esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Skeletal, Dental and Soft Tissue Changes after Treatment with ASSD Appliance
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**Mean (SD)**} & \multicolumn{1}{c}{**Mean Difference (95\% CI)**} & \multicolumn{1}{c}{**t-Statistics (df = 25)**} & \multicolumn{1}{c}{_p-Value_} \\ \hline \multicolumn{5}{c}{**T1**} & \multicolumn{1}{c}{**T2**} & \\ \hline Skeletal & & & & \\ SNA (\({}^{\circ}\)) & 81.7 (3.36) & 84.4 (3.88) & 2.73 (1.63, 3.84) & 5.09 & \(<\)0.001 \({}^{\ast}\) \\ \hline Co-A (mm) & 74.3 (4.67) & 79.0 (5.40) & 4.62 (2.37, 6.86) & 4.23 & \(<\)0.001 \({}^{\ast}\) \\ \hline A-Nperp (mm) & \(-\)5.7 (3.95) & \(-\)2.0 (3.98) & 3.69 (2.44, 4.95) & 6.05 & \(<\)0.001 \({}^{\ast}\) \\ \hline OP/SN (\({}^{\circ}\)) & 14.8 (4.42) & 13.4 (3.91) & 1.46 (\(-\)2.51, \(-\)0.41) & \(-\)2.87 & 0.008 \({}^{\ast}\) \\ \hline SNB (\({}^{\circ}\)) & 87.3 (3.68) & 86.6 (3.65) & 0.71 (\(-\)1.34, \(-\)0.08) & \(-\)2.32 & 0.029 \({}^{\ast}\) \\ \hline Co-Gn (mm) & 112.2 (5.08) & 114.1 (5.18) & 1.92 (0.66, 3.19) & 3.13 & 0.004 \({}^{\ast}\) \\ \hline Pog-NB (mm) & 0.8 (1.31) & 0.9 (1.03) & 0.16 (\(-\)0.15, 0.38) & 0.90 & 0.376 \\ \hline MP/SN (mm) & 34.5 (6.20) & 36.9 (5.93) & 2.35 (1.50, 3.19) & 5.71 & \(<\)0.001 \({}^{\ast}\) \\ \hline AO-BO (mm) & \(-\)13.8 (3.33) & \(-\)7.3 (7.89) & 6.46 (3.41, 9.51) & 4.36 & \(<\)0.001 \({}^{\ast}\) \\ \hline ANB (\({}^{\circ}\)) & \(-\)5.6 (2.66) & \(-\)2.3 (2.79) & 3.29 (2.25, 4.32) & 6.55 & \(<\)0.001 \({}^{\ast}\) \\ \hline ANS-Gn (mm) & 63.3 (3.86) & 64.4 (3.93) & 1.16 (0.33, 1.90) & 2.92 & 0.007 \({}^{\ast}\) \\ \hline Co-MP (mm) & 40.8 (5.68) & 40.6 (5.13) & 0.23 (\(-\)1.29, 0.82) & \(-\)0.45 & 0.656 \\ \hline
**Dental** & & & & \\ UI/NA (\({}^{\circ}\)) & 36.1 (5.68) & 35.9 (5.90) & \(-\)0.19 (\(-\)2.35, 1.97) & \(-\)0.18 & 0.856 \\ \hline LI/NB (\({}^{\circ}\)) & 21.8 (6.33) & 24.5 (6.24) & 2.67 (0.75, 4.60) & 2.86 & 0.008 \({}^{\ast}\) \\ \hline
**Soft tissue** & & & & \\ PogS-NB (mm) & 11.8 (2.42) & 11.8 (2.63) & 0.12 (\(-\)0.47, 0.71) & 0.42 & 0.677 \\ \hline LS-NB (mm) & 15.0 (3.09) & 17.1 (3.30) & 2.08 (1.32, 2.83) & 5.65 & \(<\)0.001 \({}^{\ast}\) \\ \hline UL/EL (\({}^{\circ}\)) & \(-\)3.9 (2.99) & \(-\)2.0 (2.47) & 1.87 (1.07, 2.66) & 4.81 & \(<\)0.001 \({}^{\ast}\) \\ \hline LL/EL (\({}^{\circ}\)) & 0.8 (2.57) & 1.8 (2.10) & 1.00 (0.36, 1.64) & 3.20 & 0.004 \({}^{\ast}\) \\ \hline \hline \end{tabular}  
\({}^{\ast}\)_p_ < 0.05.  
\end{table} 
Table 6: Difference in pretreatment (T1) and post-treatment (T2) cephalometric variables in ASSD group (n = 26). lower incisor inclination by 2.67\({}^{\circ}\) (\(p\) = 0.008), and more protrusive position of the lower lip (LL-EL) by 1 mm (\(p\) = 0.004).
## 2 The Value of Module Efficiency Resides in Non-Module Costs  
PV modules of higher efficiencies enjoy price premiums because they help lower the unit cost (per watt) of non-module components in a PV system. There are various ways of categorizing the costs of a PV project. Figure 4 shows a simple structure of a utility PV project's cost to illustrate the value of module efficiency. The category referred to as 'area-proportional' includes spending on racking, land, construction, transportation, etc. 'Others' refers to costs of electronic components that are quoted in terms of power rather than area, and soft costs related to administration, due diligence, etc.  
In a case with a restricted grid interconnection capacity or a defined project power capacity, when modules of a higher efficiency are adopted, the required number of modules, land area, and 'area-proportional' costs are all reduced linearly. Therefore, the per-watt spending on area-related items is inversely proportional to module efficiency. In a case with a defined land area, when modules of a higher efficiency are adopted, all area-proportional costs are averaged over more power, so their per-watt value is reduced proportionally. Additionally, some soft costs categorized as 'others' remain the same or increase only marginally. Depending on the specific correlation between
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 1 Introduction
This paper presents the design of a novel implantable antenna that achieves compactification and broadens the operating bandwidth. Silicone material is used to cover the antenna, increasing its durability and biocompatibility. Therefore, the antenna proposed in this paper can be better applied in implantable medical devices, offering potential advantages in terms of comfort and adaptability. The following sections will provide a detailed introduction to the design methodology, simulation results, and experimental validation process of the proposed antenna.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 3 System Model
### Beamforming Model  
Directional beamforming is implemented at BSs and UEs using uniform linear antenna arrays. The beamforming vectors define the direction and beamwidth of each link.
## 4 Loss Reduction Optimization Design
### Comparative Analysis of Losses Before and After Optimization  
Comprehensive evaluation of the electromagnetic losses before and after implementation of the proposed design improvements reveals significant performance enhancements across the entire operational envelope of the high-speed magnetic field-modulated motor. The optimization strategies collectively achieve a 43.2% reduction in total electromagnetic losses at rated speed, with component-specific improvements varying according to the targeted loss mechanisms, as illustrated in Figure 9. The most substantial improvement occurs in permanent magnet eddy current losses, which decrease by 68.7% due to the combined effects of segmentation, material hybridization, and magnetic shielding. Core losses demonstrate a 37.4% reduction primarily attributable to the optimized modulator geometry and improved material selection, with the greatest benefits observed in the intermediate speed range where the modified flux patterns effectively mitigate harmonic components. Copper losses exhibit a more modest 26.5% reduction through enhanced winding configuration and proximity effect management, remaining largely dependent on current density requirements dictated by torque production. Mechanical losses show similar behavior between original and optimized designs at lower speeds but diverge substantially above 80% of rated speed, with the optimized design maintaining nearly linear scaling compared to the exponential increase observed in the original configuration. The comparative efficiency maps reveal a significant expansion of the high-efficiency operational region, with peak efficiency increasing from 92.3% to 95.8% and the \(\eta>90\%\) region expanding by 42% in thespeed-torque plane. Temperature distribution analysis demonstrates that maximum temperature hotspots decrease from 143 \({}^{\circ}\)C to 98 \({}^{\circ}\)C under identical operating conditions, with temperature gradients reduced by 58% across active components. Transient performance evaluation indicates that the optimized design maintains efficiency above 93% during rapid speed transitions, whereas the original design experiences substantial efficiency degradation to approximately 86% during similar operating profiles. A full three-dimensional comparison is given in Figure 10. These improvements collectively enhance the viability of high-speed magnetic field-modulated motors for flywheel energy storage applications requiring extended operational durability and enhanced thermal stability.  
Figure 9: Comparison of losses and efficiency before and after optimization.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 4 Simulation Results
### Comparison of Total Energy Consumption of the Network  
To prove that the performance of the proposed protocol is better than that of the existing protocols, the total energy consumption of the proposed protocol is compared with that of LEACH, LEACH-M, O-LEACH, LEACH-C [18], EE-LEACH [36] and SEEN, under the same simulation conditions. Figure 11 shows the total network energy consumption of the protocols versus the number of rounds. As shown in Figure 11, the LEACH-C protocol optimizes the cluster head selection and consumes less network energy than the LEACH protocol in the same round. The LEACH-C prolongs the network lifetime to 2723 rounds as compared to 2267 rounds of the LEACH protocol. In addition, the LEACH-M and O-LEACH prolong the network lifetime to 2775 and 2972 rounds, respectively. Because the residual energy of nodes is taken into account when selecting cluster heads and the multi-hop communication mode is applied in EE-LEACH, the EE-LEACH further prolongs the network lifetime to 3114 rounds. The proposed IEE-LEACH protocol not only considers the residual energy and initial energy of nodes when selecting cluster heads, but also considers the average energy of all nodes and the total energy of network to further optimize cluster head selection. Thus, the total network energy consumption of the IEE-LEACH protocol is less than that of the other protocols in the same round, and the proposed IEE-LEACH protocol prolongs the network lifetime to 3242 rounds. Moreover, because single hop, multi-hop and hybrid communication modes are adopted, and the nodes closer to BS than to the CH  
Figure 10: Comparison of the number of cluster heads.  
do not participate in cluster formation, the proposed IEE-LEACH protocol further extends the lifetime of the network.
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 3 Laboratory Testing and Results
### Direct Shear Tests  
The direct shear tests were conducted on soil-concrete specimens prepared in accordance with ASTM D3080 [41] standards. Three soil specimens, each with different initial degrees of saturation, were placed in a 101.3 mm \(\times\) 101.3 mm \(\times\) 30 mm mold and compacted in layers using static compaction. Concrete specimens (cement/sand/water ratio = 1:2:0.45) filled the lower half of the direct shear device as shown in Figure 3. The specimens were tested under three net normal stresses (100, 200, and 400 kPa) to determine shear strength parameters (cohesion, C\({}^{\prime}\), and effective friction angle, \(\phi^{\prime}\)). Soil specimens were consolidated under varying vertical pressures for one day to dissipate excess pore pressure, followed by shearing at 0.0049 mm/min.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Symbols and Units**} & **Upper** & **Lower** & **Upper** & **Lower** & **Upper** & **Lower** & **Upper** & **Lower** \\  & & **Part** & **Part** & **Part** & **Part** & **Part** & **Part** & **Part** & **Part** \\  
Figure 4 shows the relationship between shear stress and net normal stress. As shown in Figure 4, the friction angle (\(\phi^{\prime}\)) was determined to range from 9\({}^{\circ}\) to 12\({}^{\circ}\), indicating no significant variation in friction angles across different initial degrees of saturation, while cohesion varies with matric suction. The coefficient of determination (R\({}^{2}\)) for the fitted lines was calculated to assess the reliability of the trend. The R\({}^{2}\) values shown in Figure 4 range from 0.874 to 0.997, indicating a strong correlation between the measured data and the regression lines. Figure 5 presents the shear stress vs. matric suction for the samples tested. The interface friction angle (\(\phi^{\text{b}}\)) determined for the samples tested ranges from 4\({}^{\circ}\) to 8\({}^{\circ}\), indicating that the interface friction angle (\(\phi^{\text{b}}\)) increases as the normal stress increases. The R\({}^{2}\) values shown in Figure 5 range from 0.481 to 0.980. The R\({}^{2}\) value for the 100 kPa case was relatively low, suggesting a weaker correlation under low confining pressure. However, the determined \(\phi^{\text{b}}\) value is approximately two-thirds of the \(\phi^{\text{b}}\) value, which is consistent with general findings provided in [PERSON] et al. (2012) [20].  
Figure 4: Variation in shear strength with the net normal stress under different DOS.  
Figure 3: Schematic diagram of the direct shear test.
## 2 Materials and Methods
### Model and Method
#### 2.3.3 Optimal Parameter-Based Geographic Detector  
Geographic detectors, as a statistical method for detecting the effects between factors in multiple spatial units, are widely used in detecting spatial dissimilarities and identifying interactions between factors [47; 48]. However, the analysis of traditional geographic detector models is often affected by spatial scale and zoning effects, and the results are often uncertain due to the spatial scale dependence of the process mechanism [49]. [PERSON] et al. [50], in order to avoid spatial scale dependence and reduce the influence of zoning effects, established a geographic detector model based on optimal parameters, which can better identify the relevant combinations of spatial scale parameters, greatly reduce the influence of spatial differentiation, and more accurately explain the correlation relationship between variables. Therefore, in this paper, based on parameter optimisation, the driving factor of the spatial divergence of NEP in the Taihang Mountain area is detected by a geo-detector.  
The factor detection can detect the degree of influence of the driving factor X on NEP, and the result is measured by q. The specific formula is as follows:  
\[q=1-\frac{1}{N\sigma^{2}}\sum\limits_{h=1}^{L}N_{h}\sigma_{h}^{2} \tag{9}\]  
where \(L\) is the stratification of variables, that is, classification or partitioning; \(N\) and \(N_{h}\) represent the number of units in the entire region and the h-th layer, respectively; \(\sigma^{2}\) and \(\sigma_{h}^{2}\) represent the discrete variances of vegetation carbon sinks in the entire region and the h-th layer, respectively. The value of \(q\) is between 0 and 1. The larger \(q\) is, the stronger the explanation of NEP.  
In the actual region, the vegetation carbon sink is not a natural factor acting alone, but often multiple factors acting together, such as temperature and precipitation, precipitation slope direction, slope elevation, and other factors closely linked and acting together. Therefore, in addition to factor detection, this paper further detects factor interactions and assesses whether the influence on vegetation NEP increases or decreases when two closely related driving factors act together or whether the effects of these factors on NEP are independent of each other.  
This was assessed by first calculating the q-values of the two factors X1 and X2 on Y: q(X1) and q(X2), respectively, and by calculating the q-values when they interacted (new polygonal distributions formed by a tangent of the two layers of the superimposed variables X1 and X2): q(X1(\(\cap\)X2) and comparing q(X1), q(X2) with q(X1(\(\cap\)X2).
## 4 Results and Discussion
### Overall and Shell-Side Heat Transfer Coefficient  
Figure 6 shows the variation of the overall heat transfer coefficient \(U_{\mathrm{o}}\) with the shell-side Reynolds number \(Re_{\mathrm{o}}\) of non-Newtonian base fluid and its nanofluids at different concentrations. As can be seen from Figure 6, \(U_{\mathrm{o}}\) increases remarkably with the increasing of \(Re_{\mathrm{o}}\) and MWCNTs concentration from 0.2 wt % to 1.0 wt %. For a given \(Re_{\mathrm{o}}\), it can be found that the \(U_{\mathrm{o}}\) of the non-Newtonian nanofluids at weight fractions of 1.0 wt %, 0.5 wt % and 0.2 wt % increase by 22%, 14% and 6% on average, respectively. Similar trend can also be observed in shell-side heat transfer coefficient \(h_{\mathrm{o}}\) shown in Figure 7. It is apparent that the addition of MWCNTs enhances the shell-side heat transfer performance of heat exchanger.  
Figure 6: Overall heat transfer coefficient versus shell-side Reynolds number.  
Figure 7: Shell-side heat transfer coefficient versus shell-side Reynolds number.  
Figure 8 presents the thermal resistance analysis of tested heat exchanger for the non-Newtonian base fluid and its nanofluids at different concentrations. \(R_{\mathrm{s}}\) and \(R\) are the shell-side and overall thermal resistances given as follows:  
\[R_{\mathrm{s}}=h_{\mathrm{o}}^{-1} \tag{13}\]  
\[R=U_{\mathrm{o}}^{-1} \tag{14}\]  
As can be seen from Figure 8, the ratio of shell-side thermal resistance to overall thermal resistance decreases with the increasing in both shell-side Reynolds number and weight fraction of MWCNTs. In comparison to the base fluid, the thermal resistance ratio of the nanofluids with 0.2 wt %, 0.5 wt %, and 1.0 wt % MWCNTs decrease approximately 4%, 16%, and 20% at the same Reynolds number. Thus, the addition of MWCNTs into base fluid significantly increases the heat transfer coefficient in the shell side.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 2 Measurement Setup Description
### Measurement Equipment  
A Rohde & Schwarz (Munich, Germany) SMB100A signal generator was used to generate a narrowband transmission signal. A Rohde & Schwarz CMW500 signal generator was used to generate a 5G signal. The received power of the narrowband and 5G signals was measured using a spectrum analyzer (SA) model Anritsu (Atsugi, Kanagawa Prefecture, Japan) MS2090A. For the LoRa and the Zigbee transmissions, a LoRa PyCom (Eindhoven, NB, The Netherlands) LoPy4 module and a Zigbee XBee (Hopkins, MN, USA) 868 LP module were used, respectively. All transmissions were made with vertical polarization and \(\lambda/4\) monopole type antennas with resonance at the frequency of 868 MHz.
## 5 Conclusions  
The ball-disk wear experiments were performed under oil lubrication using a wear tester. The vibration and sound pressure signals were measured during the experiments. By the spectrum analysis of CCF calculated from the two types of signals, the \"weak\" FIV components submerged in the original signals were detected. Then, the \"weak\" FIV components were extracted using HWPT. The following conclusions were drawn:  
(1) The \"weak\" FIV components submerged in the original vibration and sound pressure signals could be identified by spectrum analysis of the CCF calculated from the two types of original signals.  
(2) The RMS change of the \"weak\" FIV components extracted from the vibration and sound pressure signals was highly consistent with the friction coefficient change. It could effectively characterize the transition of the wear stage from running-in to stable wear of the friction pairs.  
By analyzing the CCF of measured vibrations and sound pressure signals in spectral form, the method proposed here directly identified the frequency distribution of \"weak\" FIV, thereby eliminating the need for extensive iterative calculations, unlike methods based on cross-correlation coefficients, friction coefficients, etc.  
In the future, real friction pairs will be used in bench-scale experiments in order to validate the efficacy of the proposed methodology in this study, such as cylinder liner-piston rings, sliding bearings, rolling bearings, etc. Additionally, the FIV propagated both tangentially and normally. Despite this, this study focused solely on the normal vibrations, disregarding tangential vibrations or stick-slip motions. Further research is needed to better understand the correlation between FIV signals and friction-induced sound pressure signals during wear.  
Conceptualization, G.L., T.L. and H.Z.; data curation, Y.Z.; formal analysis, P.X., Y.Z. and Y.S.; funding acquisition, G.L., P.X. and T.L.; investigation, T.L., H.G. and H.Z.; methodology, G.L.; software, Y.Z., P.X., H.G. and Y.S.; supervision, G.L.; visualization, P.X.; writing--original draft, P.X. and Y.Z.; writing--review and editing, G.L. and P.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities grant number 313202222, the National Natural Science Foundation of China grant number 51879020, and the high-level talent innovation support program youth science and technology star project grant number 2021 RQ133.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We appreciate each of the anonymous reviewers for their valuable comments and suggestions for improving the quality of this paper.  
The authors have no relevant financial or non-financial interests to disclose.
## 5 Thermal Characterization  
Reliability issues such as self-heating and the rise of junction temperature are among the crucial concerns for modern electronic technologies and are becoming increasingly important from the scaling point of view. Additionally, heat-dissipation in photodiodes is also a prominent source of concern that may be coupled to the thermal instability in the electronic part, and can thus further deteriorate the performance of an integrated hybrid monolithic circuit through thermal feedback between the interconnected devices. Thermal management is thus absolutely important for both devices, in order to ensure stable operation. In order to alleviate the thermal issues, thermal impedance modeling approaches have been proposed for InP heterojunction bipolar transistor (HBT) s [14] including the accurate estimation of thermal resistances and thermal R-C network representations. Similar strategies have been adopted for UTC photodiodes, in this work, to provide an estimation of the thermal resistances and ultimately to develop a unified thermal management strategy for monolithic integrated circuits.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 4 Discussion  
The results demonstrate that the GCSG model consistently outperformed other models in predicting time-series load data, achieving higher R-squared values and lower MAE and MSE across different window sizes and prediction steps. This suggests that the integration of GAF, CNN, SENet, and GRU in the GCSG model contributes significantly to its predictive accuracy. These findings align with prior studies emphasizing the importance of incorporating attention mechanisms and sequential learning in time-series forecasting tasks.  
A comparison with the existing literature reveals both similarities and differences. While our study corroborated previous findings regarding the effectiveness of LSTM-based models in time-series prediction, the novel combination of GAF and SENet in the GCSG model provides additional improvements in capturing temporal dependencies and feature extraction.  
We acknowledge several limitations in our study. The choice of hyperparameters and model configurations could have influenced the results, and the generalizability of the GCSG model to other domains requires further investigation.  
In future research, exploring the application of the GCSG model in different domains and incorporating domain-specific features could enhance its robustness and applicability. Further investigations focused on the optimization of hyperparameters and evaluating model interpretability will be valuable for advancing the understanding of deep learning techniques in time-series analysis.