esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Characteristic of the Sample  
The results of pressure measurements provided during a total of 50 RIRS procedures in 3D printed models with chemically synthesised stones (25 RIRS procedures with the use of 10/12 Fr UAS and 25 with the use of 12/14 Fr UAS) were eligible for the study. The sample of \(N\) = 4,880,251 IPP readings in the 10/12 Fr AUS (\(n_{1}\) = 2,957,775) and 12/14 Fr UAS (\(n_{2}\) = 1,922,476) groups was analysed for seven parameters. Each parameter represented an aggregate holmium:YAG laser setting of the watts/hertz/joule value.  
The descriptive statistics of the IPP distributions for groups 10/12 Fr and 12/14 Fr UAS without breakdown of parameters are shown in Table 1.  
The descriptive statistics of the IPP distributions for the 10/12 Fr and 12/14 Fr UAS groups by individual parameters can be found in Table 2.  
Estimation of the group effect on the IPP value was first investigated in the framework of a mixed model with a random effect in the form of parameter categories (formula: IPP \(\sim\) group + (1 \(|\) parameter)). However, the results of the values of the coefficients of determination \(R^{2}{}_{Conditional}\) = 0.58, \(R^{2}{}_{Marginal}\) = 0.56, and the very low value of \(ICC\) = 0.04 evidenced a lack of variance differences within each category of random intercepts, so the effect of the group was estimated based on the OLS model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{_Group_} & \multirow{2}{*}{\(n\)} & \multicolumn{4}{c}{_cmH\({}_{2}\)O_} \\ \cline{3-6}  & & **\(M\) (SD)** & **\(M\)dn (Q1, Q3)** & **\(Min\)** & **\(Max\)** \\ \hline
10/12 Fr & 2,957,775 & 175.0 (83.4) & 172.0 (114.0, 228.0) & 6.8 & 461.7 \\
12/14 Fr & 1,922,476 & 22.6 (10.9) & 21.3 (13.9, 29.0) & 6.8 & 106.5 \\ \hline \end{tabular}
\end{table}
Table 1: The descriptive statistics of the IPP distributions for groups 10/12 Fr UAS and 12/14 Fr UAS. _Mdn_—median; _Q1_—the first quartile (25%); Q3—the third quartile (75%).
## 3 Results
### Forest Degradation Maps in the Three-North Forest Shelterbelt
#### 3.1.3 High Resolution (30 m) Forest Degradation Maps in Typical Areas  
We obtained the high resolution (30 m) forest degradation map in four typical areas (Figure 6), Maowusu, Hulun Buir, Khorchin and Loess Plateau in the Three-North Forest Shelterbelt, in which forest degradation were in a serious situation. The high resolution (30 m) forest degradation maps were derived by using the classification strategy demonstrated in Section 2.3.2 based on the Landsat data from 2000 to 2010. Total forest degradation area in the four typical areas were about 250 \(\times\) 10\({}^{4}\) hectares, occupying 52.87% of the total forest degradation area in the Three-North Forest Shelterbelt (The forest degradation area were 93.87 \(\times\) 10\({}^{4}\) hectares, 23.43 \(\times\) 10\({}^{4}\) hectares, 103.04 \(\times\) 10\({}^{4}\) hectares, 33.87 \(\times\) 10\({}^{4}\) hectares in Maowusu, Hulun Buir, Khorchin and Loess Plateau, respectively). We found that the serve forest degradation mainly distributed in the middle and southern areas of Maowusu (12.92 \(\times\) 10\({}^{4}\) hectares), eastern areas of Hulun Buir (6.66 \(\times\) 10\({}^{4}\) hectares) and some areas in the western Khorchin (4.56 \(\times\) 10\({}^{4}\) hectares).
## 3 Results and Discussion
### Noise
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Materials, Methods** & **Linearity Ranges** & **Sensitivity (V/T)** & **Size** & **Resolution** & **Noise Level** & **Opering** & **Reference** \\ \hline Permalloy-based MEMS-micro-fluague sensor & \(\pm\)300 \(\mu\)T & 327 & 3 \(\times\) 4 mm\({}^{2}\) & – & – & 150 nA & [29] \\ \hline Co-based amorphous ribbon flawgate sensor & \(\pm\)1 mT & 593 & 3 \(\times\) 6.5 cm\({}^{2}\) & – & 790 \(\mu\)T/Hz\({}^{1/2}\) & 600 nA & [7] \\ \hline Go-based amorphous ribbon giant magnetupimpedance & \(\sim\)\(\pm\)1 mT & -1800 & 1 \(\times\) 9 mm\({}^{2}\) & – & 17 \(\mu\)T/Hz\({}^{1/2}\) & 20 mA & [30] \\ \hline Magnetuplectic composition-based sensor & \(\pm\)1 mT & 3800 & 4 \(\times\) 4 mm\({}^{2}\) & – & 27 \(\mu\)T/Hz\({}^{1/2}\) & – & [31] \\ \hline Hall sensor based on bilayer graphene & \(\pm\)8 mT & 32 & 0.7 \(\times\) 21 mm\({}^{2}\) & 118 \(\mu\)T & – & 1.2 mA & [32] \\ \hline Commercialized GMI sensor (Type DDH) by ACHI & \(\pm\)0.1 mT & 10\({}^{8}\) & 35 \(\times\) 11 mm\({}^{2}\) & 1 nT & 30 \(\mu\)T/Hz\({}^{1/2}\) & 15 mA & [33] \\ \hline Commercialized HMR sensor (Type 3000 Jy) & \(\pm\)200 \(\mu\)T & – & 82 \(\times\) 38 mm\({}^{2}\) & 10 nT & – & 35 mA & [34] \\ \hline Homers/Well Co., Ltd., Seoul, Korea & \(\pm\)40 \(\mu\)T & – & 25 \(\times\) 30 mm\({}^{2}\) & 67 nT & – & 27 mA & [58] \\ \hline Commercialized Flugage sensor (Type uMg-01/20) & \(\pm\)2 mV\(\pm\)200 \(\mu\)T & – & 12 \(\times\) 27 mm\({}^{2}\) & 1 nT & – & – & [36] \\ \hline Commercialized Flugage sensor (Type MagS19) & \(\pm\)60 nT & – & 25 \(\times\) 20 mm\({}^{2}\) & Several nT & \(\leq\)50 pT/Hz\({}^{1/2}\) & 38 mA & [37] \\ \hline Commercialized TMR sensor (Type TMR003) by & \(\pm\)1.5 mT & 300 & 6 \(\times\) 5 mm\({}^{2}\) & – & 790 pT/Hz\({}^{1/2}\) & 29 \(\mu\)A & [88] \\ \hline Dwysych Co., Ltd., San Jose, CA, USA & \(\pm\)1.05 mT & 1985 & 27 \(\times\) 7.3 mm\({}^{2}\) & 5 nT & 3k pT/Hz\({}^{1/2}\) & 70 mA & Current study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recently reported magnetic sensors.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 2 Framework
### Operation Strategy
#### 2.2.2 Second Stage  
According to the short term forecast information collected from MGs and the prices of power grids, the EMO establishes the internal price optimization model for next period based on Stackelberg game theory. The optimization target of the EMO is to maximize its own profits while considering the demand responses of MGs. Furthermore, the EMO broadcasts the results of the internal prices to the MGs, which respond to the prices by adjusting load demands.
## 2 Materials and Methods
### Methods
#### 2.2.9 Analysis of Aroma Compounds  
Extraction of the volatile compounds from treebinth fruit oil was performed with some modification using headspace solid-phase microextraction (HS-SPME) methods described by [PERSON] et al. (2019) and [PERSON] et al. (2019). Approximately 3 g of oil was taken into a 20 mL glass vial with 1 \(\mu\)L of 4-nonanol as the internal standard. Then, it was agitated at 500 rpm to equilibrate at 60 \({}^{\circ}\)C for 45 min. SPME fibre coated with (50/30 mm DVB/Carboxen/PDMS, 1 cm, Supelco, Bellefonte, PA) was used as a sorbent. After extraction, fiber was removed from the vial and immediately transferred to the injection port of GC-MS to thermally desorb the analytes at 250 \({}^{\circ}\)C for 5 min.  
Headspace Solid-Phase Microextraction (HS-SPME) for Powder  
The method proposed by [PERSON] et al. [44] was followed to extract volatile compounds from the powder samples using HS-SPME. Simply, 3 g of sample was weighed into a 20 mL glass vial with 1 \(\upmu\)L of 4-nonanol as the internal standard, followed by the addition of 5 mL saline solution (25%). It was equilibrated and desorbed, as described above for the oil.  
GC-MS analysis was conducted as previously described by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [43] using GC (Agilent 6890) equipped with a flame ionization detector (FID) and a mass selective detector (Agilent 5973-MSD). Aroma compounds were separated on a DB-Wax column (30 m length \(\times\) 0.25 mm i.d. \(\times\) 0.5 \(\upmu\)m thickness, CA, USA). Helium was used as the carrier gas at a flow rate of 1.5 mL-min\({}^{-1}\). The temperature of the FID detector and injector were set at 280 and 270 \({}^{\circ}\)C, respectively. The oven temperature is programmed as follows: initially the temperature was held at 40 \({}^{\circ}\)C for 4 min, increased to 90 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, to 130 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, and finally to 240 \({}^{\circ}\)C. 5 \({}^{\circ}\)C/min and held at this temperature for 8 min. Mass spectra were obtained in an electronic impact mode of 70 eV with a mass range of 30-300 amu. Identification of all volatile compounds was confirmed by matching the mass spectra obtained with the mass spectra available in the NIST database and the injection of standard compounds. The experimentally determined linear retention index (LRI) by [PERSON] was also compared with the RI in NIST WebBook and literature. The quantification of identified volatile compounds was made by utilizing the internal standard (4-nonanol). Two repetitions were conducted for each sample. After the quantification, the aroma retention (%) for aroma compounds (\(\upalpha\)-pinene, sabinene, \(\upbeta\)-myrcene, (Z)-\(\upbeta\)-ocimene, ocimene and linalool) were calculated on the basis of oil according to the formula given below.  
\[\%\ Retention=\frac{Concentration\ in\ the\ total\ oil\ in\ powder\left(\frac{\upmu\upmu}{\text{kgoil}}\right)}{Concentration\ in\ initial\ oil\ load\left(\frac{\upmu\upmu}{\text{kgoil}}\right)}\times 100 \tag{6}\]
## 2 First Wall Mock-Up Description  
The design solutions of the DEMO breeding blanket are continuously evolving to address various requirements resulting from ongoing studies. For this reason, the manufacturing of mock-ups closely following the design is impractical. For many experimental studies it is then necessary to rely on existing manufactured mock-ups. This is also the case of the present campaign conducted, which employs an existing First Wall Mock-Up (FWMU) manufactured in the frame of the Test Blanket Module (TBM) studies [7].  
The FWMU is a EUROFER P92 plate (Figure 1a) in which cooling channels have been manufactured using a spark erosion process developed as part of a study concerning various fabrication paths for a blanket FW [10]. The plate has dimensions of \(710\,\mathrm{mm}\times 405\,\mathrm{mm}\times 45\,\mathrm{mm}\). The cooling channels have a square cross-section of \(15\times 15\,\mathrm{mm}^{2}\) with a 4-millimeter fillet radius at the corners. There are 20 channels in total that are oriented parallel to the long side of the plate. Each two adjacent channels are paired together forming ten U-shaped flow paths (Figure 1c). This solution allows the increase in the length of the flow path from 700 mm to about 1410 mm and, consequently, the increase in the coolant outlet temperature. The wall between two neighboring channels has a width of 5 mm, while the \"plasma facing side\"--on which the surface heat load is applied--has a thickness of 3 mm. The coolant flows to the channels through circular nozzles (8-millimeter inner diameter). Due to manufacturing reasons, each channel has a horizontal nozzle and a vertical nozzle. The horizontal nozzle is connected at the end of one of the two sides of each channel. The vertical nozzle is attached on the back of the plate at a few centimeters from the end of the channel, thus leaving a dead-end zone on one side of the channel (Figure 1b).  
Figure 1: First wall mockup: (**a**) Mock-up drawing with dimensions; (**b**) Ansys CFX model of a single cooling channel; the arrows at the end of the nozzles indicate the assumed flow direction; (**c**) two channels Ansys CFX model.  
The flow characteristic of the mock-up channels was experimentally investigated and reported in [11]. Thus, for designing of the LOFA experiment we had access to the pressure losses in the channels at different flow rates for both flow directions: one using the horizontal (axial) nozzle as an inlet and the other using the vertical one, transverse to the channel flow. The data reported in [11] show a significantly higher pressure drop in channel 1 and 2 as the flow rates are above 30 g/s compared to the values shown by the remaining channels. This is mainly due to pronounced welding seams that reduce the cross-section of the corresponding horizontal nozzles.
## 4 Content and Results of Research  
### Content of Research  
#### 4.1.1 Framework Construction of Project-Based Teaching Model Driven by Digital Twin Technology  
Teaching models are developed under the guidance of theories of teaching and learning, utilizing learning environments, resources, and tools to establish stable relationships among various elements of teaching and learning activities, design the process of teaching and learning activities, and ultimately form stable teaching activity procedures or configurations. This article utilizes the basic principles of project-based teaching models, guided by embodied cognition theory and constructivist theory and driven by digital twin technology. The project-based teaching model primarily consists of project preparation, project implementation, and project summary. In the project-preparation phase, teachers need to gain an in-depth understanding of students' learning needs, characteristics, and the set learning objectives. Through meticulous project scenario design and task subdivision, the completeness and feasibility of the project are ensured. At the same time, teachers need to adjust the difficulty and complexity of tasks according to students' actual situations to promote their participation and learning motivation. Digital twinning technology plays a crucial role in this phase. By collecting student data and generating digital twin models, teachers can better understand students' learning status and needs, providing effective guidance for project implementation. During the project-implementation phase, students transition from theoretical knowledge to practical skills. They need to collaboratively develop project plans in a team environment and solve practical problems through embodied experiences and collaborative exploration. Teachers' role in this phase is to guide and instruct. By providing necessary resources and support, they help students overcome difficulties and achieve project goals. Meanwhile, digital twinning technology serves the function of monitoring and optimization, tracking students' learning status in real time, and simulating their learning processes based on specific algorithms to provide personalized learning guidance and feedback. Finally, in the project-summary phase, students showcase their  
Figure 6: Research model diagram.  
achievements and receive evaluation and feedback from teachers and businesses. This not only assesses students' learning outcomes but also reflects on and improves the teaching process. Digital twinning technology in this phase collects evaluation data and optimizes the teaching process based on feedback results, providing more personalized and effective teaching support. In summary, the project-based teaching mode combined with digital twinning technology can not only enhance students' learning interest and engagement but also promote their practical skills and innovative thinking. Meanwhile, teachers can obtain more student feedback and data support through digital twinning technology, providing more scientific and effective means for optimizing the teaching process. The specific model design is illustrated in Figure 7.
## 5 Post-Processing EMG Signals: Proposed Supervised Learning Model  
A supervised learning model (SLM) that has shown great promise for analyzing temporal and visual data is the convolutional neural network (CNN) [50, 51]. CNNs' distinctive architecture, composed of convolutional, pooling, and fully connected layers, makes them especially good at identifying patterns and structures in data. By applying convolution operations to the input data, the convolutional layers enable the network to recognize regional patterns and characteristics. The dimensionality of the data is reduced by pooling layers. This improves computation efficiency and increases the network's resistance to even tiny changes in the input. Even though traditional CNNs are designed for image data, they can be adapted to handle sequential data such as time series or signal data through one-dimensional convolutional layers [52, 53].  
A recent work has shown that one-dimensional convolutional neural networks (1D-CNNs) are specifically intended to process sequential data, making them highly suitable for analyzing EMG signals [54]. The proposed SLM is an advanced 1D-CNN trained from scratch, which analyzes sEMG signals and detects complex patterns to identify healthy subjects or those with potential neuropathy.
## 3 Proposed Method
### Optimized Segmentation Method  
Based on ML-SQ, we proposed an optimized segmentation method. We control the number of ones in the slopes of segments instead of the fractional bit widths of the slope. This method can make full use of the multiplexers in the SAA part. As a result, less segments are required and the number of SAA operations is fewer in some cases. The difference in segmentation method between ML-PLAC and OML-PLAC is shown in green in Figure 3 and replaced by the pseudocode in Table 2.  
In the segmentation process of OML-PLAC, the slope fractional bit width \(\text{kw}_{1}\) is dynamic, no longer fixed, and depends on the slope value. Compared to ML-PLAC, the restriction of the same number of \"1\" in the slope value makes the number of SAA operations more even. We illustrate with two different slopes: \(\text{kp}_{1}=110.1010110_{2}\), \(\text{kp}_{2}=0.1100101_{2}\). If we take kw as 3, the two slopes truncated in ML-PLAC are \(\text{kp}_{\text{m11}}=110.101_{2}\), \(\text{kp}_{\text{m12}}=0.110_{2}\) while in OML-PLAC are \(\text{kp}_{\text{om11}}=110.1_{2}\), \(\text{kp}_{\text{om12}}=0.11001_{2}\). Then in ML-PLAC, the operations with \(\text{kp}_{\text{m11}}\) and \(\text{kp}_{\text{m12}}\) generate 4 and 2 shifted data, respectively. As a result, three adders are required, even considering reuse in hardware architecture. However, in OML-PLAC, different slope values produce the same shift data (3) and require the same adders (2). In the OML-PLAC method, the number of shift-add operations is not always strictly flat because of the rounding of the \(\text{kp}_{\text{i}}\) quantization (line 5_9 in Table 2), when \(\text{kp}_{\text{i}}=0.011101_{2}\) and \(\text{kw}=2\), for example, then the truncated datum is \(0.1_{2}\) but not \(0.011_{2}\)Although this scenario occurs from time to time, the overall flattening of OML-PLAC is still better than that of ML-PLAC. Moreover, the rounding operation brings the approximation closer to the actual value.  
As we can see in Table 2, the new lines form 5_1 to 5_15 are used to replace the original lines 2 and 5.1 to 5.6 in OML-PLAC. Lines 5_1 to 5_8 of OML-PLAC are used to get the new fractional bit widths of the slope \(\kw_{1}\) for each segment according to the number of \"1\" in the slope kw. It should be noted that \(\kw_{1}\) can be negative and the magnitude represents the position of the least significant \"1\" in the integer part of the slope. Suppose \(\kw_{i}=110.01_{  
In order to reveal the improvement of segmentation method in OML-PLAC. we summarize the segmentation results of OML-PLAC and ML-PLAC in Table 3. As can be seen from the table, the segmentation method in OML-PLAC can significantly reduce the number of segments under the same conditions. Note here that kw can be zero in ML-PLAC but cannot in OML-PLAC, so we set kw to 1 when calculating hyperbolic tangent function and sigmoid function. In addition, when we set kw the same value in ML_PLAC and OML_PLAC, the latter needs less shift data and adders, which can be seen from the previous example.
## 4 Discussion
These findings support the hypothesis that muscle TrPs could be more relevant in subgroups of patients with TTH rather than in all patients [45]. It is also possible that this subgroup of patients with FETTH exhibits differences in brainstem processing [46], explaining the observed differences; however, this hypothesis should be investigated in future studies. Current findings have several implications for daily clinical practice. First, early therapeutic interventions for preventing the development of central sensitization should be encouraged in the subgroup of patients with FETTH with higher sensitization (cluster 0). For instance, clinicians should try to identify patients with FETTH exhibiting more active myofasical TrPs, since earlier managementof TrPs seems to be relevant for positive outcomes [47]. Second, the SC analysis identified that patients with CTTH (cluster 2) exhibiting higher levels of central sensitization also showed higher anxiety and depressive levels. Since stress is one of the most common trigger and aggravating factors of TTH [48; 49], it is possible that this subgroup of patients would be more susceptible to stressful situations and therefore to promoting more hyperalgesia. Consequently, psychological approaches targeting mood disorders should also be implemented in this subgroup of patients. Identification of the subgroup (cluster) to which the patient is assigned could lead to therapeutic approaches better targeting peripheral or central mechanisms. Although this is the first study investigating a classification system in patients with TTH by using SC analysis, some potential limitations are recognized. First, we recruited our patients from tertiary care hospitals, which could lead to the inclusion of patients with more severe headaches. Therefore, studies including participants recruited from the general population are needed for a better extrapolation of the results. Second, we only tested widespread pressure sensitivity as a feature of central sensitization [38]. It would be interesting to investigate other outcomes of sensitization, e.g., thermal or electrical thresholds, conditioning pain modulation or nociception flexor reflex to further determine the differences between the identified clusters.
# Rearrangeable and Repackable S-W-S Elastic Optical Networks for Connections with Limited Bandwidths
###### Abstract  
Elastic optical networks flexibly allocate bandwidth to a connection for improving utilization efficiency. The paper considers an optical node architecture that is similar to a three-stage Clos network for elastic optical networks. The architecture, which employs space switching in the first and the third stages and wavelength switching in the second stage, is called an S-W-S switching fabric. In this paper, we propose a graph-theoretic approach and different routing algorithms to derive the sufficient conditions under which an S-W-S switching fabric will be rearrangeable nonblocking and repackable nonblocking. The proposed rearrangeable and repackable nonblocking S-W-S switching fabrics for connections with limited bandwidths consume around half the number of middle wavelength switches compared to strictly nonblocking S-W-S switching fabrics.  
elastic optical network; rearrangeable nonblocking; space-wavelength-space switching fabric; repackable nonblocking +
Footnote †: journal: Applied Sciences