esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Strain/Load Prediction Based on Deep Learning Model  
The input data were obtained through the previous sections, which included preprocessing, flight parameter maneuver recognition classification, and wing virtual ground calibration tests. The deep learning model incorporated the AE, RNN, and LSTM models, considering the time-sequential characteristics of the flight parameters and strains/loads.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Shear Force** & **Bending Moment** & **Torque** & **The Bending Moment** & **Shear Bridge (\(\mu\epsilon\))** & **Torque Bridge (\(\mu\epsilon\))** \\
**(kN)** & **(kN\(\cdot\)m)** & **(kN\(\cdot\)m)** & **Bridge (\(\mu\epsilon\))** & **Shear Bridge (\(\mu\epsilon\))** & **Torque Bridge (\(\mu\epsilon\))** \\ \hline \(-1.0000\) & \(-1.0000\) & \(-1.0000\) & \(-1.0000\) & \(1.0000\) & \(0.8874\) \\ \(-0.6000\) & \(-0.0314\) & \(-0.2063\) & \(-0.0270\) & \(0.6810\) & \(1.0000\) \\ \(-0.3333\) & \(0.1675\) & \(0.0658\) & \(0.1724\) & \(0.4242\) & \(0.7919\) \\ \(0.3333\) & \(1.0000\) & \(0.9388\) & \(1.0000\) & \(-0.1898\) & \(0.4737\) \\ \(0.7333\) & \(0.6953\) & \(1.0000\) & \(0.6873\) & \(-0.6324\) & \(-0.2143\) \\ \(1.0000\) & \(-0.1779\) & \(0.6552\) & \(-0.1944\) & \(-1.0000\) & \(-1.0000\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Normalized load strain data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loads** & **Strain of Critical** & **The Bending** & **Shear Bridge** & **Torque Bridge** \\  & **Position (\(\mu\epsilon\))** & **Moment Bridge (\(\mu\epsilon\))** & **(\(\mu\epsilon\))** & **(\(\mu\epsilon\))** \\ \hline \(5\) & \(-1\) & \(-1\) & \(1\) & \(0.88742\) \\ \(8\) & \(-0.1288\) & \(-0.02702\) & \(0.68099\) & \(1\) \\ \(10\) & \(0.1224\) & \(0.17243\) & \(0.42418\) & \(0.79186\) \\ \(15\) & \(1\) & \(1\) & \(-0.18977\) & \(0.4737\) \\ \(18\) & \(0.9288\) & \(0.68733\) & \(-0.6324\) & \(-0.2143\) \\ \(20\) & \(0.3846\) & \(-0.19441\) & \(-1\) & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Raw strain data for critical sites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Shear Force** & **Bending Moment** & **Torque (kN\(\cdot\)m)** & **The Bending Moment** & **Shear Bridge (\(\mu\epsilon\))** & **Torque Bridge (\(\mu\epsilon\))** \\
**(kN)** & **(kN\(\cdot\)m)** & **(kN\(\cdot\)m)** & **Bridge (\(\mu\epsilon\))** & **Shear Bridge (\(\mu\epsilon\))** & **Torque Bridge (\(\mu\epsilon\))** \\ \hline \(5\) & \(14.91\) & \(1.66\) & \(2014.02\) & \(-184.3\) & \(63.2\) \\ \(8\) & \(20.67\) & \(2.38\) & \(2788.74\) & \(-319.26\) & \(73.72\) \\ \(10\) & \(21.85\) & \(2.62\) & \(2947.55\) & \(-427.91\) & \(54.27\) \\ \(15\) & \(26.8\) & \(3.41\) & \(3606.49\) & \(-687.65\) & \(24.54\) \\ \(18\) & \(24.99\) & \(3.47\) & \(3357.53\) & \(-874.91\) & \(-39.75\) \\ \(20\) & \(19.8\) & \(3.16\) & \(2655.46\) & \(-1030.43\) & \(-113.17\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Load modelling raw data.
## 3 Results for the Research Questions
### RQ2: What Are the Expertise Sources Used for Experts Finding Systems?
Usually, building and updating such databases is an expensive task, but small organizations are able to keep their databases up-to-date with good quality information [4]. Thus, the shortcomings of this expertise source are (1) the completeness and update of information depends on the employees, and sometimes they are busy or unwilling to carry out this task [71]; (2) the employees need motivation and rewards from their organization to update their profiles regularly; (3) in an organization's databases, the descriptions of expertise tend to be unstructured and general, and it is difficult to seek experts in a specific area of expertise with these descriptions; and (4) it is difficult to update the information in these databases in real time because of the static nature of these databases [1; 4]. Additionally, emails have been used as expertise evidence by the authors in reference [87]. 2. Social Networks (SNs)--The rapid development in internet technologies creates a huge amount of user-generated data disseminated on SNs, which is becoming one of the most important expertise sources [53; 86]. This user-generated data holds a lot of information such as users' posts, question tags, opinions, activities, experience, etc., which can be valuable for expert finding [71]. SNs are one of the valuable expertise sources since they commonly contain up-to-date information and show meaningful relationships between people. Experts' SNs can be used to extract the effective factors for their ranking and selection (e.g., organisation-related factors, temporal factors, and contextual factors). SNs were used widely as expertise sources by the authors of references [20; 21; 37; 56; 57; 76; 86]. Also, forums have been used by the authors in references [84; 85]. Additionally, posts and questions tags have been used as expertise evidences by the authors of references [16; 17; 19; 44; 82; 83]. 3. Hybrid expertise sources--Recent studies recommended that identifying an expert should not be based on a single expertise source. Thus, current expert finding systems studies integrated heterogeneous expertise sources to enhance the quality of expert finding systems. Several researchers combined different expertise sources, and these are presented in Table 4.  
RQ3: What Are the Differences between Expertise Retrieval and Expertise Seeking, and What Are the Contextual Factors and Theories That Have Been Integrated with Expert Finding Systems?
## 3 Results
### Effect of Bulk Density and Water Holding Capacity on Cucumber Fruit Yield and Quality
The higher the bulk density of lignite substrate fed with standard nutrient solution, the firmer the cucumber fruits were, as confirmed by the high (77.94%) coefficient of determination for this parameter (Table 11). In the case of plants grown in mineral wool and fed with high EC of nutrient solution, the increase in moisture content of this substrate had a decreasing effect on cucumber fruit hardness (Table 10). The multiple correlation coefficient in this case was (R = 0.7569) and the coefficient of determination was 57.29% (Table 11). In both treatments, when lignite growing mats were fed with standard nutrient solution and when in mineral wool mats fed with high EC nutrient solution, a statistically significant negative correlation was found between bulk density of these substrates and cucumber fruit skin color saturation (C*). This was confirmed by the coefficient of determination, which was 75.03% and 75.84%, respectively (Tables 10 and 11). Cucumber fruit skin color intensity (H*) and coloration index (a*/b*) were positively correlated with the bulk density of the medium, but only in the mineral wool medium treatment, where standard medium was used (Tables 10 and 11). In the case of lignite substrate and high EC medium, a significant multiple correlation was also found between physical properties of the substrate and color intensity (H*) and color index (a*/b*) of cucumber fruit peel (Table 11). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Physical Parameter of the Substrate**} & \multicolumn{3}{c}{**Total Yield**} & \multicolumn{3}{c}{**Marketable Yield**} & \multicolumn{2}{c}{**Unmarketable Yield**} & \multirow{2}{*}{**Number of Aborted**} \\ \cline{3-3} \cline{5-10}  & & **Number** & & & & & & **Number** \\  & & **of Fruits** & **of Fruit** & **of Fruits** & **of Fruit** & **of Fruits** & **of Fruit** & **of Fruit** \\ \hline MW/control & BD & \(-\)0.5195 & \(-\)0.1661 & \(-\)0.1675 & \(-\)0.1397 & \(-\)0.6762 & \(-\)0.6763 & 0.4606 \\ EC & WHC & \(-\)0.0597 & 0.2209 & 0.0108 & 0.0176 & \(-\)0.1017 & \(-\)0.1019 & 0.2020 \\ \hline \multirow{2}{*}{L/control} & BD & 0.8415 * & 0.8409 * & 0.6908 * & 0.6373 & 0.9798 & 0.9588 & 0.8384 * \\  & WHC & 0.9606 * & 0.9485 * & 0.8678 * & 0.8239 * & 0.9920* & 0.9896 * & 0.7560 * \\ \hline MW/high & BD & 0.3216 & 0.7005 & 0.8114 & 0.8097 & \(-\)0.4821 & \(-\)0.3462 & \(-\)0.7862 \\ EC & WHC & 0.4177 & 0.7175 & 0.8055 & 0.8035 & \(-\)0.3735 & \(-\)0.2003 & \(-\)0.7108 \\ \hline \multirow{2}{*}{L/high EC} & BD & 0.5626 & 0.5961 & 0.7175 & 0.6840 & 0.1690 & 0.3498 & 0.0107 \\  & WHC & 0.7189 & 0.4632 & 0.5545 & 0.5446 & 0.2377 & 0.2578 & 0.3161 \\ \hline \hline \end{tabular}
* significant at \(p\leq 0.05\).  
\end{table} 
Table 8: Linear correlation coefficient(2) for the relationship between cucumber fruit yield and physical parameters of the medium (BD, WHC) depending on the type of medium and medium EC.
## 2 Materials and Methods
### Objective Functions  
We used 12 mathematical objective functions of different levels of complexity in our study. All functions are defined in multiple dimensions and are to be minimized, with the minimum value being zero. The objective functions together with their search range, the optimum value \(f(\mathbf{x}^{*})\), the location of the optimum \(\mathbf{x}^{*}\) in the design space and some other properties are presented in Table 3. More details on the functions can be found in Appendix B, where the functions are plotted for the two-dimensional case (\(D=2\)). The aim of every optimization algorithm used in the study is to find the design vector \(\mathbf{x}^{*}\) that minimizes the value of \(f\), for each function \(f_{1}\) (\(I=1,2,\ldots,12\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No of Dimensions, \(D\)** & \(\mathbf{D=5}\) & \(\mathbf{D=10}\) & \(\mathbf{D=30}\) & \(\mathbf{D=50}\) \\ \hline Population size \(NP\) & & & & \\ \(NP=10\)-\(D\) & & & & \\ Max. Generations/Iterations & & & & \\ _MaxLiter_ & 50 & 150 & 550 & 950 \\ _MaxLiter_ = 20-\(D-50\) & & & & \\ Max. Obj. function evaluations & & & & \\ _MaxFE_ & 2500 & 15,000 & 165,000 & 475,000 \\ _MaxFE_ = \(NP\)-_MaxIter_ & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization parameters and convergence criteria used for each category of problems based on the number of dimensions.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  
The 12 functions exhibit different computational complexities. Some are very easy to calculate, while others are much harder. Figure 3 shows the computational time needed for the calculation of the values of the objective functions 100,000 times, for a vector of 5, 10, 30 or 50 variables (\(D\) = 5, 10, 30, 50). All calculations were done in MATLAB with a 64-bit computer running Windows 10, equipped with an Intel i9-8950 HK CPU @ 2.90 GHz and 32 GB RAM. In the \(D\) = 5 and \(D\) = 10 cases, most functions take 0.1 to 0.7 s to calculate. The exception is function F06 which is much harder and requires 5.9 s and 11 s to calculate, for \(D\) = 5 and \(D\) = 10, respectively. Note that the vertical axis of each diagram is in logarithmic scale, for better clarity of the results. It appears that some functions become much harder to calculate as the dimensionality increases (for example functions F03, F04, F06, partly F08) while for others this is not the case.
## 1 Introduction  
Mechanical ventilation is an important treatment in an intensive care unit (ICU) which usually helps patients who cannot breathe adequately on their own [1; 2; 3]. In recent years, there has been a rapid development of mechanical ventilation systems, and studies of respiratory systems can be referred to as a focus on clinical diagnosis and treatments [4; 5; 6; 7; 8; 9].  
Because of the establishment of an artificial airway and the use of sedatives and muscle relaxants, patients cannot cough when they are in a coma [10; 11; 12; 13; 14]. Due to this, espectoration capacity of ventilated patients will further decline and even be lost [15; 16; 17]. Furthermore, secretions deposited in the patient's airway aggravate their hypoxia and carbon dioxide retention [18; 19; 20] and may result in bacteria collection, which can cause the occurrence or aggravation of a pulmonary infection, such as ventilator associated pneumonia [21; 22; 23; 24; 25; 26]. Therefore, secretion clearance has become an important curing and nursing measure for secretion management, which involves members of the ICU care team such as doctors, physiotherapists, respiratory therapists, and speech and language therapists [27; 28; 29; 30], and it is worthwhile studying the model with secretion clearance function.  
Most of the previously studied systems are complex and changeable, and the influence of individual parameters on the whole system is difficult to analyze. Some dynamic characteristics and properties of the respiratory system cannot be measured directly. For a multiparameter mechanical ventilation system, the research method is important. The dimensionless model can simplify the system to analyze the impact of each parameter on the system.  
However, many studies regarding mechanical ventilation systems with secretion clearance function are based on a single lung and electric models [31; 32; 33], which are only approximately equivalent to the human respiratory systems. However, these studies are not consistent with the actual situation, and their reliability and applicability are greatly reduced, which may not be useful for clinical practice and treatment. Therefore, the establishment of a mathematical model of the mechanical ventilation pneumatic system with double lungs is helpful for promoting the development of mechanical ventilation system research and can be safely and effectively applied to clinical treatment and research.  
In this paper, a dimensionless pneumatic mathematical model of a mechanical ventilation system of double lungs with secretion clearance function was built. An experiment with a ventilator with pressure-controlled ventilation mode has been constructed. After comparing the experimental data with the simulation data, the mathematical model can be verified. Then, we can use the simulation model to get data to analyze the influence of each parameter on the coupling of the two lungs. Finally, the impact of each parameter on the respiratory system is obtained by incorporating the orthogonal experiment data.
## 4 Simulation and Test Verification
### Simulated Test
#### 4.1.1 Signal Power Spectrum Simulation Before and After Anti-Jamming  
Firstly, analyze the changes in the power spectrum before and after PFM interference suppression to verify the algorithm's effectiveness in terms of interference mitigation. Considering the analysis in Section 3.1, the single-component PFM interference scenario can be regarded as a special case of multi-component PFM interference with consistent carrier frequencies. Therefore, two interference scenarios are designed. The parameter settings for these scenarios are shown in Table 1, where Scenario 1 and Scenario 2 correspond to two-component PFM interference with consistent and inconsistent carrier frequencies, respectively.  
In the two scenarios, two sets of comparative experiments were conducted: one with strong interference signal with a Jamming-to-Signal Ratio (JSR) of 25 dB and the other with weak interference signal with a JSR of 15 dB. The changes in the signal power spectrum before and after interference suppression were compared, with the results shown in Figures 3-6. Among them, (a) in Figures 3-6 represents the power spectrum diagram of the received signal before interference suppression (containing Iridium signal, PFM interference, and noise); (b) shows the resampled data obtained by optimizing the sampling of the original received signal after estimating the modulation period of the PFM interference signal; and (c) displays the power spectrum diagram of the Iridium signal and noise after activating the SAIOR algorithm and applying frequency-domain filtering to the interference signal, where the red dashed box highlights specific details of the frequency-domain-filtered interference signal, and the small inset in the upper right corner provides a magnified view of the details within the red dashed box.  
In Figures 3-6, a comparison between (a) and (b) shows that the SAIOR algorithm effectively converts the dispersed PFM interference over a wide bandwidth into one or several single-frequency interferences in the newly sampled data through appropriate secondary sampling, enhancing the aggregation of interference. Meanwhile, the energy distribution of LEO satellite signals and noise remains largely unaffected, effectively reducing the overlap between LEO satellite signals and interference, thereby minimizing the damage to the desired signal during interference suppression. A comparison between (a) and (c) demonstrates that, in a dual-component PFM interference scenario for LEO satellite signals, regardless of whether the carrier frequencies of the interference components are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interference Scenario** &
\begin{tabular}{c} **Carrier Frequency** \\ **(MHz)** \\ \end{tabular} & **Modulation Period (\(\upmu\)s)** & **Bandwidth (kHZ)** \\ \hline Dual-Component PFM Interference & & & \\ Scenario 1 & 270 & 360; 420 & 400; 250 \\ \hline Dual-Component PFM Interference & & & \\ Scenario 2 & 270; 280 & 360; 420 & 400; 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment parameter setting of signal power spectrum simulation.
## 3 Results and Discussion  
The Effect of Chloroform Treatment of Cereal Caryopses on the Growth of Their Seedlings in the Presence of Phytopathogenic Fungi  
In this work, the role of 5-\(n\)-alk(en)ylresorcinols of Caryopsis surfaces in the infection process of their seedlings by _Rhizoctonia solani_ and _Fusarium cultororum_ was evaluated. Moreover, only a short 15-s chloroform extraction was used to remove these phenolic compounds from the grain surface to allow them to germinate and develop into seedlings. No significant effect of chloroform on the growth of either roots or coleoptiles of 5-day-old cereal seedlings compared to untreated controls was observed. However, considerable discrepancies in the impacts of phytopathogenic fungi on seedling growth of tested cereals were observed (Tables 1 and 2). In comparison to the untreated control, the coleoptile heights of barley cv. Rabelwere decreased by both strains of _R. solani_ to similar extents; on average, by 34.90%. In the case of barley cv. Scarlett, only strain F93 reduced its shoot growth by 23.28%. In turn, both strains of this fungal species were potent inhibitors of the root growth of either barley cultivar. Concerning the Scarlett cultivar, a stronger 58.94% decline was observed after the F93 application, whereas the F92 strain turned out to be more deleterious for the Rabel root system (the 65.88% growth reduction). Removal of the outermost AR layer of barley grains of these two cultivars (Scarlett and Rabel) amplified the negative impact of _R. solani_ strains on their seedlings (Table 1). This \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Celeval/Cultivar**} & \multicolumn{2}{c}{**Caryopsis/Sand Treatment**} & **RootLength** & **ShooftHeight** \\ \cline{2-5}  & **Chloroform** & **Pathogen** & **[cm] a** & **[cm] a** \\ \hline \multirow{5}{*}{Scarlett} & \(-\) & \(-\) & 6.60 \(\pm\) 0.39 a & 5.24 \(\pm\) 0.94 ab \\  & + & \(-\) & 6.58 \(\pm\) 0.42 a & 5.96 \(\pm\) 0.77 a \\  & \(-\) & _R. solani_ F92 & 3.28 \(\pm\) 0.33 b & 4.78 \(\pm\) 0.64 bc \\  & + & _R. solani_ F92 & 2.38 \(\pm\) 0.13 c & 2.86 \(\pm\) 0.62 d \\  & \(-\) & _R. [PERSON]_ F93 & 2.71 \(\pm\) 0.49 c & 4.02 \(\pm\) 1.58 c \\  & + & _[PERSON]. [PERSON]_ F93 & 2.44 \(\pm\) 0.29 c & 4.04 \(\pm\) 0.53 c \\ \hline \multirow{5}{*}{Rabel} & \(-\) & \(-\) & 6.83 \(\pm\) 0.65 a & 6.39 \(\pm\) 0.80 a \\  & + & \(-\) & 7.10 \(\pm\) 0.82 a & 6.77 \(\pm\) 0.79 a \\  & \(-\) & _[PERSON]. [PERSON]_ F92 & 2.33 \(\pm\) 0.58 bc & 4.01 \(\pm\) 0.77 bc \\  & + & _[PERSON]. [PERSON]_ F92 & 2.03 \(\pm\) 0.46 b & 4.13 \(\pm\) 0.93 bc \\  & \(-\) & _R. solani_ F93 & 3.00 \(\pm\) 0.42 d & 4.31 \(\pm\) 0.69 b \\  & + & _R. solani_ F93 & 2.62 \(\pm\) 0.48 c & 3.87 \(\pm\) 0.56 c \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effect of chloroform treatment of barley grains on the growth of their seedlings in the presence of _R Rhizctonia solani_.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
### Text Detection
#### 6.1.2 Segmentation-Based Methods
Another real-time text detector is DBNet [124] which contains a Differentiable Binarization module to simplify the post-processing and achieve better results, being improved with DBNet++ [125]. The Adaptive Scale Fusion module was additionally introduced, enabling greater robustness of the model and maintaining its efficiency. [PERSON] et al. [126] proposed a lightweight model, called MOSTL, to detect irregular text, including curved and vertical texts. The framework contains an improved ReLU layer (i.ReLU) and an improved inception layer which extract more valuable text information. The RSCA [127] network also had the ability to detect curved texts in real time. The strategy was to apply local-context-aware upsampling to be effective with less computational cost and dynamic text-spine labeling for simplifying label generation and assignment. [PERSON] et al. [128] proposed an innovative and robust framework combining position and semantic information to avoid degrading segmentation-based methods due to inaccurate annotations of text pixels. The SAST model suggested in [129] integrates high-level object knowledge and low-level pixel information in a single shot to overcome the difficulty of separating adjacent text instances. The model detects scene text of arbitrary shapes with high accuracy and efficiency. A state-of-the-art unified detector was proposed in [130]. Without complex post-processing, the model detects text as masks and posteriorly groups them into clusters. Automatic text detection is challenging, namely when dealing with blur images, crowded backgrounds, multi-orientations, arbitrary shapes and font size variations. Some methods proposed axis-aligned bounding boxes or polygons to better enclose the text. Considering the background information that is still present in more irregular texts, other researchers proposed segmentation methods that adapt to arbitrary shape texts. The segmentation approach has the disadvantage of being vulnerable to false detection when two text instances are near one another. Despite the use of several strategies to address these issues, some text instances are not detected, some regions are wrongly classed as containing text, text regions are incorrectly separated into words, or individual portions are erroneously linked. Further studies are required in the text detection area.