esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Objectives: This pilot study was conducted to test the protocol of a randomized controlled trial evaluating whether rapid maxillary expansion (RME) can relieve nocturnal enuresis (NE) and improve breathing in children, after ruling out a placebo effect, and investigating whether the effects of RME and NE are related to the morphology of the upper airway. **Methods:** Seventy 6-15-year-old patients with NE were assessed for eligibility (e.g., constricted maxilla). Enrolled subjects were randomized to immediate treatment with RME (Group 1) or to have the same treatment (RME) delayed for at least six weeks (Group 2). Outcomes comprised the number of wet nights per week, the nocturnal urine production, and the scores of a pediatric sleep questionnaire at baseline, after active treatment (Group 1) or delayed treatment (Group 2), and after 3 months' retention. Cone beam computed tomographies were taken at baseline and after retention. **Results:** Six patients were randomized: three in each group. In four of six patients, the number of wet nights per week decreased. Moreover, in responders, nocturnal urine production was reduced following RME. **Conclusions:** This pilot study suggested that RME might reduce the severity of NE and showed that the protocol of this randomized controlled clinical trial was appropriate.  
capid maximly expansion; nocturnal enuresis; sleep-disordered breathing +
Footnote †: journal:  
symptoms of SDB in a young patient suffering bedwetting suggest the need for a referral for sleep studies, sometimes in combination with orthodontic treatment [14].  
SDB is common in children in need of orthodontic treatment because of skeletal and dental transversal deficiencies [15]. The orthodontic treatment aiming at resolving these deficiencies often sees, as a secondary effect, the enlargement of the upper airway (UA), and the resolution of NE is occasionally seen at treatment completion [12].  
One of the available treatment modalities to correct the transversal discrepancy is rapid maxillary expansion (RME). This procedure increases the maxillary arch width by opening the mid-palatal suture, thus achieving skeletal expansion [16; 17]. As such, RME has been associated with an improvement in breathing [18]. In the literature, some evidence is available regarding the effect of RME on NE [12; 19; 20; 21].  
Therefore, a randomized controlled clinical trial (RCT) was designed to evaluate whether RME could reduce the frequency of NE and improve quality of life in children after ruling out a placebo effect to investigate whether the effects of RME on NE are related to the morphology of the nasal and pharyngeal airway. The hypothesis was that RME treatment would lead to a reduction in nocturnal urine production as a result of improved breathing obtained by enlarging the nasal and pharyngeal airway. The purpose of this pilot study was to investigate the appropriateness of this RCT protocol.
###### Abstract  
The present study aimed to evaluate the influence of manual expertise on static computer-aided implantology (s-CAI) in terms of accuracy and operative timings. After the cone-beam CT (CBCT) scanning of eleven mandibular models, a full-arch rehabilitation was planned, and two different skilled operators performed s-CAI. The distances between the virtual and actual implant positions were calculated considering the three spatial vectorial axes and the three-dimensional Euclidean value for the entry (E) and apical (A) points, along with the axis orientation differences (Ax). These values emerged from the overlapping of the pre-op CBCT to post-op CBCT data (method 1), from scanning the data from the laboratory scanner (method 2), and from the intra-oral scanner (method 3) and were correlated with the operators' expertise and operative timings. The mean values for accuracy from the three methods were: E = 0.57 (0.8, 0.45, 0.47) mm, A = 0.6 (0.8, 0.48, 0.49) mm, and Ax 1.04 (1.05,1.03,1.05) \({}^{\circ}\) for the expert operator; and E = 0.8 (0.9, 0.87, 0.77), A = 0.95 (1.02, 0.95, 0.89), and Ax =1.64 (1.78, 1.58, 1.58) for the novice. The mean value of the operative timings was statistically inferior for the expert operator (\(p<0.05\)), with an improved accuracy over time for both operators. A significant difference (\(p<0.05\)) emerged between method 1 and methods 2 and 3 for seven of the nine variables, without differences between the evaluations from the two scanners. The support from digital surgical guides does not eliminate the importance of manual expertise for the reliability and the shortening of the surgical procedure, and it requires a learning pathway over time.  
clinical skill; data accuracy; dental implant; outcome assessment 2022
## 2 PV System Modeling  
A PV system consists of modules that are connected in parallel and series. PV modules are the basic building blocks of a PV system, consisting of a series and parallel combination of PV cells. In general, a single diode model is used for modelling and simulation of a PV system [31; 32]. This model is composed of a series resistance \(R_{s}\), a shunt resistance \(R_{sh}\), a diode \(D\), and a current source \(I_{ph}\) (see Figure 2) [33].  
PV system \(I_{pv}\) represents output current, which is mathematically modeled as  
\[I_{pv}=N_{p}I_{ph}-I_{d}-I_{sh} \tag{1}\]  
where \(I_{s}h\) is the current going through the shunt resistance, \(I_{d}\) is the current travelling through the diode, \(N_{p}\) is the number of parallel linked cells, and \(I_{ph}\) is the source current, which is temperature and irradiance dependent.  
\[I_{ph}=\bigg{(}\frac{G}{G_{ref}}\bigg{)}[I_{sc}+K(T-T_{ref})] \tag{2}\]  
In the above expression, \(T\) signifies temperature, \(K\) denotes temperature coefficient, \(G\) denotes solar irradiance, and \(I_{sc}\) denotes short circuit current at standard solar irradiance \(G_{ref}\) and temperature \(T_{ref}\). [PERSON]'s model may be used to obtain the expression for diode current, as shown below.  
\[I_{d}=I_{rs}N_{p}\bigg{[}exp\bigg{(}\frac{V_{d}}{nV_{t}}\bigg{)}-1\bigg{]} \tag{3}\]  
Figure 1: Proposed control methodology.  
Figure 2: Single diode model of PV system.  
where \(n\) is the ideality factor of the diode, \(I_{rs}\) is the reverse saturation current of the diode, and \(V_{t}\) is the thermal voltage provided by  
\[V_{t}=\left(\frac{kT}{q}\right) \tag{4}\]  
where \(q\) is the electron charge, \(T\) is the temperature and \(k=1.38\times 10^{-23}J\) is the Boltzmann's constant. The voltage across diode \(V_{d}\) is  
\[V_{d}=\left(\frac{V_{pv}+I_{pv}R_{s}}{N_{s}}\right) \tag{5}\]  
where \(V_{pv}\) denotes the array output voltage, \(N_{s}\) denotes the number of series-connected cells, and \(R_{s}\) is the series resistance. Incorporating (4) and (5) in (3), the expression for diode current becomes  
\[I_{d}=I_{rs}N_{p}\left[exp\left(\frac{q(V_{pv}+I_{pv}R_{s})}{nkN_{s}T}\right)-1\right] \tag{6}\]  
The current through shunt resistance can be mathematically expressed as  
\[I_{sh}=\left(\frac{V_{pv}+I_{pv}R_{s}}{R_{sh}}\right) \tag{7}\]  
Considering (2), (6) and (7), the output current of PV system can be expressed as  
\[\begin{split} I_{pv}=&\Bigg{(}\frac{G}{G_{ref}} \Bigg{)}[I_{sc}+K(T-T_{ref})]N_{p}-I_{rs}N_{p}\bigg{[}exp\left(\frac{q(V_{pv}+ I_{pv}R_{s})}{nkN_{s}T}\right)-1\bigg{]}\\ &-\left(\frac{V_{pv}+I_{pv}R_{s}}{R_{sh}}\right)\end{split} \tag{8}\]  
The PV system curves like P-V and I-V are shown in Figures 3 and 4 under varying irradiance and temperature.  
Figure 3: Characteristic curves of P-V and I-V under varying temperature.  
We have modeled the current of the PV system in (8). The non-inverted DC-DC buck-boost topology converter detailed modeling is discussed in subsequent section.
## 2 Materials and Methods
### Human Joint Rotation Angle Estimation Model Based on SMA-BLS
#### 2.2.3 Role of SMA in BLS Parameter Optimization  
1. Global Search: The SMA efficiently searches large solution spaces to find near-optimal parameters for BLS, such as the number of feature nodes and projection dimensions.
2. Local Fine-Tuning: After finding good solutions, the SMA refines them further, improving the model's performance through precise adjustments.
3. Avoid Premature Convergence: The SMA prevents getting stuck in suboptimal solutions by dynamically adjusting its search strategy, leading to better results. Advantages of Using SMA for BLS Parameter Optimization
1. Flexibility: The SMA works well with various types of optimization problems, including those without gradient information or highly nonlinear functions, making it ideal for complex models like the BLS.
2. Efficiency: The SMA is computationally efficient, finding good solutions quickly, which is especially useful for large datasets and high-dimensional parameter spaces.
3. Ease of Use: The SMA is simple to understand and implement, making it easy to integrate into existing machine learning frameworks.
4. Improved Performance: By optimizing BLS parameters, the SMA enhances the model's accuracy, stability, and ability to generalize, leading to better practical outcomes.
## 4 Results and Discussion
### Model Architectures
#### 4.1.2 Model Analysis
These are the layers in which the PU and QCD reach peak energy deposition (for reference, please see Figure 5). * Similar to the MLP model, the CNN architecture undergoes a feature importance analysis using SHAP values. However, the SHAP documentation does not provide a way in which to visualize the 3D regions with the highest influence on model decisions. Figure 11: A summary plot for MLP the most influential features in a model. The features (pixel positions) are ranked based on their effect on the model’s predictions. The x-axis represents the SHAP value—a measure that quantifies the influence of a feature on a specific prediction. The y-axis displays the features, while the plot also demonstrates the distribution of SHAP values for each feature. Additionally, the color of the plot represents the value of the features, arranged from low to high. Therefore, we created a custom visualization that follows standard SHAP rules. This visualization produces a figure wherein red pixels indicate the features that cause the model to predict that an HGCAL image corresponds to an EM shower, while blue pixels indicate the features that push the model away from predicting that an image belongs to the EM class. Figure 12 gives a visualization of an example of SHAP local interpretation. Essentially, local interpretation involves calculating the SHAP values for each feature for a specific instance, providing insight into which features were most important for that decision. It is noticeable that most of the red areas are within the layers where the EM showers have the most energy deposits (for reference, please see Figure 5). This indicates that the model pays significant attention to these areas when predicting an EM shower.
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 3 Shortest Paths with Vertex Transfer Penalties
### Kirby-Potts Expansions  
Although the algorithms reviewed in Section 2 are all known to solve their respective shortest path problems in edge-weighted graphs, note that they cannot be directly applied to graphs that feature transfer penalties at the vertices. Instead, graphs will usually be _expanded_ in such a way that transfer penalties are expressed via additional \"transfer edges\". Once this has been done, shortest path methods can then be applied as before. The most prominent method of expansion is due to [PERSON] and [PERSON] (2016) who use a cluster of dummy vertices for each vertex in the original graph. Specifically, using an edge-coloured graph \(G=(V,E)\), a new larger graph \(G^{\prime}=(V^{\prime},E^{\prime})\) is formed by creating two sets of dummy vertices for each vertex \(v\in V\): one for each incoming colour in \(v\) and one for each outgoing colour in \(v\). Transfer edges are then added between the dummy vertices in each set using edge weights equivalent to the corresponding transfer penalties.  
Examples of the Kirby-Potts expansion method are shown in Figure 3a,b. As illustrated, each cluster is composed of a set of \"in-vertices\" and \"out-vertices\". Transfer penalties are specified using edges that start at the in-vertices and end at the out-vertices (forming a directed bipartite graph). This results in a new graph \(G^{\prime}=(V^{\prime},E^{\prime})\) comprising \(n^{\prime}\) vertices and \(m^{\prime}\) edges, where  
\[n^{\prime} = \sum_{i=1}^{n}|C^{-}(v_{i})|+|C^{+}(v_{i})| \tag{1}\] \[m^{\prime} = m+\sum_{i=1}^{n}|C^{-}(v_{i})|\cdot|C^{+}(v_{i})|. \tag{2}\]  
Note that a shortest path between two vertices in \(G^{\prime}\) now also specifies the starting colour and arrival colour in the original edge-coloured graph's path. For example, the shortest path between the vertices marked by X and Y in Figure 3a corresponds to the shortest \(v_{1}\)-\(v_{5}\)-path in Figure 2 in which \"arrival\" at \(v_{1}\) is assumed on a black edge and arrival at \(v_{5}\) is on a red edge.
# Study on the Minimum Operation Width of Human-Powered Bicycles for Safe and Comfortable Cycling
## 4 Results
### Minimum Operation Width Determination Based on Lateral Distance Analysis  
Table 9 presents the results of the normality tests for the five cycling operation widths. The findings indicate that the lateral distances for all operation widths conform to a normal distribution.  
The lateral distances of different cyclists across various width scenarios were compiled, and the cumulative frequencies for each lateral distance were calculated, resulting in Figure 14.  
Figure 14 illustrates that under all preset width conditions, the lateral distance required to accommodate 85% of cyclists is 0.268 m. Considering the bicycle width of 0.62 m, the total operation width required is 0.888 m, which is rounded up to 0.90 m. Conversely, the lateral distance required to accommodate 95% of cyclists is 0.365 m. Adding the bicycle width of 0.62 m results in a total requirement of 0.985 m, thus necessitating an operation width of at least 1.0 m to meet these conditions.
## Materials and Methods
### Headspace Collection and Analysis of Plant Volatiles  
Three varieties of tomato plants (2 weeks after transplanting) were selected to collect the volatile emissions (healthy plants). Firstly, to obtain volatile profiles, we employed the headspace-solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GCMS) system. Individual plants were placed into a polypropylene desiccator previously adapted with a charcoal-activated trap, and flow-air (6 L/min) was passed on the plant for 5 min (Floradade); polyethylene bags (Sunbag, MERCK KGaA, Darmstadt, Germany) were utilized for Micro-Tom and wild. To obtain the VOC profiles, three varieties with three biological replicates (_n_ = 3) were performed for a single bioassay in healthy tomato plants (psyllid-free).  
To evaluate the VOCs released in the three varieties during _B. cockerelli_ nymph feeding, two bioassays were performed independently with three biological replicates for all varieties. With a small brush, 25 nymphs of _B. cockerelli_ (fourth and fifth stages) were gently placed on each plant and allowed 2 h feeding on the tomato leaves (nymphs were left in plants). After feeding, individual plants were collected, using either bags or a desiccator, to evaluate the headspace of each treatment by SPME. For SPME, gray fiber (50/30 \(\upmu\)m, DVB/CAR/PDMS, MERCK KGaA, Darmstadt, Germany) was used in all evaluations. The fiber was exposed during 2.5 h. Healthy plants (nymphs-free) were exposed at the same time for SMPE. The bioassays were performed at 28 \(\pm\) 2 \({}^{\circ}\)C in greenhouse conditions.  
All fibers were injected and desorbed in a gas chromatograph (GC 7890A, Agilent Technologies, Wilmington, DE, USA) coupled to mass selective detector (Agilent Technologies MSD 5975 C). All analyses were performed under running conditions: initial oven temperature of 60 \({}^{\circ}\)C for 1 min, increasing 8 \({}^{\circ}\)C/min until 280 \({}^{\circ}\)C for 1 min. Injector temperature was 230 \({}^{\circ}\)C. The VOCs were analyzed employing a capillary column Agilent Technologies HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). After 15 minutes, the fiber was removed from the injector and stored until use. All the compounds were identified by a NIST92 and Wiley library, according the percentage of quality presented (\(>\) 90%); retentions times were compared with library, and Kovats indices also were determined for individual compounds.
# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
## 1 Introduction
If the effect of shadow detection is not very good, it is impossible to obtain a high-quality, shadow-free picture in the subsequent removal process. Inspired by the classical adaptive algorithm, [PERSON] and [PERSON] [20] used the convolutional automatic encoder for image binarization, which is an end-to-end network structure and shows good performance in image binarization processing. Image binarization can be formulated as a two-class classification task at the pixel level, which is similar to the shadow removal task. At the same time, one of the biggest drawbacks of the current image shadow removal is the lack of the end-to-end channel. Inspired by this, the purpose of this paper is to design an end-to-end and fully automated network structure to remove image shadows. Thereby, through the ex-trained network structure, the input of a single shadow image can be transformed to a high-quality, shadow-free image, which provides a good basis for subsequent target detection and target tracking. Different from traditional methods, this paper applies the most prominent features of the deep encoding-decoding model framework of end-to-end and convolutional neural network (CNN) design. Inputting a single shadow image, a shadow mask is obtained. It can describe the global structure of the scene and the high-level semantic information of shadow characteristics. Using the relationship between the shadow ratio and shadow image, Equation (1) realizes an end-to-end fully automated algorithm to restore the shadow-free image. At the same time, this paper designs a small network structure for refinement in order to better handle local information, thus predicting a more accurate alpha mask and sharper edge information.
# Effect of Cooking Methods on Bioactive Pigments in Purple Carrots (_Daucus carota_ L.)
###### Abstract  
Carotenoids and anthocyanins are plant-based molecules that have shown potent antioxidant and anti-inflammatory activities contributing to human health. Purple carrots contain not only the carotenoids occurring in the typical orange carrots, but also a high content of anthocyanins, which makes them nutritionally more interesting. However, most of these bioactive compounds are partially lost during cooking. This study aimed to determine total carotenoid content (TCC), total anthocyanin content (TAC), total phenol content (TPC) and antioxidant activity (AA) in terms of DPPH and ABTS radical scavenging activity in cooked purple carrots. The identification of the main anthocyanins was also performed by HPLC-DAD. With a view to evaluating the effect of cooking, purple carrots were subjected to freeze-drying, boiling, steaming and roasting. To our knowledge, this is the first extensive study about the effect of various cooking methods on different parameters related to the beneficial health properties of purple carrots. As a result, freeze-drying brought about significant loss of carotenoids (TCCs 554.90 \(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro s}\) 1136.66 \(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro s}\) in fresh carrots) whereas boiling resulted in a significant decrease of polyphenols (TPCs 15.71 \(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro s}\) 66.71 \(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro s}\) in fresh carrots) and anthocyanins (TACs 1.41 \(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro s}\) 4.83 \(\mathrm{\SIUnitSymbolMicro g}\)-\(\mathrm{\SIUnitSymbolMicro s}\) in fresh carrots). The results in this paper can be used by the food industry to elaborate more nutritious purple-carrot-based products.  
purple carrot; anthocyanins; carotenoids; bioactive compounds; antioxidant activity; cooking methods Article
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 5 Conclusions  
This paper presented a method called P-NG for simulating the SGD based on the neighbor gradient algorithm. Compared to the classical F-ALS method, the SGD masks derived by the P-NG method are more complete due to the detection of passive distortion. By employing the ascending and descending ALOS PALSAR pairs acquired over the Chongqing area of China, it was found that the areas affected by layover/shadow generated by the P-NG method were dominated by the non-interferometry regions. Therefore, the SGD derived by the P-NG method can also be used as a basis for selecting or optimizing the best collocate of SAR data. In addition, the P-NG method can correct the boundaries among the different effects of the SGD, which greatly improves the accuracy of simulation results. The SGD derived by the P-NG method can greatly improve the use rate of SAR images and enhance the applicability of InSAR in the monitoring of landslides.  
Also, the performance of the presented P-NG method depends on the resolution of the used DEM. The realization of a high-resolution Global Elevation Digital Map will be an opportunity for the P-NG method, and the extension of the P-NG method to high-resolution DEM will be our future research topics.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. All authors reviewed and approved the manuscript.  
This research received no external funding.  
The ALOS PALSAR data were provided by the Japan Aerospace Exploration Agency (JAXA). The research was supported by the National Natural Science Foundation of China (Nos. 41774023, 41674010), the Science and Technology Project of Hunan Province (Nos. 2018 JJ3347, 2017 RS3001), the Project of Education  
Figure 9: Intensity image of the ALOS descending data (**a**); the layover/shadow simulated based on 90-m SRTM DEM (**b**) and 30-m SRTM DEM data (**c**). V: “real” layover/shadow by visual interpretation; AS: active shadow; PS: passive shadow; AL: active layover; PL: passive layover.  
Department of Hunan Province (No. 16K053), and the Land and Resources Research Project of Hunan Province (No. 2017-13). The authors thank three anonymous reviewers for their constructive comments. The authors declare no conflict of interest.