esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology
### Methodology  
#### 2.2.1 Ascertainment of Quintile Rankings for Pentad Soil Moisture Levels  
The heterogeneity of soil moisture, underscored by pronounced regional disparities and seasonal fluctuations, necessitates a nuanced approach to encapsulate the transient states of soil hydration [31; 32]. Traditional metrics, such as soil water depth or volumetric moisture content, offer snapshots of soil moisture yet falter in their utility for longitudinal drought analyses over expansive temporal and spatial domains [7; 10]. To surmount this limitation, the present study advocates for the transformation of soil moisture readings into percentile rankings [2], thereby augmenting the spatial-temporal coherence in drought identification outcomes. Herein, we introduce a dual-faceted percentile framework, comprising the following: (a) the annual pentad average soil moisture percentile, recalibrated annually, and (b) the comprehensive time series pentad average soil moisture percentile. The former aims to mirror the moisture conditions across identical temporal windows within a given region, enhancing intra-annual comparability, whereas the latter seeks to illuminate shifts in soil moisture across successive intervals within the same locale, bolstering inter-temporal analysis.  
The methodological blueprint for calculating the annual pentad average soil moisture percentile unfolds as follows: (1) implementing a pentad moving average on soil moisture data to smooth short-term variability; (2) segmenting the chronology into 365 distinct pentad series (leap year's February 29 excluded), thereby stratifying the dataset into quintiles on a pentad basis to distill soil moisture percentiles; and (3) synthesizing these 365 percentile sequences in chronological succession to fabricate a unified, extended time series. The computation of the comprehensive time series pentad average soil moisture percentile adheres to a similar procedural paradigm, albeit anchored to the entirety of the time series, ensuring a consistent methodology across both percentile derivations.
[PERSON]  
3 Holst Centre/TNO, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands; [EMAIL_ADDRESS] (H.B.A. ); [EMAIL_ADDRESS] (A.J.M.v.B. ); [PERSON]] (I.K. ); [EMAIL_ADDRESS] (B.P.) [PERSON]. [PERSON]  
3 Holst Centre/TNO, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands; [EMAIL_ADDRESS] (H.B.A. ); [EMAIL_ADDRESS] (A.J.M.v.B. ); [PERSON]] (I.K. ); [EMAIL_ADDRESS] (B.P.) [PERSON]  
3 Holst Centre/TNO, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands; [EMAIL_ADDRESS] (H.B.A. ); [EMAIL_ADDRESS] (A.J.M.v.B. ); [PERSON]] (I.K. ); [EMAIL_ADDRESS] (B.P.) [PERSON]  
3 Holst Centre/TNO, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands; [EMAIL_ADDRESS] (H.B.A. ); [EMAIL_ADDRESS] (A.J.M.v.B. ); [EMAIL_ADDRESS] (I.K. ); [EMAIL_ADDRESS] (B.P.) [PERSON]  
3 Holst Centre/TNO, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands; [EMAIL_ADDRESS] (H.B.A. ); [EMAIL_ADDRESS] (A.J.M.v.B. ); [EMAIL_ADDRESS] (I.K. ); [EMAIL_ADDRESS] (B.P.) [PERSON]  
3 Holst Centre/TNO, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands; [EMAIL_ADDRESS] (H.B.A. ); albert.vanbreemen@tno. allowing better conformity of the dose to the tumor [2], but also requires very accurate dose planning. A precise verification of the dose delivered to the patient is, therefore, mandatory and has to be guaranteed by appropriate quality assurance (QA) procedures with high spatial resolution. A proper set of detectors for measuring the beam parameters, in particular, the beam position and the delivered dose, is fundamental to achieve an efficient QA protocol [3]. Nowadays, there is still room for improvement toward a robust and complete solution providing accurate and real-time measurements with high spatial resolution and a uniform response to the beam energy. The LaGEMPix detector, which is the subject of this paper, is a promising tool for more efficient QA procedures with high spatial resolution. It consists of a triple-GEM (Gas Electron Multiplier) [4] coupled to a highly pixelated readout based on a matrix of organic photodiodes (OPDs). The potential of a triple-GEM detector combined with high pixel granularity for QA in hadron therapy was initially demonstrated with the GEMPix [5; 6]. The GEMPix has shown very promising results but its application is limited by the relatively small active area of 28 \(\times\) 28 mm\({}^{2}\). In hadron therapy, typical sensitive areas of 200 \(\times\) 200 mm\({}^{2}\) are required to cover the maximum clinical field size [7; 8]. The new readout, based on the detection of the scintillation photons generated in the GEM holes, allows for the development of a compact detector to perform imaging at larger areas, further scalable with an expected high spatial resolution. In this paper, we report on the development of the first LaGEMPix prototype with an active area of 60 \(\times\) 80 mm\({}^{2}\), which is six times larger than that of the GEMPix. The detector and the experimental set-up are described in Section 2. The results obtained using an X-ray irradiator are reported in Section 3 and discussed in Section 4. Finally, Section 5 draws the conclusions and outlines the next steps of the project.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 2 Antenna Design and Analysis
### Current Distribution  
Figure 5 shows the simulated current distributions of the proposed antenna at 2.5 GHz and 4.7 GHz. After adding the DGS, one can see that the current from port 1 to port 2 is cut off. Therefore, high isolation is achieved because the surface current and near fields are concentrated within the decoupling structure.  
When Ant_a is excited, the current is mainly concentrated on the edge of a circular patch at 2.5 GHz. While when it is working at 4.7 GHz, there is a strong current on the L-shaped branch and step-shaped part. Thus, each branch in the radiation elements is responsible for stimulating resonance. The formula controlling the relationship between excitation resonant frequency, antenna geometric parameters, and the physical characteristics is as follows:  
\[f_{r}=\frac{c}{2L_{e}}\sqrt{\frac{2}{e_{r}+1}} \tag{1}\]  
where \(Le\) is the total electric length of the structural antenna elements.
## 5 Parameter Optimization
### Validation Experiments  
In order to verify the accuracy of the obtained mechanism parameters, this paper substitutes the optimized mechanism parameters into MATLAB R2021b for the calculation of the posture adjustment mechanism volume ratio, which is 0.0626. The relative error with the predicted volume ratio of the posture adjustment mechanism is 2.19%, which is not significant, indicating that the optimal values of the obtained parameters are accurate and reliable. Figure 16 shows the comparison of the attitude range before and after optimization, in which the volume of the posture adjustment mechanism workspace increases from \(9.8648\times 10^{7}\) to \(1.11\times 10^{8}\), an increase of 12.52%. Figure 16a is the projection of the workspace in the XOZ plane before optimization, and Figure 16b is the projection of the workspace in the XOZ plane after optimization.  
Figure 15: Three-dimensional response surface and contour diagrams of the interaction between parameters affecting posture adjustment mechanism volume ratio. (**a**) Interaction between B and C; (**b**) interaction between C and D; (**c**) interaction between C and E.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 4 Our Proposal  
This research proposes a new strategy for placing multiple sinks in the best positions, and for transmitting and conveying data through an optimal short path that minimizes energy consumption, maximizes the network's lifetime, and reduces data transmission delay.
## 4 Optimization
### Mode Switching Order Range Estimation Using Latin Hypercube Sampling  
In the proposed method, the mode-identification process consumes most of the computational time. To improve the efficiency of mode identification and reduce the time cost, it is necessary to avoid the identification of all modes one-by-one. Therefore, Latin hypercube sampling (LHS) was employed to perform probability statistics of the switching range of the concerned modes, and the mode orders to be identified were reduced according to the statistical results.  
LHS is an approximate random sampling method from multivariate parameter distribution, which was first proposed by [PERSON] [26]. The samples generated by LHS are distributed evenly in the variable domain, and the objective domain of these samples has a high similarity to the actual objective domain. Thus, the statistical results of LHS can represent the actual results with high credibility. For the model-updating approaches proposed in this work, the distribution of LHS samples in the design variables space and the switching range of the concerned mode orders are presented in Figures 11 and 12, respectively.  
Figure 11: Distribution of LHS samples. (**a**) First model-updating approach and (**b**) second model-updating approach.  
Based on the above statistical results, the mode orders to be tracked in the two model-updating approaches were determined, as shown in Table 9.
## 4 Control Threshold for Design Based on Dynamic Shakedown Characteristics
### Control Threshold for Dynamic Shakedown Design of High-Speed Railway Subgrade  
Considering the shakedown characteristics of the filler, the dynamic stress \(\sigma_{di}\) and dynamic strain \(\epsilon_{di}\) of the \(i\)-th layer of the subgrade should be less than the shakedown thresholds during the design of high-speed railway subgrade, as shown in Equations (10) and (11).  
\[\left\{\begin{array}{l}\sigma_{di}\leq n\cdot CSR^{*}_{A-B}\cdot\sigma_{3i}= 1/n\cdot CSR^{*}_{A-B}\cdot k_{i}\cdot\rho_{i}\cdot h_{i}\\ \sigma_{di}\leq CSR^{*}_{B-C}\cdot\sigma_{3i}=CSR^{*}_{B-C}\cdot k_{i}\cdot \rho_{i}\cdot h_{i}\end{array}\right. \tag{10}\]  
\[\left\{\begin{array}{l}\epsilon_{di}\leq 1/n\cdot\epsilon^{*}_{d}\;A-B\\ \epsilon_{di}\leq\epsilon^{*}_{d\;B-C}\end{array}\right. \tag{11}\]  
where \(\rho_{i}\), \(h_{i}\), and \(k_{i}\) are the density, thickness, and earth pressure coefficient of subgrade filler corresponding to the i-th layer, respectively, and the confining pressure \(\sigma_{3i}\) can be calculated; \(n\) is the safety factor proposed according to the design requirements of different control levels, considering the deformation stability. With stricter requirements for deformation control in a design, the value of \(n\) increases.
## 3 The Proposed Method  
A constrained polygonal space and an extremely sparse waypoint graph-based PP method are imperative for addressing the time complexity issues that emerge due to unnecessary path exploration of low-probability spaces on an obstacles-rich map. The proposed PP method limits path exploration to only the constrained spaces that have a higher probability of containing optimal/quasi-optimal paths, and it safely removes the unlikely spaces in order to hasten the pathfinding computations. It removes the only spaces from a map that likely cannot assist in finding a low-cost solution with high probability. It effectively resolves the two competing goals of efficiency and path length while finding paths for UAVs in urban environments. This section provides a brief overview of our proposed PP method and outlines its workings. In Figure 1, we demonstrate the proposed PP method's conceptual overview.  
To find a path, \(P\), between source \(s\) and target location \(t\) for a UAV, while safely bypassing obstacles in UAV flying environment \(W\), the following six key conceptualizations are introduced: (i) operating-environment modeling by using data from a real environment map; (ii) generation of a constrained polygonal space by exploiting obstacle geometry information; (iii) determining and analyzing the complexity of the constrained polygonal space using a multiple criteria-based method leveraging six different complexity parame  
Figure 1: The proposed PP method’s conceptual overview.  
ters; (iv) providing need-based extension of the constrained polygonal space to the next level; (v) task-modeling with an extremely sparse waypoint graph that has as few nodes and edges as possible by utilizing the concepts of far distance reachability and direction guidance; and (vi) abstract pathfinding with the A* algorithm from the CPS using an ESWG, and enhancing the abstract path quality by generating additional nodes and edges in the vicinity of abstract path nodes. Concise descriptions of each main component, with relevant equations/procedures, are summarized below.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 4 Results
### VOD Computed using SAR Data in VH Polarization  
In this section, the time variations of VOD-VH values (VOD-VH\({}_{\text{A,av}}\), and VOD-VH\({}_{\text{D,av}}\)) obtained for the VH polarization are compared to that of NDVI\({}_{\text{av}}\). Figure 5 shows that the temporal dynamic of VOD-VH in both crop growth cycles does not match that of NDVI\({}_{\text{av}}\). During the growing period, VOD-VH values increase slightly (by about 0.10) while NDVI\({}_{\text{av}}\) presents a sharp increase before reaching its peak value. For instance, VOD-VH\({}_{\text{A,av}}\) for wheat increases from 0.16 to 0.27 while NDVI\({}_{\text{av}}\) increases from 0.2 to 0.8 (Figure 6d). Finally, the coefficient of determination (R\({}^{2}\)) between VOD-VH and NDVI\({}_{\text{av}}\) is about 0.45 for barely, 0.20 for fallow, 0.26 for oats, 0.40 for wheat, and 0.01 for rapeseed (Figure 6).  
Figure 4: VOD values computed at VV polarization (VOD-VV\({}_{\text{A,av}}\) and VOD-VV\({}_{\text{D,av}}\)) as a function of NDVI\({}_{\text{av}}\) values. (**a**) Barley, (**b**) Fallow, (**c**) Oat, (**d**) Wheat, (**e**) Rapeseed.  
Figure 5: Temporal patterns of VOD values computed from VH polarization (VOD-VH\({}_{\text{A,av}}\) and VOD-VH\({}_{\text{D,av}}\)) and NDVI\({}_{\text{av}}\). (**a**) Barely, (**b**) Fallow, (**c**) Oat, (**d**) Wheat, (**e**) Rapeseed. The shaded region represents the mean \(\pm\) standard deviation of VOD values for all plots at a given date.
# Microstructural Evaluation of Dental Implant Success Using Micro-CT: A Comprehensive Review
## 4 Evaluation of Bone Around Implants Using Micro-CT  
Micro-computed tomography (micro-CT) is emerging as a valuable non-destructive 3D imaging modality for assessing bone-implant contact (BIC) and osseointegration. Studies have shown a strong correlation between micro-CT and traditional histomorphometry for BIC measurement [62; 63]. [PERSON] et al. (2019) compared the results of bone-implant contact examination with the use of 2D and 3D imaging. From the results obtained, no significant correlation was found between 3D and 2D BIC ratios. In addition, the standard deviation for BIC examination using 2D imaging was twice that of BIC examination using 3D imaging. This indicates a higher variability in 2D measurements [64].  
The condition of the bone around the implant can be visualized using micro-CT [22]. Bone formation around implants occurs in two directions or involves two types of osteogenesis, namely distance osteogenesis and contact osteogenesis. In distance osteogenesis, bone forms from the host bone tissue toward the implant surface, and in contact osteogenesis, new bone forms from the implant surface toward the bone. The granulation process is followed by the formation of woven bone, which then transforms into more stable lamellar bone. This process increases bone contact with the implant and will contribute to the secondary stability of the implant [65].  
The use of micro-CT allows for the observation and evaluation of all processes occurring during dental implant placement. This enables the rapid assessment of the implant and surrounding bone, facilitating informed decision-making. This method can be employed to quantify bone volume, bone surface area, trabecular thickness, trabecular number, trabecular separation, and bone-bonding ability. By utilizing this method, bone tissue in close proximity to the implant surface can be examined at a thickness of several microns, facilitating the evaluation of the implant osseointegration process in both quantitative and qualitative terms [22].
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 1 Introduction
Compared with the traditional hydraulic fluid working medium, the density, viscosity, and other physical parameters of CO\({}_{2}\) change with changes in temperature and pressure, which leads to geometric deviations between the actual suction/discharge medium volume and the theoretical geometric displacement of the piston pump under high pressure and high speed. Clearance leakage of the three important friction pairs (the port pair, plunger pair, and slipper pair) of the plunger pump also causes volume loss and volume efficiency reduction. In this study, the computational fluid dynamics (CFD) numerical simulation method is used to simulate the volumetric efficiency loss of a CO\({}_{2}\) swashplate axial piston pump, and the mechanism of working fluid compression retention volumetric loss, port pair leakage volumetric loss, plunger pair volumetric loss, and slipper pair leakage volumetric loss is analyzed. The results provide a reference for volumetric efficiency loss analyses of CO\({}_{2}\) swashplate axial piston pumps.
## 5 Experimental Investigations
### Experimental on the Public Datasets  
This paper presents a comprehensive evaluation conducted on two publicly available datasets. Experimental results on MixATIS and MixSNIPS datasets are given in Table 5. The primary objective of the evaluation was to analyze the performance of multi-intent detection models when applied to the English dataset without the use of BERT or other pre-trained language models. To ensure a fairer comparison, the Chinese BERT pre-trained model in Auto-HPIF was replaced with a bidirectional LSTM during the experiments. The modified model was then compared against other models.  
* **Experimental Findings:** The experimental results on the MixATIS dataset showed that Stack-Propagation had lower slot filling F1, intention detection accuracy, and overall accuracy compared to Auto-HPIF (w/o Chinese-BERT) by 2.0%, 3.8%, and 8.5%, respectively. However, some recently proposed models, such as GL-GIN introduced a global-local graph interaction network structure, which improved the overall accuracy to 43.5% and 75.4% on the MixATIS and MixSNIPS datasets, respectively. Another model called SDJN achieved cyclic optimization by exchanging intention and slot information, resulting in an overall accuracy improvement of 1.1% and 0.3% on the MixATIS and MixSNIPS datasets, respectively, relative to GL-GIN. Importantly, Auto-HPIF (w/o Chinese-BERT) achieved the highest intent accuracy of 78.4% and overall accuracy of 47.6% on the MixATIS dataset, surpassing the second-best model by 1.3% and 3.0%, respectively. Additionally, on the MixSNIPS dataset, Auto-HPIF (w/o Chinese-BERT) achieved an overall accuracy of 76.4%, outperforming the second-best model by 0.7%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**CADS**} \\ \cline{2-5}  & **Overall (Acc)** & **Slot (F1)** & **Intent (Acc)** & **Intent (F1)** \\ \hline AGIF [5] & 83.27 & 94.58 & 90.31 & 96.58 \\ GL-GIN [18] & 84.96 & 95.00 & 90.45 & 98.62 \\ Co-Guiding Net [20] & 85.87 & 95.40 & 90.53 & 98.60 \\ Ours (w/o Chinese-BERT) & 86.26 & 95.90 & 90.57 & 98.18 \\ Auto-HPIF (ours) & **87.94** & **96.80** & **90.61** & **98.90** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison (%) of multiple-intent detection and slot filling on CADS datasets with baseline methods.  
* **Analysis of Experimental Results:** The experimental findings underscore the potential and applicability of the Auto-HPIF model in addressing complex multi-intent detection tasks. From an algorithmic design perspective, considering whether the proposed Auto-HPIF approach is susceptible to the issue of vanishing gradients is of paramount importance. This study observes that the Auto-HPIF model demonstrates robust performance across multiple indicators, indicating its effective training and alleviation of the gradient vanishing problem. This achievement aligns with the findings in reference, which introduces the oriented stochastic loss descent algorithm. This algorithm addresses the challenge of gradient vanishing, enabling deep networks to be trained without encountering the aforementioned issue.
## 2 PMSM Control
### Mathematical Modelling of PMSM  
The electrical dynamic equations were transformed from a three-phase rotating time frame (a, b, c) into a two-coordinate rotating position frame (\(d\), \(q\)) by the Clarke and Park transformation [10]. Hence, the \(d\)-\(q\) axis model of the three-phase dynamic equation under the steady state is expressed as  
\[\left[\begin{array}{c}v_{d}\\ v_{q}\end{array}\right]=\left[\begin{array}{cc}R_{s}+pL_{s}&-wL_{s}\\ wL_{s}&R_{s}+pL_{s}\end{array}\right]\left[\begin{array}{c}i_{d}\\ i_{q}\end{array}\right]+\left[\begin{array}{c}0\\ w\lambda_{pm}\end{array}\right] \tag{1}\]  
where \(v_{d}\) and \(v_{q}\) are the voltages in the \(d\)-\(q\) axis, and \(w\) is the angular frequency. \(R_{s}\), \(L_{s}\), and \(p\) are the phase resistance, synchronous inductance, and differential operator, respectively. The rotational speed of the motor can only be calculated by assuming that the torque point is in a steady state. The expansion of Equation (1) shows that the current and voltage are restricted by the supply voltage; therefore, the rotational speed can be expressed using the following equation [11]:  
\[\left(\frac{V_{s\max}}{w_{r}}\right)^{2}\geq(L_{d}i_{d}+\lambda_{pm})^{2}+(L_ {q}i_{q})^{2} \tag{2}\]  
\[w_{r}=\frac{V_{s\max}}{\sqrt{\left(L_{d}i_{d}+\lambda_{pm}\right)^{2}+\left(L_ {q}i_{q}\right)^{2}}} \tag{3}\]  
where \(V_{s\max}\), \(L_{d}\), \(L_{q}\), \(\lambda_{pm}\), \(w_{r}\) are the voltage limit, \(d\)- and \(q\)- inductances, no-load flux, and base speed, respectively. To obtain speeds above the base speed, flux-weakening control was performed in the permanent magnet (PM) machines. The optimized current references for the torque reference in the flux-weakening region are as follows [12]:  
\[\vec{i_{d}}=\frac{L_{d}\lambda_{f}-\sqrt{\left(L_{d}\lambda_{f}\right)^{2}+ \left(L_{q}^{2}-L_{d}^{2}\right)\left(\lambda_{f}^{2}+\left(L_{q}I_{s}\right) -\left(\frac{V_{s\max}}{w_{r}}\right)^{2}\right)}}{L_{q}^{2}-L_{d}^{2}} \tag{4}\]  
\[\vec{i_{q}}=\pm\sqrt{l_{s\max}^{2}-\vec{i_{d}}^{*}2} \tag{5}\]  
where \(\lambda_{f}\), \(\vec{i_{d}}^{*}\), and \(\vec{i_{q}}^{*}\) are the load magnetic flux and \(d\)- and \(q\)- current references, respectively.