esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
It is well established that most patients undergoing different types of orthodontic treatment experience pain [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Pain perceived during orthodontic treatment is considered an inevitable undesirable adverse effect caused by stimulating the trigeminal nerve by inflammatory factors and cytokines [11, 12]. In general, it usually begins 4 h after the onset of orthodontic force [13]. A significant effect on orthodontic pain arises from age and sex [14].  
Semi-rapid maxillary expansion (SRME) has been defined as 1 mm to 1.5 mm expansion per week, producing lower forces than RME [6,15,16]. Activation of a Penn expander supported on four miniscrews resulted in discomfort and functional impairment that were higher in patients who had the appliance activated twice a day compared to those who had the screw activated once a day [17]. Similar results related to comparisons between one versus two turns daily were reported in other studies [5,6,16,17,18,16]. All of the above studies concluded that one turn a day was less painful than two turns a day for patients in different ages.  
Pain experienced during rapid maxillary expansion (RME) has been investigated from different points of view [5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19, 20]. [PERSON] et al. (2023), as well as [PERSON] and [PERSON] (2021), compared RME to a quadhelix appliance and found no difference in pain or discomfort levels [10,19]. A recent systematic review by [PERSON] et al. (2022) proved that pain was characterized by a lower intensity in SME than in RME, only during the first week of treatment [6]. A comparison between tooth-borne RME with special predetermined MEMORIA\({}^{\text{\textregistered}}\)_Leaf_ springs, calibrated with constant forces released by a nickel-titanium hybrid Hyrax expander showed no statistically significant difference in pain levels. According to [PERSON] et al. (2020) and [PERSON] et al., the use of a Hyrax appliance was associated with a significantly higher pain than of the LEAF expander [6, 7, 8]. However, a recent systematic review showed inconclusive results regarding pain levels from various maxillary expansion appliances in growing patients [20].  
Pain appears difficult to objectively quantify; moreover, pain level related to the same procedure can be sensed differently among people [21]. To our knowledge, no studies compared the pain perceived during two different orthodontic procedures in the same patients. Nevertheless, conducting a comparison between SRME and another orthodontic procedure performed on the same persons at the same time may provide the most reliable answer to the questions related to discomfort with the SRME procedure.  
Thus, the aim of the present study was to analyze pain and discomfort related to SRME according to chronological age, gender, skeletal maturation and presence/absence of diastema, and comparing them to pain and discomfort related to the fixed appliance (FA) procedure. The null hypothesis was that to adults, both procedures (SRME and FA therapies) would be more painful than to adolescents. The second hypothesis was that SRME would be more painful than FA therapy.
# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 4 Machine Learning Models
### eXtreme Gradient Boosting  
Recently, the XGBoost algorithm has gained popularity due to its high accuracy and efficiency. XGBoost is an ensemble method that combines multiple weak learners such as decision trees into a single strong learner [78; 79]. The algorithm iteratively adds decision trees to the model, with each tree attempting to correct the errors of the previous trees.  
XGBoost package [78] is usually utilized to implement the XGBoost regression model in R. The XGBoost package also offers support for hyperparameter tuning, which can significantly improve the model's performance. It provides a range of options for tuning its hyperparameters, including learning rate (eta), maximum depth of each tree (max_depth), number of trees, and regularization parameters (alpha and gamma). Bayesian optimization can be used for hyperparameter tuning in XGBoost.
# A Laser Irradiation Method for Controlling _Pieris rapae_ Larvae
## 3 Results
#### 3.1.2 Irradiation Area  
This effectiveness of killing larvae using laser irradiation was significant at each 24 h interval (antifeedant percentage: 24 h, \(F\) = 22.04, df = 3, 8, \(p\) < 0.001; 48 h, \(F\) = 33.66, df = 3, 8, \(p\) < 0.001; 72 h, \(F\) = 35.04, df = 3, 8, \(p\) < 0.001) (Figure 2b). If the irradiation area was greater than 0.01 mm\({}^{2}\), a mortality rate of 100% was observed 48 h posttreatment (Figure 2f). Moreover, at 24 h posttreatment, the antifeedant percentage and mortality rate obtained with an irradiation area of 0.01 mm\({}^{2}\) were 79.14% and 66.67%, respectively. No significant difference was found between irradiation areas of 0.79 mm\({}^{2}\) and 0.01 mm\({}^{2}\) at all tested posttreatment times (0.79 mm\({}^{2}\) vs. 0.01 mm\({}^{2}\): antifeedant percentages, 24 h, \(F\) = 0.006, df = 1, 4, \(p\) = 0.943; 48 h, \(F\) = 0.066, df = 1, 4, \(p\) = 0.811; 72 h, \(F\) = 0.074, df = 1, 4, \(p\) = 0.799; mortality rate: 24 h, \(F\) = 0.500, df = 1, 4, \(p\) = 0.519; 48 h, Same, all 100%). Therefore, the diameter range of the irradiation area was set to 1-3 mm, which indicates that the irradiation area was between 0.79 and 7.07 mm\({}^{2}\).  
Figure 2: Antifeedant percentages and mortality rates (Mean \(\pm\) SE) of 4 th-instr _P. rapae_ larvae after 24 h, 48 h and 72 h under varying working parameter tests. We repeated the experiment 3 times of putting 20 _P. rapae_ larvae into the petri dish in turn for laser irradiation and observation. Only one working parameter was tested in each experiment, and the other working parameters were fixed (Table 1). The tested working parameters were laser power (**a,_e_**), irradiation area (**b,_f_**), laser opening time (**c,_g_**) and irradiation position (**d,_h_**). Bars with the same letters indicate no significant difference among the tested groups within each observation time (ANOVA followed by [PERSON]’s honestly significant difference test, \(p\) > 0.05), while test between different observation times cannot be compared with each other.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 3 Flexible Economic Dispatch Model
### Modeling for the Power System (Adjustable Capacity Adequacy Test Sub-Problem)
#### 3.3.2 Natural Gas System Constraints  
Natural gas system constraints should include the operational limits of gas wells, pipelines, compressors, P2G facilities, storage facilities, and gas loads.  
\[f_{w,\min}\leq f_{w,t}\leq f_{w,\max} \tag{37}\]  
\[p_{i,\min}\leq p_{i,t}\leq p_{i,\max} \tag{38}\]  
\[\widetilde{f}_{ij,t}=\text{sgn}\big{(}p_{i,t},p_{j,t}\big{)}C_{ij}^{gt}\sqrt{ \left|p_{i,t}^{2}-p_{j,t}^{2}\right|} \tag{39a}\]  
\[\widetilde{f}_{ij,t}=\left(f_{ij,t}^{out}+f_{ij,t}^{in}\right)/2 \tag{39b}\]  
\[\text{sgn}\big{(}p_{i,t},p_{j,t}\big{)}=\left\{\begin{array}{cc}1&p_{i,t} \geq p_{j,t}\\ -1&p_{i,t}<p_{j,t}\end{array}\right. \tag{39c}\]  
\[L_{ij,t}=L_{ij,t-1}+f_{ij,t}^{in}-f_{ij,t}^{out} \tag{40a}\]  
\[L_{ij,t}=C_{ij}^{lp}\big{(}p_{i,t}+p_{j,t}\big{)}/2 \tag{40b}\]  
\[R_{c,\min}\leq\frac{p_{c},m,t}{p_{c},n,t}\leq R_{c,\max} \tag{41}\]  
\[E_{s,t}=E_{s,(t-1)}+f_{s,t}^{in}-f_{s,t}^{out} \tag{42a}\]  
\[E_{s,\min}\leq E_{s,t}\leq E_{s,\max} \tag{42b}\]  
\[f_{s,\min}^{in}\leq f_{s,t}^{in}\leq f_{s,\max}^{in} \tag{42c}\]  
\[f_{s,\min}^{out}\leq f_{s,t}^{out}\leq f_{s,\max}^{out} \tag{42d}\]  
\[0\leq f_{\text{P2G},p,t}\leq f_{\text{P2G},p,t,\max} \tag{43}\]  
\[\sum\limits_{j\in\text{G}(i)}\left(f_{ij,t}^{out}-f_{ij,t}^{in}\right)+\sum \limits_{s\in\text{G}(i)}\left(f_{s,t}^{out}-f_{s,t}^{in}\right)+\sum\limits _{w\in\text{G}(i)}f_{w,t}+\sum\limits_{p\in\text{G}(i)}f_{\text{P2G},p,t}=\sum \limits_{b\in\text{G}(i)}f_{\text{L},b,t}+\sum\limits_{i\in\text{G}(i)}(f_{ \text{GT},i,t}-f_{\text{NL},i,t}) \tag{44}\]  
Constraints (37) is the gas well constraint. (38) is the nodal pressure constraint. (39a)-(39c) represent the pipeline flow constraint. (40a) and (40b) are line-pack constraints. (41) represents the compressors constraint. (42a)-(42d) are the gas storage constraints. (43) is the P2G facility flow constraint. (44a) and (44b) are the nodal gas balance.
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 3 QTGrid Routing Protocol
### CH and PCH Election  
#### 3.2.1 CH Election  
CH election is crucial for GRs. In the initial stage, QTGrid selects the node closest to the center of the grid as the CH [11; 26]. Then, in the re-selection of CH, QTGrid comprehensively considers energy consumption and load balancing. The CH nodes were selected based on metrics, such as distance from the candidate node to the grid center and the eight adjacent CHs and residual energy. QTGrid defines _cls_ as the coefficient of CH election and selects the node with the minimum _cls_ as the CH. _cls_ can be calculated with Equation (2).  
\[chs=\alpha(1-\frac{E_{current\ i}}{E_{initial\ i}})+\beta\frac{\sqrt{2}d}{l}+ \gamma\frac{\sum\limits_{i=1}^{8}D_{i}}{8\times 2\sqrt{2}l} \tag{2}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are equilibrium coefficients; \(a>0\), \(\beta>0\), \(\gamma>0\) and they meet \(\alpha+\beta+\gamma=1\); \(E_{current\ i}\) and \(E_{initiali}\) are the current residual energy and initial energy, respectively; \(D_{i}\) is the sum of the distance between the nodes to the eight adjacent CHs; \(d\) is the distance between the node and grid center; \(l\) is the side length of the current level grid.  
QTGrid periodically broadcasts the CH election message to all nodes in the network at an interval of \(T\) seconds. After receiving the message, each cluster starts a new round of CH election. When a node in the cluster was elected as the CH by Equation (2), the election information was broadcasted and recorded by the CMs and adjacent clusters. In this manner, CHs with poor performance were replaced by more suitable CMs to avoid the premature death of several CHs and balance the energy loss of the intra-cluster nodes.
## 3 Numerical Experiments and Analysis
### Hybrid Algorithm Based on CC-SHADE-ML and MTS-LS1
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metaheuristic** & **C1** & **C2** & **C3** & **C4** & **C5** & **Total** \\ \hline SHADEILS & 8.67 & 11.75 & 11.00 & 14.67 & 15.00 & 61.08 \\ MOS & 12.67 & 10.75 & 9.50 & 12.33 & 10.00 & 55.25 \\ MLSHADE-SPA & 13.00 & 12.00 & 11.75 & 11.67 & 3.00 & 51.42 \\ CC-RDG3 & 5.83 & 12.25 & 12.63 & 9.33 & 11.00 & 51.04 \\ BICCA & 10.67 & 10.50 & 11.00 & 9.00 & 8.00 & 49.17 \\ IHDELS & 10.33 & 6.50 & 6.25 & 9.67 & 13.00 & 45.75 \\ SGCC & 8.00 & 8.00 & 8.38 & 12.00 & 9.00 & 45.38 \\ SACC & 2.00 & 6.63 & 8.38 & 8.33 & 14.00 & 39.33 \\ CC-SHADE-ML-LS1 & 12.00 & 4.75 & 4.50 & 4.33 & 12.00 & 37.58 \\ CC-CMA-ES & 8.67 & 5.38 & 7.75 & 7.33 & 2.00 & 31.13 \\ VMODE & 6.00 & 5.38 & 6.13 & 6.67 & 6.00 & 30.17 \\ DGSC & 6.33 & 7.38 & 5.38 & 5.00 & 4.00 & 28.08 \\ MPS & 4.33 & 8.25 & 7.50 & 3.00 & 5.00 & 28.08 \\ DECC-G & 9.33 & 6.00 & 4.38 & 3.33 & 1.00 & 24.04 \\ DEEPSO & 2.17 & 4.50 & 5.50 & 3.33 & 7.00 & 22.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The ranking of state-of-the-art metaheuristics.
## 5 Tests and Results Analysis
### Experiment and Analysis
In Figure 7b, because the branch based on background prediction was added, the background was detected in the complex environment, avoiding the object to be affected by the environment and resulting in wrong detection or missing detection. The vehicles that blocked each other did not affect vehicle detection and non-vehicle targets were not mistakenly detected. The model correctly recognized pedestrians riding electric vehicles and detected some vehicles in the corner or far away from the surveillance perspective. The differences detected by the two methods are indicated directly in the figure by different colored arrows. Therefore, the model training performance is improved based on group normalization and improved loss function in the vehicle training process. Figure 5: Comparison of improved one-stage detector and one-stage detector in highway environment. Then, the paper compares the P-R curves of different algorithms, as shown in Figure 8. It can be seen that the recall rate and accuracy rate of SSD and SINet were both lower than that of YOLO v4 and the improved algorithm in this paper. While under the same precision rate, the recall rate of the improved algorithm was higher than the YOLO v4 algorithm in this paper. The one-stage detector algorithm based on background prediction and group normalization for the vehicle detection model showed that the omission rate and error rate of the model were lower than that of the YOLO v4 model, which had a better detection effect. Figure 8: P-R curves for different models. Figure 6: Comparison of improved one-stage detector and one-stage detector in urban environment. Figure 7: Comparison of improved one-stage detector and one-stage detector in rural environment. Table 1 shows the performance comparison between the proposed model and other important target detection models. All models were trained with traffic monitoring training datasets. Among them, the detection accuracy of the proposed model in this paper was the highest, but its detection speed was only slightly lower than that of YOLO v4. This is because the network structure of this model is more complex than the latter, but it also fully meets the requirements of real-time detection.
## 1 Introduction
The attenuation coefficients of different tissues can be assessed automatically, and adaptive TGC can be achieved by means of software. Thus, it will have an important impact on further improving the quality of the breast ultrasound images [22]. The key of this algorithm is the automatic estimation of the attenuation coefficient. The methods of extracting the attenuation coefficient mainly include time domain and frequency domain approaches [23; 24]. Because the time domain method cannot remove the effect of ultrasonic diffusion on the attenuation coefficient evaluation and the calculation is complex [25; 26; 27], the frequency domain method is used to obtain local attenuation information. In this study, an improved ultrasound tomography algorithm based on a system with cylindrical motions of the PMUT linear array was designed. Spectral cross-correlation was adopted to evaluate the attenuation coefficients. According to the acquired attenuation coefficients, TGC compensates for the pulse-echo signal. The algorithm was validated by simulation and experiment.
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### AA-ILs with Application in the Processing of Biomaterials
#### 2.6.1 Cellulose Treatment and Processing
Meanwhile, ILs containing 1,5-diazabicyclo[4.3.0]non-5-ene (DBN) or 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) with Asp or Pro anions are able to dissolve cellulose and efficiently form refined cellulose films by hydrogen bonding between ILs and the material [115]. The thin cellulose film based on [DBN][Pro] exhibited a favorable combination of properties, including high tensile strength, high Young's modulus, good transparency (\(\approx\)70% at 550 nm), and smooth surface morphology. Furthermore, the two-step transglycosylation of microcrystalline cellulose to produce methyl \(\beta\)-D-glucoside was significantly enhanced by an initial pretreatment with a [P\({}_{4444}\)][Gly]-DMSO mixture, yielding a 38% higher amount of target product compared to reactions without pretreatment [116]. In addition, [N\({}_{1112\text{-OH}}\)][Gly] has been used for the pretreatment of brewer's spent grains prior to enzymatic hydrolysis to facilitate saccharification and achieve higher yields of sugar components [117]. [PERSON] et al. found that homogeneous solutions could be obtained by using a [Ch][Gly]-water solution to dissolve starch [118]. In addition, starch molecules remain disaggregated upon heating due to strong interactions between ions and polysaccharide molecules.
## 5 Electrical Characterization
### Response Characteristics
As discussed in Section 5.2, the response statistics must be categorized by bit-line, as the bit-line CMOS series resistance heavily affects the cell's response. After enrolling 30 devices in both high- and low-resistance states, the enrollment data showed that, if the average response of a toggle MRAM cell written high (with CMOS series resistance) is \(T\), the responses will range from \(\pm 31.16\%\) of \(T\). The average intra-cell variation was only 0.231% of \(T\), with the average bit-line inter-cell variation being 5.794% of \(T\), giving an inter-cell to intra-cell variation of 25.03. The toggle MRAM cells written to a low-resistance state with CMOS series resistance had an average response of \(L=T\times 0.852\), with responses ranging from \(\pm 46.77\%\) of \(L\). The average intra-cell variation was 0.203% of \(L\), with the average bit-line inter-cell variation being 5.261% of \(L\), giving an inter-cell to intra-cell variation of 25.88. The response statistics for both states can be found in Table 1.  
The average bit-line inter-cell to intra-cell ratio increased by 0.85 from a high to a low resistance state. Both MRAM states showed excellent statistical values for use as PUFs. While the low-resistance statistical values showed slightly better electrical characteristics, the difference was so minuscule that both states could be used to generate responses.
## 2 Materials and Methods
### Site Information  
The EC flux data and in situ meteorological measurements from 68 FLUXNET sites (The LaThuile free fair-use Dataset), were extracted from the Level 4 product, which was downloaded directly from the FLUXNET website (Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), 2013). Data for 8 additional sites were obtained from the Chinaflux network. After removing the sites for which there was less than one year of measurements and no net radiation measurements or NDVI data were available, 63 sites remained for analysis in this study (Table S1), including 7 cropland sites, 11 grassland sites, 5 savanna sites, 3 wetland sites, 3 shrubland sites, 7 deciduous broadleaf forest sites (DBF), 5 evergreen broadleaf forest sites (EBF), 19 evergreen needleleaf forest sites (ENF) and 3 mixed forest sites (MF).
## 4 Discussion
### Gamma Irradiation Effect on Antiradical Capacity
It was reported that morin, p-cumaric acid, and caffeic acid, which is a metabolite of caffeoylquinic acid (with FRSA equivalent to those of ascorbic acid and Trolox), exhibit strong DPPH radical scavenging activity [34; 35; 36]. Moreover, ferulic acid and a significant amount of hesperidin were detected in 5 kGy irradiated SS. These compounds could contribute to the highest TEAC values obtained here and for the strong radical-scavenging activity. Ferulic acid (FA) is the most abundant hydroxycinnamic acid, followed to a lesser extent by coumaric acid found in plant cell walls, which are covalently linked to polysaccharides and lignin. Probably FA was bound on the cell wall matrix of shell seeds and after application of 5 kGy dosage gamma radiation, the bond between FA and \(\alpha\)-L-arabinofuranose in hemicellulose released FA and degraded the crosslinking between hemicellulose and lignin [37]. As a result of irradiation with certain doses of radiation, the release of various hydroxycinnamic acids (HCAs) from the biomass of shell chestnut seeds was observed. The current study showed that hydroxycinnamic acids have the potential to provide a significant source of antioxidant characteristics. A wide range of lignin depolymerization approaches have been investigated including enzymatic, catalytic, thermal, and alkaline pretreatment, but information on the irradiation application for this purpose was not found in the literature [38]. These are the preliminary investigations on the application of gamma irradiation as a method for partial degradation of lignin and the release of biologically active HCAs from plant biomass, used as an excellent renewable resource for phytochemicals possessing bioactive properties. On the other hand, other studies reported increase of the amount of reducing sugars and better antioxidants extractability from the plant source during irradiation [39].
# Design of a 90 GHz SOI Fin Electro-Optic Modulator for High-Speed Applications
## 1 Introduction
In one alternative, silicon can be thermally modulated since it has a high thermo-optic coefficient of \(1.8\times 10^{-4}\) K\({}^{-1}\). However the modulation speed is very limited [27]. In another alternative, silicon is covered with a cladding material which possesses a much stronger electro-optic coefficient [28; 29; 30]. The cladding material modulates the part of the mode that overlaps with it. Other platforms such as polymers [31], III-V semiconductors [32; 33] and graphene [34; 35; 36] were also proposed. However, it is always advantageous to stick to the silicon-on-silica platform to keep fabrication simplicity and low cost. In this work, a MOS based modulator is proposed. The structure utilizes the fin-gate structure which is widely used as a part of the field effect transistor (FET) [37; 38; 39]. The proposed design has a main target of providing the maximum possible speed in order to make this modulator a candidate for the bandwidth aggressive applications, like those mentioned above. Such applications require speeds, for example, more than 100 Gbps of non-return-to-zero (NRZ) code [13]. Other design targets are small size and low optical losses. Finally, the design is confined to the mature silicon-on-silica platform which guarantees possible future implementation at low-cost.