esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Simulation Study
#### 3.1.3 Third-Order Process Model
Strategic analysis is carried out by changing the initial set points, starting at 1 at time 0, moving to 0.5 at 20 s, and progressing to 2 at 40 s while keeping track of positive and negative changes. This test bench provides a comprehensive analysis of the controller's functionality. This step response cleverly adjusts its output to match the specified set-point change, highlighting the superior overall performance of the proposed complex slave controller. The complex slave controller consistently outperforms comparable controllers with its high adaptability and accuracy, making it ideal for applications where control and adaptability to varying conditions are key performance indicators. Complex-order controllers have proven indispensable partners in set-point tracking, providing improved control dynamics and higher performance standards in the face of dynamic operating requirements. The comprehensive evaluation of disturbance rejection involved the construction of a deliberate and pertinent scenario. In this scenario, an intentional disturbance value of 2 was injected into the controller structure after 8 s.  
These simulation results accurately mimic real-life situations where disturbances can permeate the entirety of a process plant. The intricate dynamics of this scenario are depicted in Figure 11, where each control structure adeptly navigates the challenges posed by disturbances and dynamically adjusts the output back to the set point. This observed response aligns seamlessly with the step response illustrated in Figure 9. The proposed complex-order controller consistently emerges as the front-runner in this evaluation, outperforming its competitors with a demonstrable superiority in effectively managing and suppressing disturbances. The nuanced and resilient performance of the complex-order controller underscores its efficacy in maintaining overall system control amidst external disruptions. This empirical evidence not only highlights the specific prowess of the complex-order controller in disturbance rejection but also positions it as the optimal choice for applications  
Figure 9: Third-order process model’s step response with compared controllers. where superior disturbance management and sustained system control are paramount performance criteria. The complex-order controller is a robust solution, offering enhanced control dynamics and a heightened ability to contend with real-world disturbances within dynamic operational environments.
## 2 Experimental
### Testing Methods  
#### 2.2.1 Bond Strength  
The bond property between carbon fiber sheets and concrete is a key factor controlling the behaviour of the strengthened concrete elements. There are many different experimental methods used for determining the bond strength. [PERSON] _et al._[21] classified the existing measurement approaches into the following three types (see Figure 1): (a) single-shear test; (b) double-shear test; and a (c) beam test. Due to the simplicity and reliability [22], the double-shear test was adopted in this study for measuring the bond strength between the carbon fiber sheets and concrete specimen.  
During the double-shear test, the loading was imposed on the prepared specimen by using a computer controlled electronic universal testing machine until failure, under a displacement control at a speed of 0.2 mm/min. The critical load at failure was recorded and the bond strength can be calculated according to the following equation:  
\[\tau=\frac{P_{\mathrm{u}}}{2b_{\mathrm{f}}L_{\mathrm{f}}}. \tag{1}\]  
In the above equation, \(\tau\) = bond strength (MPa); \(P_{\mathrm{u}}\) = critical load imposed on the specimen at failure (kN); \(b_{\mathrm{f}}\) = the bonding width of carbon fiber sheets (mm); and \(L_{\mathrm{f}}\) = the bonding length of carbon fiber sheets (mm).
## 2 System Model
### Device Energy Consumption Estimation
where \(E_{Tx-sloc}\) and \(E_{Rx-sloc}\) are defined as the energy consumed per bit by \(Tx\) and \(Rx\) circuits. Also, \(\varepsilon_{f_{g}}\) and \(\varepsilon_{amp}\) are the power amplification factors of the free space and multi-path radio models, respectively. Whereas, the threshold value (\(d_{0}\)) is estimated via \(d_{0}=\sqrt{\varepsilon_{f_{g}}/\varepsilon_{amp}}\). Equations (1) and (2) calculate the energy consumption of the device for \(k\) number of data packets transmission. The proposed routing scheme also utilizes maximum energy (\(RE_{\max}\)) and residual energy (\(RE\)) of the intermediate device for the selection of the optimal path. The \(RE_{\max}\) and \(RE\) values are estimated by exploiting the linear battery model [44]. Thus, the rank of intermediate devices \(c\) based on drain rate \(DR^{c}_{(x,y)}\) for the selection of optimal route between the source and destination devices can be calculated as follows:  
\[DR^{c}_{(x,y)}=\frac{RE_{c}(k)}{RE_{\max}(k)\times\left\{E^{c}_{Tx}(k,\lambda) +E^{c}_{Rx}(k)\right\}} \tag{3}\]
## 3 Results
### Post-Workshop Survey  
Figure 1 shows the distribution of responses collected from the survey delivered after the end of each workshop, in each of the three patient groups (medication, lifestyle, and mixed). The six questions in the survey (see Appendix B) prompted participants to reflect about the following aspects, in relation to the attended workshop: (1) Feeling better about diabetes management; (2) Learning new information; (3) Usefulness of the information; (4) Plans to act based on the information obtained; (5) Self-belief towards achieving individual goals within three months; and (6) Motivation to set a goal.  
The distribution of responses shown in Figure 1 suggests differences between the groups. In general, the majority of patients in the lifestyle group tended to agree (from moderately agree to strongly agree) with the feelings and attitudes captured by the proposed statements. Only one patient from the lifestyle segment reported a neutral response (neither agree nor disagree) in one of the questions, and none of the patients in the lifestyle segment disagreed. Noticeably, in three out of six questions all patients in the lifestyle segment strongly agreed with the proposed statement (i.e., Q3: The information and interactions during the workshop were useful to me; Q4: I plan to act on the information I obtained in the workshop; Q6. I am motivated to set a goal for improving my diabetes).  
Vice versa, patients in the medication group had varying opinions about the statements presented in the survey. Only the distribution of responses to question Q2 was similar between patients in the lifestyle and medication group, with 50% or more patients reporting that they moderately agreed with the fact that they learned new information, and the remaining participants reporting that they either strongly agreed or that they did neither agreed nor disagreed. Regarding questions Q1, Q3, Q4, Q5, and Q6, patients in the medication group were less optimistic than those in the lifestyle group, particularly in relation to having motivation to set a goal or make plans to act, and having the self-belief to achieve the goal. Overall, the distribution of responses observed in patients in the mixed group shows an intermediate pattern that is between those of patients in the lifestyle and medication segments.
## 4 Feature Extraction of RC Valve Fault
### Fault Characteristics Analysis of Response Signal  
#### 4.2.1 Spectrum Analysis of Response Signal  
Figure 11 shows the frequency-domain waveforms of each group of vibration signals. Among them, \(f_{n1}\), \(f_{n2}\), \(f_{n3}\), and \(f_{n4}\), respectively, represent the rotation frequency of RC under the conditions of normal, 2 mm leakage, 6 mm leakage, and 8 mm leakage of the valve.  
In Figure 11, \(f_{n1}=14.336\) Hz, \(f_{n2}=15.360\) Hz, \(f_{n3}=16.378\) Hz, and \(f_{n4}=16.381\) Hz, it shows that the rotation frequency of the RC increases with the aggravation of valve leakage fault. During the actual operation of the RC, the pressure in the cylinder is reduced, due to the presence of the leak, which results in the reduction of the torque of the crankshaft, so that the rotational speed of the crankshaft is increased. The frequency spectrum of vibration signal contains a large number of frequency doubling of rotation frequency, and the frequency doubling will fluctuate when the rotation frequency changes. Therefore, the characteristic information of valve failure can be extracted from the rotation frequency and its frequency doubling of RC.
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
## 5 Parameter Optimization of the Controller Based on PSO
### Algorithm Flow  
In this algorithm, the input parameter is the particle group's parameter and the acceleration constant of the sliding mold controller of the supplied material device, and the output result is the optimal parameter of the sliding mode controller. The specific processes are shown in Figure 7.  
First, initialize the algorithm parameter and set the number of iterations, t, and number of sliding mode controller group particles, n. While randomly defining the initial position, flight speed, and acceleration constant of the particles, assume that the individual extremum of each particle is its current optimal position, and that the global extremum is the best particle position in the individual extremum. Calculate the fitness of each particle in the group using Formula (18); then, compare the particle fitness values to the individual extremum and the global extremum. If the current position of the particle is superior to the individual extremum, update the individual extremum, and if the particle swarm position  
Figure 7: Optimization process for sliding mold controller parameters based on PSO.  
is superior to the global extremum, update the global extremum. Obtain the particle in question's up-to-date location and flight speed based on the updated individual extremum, the global extremum, and Formula (17). Finally, if it is found that the number of iterations exceeds the set maximum number, the iteration is stopped and the optimal solution is obtained, i.e., the optimal parameter of the sliding mode controller for the trajectory of the supplied material device; otherwise, Formula (18) is used to calculate the fitness of each particle in the group.  
After the MATLAB/Simulink R2016b simulation iteration, the optimized results of PSO are obtained, as shown in Figures 8 and 9.  
As shown in Figure 8, after 15 iterations, the PSO has found the global extremum, at which point the target function achieves the minimum value. As shown in Figure 9, the red line represents the c value; the green line represents the k value; and the blue line represents the \(\varepsilon\) value, so the controller parameters have reached the optimum: c = 4.27, k = 1.5, \(\varepsilon\) = 2.1.
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 7 Simulation Results and Analysis
### BER with Time Delay Error CSI  
In time delay error CSI, Figure 4 simulates the BER performance of several typical algorithms when configured as [2,2,4,13,8]. When using QPSK for modulation, the proposed algorithm uses power allocation and aligns the phase of the transmitter and receiver signals to reconstruct the interference into a green energy source, which enhances the power of the desired symbol, and BER is greatly improved. The BER of the proposed algorithm is lower than the algorithms of literature [11,17].  
Figure 4: BER comparison under time delay error CSI.  
Figure 3: Average system capacity comparison under time delay error CSI.
## 3 Results
### Results of Statistical Analysis
#### 3.2.1 McNemar Test  
The McNemar exact test [19] was used to evaluate all variables, except \"maximum mouth opening\". This test is typically employed to assess differences in dichotomous data (presence/absence) before and after a treatment, effectively evaluating the treatment's efficacy. In other words, it determines whether two dependent variables are statistically significantly different from each other. In our case, it assesses whether the combined orthodontic-surgical treatment influenced the outcomes.  
For the statistical analysis, cases where the analyzed elements decreased in intensity or frequency were considered as \"0\", indicating the disappearance of the symptom. We assumed that any reduction in symptoms indicates a positive trend in our treatment compared to the baseline.  
Statistical significance was considered if \(p<0.05\).  
The results obtained are as follows:  
* Click before and after: Exact McNemar significance probability 0.0192, indicating a significant association.
* TMJ pain before and after: Exact McNemar significance probability 0.0117, indicating a significant association.
* Bruxism or clenching before and after: Exact McNemar significance probability 0.0386, indicating a significant association.
* Masticatory Muscle Pain before and after: Exact McNemar significance probability 0.0074, indicating a significant association.
* Headaches before and after: Exact McNemar significance probability 0.0000, indicating a significant association.
* Locking before and after: Exact McNemar significance probability 1.0000, indicating no significant association.
# Vitamin C in Plants: From Functions to Biofortification
## 4 Vitamin C Biosynthesis in Plants
### d-Mannose/l-Galactose Pathway
l-galactose dehydrogenase (GDH) is the NAD-dependent enzyme catalyzing the conversion of l-galactose into l-galactono-1,4-lactone [60]. This step of ASC biosynthesis is not limiting. Indeed, in _Arabidopsis_, the overexpression of GDH does not change the vitamin C content, and antisense plants show vitamin C decrease only under high light [150]. The last step of the Smirnoff-Wheeler pathway is catalyzed by GLDH, a flavoprotein which converts l-galactono-1,4-lactone into l-ascorbate, transferring electrons to cytochrome c [151; 120; 152]. An important observation is that GLDH does not release H\({}_{2}\)O\({}_{2}\), as happens with l-gulonolactone oxidase in animals, and therefore the production of vitamin C in plants does not affect the redox state of the cell [152]. Unlike the other enzymes of the d-mannose/l-galactose pathway, which are all localized in the cytosol, GLDH is an integral protein of the mitochondrial inner membrane [153; 97; 154]. Specifically, GLDH has been detected in complex I and acts as an essential plant-specific factor for complex I assembly [155; 156; 157; 158]. Due to the GLDH localization, vitamin C biosynthesis is very sensitive to stresses that cause impairments of electron flux [111; 114; 159]. On the other hand, an increase in respiration, like that observed during tomato ripening, is associated with an enhancement of vitamin C content [160]. The different steps of the Smirnoff-Wheeler pathway are schematized in Figures 2 and 3.
## 5 Conclusions  
In this paper, an ISBOA-optimized BP neural network-based error compensation method for rock-drilling robotic arms is proposed to address the end positioning accuracy problem. The good point set strategy is used to initialize the ISBOA population position, facilitating a faster search for better solutions. ISBOA optimizes the initial weights and biases of the BP neural network, effectively preventing the BP neural network model from falling into local extrema and improving training speed and accuracy. The ISBOA-BP optimal model is then established based on the joint values of the rock-drilling robotic arm and the corresponding end position errors. Compared to the BP, GA-BP, PSO-BP, and SBOA-BP algorithms, the ISBOA-BP algorithm enhances both the convergence accuracy and the generalization ability of the neural network. The experimental results indicate that the average total positioning error is reduced from 187.972 mm to 28.317 mm, representing an 84.94% improvement in positioning accuracy. This significantly enhances the positioning accuracy of the rock-drilling robotic arm, meeting the requirements of practical engineering applications. However, differences in hardware conditions, error characteristics, and application environments exist among various robots. No in-depth research has been conducted on how to directly apply the neural network trained on the G3 Zi rock-drilling robot to other types of robots through transfer learning for error compensation. Therefore, this will be the focus of future research.  
Q.X.: conceptualization, writing--original draft, data curation, formal analysis, validation, visualization. W.J.: writing--review and editing, conceptualization, data curation, formal analysis, validation. Y.L.: writing--review and editing. T.Z.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Error Range (mm)** & \
\begin{tabular}{c} **Average Absolute** \\ **Error (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation (mm)** \\ \end{tabular} \\ \hline X-error & Before & [51.175, 166.243] & 92.282 & 32.644 \\ After & [\(-16.974\), 12.633] & 7.321 & 7.901 \\ Y-error & Before & [\(-25.489\), 184.063] & 73.824 & 55.757 \\ After & [\(-26.443\), 25.110] & 12.305 & 12.431 \\ Z-error & Before & [\(-297.178\), 30.573] & 129.112 & 94.319 \\ After & [\(-32.986\), 30.905] & 11.755 & 15.131 \\ Total error & Before & [55.387, 350.901] & 187.972 & 85.280 \\ After & [0.876, 36.095] & 28.317 & 9.091 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Position error before and after ISBOA-BP neural network compensation.
# The Reproduction and Evaluation of Star Fields with the Milky Way in a Planetarium
## 4 Conclusions  
A natural starry sky with the Milky Way is created by a set of point light sources, and its reproduction has not been sufficiently investigated. In this study, to investigate the important physical factors that reproduce the star field with the Milky Way in a planetarium, we analyzed three evaluation indices--faithfulness, preference, and depth feeling--with psychometric experiments, using the projected stars as experimental stimuli and changing three physical factors (transmittance, representation size and star density). A standard projection pattern was designed by a group of experienced observers with abundant astronomical observation experience. The standard was faithful to the actual starry sky, perceptually but not physically. In evaluation experiments, 37 observers were engaged to evaluate 12 types of star patterns projected on a dome screen. Based on the evaluation results, we proposed a common estimation model for describing the faithfulness, preference, and depth feeling with a log-normal distribution. The resultant model exhibited good accuracy with high correlation coefficients.  
Many studies about human perception of the real sky at night have been discussed since [PERSON]'s works. As future work, extensive and insightful discussions are further required based on such studies. In a further analysis involving clustering observers, we identified two clusters defined by evaluation tendencies. The major cluster focused on density reproduction for the appearance of the Milky Way in a planetarium.  
Conceptualization, K.O.; methodology, M.T.; validation, K.O. and S.S.; formal analysis, M.T.; investigation, M.T. and Horiuchi; resources, K.O. and S.S.; writing--original draft preparation, M.T.; writing--review and editing, M.T. and T.H.; supervision, T.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Konica Minolta Planetarium Co., Ltd.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data not available due to commercial restrictions.  
The authors declare no conflict of interest.  
Figure 9: Weights in two clusters for each evaluation. (**a**) Faithfulness; (**b**) Preference; (**c**) Depth feeling; (**d**) Total comparison.
## 1 Introduction
Then, in the Text-to-SPARQL tasks, the literal inside the final output is mapped back to the IRI encoding. These measures ensure that the semantic information is preserved during the training process, allowing for the maximum utilization of the pre-trained model's weights and generating directly executable SPARQL queries as the final output. Experimental results show that our model can significantly improve the quality of SPARQL query generation. On three well-known KBQA datasets, LC-QuAD 2.0 [18], QALD-9 plus [19], and QALD-10 [20], TSET surpasses all previous methods in answer \(F1\) score and Query Match (QM) accuracy, achieving new state-of-the-art performance. We also do a comprehensive set of ablation studies to demonstrate the effectiveness of our proposed Triple Structure Correction (TSC) objective and the semantic transformation approach. A thorough error analysis has surprisingly shown that despite our primary focus on addressing triple errors, our model also exhibited improvements in sentence-level errors. It further underscores the effectiveness of our method in improving the overall quality of SPARQL query generation. In summary, our main contributions are as follows:  
* We present TSET, a model incorporating the novel Triple Structure Correction (TSC) objective, which improves SPARQL query comprehension. * We design semantic transformation during pretraining to enhance the model's understanding of triple-structure information while still being able to leverage the pre-trained weights. * Through exhaustive evaluation using three popular KBQA datasets, we demonstrate the superiority of TSET, setting a new State-Of-The-Art performance on query match and answer \(F1\) metric.
## 1 Introduction
The potential of entomopathogenic fungus to adjust to external habitats other than their original habitats has made them very efficient and adequate candidates for biological control measures [10]. Considered as facultative microorganisms that do not require arthropods as hosts to complete their life cycles, _Metrahizium anisopliae_ and _Beauveria bassiana_ are the best characterized and most applied entomopathogenic fungi in biological control programs [11]. The hypocrealean fungus _Beauveria bassiana_ (Bals.) Vuill. (Ascomycota: Hypocreales) [12] has shown negative effects on 33 species of insects belonging to eight orders [13]. It implements integral protection against insect pests or constrains their growth and proliferation [12]. This fungus has also exhibited dual protection ability against _Rhizoctonia solani_ and _Pythtium myriotylum_ in _B. bassiana_-treated tomato seedlings [11]. Distinct studies showed that _B. bassiana_ may be used as an effective biocontrol agent against _Helicoverapa armigera_ in broad bean [14] and _Helicoverapa zea_ in cotton and tomato [15,16]. Feeding on _B. bassiana_-treated maize plants reduced _Sitobion avenue_ survival and fecundity in single aphids [13]. In corn plants, _B. bassiana_ showed insecticidal activity against many lepidopteras including _Sesamia calamistis_ [17] and _Ostrinia nubilalis_ [18]; and when it was isolated as endophyte, against _Spodoptera frugiperda_ [19]. The mortality level of target insect pests with _B. bassiana_ depends on larval developmental stage [20], inoculation method [21], or fungal strains used [22]. As an endophyte, _B. bassiana_ mostly does not induce direct mortality in insect pests but often reduces larval growth rate, weight, or longevity [23,24]. Taken together, the aim of the present study was to evaluate the effect of _Beaureria bassiana_-seed treatment on _Zea mays_ L. against _Spodoptera frugiperda_ larval activity under laboratory and field conditions.