esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Risk Score Component  
The method developed in the first component is designed to predict the occurrence of future TLF at the time of discharge and is based on the retrospective information available at discharge. In addition to this prediction, we plan to use our method prospectively until the end of the ongoing study. This will allow clinicians to optimize the treatment plans for at-risk patients.  
First, using our training dataset (Phase I data), we learn the distribution of TLF over time (Figure 3). To model the time-to-TLF distribution, we use a variety of known probability distributions and find the best fit. Here, we use the Kolmogorov-Smirnov test, a non-parametric test which evaluates the goodness of fit based on the difference between the empirical CDF of the TLF events \(F_{0}(X)\) and the CDF of a candidate probability distribution \(F_{r}(X)\). The null hypothesis \(H_{0}\) is that the data have been sampled from the candidate distribution i.e., \(F_{0}(X)=F_{r}(X)\). The test statistic \(D\) is defined as the maximum difference of two CDFs over the samples \(X\):  
\[D=\max_{x}|F_{0}(X)-F_{r}(X)| \tag{1}\]  
For a good candidate \(r^{*}\), \(D\) goes to zero as the sample size \(n\) goes to infinity. The critical value for \(D\) is found from the Kolmogorov-Smirnov table values for one sample test, which we use to infer the \(p\)-value. As shown in Figure 3, the best fitted model for risk score is the generalized extreme value distribution (parameters: shape = \(-0.24\), loc = 147, scale = 116, and \(p\)-value = 0.62). Finally, the risk score of a given patient for developing TLF after time \(\tau\) is measured as:  
\[\text{risk}(\tau)=1-F_{r^{*}}(X=\tau) \tag{2}\]  
Figure 3: Fitted risk score model to TLF distribution: **(left)** histogram of time to TLF; **(middle)** candidate distributions tested against the empirical distribution using the Kolmogorov–Smirnov test; **(right)** the constructed risk score for each distribution.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Rule-Based Energy Management Strategies
#### 3.1.2 Fuzzy Rule-Based Energy Management Strategies
Typically, the development of such strategies requires a great deal of time and qualified engineers due to the long process of defining and calibrating the rules. However, the driving conditions of vehicles are often highly variable, which also leads to a necessity to redefine the rules for each driving condition and powertrain, so placing high demands on the robustness of the strategies [78; 79; 80]. At the same time, rule-based strategies always suffer from poor fuel economy compared to the following strategies based on optimization. This is most probably owing to their inability to adjust their control strategy in real-time [81].
## 2 Relative Position and Attitude Measurement of Maglev Vibration Isolation System
### Relative Position and Attitude Measurement Model of Maglev Vibration Isolation System  
The six-DOF relative position and attitude measurement system is used to measure the three-axis relative attitude and three-axis relative centroid position of the support module relative to the payload module. At present, there are mainly 4-bar and 8-bar sensor configuration schemes, as shown in Figure 2.  
Figure 1: Two-dimensional PSD.  
Figure 2: Unidirectional position sensors of 8-bar layout.  
In theory, at least three two-dimensional PSDs are required to achieve the calculation of three degrees of freedom displacement and angle. In order to achieve hardware redundancy and improve calculation accuracy, the system uses four two-dimensional PSDs, with the photosensitive surface fixed on the base side plate and four laser sources fixed on the floating platform side plate. When the base is disturbed, the position of the PSD photosensitive surface changes accordingly, and the position of the light spot irradiated by the laser source on the photosensitive surface also changes. By collecting and processing the output current changes of the photosensitive surface pins, the displacement changes of the light spot on the photosensitive surface can be obtained.  
Based on the three-dimensional position measurement model, the relationship between the relative position and angle changes between the floating platform and the base is established, and the displacement changes of the light spot on the photosensitive surface are determined.  
The 4-bar layout requires the configuration of bidirectional position sensors, such as two-dimensional PSD. The 8-bar layout requires the configuration of traditional unidirectional position measurement sensors, such as laser or eddy current. Compared to the others, the layout of the 8-bar position sensor has a higher system redundancy, allowing for the arbitrary failure of one out of four sets in the longitudinal and transverse directions. When there is only a one-way failure in the actuator, it does not affect the payload module's motion of 6 DOF (degrees of freedom) [1].
## 3 Results and Discussion
### Various Scan Path  
#### 3.3.1 Deposition according to Scan Path  
In the LCLD process, the horizontal feed rate of the line structure must be lowered to a certain level for sufficient selective heat energy supply and a stable reduction reaction. Therefore, to obtain a narrow line width, the patterning width and the patterning space should be reduced to maintain the horizontal feed rate. For this reason, when irradiating the laser through the vertical line shape scan path, ablation occurred due to high energy overlap in the middle region, making it impossible to form a stable deposited structure. Accordingly, the experiment was conducted by changing the scan path design to reduce the excessive overlap of the laser beam while lowering the horizontal feed rate below a certain level.  
From the previous chapters, it can be seen that deposition was effectively performed mainly at the edge of the laser scan path, and ablation occurred in the middle region where the energy density was highest, which had a negative effect on the deposition. Therefore, as shown in Figure 7a, a new triangular scan path was designed by adding a scan path to the outer region of the vertical line. In this case, since the entire laser patterning proceeded while surrounding the deposited structure, the laser beam was expected to produce a stable deposited structure through indirect heating. Patterning width A and patterning space B were fixed at 50 \(\upmu\)m and 20 \(\upmu\)m, respectively, so that the horizontal feed rate was 0.141 mm/s. However, as shown in Figure 7b, some unstable deposition was observed in the middle region, although the deposition structure was formed. Accordingly, laser patterning was designed in a form surrounding the deposited structure. Still, the scan path was modified in an elliptical shape, as shown in Figure 8a, to minimize ablation due to excessive energy overlap in a small area.  
In order to deposit a similar line width structure of the previous experiments, the major axis length A of the ellipse was set to 50 \(\upmu\)m. In order to irradiate energy evenly over the entire pattern area, the distance between the ellipse centers was fixed at half of the length B. At this time, the major axis length B of the ellipse was set to 50 \(\upmu\)m, so that the horizontal feed rate was 0.159 mm/s, a similar level to that of the previous experiment. As  
Figure 6: Result of reduced patterning width according to patterning space B: (**a**) 12.5 \(\upmu\)m; (**b**) 2.5 \(\upmu\)m.  
shown in Figure 8b, a conductive structure with a line width of about 180 \(\upmu\)m, completely connected as a single line, was obtained.
# Effect of Compressive Strain Rate on Auxetic Foam
## 2 Methods  
### Fabrication: Open Cell Foams  
Open cell polyurethane foams with two different cell structures were thermo-mechanically fabricated from 32 (z-axis) by 32 (x-axis) by 96 (y-axis) mm samples (PUR30 FR, Custom Foams, as used in [35; 55; 75]). Fabrication conditions and, hence, imparted cell structures were selected based on previous work to give high magnitude NPR in all three orthogonal axes [58; 67; 76] or just one axis [55; 57; 62; 77]. For the uniform triaxial conversions, compression to 40% engineering strain was imposed with aluminium moulds to modify cell shape in each axis (Figure 1a), giving a volumetric compression ratio (final/original volume) of five. Samples and moulds were heated in an oven (MCP Tooling Technologies LC/CD) for twenty minutes at 160 \({}^{\circ}\)C, then removed from the oven and cooled to room temperature (~30 min) [58]. Anisotropic foams were fabricated with 30% biaxial compression in the x- and z-axes, and extension by a factor of two in the _y_-axis (Figure 1b), applied using four 2 mm diameter pins passed through the foam, as in [55]. For these anisotropic samples, oven temperature was increased to 180 \({}^{\circ}\)C to maximise shape fixing [58]. Cell rise direction was parallel to the 32 mm _z_-axis in supplied open cell foam, so it was compressed during the anisotropic conversions. Three samples were converted for each condition, but one sample of anisotropic foam was discarded due to creasing.  
Triaxial foam partially re-expanded following conversion, whereas anisotropic foam partially re-expanded in both compressed axes and shrank in the axis with applied tension. Partial re-expansion was intended as it is associated with increased magnitude of NPR [58; 76; 78], but meant the sizes of samples converted with different conditions differed slightly. Triaxial cubes with ~26 mm and anisotropic cubes with ~21 mm sides were cut from cuboidal samples a week after fabrication with a utility knife (Stanley), with cuts made perpendicular to sample length only (\(y\)-axis, Figure 1) to ensure straight, undamaged sides. Unconverted cubes (-25 mm sided) of similar size to the triaxial cubes were also cut for comparative testing.
## 3 Security Level
### Dynamic Selection of Cluster Heads
In addition, if a node's trust rate is not enough to give it the ability to create a cluster, it sends a message to the nearest trusted node, which in turn (the trusted node) breaks off from its CH and creates a new cluster. As a result, a node that does not have an absolute trust will join the new cluster along with its closest nodes. However, when trusted nodes create a cluster, they compute the value of \(\omega\) for each trusted node and share its results with other trusted nodes, then the most suitable node is chosen based on the factors we discussed earlier. Finally, the created CH node sends an announcement message (_CH_Announce_) to the rest of the nodes that a new CH node has been created to link with the other CH nodes and activate the RPL protocol.
###### Abstract  
Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (\(\text{E}_{\text{b}}/\text{N}_{\text{o}}\)) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate % OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of \(\text{E}_{\text{b}}/\text{N}_{\text{o}}\) by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.  
Keywords:Intelligent Reflecting Surfaces (IRS); Multiple-Input Single-Output (MISO); Orthogonal Space-Time Block Code (OSTBC); Bit Error Rate (BER) +
Footnote †: journal: _Sensors_
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 4 Discussion
### Spectral Image Responses to Herbicides
Using a CF image analysis, herbicide effects could be detected at much earlier timings compared to other image analyses, and the extent of decrease was quite larger. Overall, the CF image analysis was more discernible in terms of differentiating herbicides depending on their modes of action, suggesting its suitability for the high-throughput screening (HTS) of herbicide molecules. Leaf temperature is strongly correlated with the overall physiological condition of a plant, particularly the conductance of stomata and the rate of photosynthesis. A symptom of plant stress is the closure of the stomata in leaves, which is accompanied by a reduction in the absorption of carbon dioxide during photosynthesis. This ultimately results in a decrease in transpiration [29], and the temperature of stressed leaves rises due to reduced transpiration. Leaf temperature is a reliable indicator for detecting numerous types of stresses in plants, such as drought, salt stress, and herbicides, that affect photosynthesis [30; 31; 32; 33]. In our study, the IR thermal images revealed that the leaf temperature reached its highest point when the effects of the herbicides and visible damage became evident (Figures 2C and 5). An early initial increase in the leaf temperature and its subsequent decrease at a certain point after herbicide treatment varied with the herbicide mode of action, implying that changes in plant leaf temperature measured by an IR thermal image analysis may also be used to explain herbicide modes of action.
## 3 Methodology
### Adaptive Variable Neighborhood Search
However, the connection of this operator contains five situations, where two of them as the same as the 2-Opt and Or-Opt operators. So this operator only checks the other three situations. The illustration of these operators is shown in Figure 3.  
Figure 2: Shaking operators. SA acceptance criterion  
We use an acceptance criterion based on the simulated annealing mechanism. For a current solution \(S_{cur}\), a neighbour solution \(S^{\prime\prime}\) is accepted if \(f(S^{\prime\prime})<f(S_{cur})\) and with probability \(P_{accept}=e^{-[f(S^{\prime\prime})-f(S_{cur})]/\tau}\) otherwise. The current temperature is \(\tau\), where \(\tau>0\). The initial temperature is \(\tau_{start}\), and there is a cooling rate factor \(\phi\) to decrease the temperature in each iteration, where \(0<\phi<1\). Adaptive mechanism  
The roulette-wheel mechanism determines the choice of which shaking operator is applied in each iteration. The probability for selection is based on the weight of each operator, which depends on its past performance, e.g., let \(\theta_{i}\) be the weight of operator \(i\), then operator \(j\) will be selected with probability \(\theta_{j}/\sum_{yi}\theta_{i}\). The weight is updated by scores. In each iteration, the applied shaking operator will gain a score. If the neighbour solution \(S^{\prime\prime}\) is better than the best solution \(S_{best}\), get \(\sigma_{1}\); if \(S^{\prime\prime}\) is better than the current solution \(S_{cur}\), get \(\sigma_{2}\); if \(S^{\prime\prime}\) is no better than \(S_{cur}\) but accepted by the SA acceptance criterion, get \(\sigma_{3}\); if \(S^{\prime\prime}\) is not accepted, get \(0\). As mentioned before, the search progress is divided into segments, and each segment contains \(seg_{cap}\) iterations. After the last iteration of each segment, shaking operators update their weights based on their scores. Let \(\pi_{i}\) represent the sum of scores in the segment for shaking operator \(i\), and \(\kappa_{i}\) is the number of times it is used in the segment. Then the weight of operator \(i\) in the next segment is updated by  
\[\theta_{i}= \begin{cases}\theta_{i}&\kappa_{i}=0\\ (1-\eta)\theta_{i}+\eta\pi_{i}/\kappa_{i}&\kappa_{i}\
eq 0\end{cases}  \tag{29}\]  
where \(\eta=[0,1]\) is called the reaction factor, controlling the adjustment speed. We set a protection threshold so that the weight will not be lower than \(0.1\) to ensure that it is still may possible to select the operator with a lower weight in the later-stage iteration  
Termination criteria  
When the temperature reaches the cut-off threshold \(\tau_{cut}\) at each planning cycle, the iterations will cease. Pseudocode  
The pseudocode of the proposed AVNS heuristic shows in Algorithm 1:  
Figure 3: VND operators.
# Graph-Driven Exploration of Issue Handling Schemes in Software Projects
## 5 Experimental Results and Analysis
### Profiles of Issue Handling Paths
#### 5.2.3 Path Timing Features
On the other hand, poor results were obtained for the Lucene project: a Q3 below 30 days covered 15.16% of issues, while for Q2, this was 22.95%. These results are due to inefficient issue processing, which can be traced in deeper analyses of paths, states, comments, and correlations with other issue documentation or processing features. Here, we observed 137 paths (with lengths in the range of 1-10) covering a total of 3983 bug issues. The timing characteristics (Q2 and Q3 in days) for some high-coverage (N issues) paths were as follows:  
\(p_{1}\): _Open, Resolved_Fixed, Closed:_ N = 1818; Q2 = 83.7, Q3 = 236.7  
\(p_{2}\): _Open, Closed:_ N = 208; Q2 = 24, Q3 = 327.4  
\(p_{3}\): _Open, Resolved_Fixed, Reopen, Resolved_Fixed, Closed:_ N = 157; Q2 = 94, Q3 = 247.8  
\(p_{4}\): _Open, Resolved_Invalid:_ N = 74; Q2 = 0.4, Q3 = 1.4  
Paths involving bug fixing states (p\({}_{1}\), p\({}_{3}\)) show high handling times, as opposed to the path with the identification of invalid bugs (p\({}_{4}\)). The two-state path p\({}_{2}\) shows the highest resolution time, and this does not reflect the involved activities; they can be derived by analyzing comment sequences. In the case of Scrum projects, the timing effectiveness of issue handling can be measured as the percentage of issues postponed to subsequent sprints. In the considered project P1 (period 2022) for 10 subsequent sprints, this percentage was 6.8%, 6.2%, 15.6%,11%, 10%, 6.8%, 15%, 6.5%, 10%, and 3.1% for all issue types, respectively. However, for bug issues, the postponed issues were negligible. Higher values for _New Function_ issues resulted mostly from the fact that they were transferred for handling with some delay (the decision of the project owner). In classical projects, we can check the ratio of issues exceeding the deadline date, but this feature is rarely encountered in repositories and is usually attributed to a fraction of issues.
## 3 Gray Value Compression
### Arithmetic Coding  
Arithmetic coding, similar to Huffman coding, belongs to a group of entropy coders; it assigns codes to symbols so that the code length corresponds to the probability of the occurrence of symbols, while other entropy coders decompose the input data stream into constituents, i.e., symbols, and replace each symbol with a codeword. Arithmetic coding encodes the entire message into a single number \(n\) for which \(n\in\langle 0,1\rangle\)[42]. Instead of the Huffman coding used in the original EEDC compression, an arithmetic coding is used, and the encoder is called _EEDC-Arith_. An equivalent analysis is performed as in the previous chapter. The encoders shown in Figure 5 were obtained by applying the new compression algorithm on the gray values series.  
From the graphical representation of the results in Figure 5, it can be concluded that the introduction of arithmetic coding did not yield better results, so this compression method is discarded.
## 1 Introduction
A closed-loop steady-state tracking error reduction approach is proposed in the field of CTOPC. The accumulative tracking errors, which are integrated into a new error state with PID structure, are considered in the proposed PID-CTOPC approach such that tracking errors within the operation envelop of the closed-loop system can be minimized compared with the PCTOPC and the [PERSON]'s approach, which only consider the current tracking errors. 2. Robustness of the closed-loop system is significantly enhanced. In the proposed PID-CTOPC approach, current, accumulative, and derivative tracking errors are minimized simultaneously such that reduction of input fluctuation, output overshoot, and steady-state tracking error can also be derived in contrast with the PCTOPC and the [PERSON]'s approach in the presence of unknown disturbances.
## 4 Datasets  
### Printed Dataset Generation  
For the study of text recognition in English, Chinese, and other public languages, relatively standard and publicly available text recognition corpora and datasets are available worldwide, so the performance of the analysis methods can be compared by using different recognition models on public datasets. However, there is no standard and open text recognition dataset available for the Uyghur language.  
In this paper, we collected 16,555 Uyghur words through Uyghur websites such as People's Daily Online Uyghur ([[http://uyghur.people.com.cn/](http://uyghur.people.com.cn/)]([http://uyghur.people.com.cn/](http://uyghur.people.com.cn/)) accessed on 4 June 2021), China Uyghur Radio Network ([[http://www.uycnr.com/lwxj/](http://www.uycnr.com/lwxj/)]([http://www.uycnr.com/lwxj/](http://www.uycnr.com/lwxj/)) accessed on 11 June 2021), and Aksu News ([[http://uy.aksxw.com/](http://uy.aksxw.com/)]([http://uy.aksxw.com/](http://uy.aksxw.com/)) accessed on 19 June 2021); the obtained Uyghur text was used as labels and the data were cleaned, sorted, and divided into words to generate Uyghur images according to Uyghur words. The training set has five w images, including one w original images with a white background, one w image with different degrees of Gaussian blurring, one w image with random waves and one w image with different backgrounds; 5 k images with a Gaussian noise background; and 5 k images with a quasi-crystal background, and each part contains different Uyghur fonts. The validation set and test set were constructed with one-third of the training set. The specific data enhancement method is shown in Figure 8.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### Fall Detection Technologies: A Classification Based on AI Paradigms
#### 2.1.2 Computer Vision-Based Systems in Fall Detection
This hybrid solution not only improves detection accuracy and responsiveness but also introduces a novel layer of interactivity and validation by reducing false positives and enhancing trust in safety environments.