esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
## 3 Results and Discussion
### Regression Analysis
#### 3.3.1 Parameter \(m\)  
The chirp parameters \(m\) and \(d\) determine the linear chirp describing the instantaneous frequency increase of the respiratory signal. These values differ among the TPs as a result of the individual interpretation of the chirp-breathing task.  
The significant predictors of \(m\) in simple regression models are Age, State, Trait, and SMBQ. When considering multiple regression models including the predictor Age, only SMBQ is still significant, whereas the variables State and Trait cease to be significant. On the other hand, the variable Gender becomes significant in models including Age. The best multiple regression model according to both AIC and \(R_{\text{adj}}^{2}\) values includes Gender, Age, and SMBQ and achieves \(R_{\text{adj}}^{2}=0.63\) (Table 3). According to this model, an increase in age corresponds to an increase in the value of \(m\). An opposite effect on \(m\) is given by being male respect to female and having a higher SMBQ.  
Figure 6: Examples of the TF estimates obtained for three TPs with the spectral estimators considered: S64 (first row); S256 (second row); WVS (third row); the MSE optimal LSCP\({}_{c}\) spectrum (fourth row); the MSE optimal LSCP\({}_{i}\) spectrum (fifth row). All the spectra for one TP are in the same column.
## 4 Results
### Direct Comparisons of Satellite and GPS IWV Values
Figure 4 shows that no clear geographical distribution of bias can be observed, though the bias and standard deviation can be quite different for 'neighboring' GPS stations. In general, the results are dominated by the overall positive or negative bias for each satellite instrument. Furthermore, no clear relationship between bias and standard deviation is observed, though it appears that stations with a higher, absolute bias usually correspond to higher standard deviations. However, the lowest standard deviations appear to be concentrated in the lowlands of Northern Germany, which can be at least partly explained by the flat geography of the region reducing retrieval errors due to height variations within one satellite pixel as well as reducing the impact of height differences between satellite pixel and GPS station on the comparison. The overall mean bias and mean standard deviation are \(-1.73\) kg/m\({}^{2}\) and \(2.01\) kg/m\({}^{2}\), \(1.19\) kg/m\({}^{2}\) and \(3.03\) kg/m\({}^{2}\), \(1.12\) kg/m\({}^{2}\) and \(2.81\) kg/m\({}^{2}\), and \(-0.31\) kg/m\({}^{2}\) and \(2.41\) kg/m\({}^{2}\), for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Figure 3: 2D histograms and probability density functions for IWV GPS vs. IWV from IASI, MIRS, MODIS and MODIS-FUB. The dashed black-white diagonal line is the 1:1 line, while the dashed red-blue line presents a linear fit through the data. Corresponding statistical quantities can be found in Table 2.
## 2 Methodology
### User and Patient Requirements  
In the analysis of energy consumption and visual comfort and performance, occupancy hours were considered for the entire week, simulating a full-time workday schedule during daytime hours, extending from 8 in the morning to 8 in the evening. Breaks are not considered, as the study focuses on the lighting conditions that affect the patient. This battery of simulations takes into account the implementation of Daylight Saving Time (DST).  
The minimum illuminance threshold for adequately conducting a simple examination in intensive care environments is tabulated at 300 lx [65], and this value is used for calculating the DA. Another standard threshold for these tasks is 500 lx [63]. Therefore, the present study uses both thresholds, 300 and 500 lx, for the calculation of DA. In cases where a higher average illuminance is required for more in-depth examinations or patient treatment (1000 lx), or in the case of emergency examinations (2000 lx) [65], specific electric lighting is considered necessary.  
To assess the adequate circadian entrainment of a patient in an ICU single cubic, the evaluation period extends throughout the entire week, from 9 to 10 in the morning [41; 42; 53; 56].  
Figure 5 presents the average spectral irradiance distribution derived from the SPD of clear and intermediate skies, considering indoor reflections, for the two Mediterranean locations indicated in Figure 4. This resulting SPD was utilized in the calculation of \(CSA\). It is worth noting that all the SPDs examined demonstrated a substantial decrease in the short-wavelength portion. This phenomenon is primarily attributed to the reduced spectral reflectance values of the surfaces within the room for these wavelengths. This spectral information is essential for assessing circadian entrainment.  
Taking into account the modified SPD shown in Figure 5 and using Equation (2), it is possible to calculate the relationship between \(CS\) and the illuminance value, as demonstrated in Figure 6. Notably, with the illuminance set at 300 lux, the resulting \(CS\) values fall within the range of 39.7% to 42.0%, considering these Mediterranean climate conditions. Therefore, this study establishes an illuminance threshold for \(CSA\) at 300 lux for the cases under examination. This threshold provides a valuable reference for assessing circadian entrainment in the specified scenarios, since in conventional situations, the effect of natural light's SPD variation, based on location and climate, has a very limited influence on melatonin suppression throughout the day.
## 3 Results and Discussion
### Precision  
Table 2 shows precision data for Hum R samples during intra- and inter-day runs for methods A and B. The %RSD for intra-day precision was \(\leq\)1.01 and inter-day precision was \(\leq\)0.75 for method A; the intra-day precision was \(\leq\)0.74 and inter-day precision was \(\leq\)0.30 for method B. The two methods showed suitable repeatability and intermediate precision.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Method A**} \\ \hline
**Std conc.** & \multicolumn{2}{c}{**Intra-Day**} & \multicolumn{2}{c}{**Inter-Day**} & \multicolumn{2}{c}{**Overall RSD**} \\ \hline
**IU/mL** & **AUP Mean \(\pm\) SD** & **\%RSD** & **AUP Mean \(\pm\) SD** & **\%RSD** & **\%** \\
200 & 80,511,102 \(\pm\) 8,10,141 & 1.01 & 81,130,312 \(\pm\) 321,212 & 0.40 & 0.80 \\
300 & 114,116,991 \(\pm\) 502,655 & 0.44 & 114,648,789 \(\pm\) 310,602 & 0.27 & 0.41 \\
500 & 197,502,242 \(\pm\) 1,337,885 & 0.68 & 197,061,936 \(\pm\) 1,478,956 & 0.75 & 0.65 \\ \hline  & \multicolumn{4}{c}{**Method B**} \\ \hline
100 & 45,340,751 \(\pm\) 79,090 & 0.17 & 44,156,436 \(\pm\) 133,078 & 0.30 & 1.47 \\
200 & 82,944,396 \(\pm\) 184,091 & 0.22 & 86,743,903 \(\pm\) 37,292 & 0.04 & 2.46 \\
400 & 156,927,295 \(\pm\) 1,166,927 & 0.74 & 162,635,381 \(\pm\) 141,610 & 0.09 & 2.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision for determination of insulin in Hum R for methods A and B.
###### Abstract  
The helical contour motion accuracy of feed drive axes is important for thread milling operations in computer numerical control (CNC) machine tools. However, the motion dynamics and external disturbances significantly affect the contour motion results, while the feed drive axes perform helical motions in thread milling operations. Although existing iterative learning contour control (ILCC) methods can improve contour motion accuracy, the problems of data recording and processing on memory usage and computational burden in control systems, wasted materials, and increased costs in thread manufacturing still limit the practical applications of ILCC. Therefore, considering the similar motion dynamics and external disturbances of the feed drive axes during the pitch cycle motions of a helical path, this study developed a pitch cycle-based iterative learning contour control (PCB-ILCC) method to address the control system and thread manufacturing problems caused by the use of ILCC. For PCB-ILCC, this study adopted contour error vector estimation by referring to the interpolated positions on the pitch cycle of the helical path to simplify the computational complexity and designed the ILCC using the cycle learning method to easily implement the ILCC structure. Thus, this study developed a permanent magnet synchronous motor (PMSM) driving control utilizing the robust control method to mitigate the problems of motion dynamics and external disturbances on the feed drive axes. Thread milling experiments performed on a five-axis CNC machining center demonstrated the feasibility of the PCB-ILCC and validated that it can significantly improve the helical contour motion accuracy of the feed drive axes and achieve an 80% contour error reduction rate in comparison with the proportional-proportional-integral control, which is extensively used in commercialized PMSM drivers and CNC controllers.  
thread milling; iterative learning contour control; CNC machine tool; helical path; pitch cycle; contour accuracy
# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
## 2 Materials and Methods
### Assessing the Accuracy of the Models  
In all cases, a confusion matrix was generated for each of the 14 models comparing predicted outcomes from the models to the true labeled data from the test dataset. Several figures of merit were calculated based on the confusion matrix [46]: accuracy, sensitivity, specificity, F\({}_{1}\)-score, Matthews correlation coefficient (MCC), and receiver operator characteristic (ROC) area under the curve (AUC).  
Accuracy is effectively a measure of generally how often a model is correct in its predictions considering both positive and negative predictions. Accuracy was determined as the ratio of the sum of true positive (TP) and true negative (TN) predictions to the total number of samples in the test set. Sensitivity is a measure of the accuracy of a model only regarding positive predictions and was calculated as the ratio of the true positive predictions to the total number of actual positives in the data. Specificity is the same as sensitivity, except only regarding negative predictions. It was calculated in a similar manner as the ratio of true negative predictions to the total number of actual positives in the data. The calculations are summarized below:  
\[\text{Accuracy}=\frac{\text{TP}+\text{FP}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}, \tag{2}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{3}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}, \tag{4}\]  
Two general goodness-of-fit measures were considered for this study: F\({}_{1}\)-score and MCC. The F\({}_{1}\)-score is a harmonic average of precision and recall, while the MCC is a more robust measure of classification models trained on unbalanced datasets, where the two classifications are not equally represented [46]. In this case, the data contain a larger proportion of significant results versus non-significant results. The F\({}_{1}\)-score ranges from 0 to 1 (0 being worst), with a value of 0.5 being equal in effectiveness to a coin-flip predictor. The MCC score ranges from \(-1\) to 1 (\(-1\) being worst), with an MCC of 0.3 being considered acceptable and an MCC of 0 representing the coin-flip predictor [46]. The two measures were calculated as follows:  
\[\text{F}_{1}=\frac{\text{2 TP}}{\text{2 TP}+\text{FP}+\text{FN}}, \tag{5}\]  
\[\text{MCC}=\frac{\text{TP}\cdot\text{TN}-\text{FP}\cdot\text{FN}}{\sqrt{( \text{TP}+\text{FP})\cdot(\text{TP}+\text{FN})\cdot(\text{TN}+\text{FP}) \cdot(\text{TN}+\text{FN})}}, \tag{6}\]  
Receiver operator characteristic (ROC) curves were also generated, and the area under the curve (AUC) was calculated as an additional measure to determine suitability for the models. An ROC AUC of greater than 0.7 was considered acceptable and values greater than 0.8 were considered good. A value near 0.5 was considered similar to a coin-flip classifier [47].
# Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes
## 3 Results and Discussion
### Leave-One-Out (LOO) Cross-Validation  
The leave-one-out cross-validation procedure was subsequently performed to test the accuracy of the quadratic polynomial regression of NDVI (700, 720), NDVI (700, 730), NDVI (708, 730), NDVI (700, 760), NDVI (708, 760), and NDVI (708, 800). The LOO regression line of groups 1, 3, and 4 showed the highest plateau, while groups 1, 2, and 4 the lowest (Figure 4A-F). Due to the great variabilities of the plateau distribution, neither setting up an absolute threshold value nor extended the range for the PI occurrence determination seems to be an ideal method. Although the plateau of regression curve between groups was different, the trend of the slopes was relatively similar. This was in agreement with preceding research [38]; however, the plateau of the curve in this study was different, which was at the PI stage rather than the booting-heading stage. The stability between the trend of the slopes and PI occurrence were tested through first-order differentiation in the leave-one-out cross-validation model. The results showed that NDVI (700, 720) was the most reliable NDVI-based VIs for PI determination since it had the lowest RE value (RE = 4.9%). A previous study represented NDVI application on wheat phenology monitoring and obtained satisfying results that achieved the lowest error of 4.61 days during the jointing stage [39]. Our study performed a slightly better approach with the application of NDVI (700, 720) with a quadratic polynomial regression model, which represented a maximum error of 68.9 GDDs (approximately three days) (Table 5). The others, NDVI (700, 730), NDVI (708, 730), and NDVI (708, 800), are potential candidates that are competent for PI assessment (Table 5). At this point, the relationship between NDVI  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**NDVI\(-\)Based VIs** & **Equation** & **Estimated GDDs** & **Actual GDDs** & **R\({}^{2}\)** & **RE (\%)** \\ \hline \multirow{4}{*}{700, 720} & y = \(-5.52\times 10^{-7}\)x\({}^{2}\) + 0.0008x \(-\) 0.161 & 724.6 & 771.3 & 0.77 & \multirow{4}{*}{5.82} \\  & y =  
based VIs and PI occurrence could be confirmed through the first-order differentiation of quadratic polynomial regression model.  
Figure 4: Leave-one-out cross-validation of the six selected PI-related NDVI-based VIs: (**A**) NDVI (700, 720); (**B**) NDVI (700, 730); (**C**) NDVI (708, 730); (**D**) NDVI (700, 760); (**E**) NDVI (708, 760); and (**F**) NDVI (708, 800). The black line is the validation of group 1, the red line is the validation of group 2, the green line is the validation of group 3, and the yellow line is the validation of group 4.
## 3 Mathematical Modeling of the System Components
### Modeling of Wind Power  
The wind model is characterized by the variation in wind velocity with gust and wind speed.  
\[\mathrm{W_{v}}=\mathrm{V_{w}}+\mathrm{V_{g}}+\mathrm{V_{wr}} \tag{6}\]  
With the following parameters:  
* \(\mathrm{V_{w}}\) is the base wind velocity;
* \(\mathrm{V_{g}}\) is the gust wind velocity;
* \(\mathrm{V_{wr}}\) is the ramp wind component.  
Wind power is given by the following expression:  
\[\mathrm{P_{w}}=\frac{\mathrm{d}\mathrm{W_{w}}}{\mathrm{dt}} \tag{7}\]  
The energy drawn by the wind turbine is expressed by the formula below:  
\[\mathrm{W_{w}}=\mathrm{V_{a}}\times\frac{1}{2}\rho\left(\mathrm{V_{1}}^{2}- \mathrm{V_{3}}^{2}\right)\]  
\[\mathrm{P_{w}}=\mathrm{d}\frac{\mathrm{V_{a}}\times\frac{1}{2}\rho\left(\mathrm{ V_{1}}^{2}-\mathrm{V_{3}}^{2}\right)}{\mathrm{dt}} \tag{8}\]where \(\rm W_{w}\) is the energy drawn by the wind turbine and \(\rho\) is the air density. Based on Betz, the maximum output power of a wind turbine is given by the following:  
\[\rm P_{M}=\frac{16}{27}\rho A\frac{3}{2}V^{3} \tag{9}\]  
where, A is the area, \(\rho\) is the air density, and V is the wind velocity.  
The substitution of the value for \(\rm V_{1}\), and \(\rm V_{3}\) enable us to obtain the following equation:  
\[\rm V_{2}=\frac{2}{3}V_{1} \tag{10}\]  
\[\rm V_{3}=\frac{1}{3}V_{1} \tag{11}\]  
where \(\rm V_{1}\), \(\rm V_{2}\), and \(\rm V_{3}\) are wind velocities in different areas.  
The modeling of a wind turbine helps to describe the captured output power of the turbine [42]. The wind power according to a given area is calculated as follows:  
\[\rm P_{w}=\frac{1}{2}\rho AW_{v}{}^{3} \tag{12}\]  
\[\rm P_{M}=P_{w}C_{p} \tag{13}\]  
\[\rm C_{p}=\frac{1}{2}\Big{[}\delta-0.22\beta^{2}-5.6\Big{]}e^{-0.17\delta} \tag{14}\]  
where \(\beta\) is the pitch angle of the blade (deg, \({}^{\circ}\)), \(\delta\) is the tip speed ratio of the turbine, and \(\rm C_{p}\) is the power coefficient.  
The maximum produced power by a wind turbine is calculated by the following equation:  
\[\rm P_{G}=V_{G}I_{G} \tag{15}\]
## 4 Discussion
It can be summarized that _OsCM_ has a vital role in the shikimate pathway due to upregulation of both Tyr and Phe, which is the ultimate source of catechol and the farnesyl moiety in the synthesis of both cq-9 and epi-cochlioquinone, respectively. Cochlioquinone has already been reported to cause toxic reactions in various cancer cells [60; 61]. In particular, [PERSON] et al. (2015) [62] identified that cochlioquinone caused cytotoxicity in the A549 cell line and SK-OA-3 cell line, which are cells that mainly cause cancer in humans. We succeeded in extracting cq-9 from rice for the first time and inoculated into the sepsis-inducing model mice. Moreover, the survival rate was increased by 60% in sepsis-induced model. It has also been reported that cq-9 has antimicrobial activity against _Phythium graminicola_[63], which causes severe damage in rice seedlings [64]. Rice extract or cq-9 induced significant reduction of U46619 -mediated contraction in endothelium-intact compared with endothelium-denuded rat aorta rings. Furthermore, rice extract or cq-9 induced a dose-dependent vasodilation in endothelium-intact rat aortic rings, which was attenuated by L-NAME, a nitric oxide synthase blocker. These findings suggest that endothelium-dependent relaxation induced by rice extract or cq-9 leads to activation of eNOS, resulting in production of NO in endothelial cells [65]. However, pretreatment with a K\({}^{+}\) channel blocker, apamin or TAE, did not affect endothelium-dependent vasorelaxation induced by rice extract or cq-9, indicating that endothelium-dependent vasorelaxation by rice extract or cq-9, does not mediate the K\({}^{+}\) channel pathway. Furthermore, this vasodilation proved that cq-9 was higher than that of rice extract. Rice extract or cq-9 extract inhibited collagen-, ADP- and thrombin-induced platelet aggregation in a dose-dependent manner. Furthermore, this collagen-induced platelet aggregation proved that cq-9 was higher than that of rice extract. In this research, 120 CNDHI lines were analyzed to QTL mapping associated with WBPH resistance. QTLs were analyzed using the phenotype of the 120 CNDHI lines and HPLC analysis data, and finally _OsCM_ was screened. _OsCM_ is an important regulatory enzyme in synthesizing cq-9, and _OsCM_ transgenic rice is highly resistant to WBPH. When cq-9 was treated in CLP-surgery mice, the survival was increased by 60%. Furthermore, the aorta of mice treated with cq-9 had an effective vasodilation response, and significantly reduced the aggregation of rat platelets induced by collagen treatment. The results of this research show that cq-9 produced by plants can be effective in treating animal diseases and can be effectively used to study the relationship between Plant-Insect-Human in the future.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 1 Introduction
The method precisely determines the pointer's position and angle using a rotated matrix bounding box, facilitated by an adaptively constructed pointer instrument template library. By leveraging feature point matching and the RANSAC algorithm, the approach achieves accurate transformation between detected and template images. Experimental results in complex industrial settings demonstrate the method's robustness against image quality challenges such as blurring, dimness, overexposure, and inclination. This integration of deep learning and traditional computer vision techniques offers a high-precision, adaptable solution for automatic pointer instrument reading in industrial scenarios, underscoring its practical significance. The paper is structured as follows. Section 2 reviews relevant literature on pointer meter reading. The principles and implementation of the proposed method are detailed in Section 3. Experimental results and comparative analyses are presented in Section 4. The paper concludes with a summary and future research directions in Section 5.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Hop Counts
#### 4.1.2 Under NDMR  
The route discovery under NDMR is either a two-way handshake process or a three-way handshake process depending on the paths discovered in the primary route discovery.  
Let \(p_{1}^{NDMR}\) and \(p_{2}^{NDMR}\) be the first and second paths, respectively. The shortest path between the source and the destination is \(p_{1}^{NDMR}\), and \(p_{2}^{NDMR}\) is the shortest path without intermediate nodes along \(p_{1}^{NDMR}\).  
Let \(n_{1}^{NDMR}\) and \(n_{2}^{NDMR}\) be the hop counts of \(p_{1}^{NDMR}\) and \(p_{2}^{NDMR}\), respectively.  
\[n_{1}^{NDMR}\leq n_{2}^{NDMR}. \tag{2}\]  
For the sample network in Figure 5, the discovered paths and their hop counts are as follows:  
* Case 1: \[p_{1}^{NDMR}=\{0,\ 1,\ 8,\ 15,\ 22\}\text{ and }n_{1}^{NDMR}=4\] \[p_{2}^{NDMR}=\{0,\ 7,\ 14,\ 21,\ 22\}\text{ and }n_{2}^{NDMR}=4\]
* Case 2: \[p_{1}^{NDMR}=\{0,\ 6,\ 12,\ 18,\ 24\}\text{ and }n_{1}^{NDMR}=4\] \[p_{2}^{NDMR}=\{0,\ 7,\ 13,\ 19,\ 24\}\text{ and }n_{2}^{NDMR}=4\]  
It is clear that node-disjoint paths discovered and established under both MP-AODV and NDMR are have similar hop counts.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 5 Simulations and Discussion
### Simulations
#### 5.1.1 Simulation Description  
We consider an ad hoc wireless network with nodes uniformly distributed in the network area of 1000 m \(\times\) 600 m. Nodes move at random speeds and in random directions in the network area.  
In these simulations, control messages were sent to discover and establish node-disjoint paths between a source and a destination in simulation networks. The number of nodes in simulation networks increased from 20 to 100 nodes.  
Performance metrics, including control overhead and route discovery time, were recorded under the MP-AODV and NDMR approaches to evaluate and compare the performance of these approaches. For the evaluation of overhead performance, in addition to the number of control messages that were transmitted and re-transmitted in the network, the number of control messages received at all nodes in the network was also recorded to evaluate and compare. Among the control messages, only request messages (RREQ/PREQ) are broadcast and re-broadcast in the network. Therefore, most of the control overhead for discovering routes is made from duplicates of the request messages. The number of the received RREQs is that of the total copies of the RREQs that are received at nodes in the network regardless of discarded copies or forwarded copies.  
Additionally, to facilitate the evaluation and comparison of the route discovery time, we assumed that the processing time at each node (\(t_{node}\)) is 10 \(\upmu\)s and that the propagation time between two adjacent nodes (\(t_{prop.}\)) is 5 \(\upmu\)s.