esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematic Analysis of Dynamic Coactivation During Arm Swing at the Shoulder and Elbow Joints
## 3 Results
### Shoulder (Flexion-Extension)  
According to the ANOVA results, statistically significant differences were observed in all kinematic variables, including ROM, the maximum and minimum joint angular velocities, and the maximum and minimum joint angular accelerations (Table 1).  
At the fastest walking speed of 7.2 km/h, the mean ROM for shoulder flexion-extension was significantly greater compared to other walking speeds. However, [PERSON]'s post hoc test indicated no significant differences in ROM among the walking speeds of 3.6, 5.4, and 7.2 km/h. Notably, the lowest mean ROM was observed at a preferred walking speed (PWS) of 4.2 km/h.  
As shown in Figure 3, the mean maximum flexion angular velocity at a walking speed of 7.2 km/h was significantly higher than at other speeds. Similarly, the mean minimum extension angular velocity was lowest at 7.2 km/h. While no significant differences were found among the walking speeds of 3.6, 4.2, and 5.4 km/h, the lowest mean maximum flexion angular velocity occurred at the PWS of 4.2 km/h. Moreover, the highest mean minimum extension angular velocity was observed at this same PWS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Joint** & **Kinematic Variable** & \multicolumn{4}{c}{**Walking Speed (km/h)**} \\ \cline{3-6}
**(Motion)** & **(Unit)** & **3.6** & **4.2 (PWS)** & **5.4** & **7.2** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Elbow \\ (flexion–extension) \\ \end{tabular} } & Range of motion * & 13.8 (5.1) & 14.3 (6.7) & 15.9 (6.9) & 17.3 (7.8) \\  & Maximum joint angular velocity ** & 32.5 (13.4) & 38.1 (14.1) & 53.8 (22.9) & 58.4 (30.7) \\  & \
\begin{tabular}{c} Minimum joint angular velocity ** \\ (deg/s) \\ \end{tabular} & \(-\)29.0 (10.5) & \(-\)33.2 (13.6) & \(-\)48.9 (14.6) & \(-\)59.0 (22.8) \\  & Maximum joint angular acceleration ** & 345.8 (128.4) & 358.2 (143.5) & 581.1 (325.4) & 618.2 (312.9) \\  & Minimum joint angular acceleration ** & \(-\)297.8 (130.1) & \(-\)336.5 (106.8) & \(-\)423.5 (142.1) & \(-\)462.9 (134.4) \\  &
\begin{tabular}{c} (deg/s\({}^{2}\)) \\ \end{tabular} & \(-\)297.8 (130.1) & \(-\)336.5 (106.8) & \(-\)423.5 (142.1) & \(-\)462.9 (134.4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 3: The mean and standard deviation of angular velocity of shoulder joint from the sagittal plane. Different letters in alphabetical order indicate Tukey’s post hoc groupings, where distinct letters denote statistically significant differences between conditions (\(p<0.05\)).  
As shown in Figure 4, the mean flexion-extension angular acceleration followed a trend similar to that of angular velocity. Specifically, there was a progressive increase in angular acceleration with walking speed, peaking at 7.2 km/h. Similarly, the mean minimum extension angular acceleration demonstrated a decreasing trend with increasing walking speed, reaching its lowest value at 7.2 km/h.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Rotation Direction**} & \multicolumn{1}{c}{\(\mathbf{M_{1}}\)} & \multicolumn{1}{c}{\(\mathbf{M_{2}}\)} & \multicolumn{1}{c}{\(\mathbf{\beta_{1}}\)} & \multicolumn{1}{c}{\(\mathbf{\beta_{2}}\)} & \multicolumn{1}{c}{\(\mathbf{k_{1}}\)} & \multicolumn{1}{c}{\(\mathbf{k_{2}}\)} \\  & & **(kNm)** & **(kNm)** & **(rad)** & **(rad)** & **(kNm/rad)** & **(kNm/rad)** \\ \hline \multirow{8}{*}{Downward rotation} & Mean & 0.709 & 0.957 & 0.025 & 0.196 & 28.378 & 1.450 \\  & SD & - & - & - & - & 4.102 & 0.114 \\  & Coeff. of variation & - & - & - & - & 0.145 & 0.079 \\  & 5 th percentile & 0.545 & 0.754 & - & - & 21.812 & 1.223 \\  & 95 th percentile & 0.925 & 1.207 & - & - & 37.002 & 1.650 \\  & Mean & 1.650 & 1.980 & 0.035 & 0.169 & 47.138 & 2.461 \\  & SD & - & - & - & - & 3.264 & 0.578 \\  & Coeff. of variation & - & - & - & - & 0.069 & 0.235 \\  & 5 th percentile & 1.493 & 1.668 & - & - & 42.650 & 1.311 \\  & 95 th percentile & 1.805 & 2.200 & - & - & 51.573 & 2.948 \\  & Mean & 0.230 & 0.301 & 0.073 & 0.197 & 3.144 & 0.577 \\  & SD & - & - & - & 1.414 & 0.236 \\  & Coeff. of variation & - & - & - & 0.450 & 0.409 \\  & 5 th percentile & 0.125 & 0.160 & - & - & 1.710 & 0.280 \\  & 95 th percentile & 0.345 & 0.460 & 4.732 & 0.921 & 4.732 & 0.921 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of bilinear rotational stiffness model. **Author Contributions:** Conceptualization, [PERSON]-[PERSON] and [PERSON]; methodology, D.-H.C.; validation, J.-H.W. ; investigation, D.-H.C. and [PERSON]-[PERSON]; writing--original draft preparation, D.-H.C.; writing--review and editing, J.-H.W. ; visualization, D.-H.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was partially supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government Ministry of Knowledge Economy (No. 20224000000070, Human Resource Training for Smart Energy New Industry Cluster). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data that support the findings of this study are available from the corresponding author upon reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Analysis of the Second-Order Time-Difference Values for LEO GPS Observations
Finally, as the ionosphere residuals in carrier phase observations vary quickly for LEO satellite GPS data, one may have the question whether \(\Delta\Delta I\) can be practically ignored in the estimation of cycle slips for satellite-borne GPS observations, even with a 1-s sampling interval. To answer this question, Figures 4 and 5 show the examples of the first-order, time-difference PIR and STPIR [11] (\(\Delta\Delta L_{1}-\Delta\Delta L_{2}=0.6469\Delta\Delta I\)) at the GOCE satellite on 26 November 2009, with different sampling intervals. As shown in Figures 4 and 5, firstly, STPIR values increase and vary more quickly as the sampling interval grows, especially at low elevation angles and near polar areas, where the quality of the carrier phase observations is poor [3; 28]. Secondly, STPIR values are usually less than 0.02 m (about 0.1 cycles) with 1-s sampling intervals, always less than 0.05 m (about 0.25 cycles) with 5-s sampling intervals, and less than 0.1 m (about 0.5 cycles) with 10-s sampling intervals. Moreover, STPIR is significantly smaller than the first-order, time-difference PIR for GOCE. It suggests that STPIR (0.6469 \(\Delta\Delta I\)) is negligible for cycle slip detection for LEO satellite phase observations, especially with a 1-Hz sampling rate, based on the conditions tested. Therefore, the second-order time-difference values for LEO GPS observations given in Equation (9), \(\Delta\Delta\overset{\rightarrow}{\tau}^{G}\), \(\Delta\Delta\overset{\rightarrow}{\tau}_{r}\), \(\Delta\Delta dt^{G}\), and \(\Delta\Delta I\), can be practically used as known or ignored for the proposed STG cycle slip detection and repair method. Figure 3: The means (**bottom**) and RMSs (**top**) of the second-order, time-difference GPS clock errors, with one-day final 30-s IGS clock products as the “true” value, with 30-s sampling intervals. Figure 4: Time-difference phase ionospheric residual (PIR), elevation angle, and satellite latitude for GPS PRN 5 at GOCE on 26 November 2009, with different sampling intervals. (**a–c**) First- or second-order time-difference PIRs for 1 s, 5 s, and 10 s, respectively; (**d**) GPS satellite elevation angle and GOCE satellite latitude. Figure 5: Time-difference PIR, elevation angle, and satellite latitude for GPS PRN 19 at GOCE on 26 November 2009, with different sampling intervals. (**a–c**) First- or second-order time-difference PIRs for 1 s, 5 s, and 10 s, respectively; (**d**) GPS satellite elevation angle and GOCE satellite latitude.
## 2 Materials and Methods
### Gynecological Cancer  
Exosomes play a significant role in intercellular female genital tract cancer interactions. They have shown immense impact on the early diagnosis, drug resistance, prognostic evaluation, metastasis and target therapies [249; 250; 251]. Exosomal proteins seem to be effective cancer biomarkers [252; 253]. In gynecologic oncology, there are reports regarding tumor expression of Hsp22, Hsp27, Hsp60, Hsp70, and Hsp90 in ovarian, cervical and endometrial cancers as potential biomarkers [254]. However, there are only a few reports of exosomal HSP levels in these malignancies, mainly in ovarian cancer.  
Ovarian cancer, with the high resistance to chemotherapeutic agents, remains the foremost cause of death in women globally. More than half of ovarian cancer patients are in an advanced stage at the moment of diagnosis [255] because there are no symptoms of early stage disease nor effective screening procedures. For this reason, finding a novel diagnostic method would have an impact on the treatment results.  
[PERSON] et al. showed that the Hsp22 expression in peritoneal fluid samples and serum-derived exosomes in patients with ovarian cancer in comparison with endometrial cancer was higher, but the difference was statistically significant only in peritoneal fluid. They also found a positive correlation between markers of cytotoxic immune response (perforin and granzyme B) and exosomal Hsp20 and Hsp22 levels [143]. Chaperones, including Hsp70 and Hsp80, were also detected in exosomes isolated from ascites effusions in patients with advanced ovarian papillary adenocarcinoma [103]. Moreover, [PERSON] et al. reported release of exosomal Hsp27 by ovarian cancer cell lines (OVCAR-3 and SK-OV-3). They found concentration-dependent incorporation of Hsp27 into exosomes, which suggests that Hsp27, liberated by exosomes from ovarian cancer cell lines, could serve as a noninvasive biomarker of ovarian cancer [97].  
One study reported release of exosomal Hsp70 and Hsp90 from cervical cancer cells. It was suggested that these Hsps may interact with survivin, allowing for its release and intercellular transport in exosomes [144].
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
###### Abstract  
This article aims to determine the most suitable cross-sectional area for a high voltage alternating current (HVAC) submarine cable in the design phase of new projects. A thermal ladder network method (LNM) was used to analyse the thermal behaviour in the centre of the conductor as the hottest spot of the cable. On the basis of the calculated cable parameters and a thermal cable analysis of transient conditions applied by a step function with a time duration greater than 1 h, this article proposes a method for a dynamic rating of submarine cables. The dynamic rating is accomplished through an iterative process. The method was tested with a MATLAB simulation and validated in comparison with a finite element method (FEM)-based approach.  
dynamic cable rating; high voltage alternating current three-core XLPE submarine cables; thermal-electrical cable analogy; thermal cable analysis of transient conditions
# An Investigation of Applying Large Language Models to Spoken Language Learning
## 3 Experiments and Results
### Experts' Evaluations of the Multi-Turn Conversations  
Compared to single-turn Q&A, people prefer interacting through dialogue interfaces. Here, we performed a more challenging evaluation in the context of language learning, specifically CAPT. From a private language learning dataset, we selected 20 English utterances produced by Chinese learners, the lengths of which were between 5 and 15 words. The speakers had different levels of pronunciation proficiency, and they were aged between 15 and 30 years old. We used the forced-alignment function provided by the Kaldi toolkit [60] to obtain the GOP [61] score as the phoneme score and, subsequently, the word score by averaging the phoneme scores. The pronunciation realizations related to phonemes, fluency, break, stress, and intonation were annotated by human experts. The evaluation experiment was conducted as follows. Firstly, we gave the LLMs a system prompt, where the data structure of the CAPT results was explained (see the initial system prompt example in Table 7). Then, the descriptions of real pronunciation realizations, annotated by human experts, of a specific English utterance were input to the LLMs as the current object of analysis. Next, a series of questions were posed, including _\"please tell me whether the pronunciation is clear, accurate and natural\", \"Please tell me how to pronounce it correctly\", \"Please provide two examples to correct my mistake\"_, etc. This evaluation was designed to assess the ability of the model to analyze and reason a given question using knowledge acquired through phonetics and second language acquisition in a longer contextualized setting. The ratings of the model's answers were divided into four groups:  
RATING-A: The response was both valid and satisfactory and was relevant to the evaluation prompt. RATING-B: This response was acceptable but contained minor errors or imperfections. RATING-C: Although this response was relevant and addressed the instruction, it contained significant errors in its content. RATING-D: This response was either irrelevant to the evaluation prompt or entirely invalid for the current topic. Table 8 lists the results of the experts' evaluations of the LLMs' answers. GPT-3.5 achieved high performance and reliability. If A and B are considered acceptable responses, its accuracy reached 83.4%, which is nearly identical to its performance on SLIQ-LL. In contrast, LLaMA2-70B-chat did not generate as many satisfactory answers as GPT-3.5, and it only achieved acceptable performance in 54% of cases.
## 4 Performance Evaluation of Protection Algorithm
### Protection Performance Under Severe CT Saturation, CT Measurement Errors, and Outliers  
From Figure 14d, when CT saturation occurs, the new algorithm still has good protection performance. When severe CT saturation occurs, external faults on the system side and internal faults can still be correctly identified. However, due to the influence of weak power supply characteristics of external faults on the PV side, the two-dimensional reconstructed current trajectory image deviates from the straight line y = \(-\)x, as shown in Figure 15a. The image is sent to the A-CNN model for identification, and the result is identified as an external fault. The two-dimensional reconstructed current trajectory protection algorithm based on deep learning operates correctly.  
If CT has a measurement error or an outlier in a sudden fault, it may cause the traditional protection to operate incorrectly. Figure 15b shows taking 10% of CT measurement error at 10 ms after an internal fault. Figure 15c shows taking a large outlier at 15 ms. The images are sent to the A-CNN model for identification, and the result is identified as an internal fault. The new protection algorithm based on deep learning operates correctly.
## 2 Materials and Methods
### Sensitivity  
The explainable machine learning known as SHapley Additive exPlanations (SHAP) was here used to analyze the sensitivity of GPP to precipitation frequency. SHAP is a game theoretic approach used to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. The advantages of this system compared to other methods include: (1) the identification of a new class of additive feature importance measures and (2) a unique solution in this class with a set of desirable properties [47].  
In this study, SHAP assigned each feature an optimized Shapley value, which was considered to be the value of sensitivity to precipitation frequency. The analysis procedure comprised the following steps. First, to ensure consistency with precipitation data, the spatial resolution was resampled to 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) in order to match the GPP with the precipitation grid, GPP data with a non-value, and the raster where non-vegetation growth was deleted. Second, using precipitation frequency data (trace, small, moderate, heavy, and very heavy) as the predicting variable and GPP data as the response variable, an XGBoost model was built to quantify the relationship between predicting (precipitation frequency) and response (GPP) variables. The grid values from all years for five precipitation categories for each season were utilized as input features for the XGBoost model, with the average GPP value of that season being the output target. Four models were established separately for each of the four seasons.  
The model employed the \"reg:squarederror\" as its objective function. In experimentation, we set the training argument num_round = 1000, and printed the root mean squareerror one by one. We observed that the minimum occurred when num_round was about 600. Subsequently, the trained XGBoost model was used to compute the SHAP values for the frequency associated with each precipitation amount. The procedure was finished using a function that comes with packages \"SHAPforxgboost\" in the software R language version 4.2.2 ([[https://liuyanguu.github.io/post/2019/07/18/visualization-of-shap-for-xgboost/](https://liuyanguu.github.io/post/2019/07/18/visualization-of-shap-for-xgboost/)]([https://liuyanguu.github.io/post/2019/07/18/visualization-of-shap-for-xgboost/](https://liuyanguu.github.io/post/2019/07/18/visualization-of-shap-for-xgboost/)) (accessed on 19 October 2022))) and the significance level was also involved. Positive (negative) SHAP values indicated the same (opposite) trend in variation for GPP and precipitation frequency, respectively. Detailed sensitivity analysis methods are provided in Supplementary Materials A.
## 2 Materials and Methods
### Extracellular Amylase Activity Analysis by DNSA Assay  
The extracellular amylase activity was analyzed using DNSA colorimetric assay as described previously [23; 31] with slight modifications. Briefly, 0.1 mL of crude enzyme was mixed with 0.9 mL of 1% (\(w/v\)) soluble starch solution dissolved in 0.05 M phosphate buffer (pH 8) (Himedia, India) and incubated at 60 \({}^{\circ}\)C for 20 min. Then, 1 mL of DNSA reagent was added, and the mixture was heated in a boiling water bath for 5 min. After cooling the reaction mixture at room temperature, 8 mL of distilled water was added to the mixture, and absorbance was measured at 540 nm using a spectrophotometer. A solution without crude enzyme was used as the control. The amount of maltose (\(\upmu\)mol) produced was determined from a maltose standard curve. All experiments were conducted in triplicate, and results are expressed as mean \(\pm\) SD. One unit (U) of crude amylase activity was defined as the amount of crude enzyme that liberates 1 \(\upmu\)mol of reducing sugars (as maltose) per minute under assay conditions, and crude extracellular amylase enzyme activity was expressed as U/mL. The crude extracellular amylase activity was calculated using the following formula [32]:  
\[\text{Amylase activity }(\text{U/mL})=\frac{\text{Maltose amount }(\mu\text{mol})}{\text{Reaction time }(\text{min})\ \times\ \text{Volume of crude enzyme }(\text{mL})} \tag{1}\]
## 4 Electric Field Simulation  
As discussed in the previous section, the proposed system has lower voltage stress over the capacitive coupling interface. Accordingly, the electric field in between the coupling plates, including the leakage electric field emissions, should decrease as well. In this section, the transient \(E\) field analysis in Maxwell software from Ansys, Pennsylvania, USA, was used to simulate the electric field strength between and around the plates. Figure 9 shows the experimental modelling used as the coupling interface with the same dimensions and materials. Measurement points were assigned between and at different distances around the plates as listed in Table 2.  
Figure 10 shows that the electric field between the single pair coupling interface plates reduced by \(D\) factor, which results in a reduction in the electric field emission to the surrounding space as illustrated in Figures 11 and 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Point** & **Distance to the Plates (cm) X, Y, Z** \\ \hline A & In the center between Tx1 and Rx1 \\ B & 2, 0, 0 (in front of Tx1 and Rx1) \\ C & 0, 0, 1 (above the center of Tx1 and Rx1) \\ D & 0, 0, 3 (above the center of Tx1 and Rx1) \\ E & 0, 0, 4 (above the center of Tx1 and Rx1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement points coordination.  
Figure 9: Plates modelling in Ansys Maxwell.  
Figure 11: Simulated electric field at measurement point B in front of Tx1 and Rx1.  
Figure 10: Simulated electric field at measure point A between Tx1 and Rx1.  
According to the Institute of Electrical and Electronics Engineers (IEEE) standard for safety levels with respect to human exposure to radio frequency electromagnetic fields of 3 kHz to 300 GHz [28], the field strength emissions for any system should be lower than 614 V/m for 0.03-1.34 MHz to ensure safe operation without harm to the human body. According to Figure 11, for the conventional CPT, the safe region starts after 2.5 cm in the X and Y directions from the plates by adding a buck converter electric field lower than 614 V/m anywhere in the XY plane around the plates. Figure 12 shows that the safe region for the conventional system, where the electric field is acceptable, is 3.5 cm above the plates. Notably, the system with a buck converter has sufficiently low electric field levels to be used safely. The proposed system helps to safely use CPT technology without the need for shielding or living object detection systems.