [PERSON] and [PERSON] showed in Reference [86] that deciding whether an arbitrary automaton admits a Wheeler order is NP-complete. This holds even when each state is allowed to have at most five outgoing edges labeled with the same character (which somehow bounds the amount of nondeterminism). On the positive side, [PERSON] et al. [65] showed that both problems (recognition and sorting) can be solved in quadratic time when each state is allowed to have at most two outgoing edges labeled with the same character. This includes DFAs, for which, however, a more efficient linear-time solution exists [65]. The complexity of the cases in between, i.e., at most three/four equally-labeled outgoing edges, is still open [87].
## 5 Experimental Results
### Close-Loop Tilt Vibration Isolation  
Firstly, the VP tiltmeter is in the horizontal state, and the cylindrical piezoelectric transducer is extended by 1 um through the output voltage of the voltage controller to generate a tilt signal to excite the VP tiltmeter, at which point the output data of the VP tiltmeter without feedback are obtained, as is shown in Figure 7. From Figure 7, we can see that the output of the VP tiltmeter is stable at about 1 V.  
Next, tilt vibration isolation experiments for the device were performed by driving the cylindrical piezoelectric transducer to tilt the supporting base, which is a similar approach to that of the open-loop calibration experiment of the VP tiltmeter. Figure 8 shows the response curve of the device working in close-loop mode to the tilt vibration. To clearly show the tilt vibration isolation procedure, the response from 4.0s to 4.4s is shown in the upper right corner of Figure 8. From Figure 8, we can see that the device is firstly horizontal, with an average voltage of 0.02977 V; it then vibrates with the maximum voltage of 0.98013 V, which is consistent with the result of Figure 6, and finally returns to the initial level, with an average voltage of 0.01681 V, in a short time (\(\sim\) 0.067 s). These experimental results demonstrate the tilt vibration isolation of the device under close-loop control, which can reduce the tilt vibration of \(\theta_{in}=6.0131\) urad to below \(\theta_{out}=0.0103\) urad. The damping factor, \(\eta_{tHB}\), of the device is \(10\log_{10}\left|\frac{\theta_{out}}{\theta_{in}}\right|\approx-27.67\) dB.
## 6 Overall Hardware Implementation
### Architecture Design  
After presenting the scheduling diagrams of the proposed model, it is required that the proposed architecture of the digital design is considered. The overall architecture of the proposed model is depicted in Figure 10.  
Figure 10: The overall structure of the proposed architecture based on the DSSN equations and scheduling diagrams.  
As can be seen in this figure, the model parameters are saved in the \(Memory_{0}\) block (parameters that are presented in Tables 1 and 2). At first step, the \(FUNC(V)\) term is realized based on the proportional scheduling diagram. After creating this basic function, some \(ShifterBlocks\) (based on the Equations (7) and (8)) are applied for generating the two basic functions, \(F(V)\) and \(G(V)\). Then, by considering some \(Multiplexer\)\(Blocks\), the final terms can be realized. These final terms and also primary signals of the basic variables (\(V\) and \(n\)) are applied to the \(Neuron\)\(Signal\)\(Calculator\). This core unit is responsible for reproducing the final voltage signals based on the DSSN neuron model. The neuron calculator unit is based on the scheduling diagram that is presented in Figure 9. Finally, the neuron signals are transferred to two buffers (\(Buffer\)\(V\) and \(Buffer\)\(n\)). Consequently, the final signals are applied to an 8-bit DAC (Digital to Analog Converter) and can be showed on the digital oscilloscope.
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 3 Model Design and Optimization  
This section will provide a detailed overview of the proposed Weak Edge Target Segmentation Network (WETS-Net) based on a dual attention mechanism, elucidating the roles and design principles of its various components. When designing WETS-Net, we focused on two key issues in weak edge target image segmentation tasks: how to enhance the network's feature extraction capability and how to effectively fuse and compensate high-level semantic features while suppressing noise and irrelevant information as much as possible. Given the excellent performance of U-shaped networks in medical image segmentation tasks and considering the similarity between medical images and weak edge target images in terms of missing texture features, we ultimately chose to base and construct WETS-Net on the U-Net architecture.  
Figure 2 illustrates the overall network architecture of WETS-Net. Compared to the classic U-Net, WETS-Net incorporates targeted improvements and optimizations in both the encoder and decoder sections. Specifically, an Edge-Aware Enhancement Module (EAEM) is embedded at the shallow feature output locations of the encoder to enhance the network's capability in extracting and representing weak edge and texture features. In the decoder section, an enhanced skip connection with a Multi-Scale Information Fusion Module (MIFM) is introduced to adaptively regulate and fuse multi-scale feature information from the encoder. Through this approach, WETS-Net can selectively enhance feature representations at different scales, thereby improving its performance in weak target segmentation tasks.  
In the following subsections, we will delve into the detailed design specifics and implementation methods of the EAEM module and MIFM model, further expounding on the overall network structure and optimization strategies of WETS-Net.
## 4 Numerical Results
### Dynamic scheduling  
We have extended the ACO to solve the problem of dynamic scheduling or re-scheduling. In practical scenarios, during the execution of the scheduled jobs, there are usually arrivals of new or urgent work orders that need to be executed at the same time with the on-going work orders. Therefore, the current schedule needs to be updated in order to include new jobs. The ACO algorithm was developed to accommodate this requirement. Assuming that during the execution process, there are new jobs arriving at the shop floor at the time point \(t^{*}\). The solver firstly extracts the operations that have not been started from current schedule. Then, the ACO algorithm is implemented for these operations together with the new jobs in order to produce a new updated schedule. This process is summarized in Figure 9.  
Figure 7: Tuning parameter \(Q\) for a use-case of 60 operations and fixed value of parameter \(A=30\): (**a**) \(Q=7000\), (**b**) \(Q=9500\), (**c**) \(Q=11,000\) and (**d**) \(Q=50,000\).  
Figure 8: Tuning parameters for total weighted tardiness performance problem with varied number of operations: (**a**) 30 operations, (**b**) 60 operations and (**c**) 100 operations.  
Figure 9: Dynamic scheduling problem illustration.  
This dynamic scheduling capability has been demonstrated in our use case. Assuming that the operations were executed based on the schedule presented in Figure 4. At the time point \(t^{*}=~{}40\) min, there are 10 new jobs arriving. The performance metric used is make-span, \(C_{max}\). Figure 10 has shown the capability of the solver in modelling dynamic job shop problem.
## 2 A Review of the Methods
### The Initial Patent Analysis System Proposed by CHI Research  
In the late 1970s, CHI Research Inc. (which later underwent a merger and was renamed 'ipIQ') worked with the US National Science Foundation in the field of patentometrics to develop the first scientific patent analysis system including the essential indicators for evaluating an institution's technological output capability. After its introduction, the system was applied to evaluate the intangible assets of companies, a task which was usually deemed as difficult an actuarial company's value assessment. Therefore, this indicator-based patent analysis system can be viewed as the initial framework that was established not only for measuring the technological strength(s) of a company but also for understanding the real value of the intangible IP assets of that company. Subsequent studies from 2001 onwards have also proposed (and patented) some relevant methods for assessing companies' intangible assets [3; 4]. The information provided by such assessments is the cogent basis for selecting stocks in which to invest.  
The patent analysis system proposed by CHI suggested that two types of sub-indicators should be considered, namely, essential indicators (including direct measures that can be calculated directly) and advanced indicators (i.e., derived measures that are computed indirectly) [5]. For example, the 'number of patents' is the most intuitive essential indicator of the overall 'patenting volume' of a company and the number of patents which have approved by an individual department (area). The annual data sets of that indicator form, in turn, the basis for calculating a company's 'patent growth in area' indicator, which measures the annual increase in patents as approved by each specific department. By multiplying these percentages with the given annual weights (i.e., the years closer to the present time are assigned heavier weights), the weighted average is obtained. This in turn becomes an important composite index which indicates 'how the studied subject is emphasising the technological developments in this area'. The '% company patents in area' is another effective essential indicator.  
In contrast, advanced indicators further measure the impacts of patents. For example,'science linkage' (SL) is an advanced indicator that measures the association between a company-owned patent and other relevant R&D works (i.e., other patents or academic papers). Another advanced indicator, 'technology cycle time' (TCT), measures the average gap between a patent's patenting time and the other previous works which it cited. So, when generalised to a broader extent, TCT is also helpful for observing the velocity of the emergence of new technologies in a given area [6].
## 4 Results and Discussion
### Case A--Tunnelling  
For the topographic survey at the tunnel exit (Figure 10), the main goal was to investigate when the receivers can recover their position after leaving the tunnel and how much they deviate from the reference position. This information can be found in Table 7.  
After exiting the tunnel, the Xiaomi Mi8 was the last device to recover its position (\(d_{along}\)) at about 57 m, while the other devices were able to recover their position slightly earlier--after 30 m in the case of the ZED-F9P (Table 8). The performance of the Xiaomi Mi8 and the ZED-F9P in the segment after leaving the tunnel was the worst among all the receivers tested (see Figure 10b). Interestingly, the Sepentrio and ZED-F9R receivers recovered their position much better than the others, which deviated up to 2 m (\(d_{cross-track}\)  
Figure 10: Case A—tunnel: performance of (**a**) Sepentrio AsteRx-U, (**b**) Xiaomi Mi8, (**c**) ZED-F9P, and (**d**) ZED-F9R.  
Septentrio were below 30 cm and 34.7% were below 10 cm, while the others, especially the ZED-F9R and the Xiaomi Mi8, were less successful, with only 4.1% of the positions where the deviations were below 30 cm and small percentages (0.3%, 2.0% and 0.3% for the ZED-F9P, ZED-F9R and Xiaomi Mi8, respectively) where the deviations from the reference positions were below 10 cm.  
Comparing the positioning of the GPS and GNSS (Tables 9 and 10 and Figure 12), it is clear that a larger number of satellites (e.g., when using the GNSS) leads to a higher number of calculated positions compared to the GPS and often to better quality positioning, as can be seen in particular for the two u-blox receivers and the Xiaomi Mi8 smartphone. For those devices, there is also a very significant improvement in the determination of  
Figure 11: Case B: performance of: (**a**) Septentrio AsteRx-U, (**b**) Xiaomi Mi8, (**c**) u-blox ZED-F9P, and (**d**) u-blox ZED-F9R.  
the fixed ambiguities in the case of the GNSS, while for the Septentrio AsteRx-U receiver, there is a decrease; this mainly leads to an improvement in accuracy for the deviations below 1 m but less for the other thresholds (30 cm and 10 cm). For positioning, it is also seen that much more data can be acquired in GNSS mode than in GPS mode. The same is true for positioning performance. The GNSS solutions show a significant improvement in deviations between the receivers and the Applanix of less than 1 m or 30 cm for all the low-cost receivers tested and 10 cm and an improvement in deviations below 10 cm for the two u-box devices, especially for the ZED-F9R. From Figure 12a,b, it can be seen that for the entire trajectory the horizontal positioning was clearly better compared to the spatial one. Unfortunately, the positioning of the Xiaomi Mi8 smartphone in urban areas has not proven to be adequate, as few GNSS solutions have managed to be positioned below the threshold of qualitative accuracy; all of them refer to adequate horizontal positioning, while spatial positioning has failed in all epochs for Case B.
# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
###### Abstract  
High-resolution optical images will provide planetary geology researchers with finer and more microscopic image data information. In order to maximize scientific output, it is necessary to further increase the resolution of acquired images, so image super-resolution (SR) reconstruction techniques have become the best choice. Aiming at the problems of large parameter quantity and high computational complexity in current deep learning-based image SR reconstruction methods, we propose a novel Recursive Swin Transformer Super-Resolution Network (RSTSRN) for SR applied to images. The RSTSRN improves upon the LapSRN, which we use as our backbone architecture. A Residual Swin Transformer Block (RSTB) is used for more efficient residual learning, which consists of stacked Swin Transformer Blocks (STBs) with a residual connection. Moreover, the idea of parameter sharing was introduced to reduce the number of parameters, and a multi-scale training strategy was designed to accelerate convergence speed. Experimental results show that the proposed RSTSRN achieves superior performance on 2\(\times\), 4\(\times\) and 8\(\times\)SR tasks to state-of-the-art methods with similar parameters. Especially on high-magnification SR tasks, the RSTSRN has great performance superiority. Compared to the LapSRN network, for 2\(\times\), 4\(\times\) and 8\(\times\) Mars image SR tasks, the RSTSRN network has increased PSNR values by 0.35 dB, 0.88 dB and 1.22 dB, and SSIM values by 0.0048, 0.0114 and 0.0311, respectively.  
super-resolution reconstruction; Swin Transformer; Laplacian Pyramid