esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits
## 5 Results
### Structural Equation Model
#### 5.3.1 Direct Effects  
Figure 2 presents the model results once the latent variable coefficients, model fit, and quality indices were estimated. As can be inferred from the \(p\)-values, all direct effects or direct relationships between the latent variables are statistically significant with a 95% reliability level. Table 6 summarizes the conclusions regarding the research hypotheses. All of the research hypotheses were accepted, since they are statistically significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Managerial** & **TPM** & **PM** & **Productivity** \\  & **Commitment** & **Implementation** & **Implementation** & **Benefits** \\ \hline R-squared & – & 0.628 & 0.407 & 0.382 \\ adj. R-squared & – & 0.626 & 0.405 & 0.377 \\ composite reliability & 0.942 & 0.939 & 0.902 & 0.956 \\ Cronbach’s alpha & 0.926 & 0.928 & 0.873 & 0.948 \\ average & 0.729 & 0.608 & 0.569 & 0.732 \\ variance inflation factor & 2.560 & 2.804 & 1.982 & 1.499 \\ Q-squared & – & 0.630 & 0.409 & 0.381 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Survey validation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value** \\ \hline average path coefficient (APC) & 0.368, \(p<0.001\) \\ average R-squared (ARS) & 0.472, \(p<0.001\) \\ average adjusted R-squared (AARS) & 0.470, \(p<0.001\) \\ average block VIF (AVIF); acceptable if \(\leq 5\), ideally \(\leq 3.3\) & 1.912 \\ average full collinearity VIF (AFVIF); acceptable if \(\leq 5\), ideally \(\leq 3.3\) & 2.211 \\ tenenhaus goodness of fit (GoF); small \(\geq 0.1\), medium \(\geq 0.25\), large \(\geq 0.36\) & 0.558 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model fit and quality indices.  
Figure 2: Evaluated model.  
Figure 2 also shows the \(R^{2}\) values from the dependent latent variables. In SEMs, \(R^{2}\) expresses the amount of variance in a dependent variable that is explained by one or more independent variables. For instance, the TPM implementation latent variable shows \(R^{2}\) = 0.628, which is explained by managerial commitment (0.400) and PM implementation (0.228). In turn, PM implementation is explained by only one latent variable, managerial commitment, with \(R^{2}\) = 0.407.  
Finally, the productivity benefits latent variable is explained in 0.382 units by three latent variables: managerial commitment (0.162), PM implementation (0.057), and TPM implementation (0.164). These results imply that to ensure proper TPM implementation, it is important to perform tasks from managerial commitment, since this latent variable explains 40% of the PM implementation. Similarly, to obtain productivity benefits, companies must have managerial commitment and TPM implementation, because those variables have a higher explanatory level.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 5 Evaluation
### Experiment Methodology & Setup
#### 5.1.2 Testbed Experiments
The slot bonding implementation deployed on the hardware testbed had a channel allocation of three channels of 200 kHz and two channels of 1667 kHz for the 50 kbps and 1000 kbps PHY, respectively, without frequency overlap in the 863-868 MHz band, as shown in Figure 8. The number of channels was limited to speed up the network bootstrap process. Two different slotframe lengths of 261 ms (i.e., 29 slots of 9 ms) and 423 ms (i.e., 47 slots of 9 ms) were considered. The nodes can actually only use 17 and 36 slots respectively in each slotframe: eight slots were included to facilitate two shared bonded slots (of four regular slots each) that use the 50 kbps PHY and padded the resulting length with unused extra slots to become a prime number of slots (to achieve pseudorandom channel hopping). The shared slots were placed at the beginning of each slotframe and were used to broadcast the TSCH Enhanced Beacon (EB) messages that advertised the network (every 7 s). The 6 top Protocol (6P) layer and the Contiki-NG _NullNet_ option were disabled, meaning there was no network or any higher layer functionality. All network topologies and schedule allocations were statically configured at the MAC layer, using the parent and PHY selections and the TSCH schedule provided by the heuristic and/or GA. The queue size was set to eight packets. The maximum number of allowed transmission opportunities per packet was set to four, as advised by the 6 TiSCH Minimal Configuration [18]. Every node transmitted a packet of 127 bytes every slotframe. For the configuration of the GA (i.e., operators, number of generation and mutation and cross-over probabilities), the reader is referred to our previous work as the GA configuration is the same [7]. However, the GA (and the proposed heuristic) now use the 50 kbps and 1000 kbps PHYs. Subsequently, the feasibility algorithm that determines the TSCH schedule was extended to be able to allocate bonded slots in the non-overlapping channels reserved for the respective PHYs. Additionally, the algorithm completely avoids interference between nodes by not allowing simultaneous transmissions using the same frequency channel. The simulator results were averaged over 20 iterations, while the testbed results were averaged over 12 iterations. The used box plot graphs show the the first quartile value (i.e., Q1, the bottom of the box), the third quartile value (i.e., Q3, the top of the box), the minimum value (i.e., the lower whisker of the box plot, which equals \(Q1-1.5\cdot IQR\) with \(IQR\) being \(Q3-Q1\)), the median value, the maximum value (i.e., the top whisker, which equals \(Q1+1.5\cdot IQR\)) and the mean value (i.e., denoted by x). Outliers are defined as values smaller or larger than the minimum or maximum values. If there are outliers, these are denoted by the diamond shapes.
## 3 Results  
Blue Light Dose 208 mJ/cm\({}^{2}\), 1000 ms Affects Malignant Cell Motility and Does Not Change Motility of Benign Cell Line PNT1A  
The motility of PC-3, A2780, PNT1A, and G361 cells was measured by using Q-PHASE multimodal holographic microscope. It was shown that light dose 208 mJ/cm\({}^{2}\)\(\times\) 1000 ms affected the motility of all types of malignant cells--PC-3, A2780, G361 (Figure 3). A2780 cells showed a distinctive decrease of motility from the beginning of the measurement while PC-3 and G361 cells decreased their motility after 15 and 20 h of measurement, respectively. The benign cell line PNT1A showed no motility changes in comparison with control by using light dose 208 mJ/cm\({}^{2}\)\(\times\) 1000 ms. The most intense light dose 208 mJ/cm\({}^{2}\)\(\times\) 500 ms decreased motility of all cell lines with the most visible effect on PNT1A and A2780 cells. Some effect was also observed in the PC-3 cells with blue light dose 24 mJ/cm\({}^{2}\)\(\times\) 500 ms.  
Blue Light Doses 208 ml/cm\({}^{2}\), 1000 ms Significantly Decrease Proliferation Activity in All Tested Cell Lines  
The relative number of cells obtained by QPI was compared with the number of cell divisions manually counted from videos edited in ImageJ 1.49v (NIH, USA and recalculated according to the length of the experiment. All control cells have positive rates of proliferation during the experiment. Although some light doses such as 24 mJ/cm\({}^{2}\)\(\times\) 500 ms in A2780 and PC-3 cells showed some proliferative effects, these effects had no statistical significance (Figure 4). The light dose of 208 mJ/cm\({}^{2}\)\(\times\) 1000 ms negatively affected proliferation in all cell lines. Interestingly, this light dose was not so deleterious for benign cell line PNT1A as for malignant cells (Supplementary Videos S1-S4). The least harmful dose in our cells was 24 mJ/cm\({}^{2}\)\(\times\) 500 ms.  
Figure 3: **Real-time QPI-measured motility of cells affected by different doses of blue light.** Real-time motility of PC-3, A2780, PNT1A, and G361 cells during 24 h of the experiment (left side of the figure). Lines are shown as the mean and standard error. K value means the slope of the least-square fitted line (increment of the value per hour). The end-point motility values of PC-3, A2780, PNT1A, and G361 cells after 24 h exposure to blue light (right side of the figure). Boxes are composed of the main box edges (25 th and 75 th percentile), central line (median) and whiskers (lowest and highest value of 1.5 of inter-quartile range).
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 2 Polar Codes
### Decoding
The information bits \((x_{1},\ldots,x_{N})\) are modulated and demodulated and input into the SCL decoder as the sequence \((\hat{x}_{1},\ldots,\hat{x}_{N})\) (the modulation and demodulation module is described in 2.1), and the SCL decoder provides \(L\) estimated information bits \((\hat{u}_{1},\ldots,\hat{u}_{k},\hat{u}_{1},\ldots,\hat{u}_{r})_{l}\), \(i\in\{1,2,\ldots,L\}\), corresponding path metrics \(PM_{l}\), and \(i\in\{1,2,\ldots,L\}\). If none of the \((\hat{u}_{1},\ldots,\hat{u}_{k},\hat{u}_{1},\ldots,\hat{u}_{r})_{l}\), \(i\in\{1,2,\ldots,L\}\) passes the CRC check, the decoding fails. If only one of them passes the CRC check, the estimation that passes the CRC check is chosen as the decoding result. If multiple estimations pass the CRC check, the estimation with the minimum metric value is selected as the decoding result among those that pass the CRC check.
# Modal Identification of Train Passenger Seats Based on Dynamic Tests and Output-Only Techniques
## 1 Introduction
* Train seat modal parameters validation based on the application of two distinct output-only techniques, one based on transmissibility functions, which is used by most of the authors in the bibliography, and the other based on the Enhanced Frequency Domain Decomposition (EFDD) method. The latter allows for visualising the modal configurations, which is a novelty in relation to the previous research works.
## 2 Centrifuge Model Test
### Soil Properties and Pile Information  
The sand used in the composition of the soil was Jumunjin sand, which is classified as SP by the Unified Soil Classification System [16]. Figure 2 shows the particle size distribution curve of the Jumunjin stand. It is important to consider the effect of the soil particle size in centrifuge model tests. [PERSON] [17] reported that the specimen particle size has no effect on the pile in centrifuge model tests if the pile diameter is 30 times the effective particle size or more. The diameter of the pile used in this study was around 87 times larger than the effective particle size of Jumunjin sand; therefore, it was considered that there was no particle size effect. The specific gravity (G\({}_{\text{s}}\)) of the Jumunjin sand used in the experiments was 2.65. The maximum dry unit weight was 16.6 kN/m\({}^{3}\). The minimum dry unit weight was 13.3 kN/m\({}^{3}\). The internal friction angle was measured as 38\({}^{\circ}\) by direct shear testing when the relative density was 80%. Table 1 shows the properties of the Jumunjin sand.  
As for the material of the pile used in the tests, it was a model monopile made out of aluminum alloy with an elastic modulus of 70 GPa. The model monopile's total length was 639 mm. It was constructed as an aluminum pipe with a thickness of 3 mm, an outer diameter of 35 mm, and an inner diameter of 29 mm. The model monopile's insertion depth was 424 mm. Because the tests were performed under a gravitational acceleration of 94.2 g, when the monopile was converted to the original scale, it was modeled as a steel monopile with an insertion depth of 40 m, a total length of 60 m, an outer diameter of 3.3 m, and an inner diameter of 2.7 m. Here, the change in thickness during conversion was calculated to take into account the flexural rigidity. In addition, the relative stiffness, which is one of the most important parameters for lateral pile behavior, was also considered in centrifugal similitude relation. The pile corresponds to a long pile, according to [PERSON] [18]. The pile specification is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**USCS** & **SP** \\ \hline Maximum unit weight (kN/m\({}^{3}\)) & 16.6 \\ Minimum unit weight (kN/m\({}^{3}\)) & 13.3 \\ Coefficient of uniformity (C\({}_{\text{u}}\)) & 1.68 \\ Specific gravity (G\({}_{\text{s}}\)) & 2.65 \\ Relative density (\%) & 80 \\ Friction angle (\({}^{\circ}\)) & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of soil.  
Figure 2: Jumunjin sand particle size distribution.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 8 Viruses  
Insect viruses are obligate intracellular parasites. They are quite ubiquitous, with over 1100 documented viruses known to infect insects from over 20 different families [227]. These viruses encompass several different families of RNA and DNA viruses. Entomopathogenic RNA viruses include copovirus, dictstrovirus, nodavirus, and tetravirus, while DNA viruses include densovirus, entomopoxvirus, asovirus, iridovirus, nudivirus, hytroavirus, iflaviruses, and baculovirus. Of these, baculoviruses are the most widely used for insect biocontrol. Epizootics of insect viruses are quite common. Many, but not all, insect viruses are surrounded by a crystalline protein matrix, called an occlusion body that protects the virons outside of the host. This allows these viruses to persist in the environment and be taken up by new insect hosts [228]. Viruses typically enter insect hosts through the gut, where occlusion bodies dissolve, releasing the virons into the midgut. The virons then infect gut epithelial cells, proliferate, and spread to other cells and tissues as secondary infections [229]. Most insect viruses are very species-specific and, as a result, safe for humans and other non-target species. They are also fast-acting, exhibit high infectivity, and are high lethality, making them great biocontrol options for insect pests.  
The use of entomopathogenic viruses for biocontrol has been studied since the 1940s. The susceptibility of various insect species to baculoviruses, specifically nucleopolyhe-droviruses and granuloviruses, has resulted in the registration of several commercial products. Although several viruses have been registered as biopesticides, the vast majority target lepidopteran, hypnenopteran, and orthopteran taxa, and no commercial products are currently available for scarab pests [35; 168; 36]. Despite this, some scarab-specific viruses have been discovered that show great potential for future development.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 4 PSIJ Sensitivity Study of Two-Stage Driver  
### Simulation Setup  
This section aims to explore the study of the sensitivity of the PSIJ transfer function (TF) to the supply voltage amplitude and frequency induced by pre-driver (\(V_{DD}\)) and last-stage (\(V_{DDQ}\)) buffer at the SS\(-\)40 \({}^{\circ}\)C corner [21]. The separate and combined PSIJ contributions of the driver's IO stages are explored. The PSIJ by the pre-driver \(V_{DD}\) supply (i.e., \(S_{p}(\cdot)\)) and last-stage supply \(V_{DDQ}\), i.e., \(S_{L}(\cdot)\)), can separately affect the driver's total output jitter (i.e., \(S_{IO}(\cdot)\)). The PSIJ of the IO device nonlinearly depends on the amplitude (e.g., \(a_{k}\)) and frequency (e.g., \(f_{k}\)) of the power supply voltage, as illustrated in Figure 8.  
\[PSIJ_{k}\ (ps)=S_{k}(a_{k}(V),f_{k}(Hz));\ k=\{P,\ L,IO\} \tag{7}\]  
where \(k\) indicates the driver (\(IO\)) or specific driver stage: pre-driver (\(P\)) or last stage (\(L\)). The amplitude and frequency of the distinct power supply of the pre-driver and last stage are swept in order to figure out the sensitivity of PSIJ on the frequency and amplitude as shown in Figure 8.
## 6 A Compressed Sensing Based Ultra-Wideband Cognitive Radio
### Compressed Sensing Background  
Reference [129] gives a most succinct highlight of the CS principles and will be followed here for a flavor of this elegant theory. Consider the problem of reconstructing an \(N\times 1\) signal vector \(x\). Suppose the basis \(\Psi=[\psi_{1},...,\psi_{N}]\) provides a \(K\)-sparse representation of \(x\), where \(K<<N\); that is  
\[x=\sum_{n=0}^{N-1}\psi_{n}\theta_{n}=\sum_{l=1}^{K}\psi_{n_{l}}\theta_{n_{l}} \tag{2}\]  
Here \(x\) is a linear combination of \(K\) vector chosen from \(\Psi\); \(\{n_{l}\}\) are the indices of those vectors; \(\{\theta_{n_{l}}\}\) are the coefficients. Alternatively, we can write in matrix notation  
\[x=\Psi\theta \tag{3}\]  
where \(\theta=[\theta_{0},\theta_{1},...,\theta_{N-1}]^{T}\). In CS, \(x\) can be reconstructed successfully from \(M\) measurements and \(M<<N\). The measurement vector \(y\) is done by projecting \(x\) over another basis \(\Phi\) which is incoherent with \(\Psi\), i.e., \(y=\Phi\Psi\theta\). The reconstruction problem becomes an \(l_{1}-norm\) optimization problem:  
\[\hat{\theta}=\arg\min||\theta||_{1}\quad s.t.\quad y=\Phi\Psi\theta \tag{4}\]  
This problem can be solved by linear programming techniques like basis pursuit (BP) or greedy algorithms such as matching pursuit (MP) and orthogonal matching pursuit (OMP).  
When applying the CS theory to communications, the sampling rate can be reduced to sub-Nyquist rate. In [130] and [131] a serial and a parallel system structure were proposed, respectively. Sampling rate can be reduced to less than 20% of the Nyquist rate. However, they were designed for signals that are sparse in frequency domain. In this paper we propose a serial system structure which is suitable for pulse-based UWB communications, which is sparse in time domain. The analog-to-information converter (AIC) structure in [130] is not suitable for UWB communications. The 3-8 GHz UWB signal is considered as an example in describing the reasons:* The multiplier, which can be a mixer, supporting such high bandwidth for 3-8 GHz UWB signal is difficult to implement.
* The system is time-variant. Each measurement is the product of a streaming signal and a changing PN sequence. This requires a huge amount of storage space and complex computation.  
A simple architecture that is suitable for UWB signals is proposed using a finite impulse response (FIR) filter-based architecture.
## 4 Experiments and Analysis
### Applications  
To verify the usability of the model, the skeleton-line-based shape classification model for residential maps in this study was applied to identify the shape of some areal settlements in Lanzhou City, with experimental data from 1:5000 Lanzhou City building data. In the vector graph of the areal settlement in Lanzhou City, this paper selected some areal settlements as the test objects, such as the yellow part in Figure 12. The test sample dataset contained 240 samples, the selected areal settlement was manually annotated, and the trained model was used to classify this dataset. The classification result accuracy reaches about 93.3%, the accuracy rate is 93.3%, and the recall rate is 93.3%. The model classification results are shown in Table 3. As can be seen from Figure 14, in practice, buildings are mostly rectangular in shape, of which Type F and Type E are well classified and can all be recognized.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \
\begin{tabular}{c} **GCN Based on** \\ **Skeleton Lines** \\ \end{tabular} &
\begin{tabular}{c} **GCN Based on** \\ **Contour Lines** \\ \end{tabular} & **Turning Function** \\ \hline Accuracy rate & 95.3\% & 64.4\% & 47.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of methods.  
Figure 13: Changing curve of loss and validation accuracy in GCN model training.  
On combining the data from the training phase in the previous section, the classification results of the model in the experimental set are slightly lower than those in the test set, but the accuracy is still strong. In distinguishing complex shapes, such as E, F, and H shapes, the measured values are mainly concentrated between 95% and 100%. The main reason is that the skeleton line is a better representation of the main body and convexity of the settlement than the areal settlements, thus providing a more accurate description of the local features of a complex-shaped settlement. However, for the T- and Y-shaped areal settlements, with similar skeleton line structures, the measured values are 75.9% and 66.7%, respectively. In the next step of the study, the training set and the experimental dataset will be further increased in order to reflect the accuracy of the model classification more objectively.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
###### Abstract  
_Swertia mussotii_ is an important medicinal plant found on the Qinghai Tibetan Plateau that has great economic and medicinal value. This plant has enjoyed a long history of use as a curative for hepatitis. The biological activity of secoriridoids, including genitopicoside and swertiamarin, has been mainly tested for its anti-hepatitis effects. Here, we identify two candidate genes (_SmIS1_ and _SmIS2_) that are homologues of iridoid synthase and that are components of the secoriridoid pathway in _S. mussotii_. Using sequencing and phylogenetic analyses, we confirm that SmIS1 and SmIS2 contain six conserved short-chain dehydrogenases/reductase (SDR) motifs and thus belong to the P5\(\beta\)Rs group. The two purified _Escherichia coli_-expressed proteins reduced 8-oxogerani to both neepetalactol and iridodials. A comparison of the kinetic parameters of SmIS1 and SmIS2 recombinant proteins revealed that SmIS2 has a lower affinity than SmIS1 for 8-oxogerani. Transcript levels of the two genes were analysed in three different tissues of _S. mussotii_ using semi-quantitative RT-PCR and RT-qPCR. SmIS1 and SmIS2 expression levels were more abundant in leaves and stems. This investigation adds to our knowledge of _P5\(\beta\)Rs_ genes in the secoriridoid synthesis pathway and provides candidate genes for genetically improving _S. mussotii_ by enhancing secondary metabolite production.  
_Swertia mussotii_; medicinal plant; secoriridoid biosynthesis; iridoid synthase; progesterone 5-\(\beta\)-reductase; heterologous expression; functional characterization