esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Efficiency and Interference Verification of a HONO Collection System  
As previously described, the efficiency of the system was determined, resulting in 97.7% for the two serial stages, with MDLs of 0.15 ppbv for HONO and 0.11 ppbv for HNO\({}_{3}\). The current spray chamber system exhibits enhanced performance in terms of both efficiency and MDLs. The concentrations used in the subsequent analysis have been adjusted for efficiency. To verify interference in the spray chamber, NO\({}_{2}\) and O\({}_{3}\) generators (49 iQPS, Thermo Fisher Scientific, Waltham, MA, USA) were employed [41; 42]. The setup of interference verification is shown at the bottom of Figure 1. In addition to NO\({}_{2}\) interference, the potential formation of OH radicals by microdroplets, which could impact HONO measurement, was examined using a OH radical probe (terepthhalic acid) [43; 44]. The O\({}_{3}\) generator produced a consistent O\({}_{3}\) concentration using compressed air, mixed with the NO2 standard in a specially designed mixer. The mixed air was then collected and analyzed in the spray chamber to verify any interference. The air mixture used had final concentrations of 50 ppb for O3 and 33 ppb for NO2, and testing confirmed that there was no interference from either NO2 or OH radicals.
## 3 Results
### Training, Test and Results of DNN  
In this section, the Deep Neural Network results are presented. The structure of this Neural Network has one normalization layer, two hidden layers and one output layer, with a total of 4,488 parameters. It uses Mean Absolute Error as the loss function, Adam as the optimizer function and Rectified Linear Unit (Relu), as the activation function. In Figure 4, it is possible to see its architecture.  
Figure 4: Deep Neural Network architecture. Prepared by the authors.  
Figure 3: Perceptron results for DRG 178: (**a**) results using AMP to predict ATP and (**b**) error count. Prepared by the authors.  
The network training process was configured with 150 epochs with batch sizes of 50 cases. It took 6 min, 16 s and 968 milliseconds to train the DNN. In Figure 5, it is possible to see the evolution of the values of the loss function during several training processes.  
It can be seen that the training process evolved quickly for the first 50 epochs and that, after that, the improvement was slight. In order to compare the results, we tested the same DRG sets as in the previous case. The table of executions for these DRGs can be seen in Table 2, which has the same columns as that in the previous case, i.e., the DRG code, the number of patients that belong to the diagnostic group, the time spent by the network to make the predictions and the accuracy achieved by the model for each group.  
This Neural Network also had a good prediction capacity, very similar to that of the Perceptron. The accuracy achieved was around 80%, slightly higher than before; the time spent running the Neural Network was slightly longer, between 4 and 4.5 s. We also plotted the results for DRG 178 and the count of the value of the errors. They can be seen in Figure 6a,b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DRG** & **Number of Patients** & **Time** & **Test Accuracy** \\ \hline
39 & 1079 & 4.490 s & 85.08\% \\ \hline
64 & 1659 & 4.439 s & 79.48\% \\ \hline
178 & 1953 & 4.415 s & 83.68\% \\ \hline
473 & 846 & 4.405 s & 83.55\% \\ \hline
536 & 1142 & 4.415 s & 83.75\% \\ \hline
690 & 2989 & 4.467 s & 83.04\% \\ \hline
811 & 1135 & 4.379 s & 80.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: DNN Sequential Neural Network results on DRG billing prediction.  
Figure 5: DNN Neural Network training evolution (from Tensorboard).  
To have a better understanding and compare the performance of both Neural Networks, in Table 3, a comparison between both models in terms of their accuracy and time spent for every DRG can be seen.  
A slightly better performance of the DNN was observed, at the cost of some processing time.
## 4 Conclusions  
In this study, three different algae species clearly showed their effect on dough rheology, bread quality, and nutritional attributes. Notably, _T. chuii_ and _P. tricornutum_ performed better than _C. vulgaris_ in these attributes. _C. vulgaris_ led to increased water absorption, coupled with decreasing dough stability, protein network, starch gelatinisation, starch ret  
Figure 4: Total phenolic content **(a)**, antioxidant capacity by FRAP (ferric ion reducing antioxidant power) **(b)**, and DPPH (radical scavenging activity) **(c)**. Control: bread without microalgae; Cv: bread with _Chlorella vulgaris_; Pt: bread with _Phaoxactylum tricornutum_; Tc: bread with _Tetraselmis chuii_. Different letters in the same parameter show significant differences (_p_ < 0.05, Tukey test).  
rogradation, and elasticity, resulting in lower bread volume and a higher crumb firmness. _P. tricornutum_ caused decreased water absorption and a reduction in the protein network but exhibited no significant impact on dough stability, starch gelatinisation, or starch retrogradation. Consequently, it had no significant effect on bread volume. _T. chuii_ decreased water absorption and the protein network, with no significant impact on dough stability, starch gelatinisation, or starch retrogradation. However, it notably increased elasticity, which did not impact bread volume or texture. Regarding nutritional attributes, the incorporation of microalgae enhanced the nutritional composition of the final product, resulting in higher levels of protein and some minerals. Additionally, it led to improvements in total phenolics and antioxidant capacity, with _T. chuii_ exhibiting the highest increase. Overall, _T. chuii_ emerged as the best formulation, exhibiting superior mechanical properties and bioactivity across the bread samples. Within the scope of the YUMAlgae project, sensory enzymes will be employed to enhance the overall aroma and colour of microalgae ingredients. These improved ingredients will undergo testing in bread formulations, aiming to attain elevated levels of microalgae that result in favourable health impacts and high sensory acceptance from consumers.  
Investigation, formal analysis, and writing--original draft, N.M.; Investigation--volatiles, J.F. Formal analysis and writing--review and editing, A.R. and M.C.N.; Supervision, A.R. and M.C.N.; Project administration, A.R. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by: (i) Project PT-INNOVATION-0075, YUMAlgae--enzYmes for improved sensory qUality of MicroALGAE ingredients in foods (EEA Grants) and (ii) Portuguese Foundation for Science and Technology (FCT), UIDB/04129/2020 of LEAF Research Center.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 2 Methods
### Planning the Review  
In the first stage--\"planning the review\"--we developed the concept for this literature review with partner organizations (ranging from academia to corporations to research non-governmental organizations) from the Sustainable Coffee Challenge [4]. We identified the need for this review through several discussions on the topic of mapping coffee production areas and the desire to measure and track sustainability goals. This guided us in developing our central research objective: determining the most effective remote sensing method for mapping coffee extent across varied geographic settings and landscape characteristics. Due to the diversity of production systems and landscape types, we aimed to capture each of the five general production systems outlined in Moguel and Toledo [34] in the literature review. This would ensure widespread applicability in different geographies and increase the likelihood of the implementation of our recommended methods. We also chose to include methods from 2010 onwards to best capture approaches that employ the newest satellite sensor technologies (e.g., the availability of the RapidEye constellation beginning in 2009, the launch of Landsat 8 in 2013, and the launches of Sentinel-1A in 2014, Sentinel-2A in 2015, Sentinel-1B in 2016, and Sentinel-2B in 2017) and advances in computing capacity, such as the increased use of cloud computing (e.g., Google Earth Engine) and deep learning.
## 4 Experiments and Results
### Proposed Model Construction
#### 4.4.3 Analysis of Number of Sampling Points  
We examined the effect of the sampling rate (\(K\)) of \(F_{0}\) on prediction performance. Based on the distribution of the duration of syllables in Figure 5, we hypothesized that the appropriate value of \(K\) would be close to the mean of syllable duration. To test this, we trained the model by varying the value of \(K\). The best model of the FF-BLSTM with the same hyper-parameters was used. The RMSE and CORR values are shown in Table 6. The model trained with \(K=45\) gave the best performance in terms of RMSE while the CORRs of all models were similar.
## 2 System Model
### Depthwise Separable Convolution  
In addition to the self-attention computation, the convolution operation has the largest computational overhead in the RIMformer block. Radar signals after the sliding window are represented as multi-channel data with localized temporal and spatial features. Convolutions efficiently extract and integrate these features. In a regular convolution operation, the input feature map of shape \(N\times M\times C_{in}\) is convolved with a filter tensor of shape \(K\times K\times C_{in}\times C_{out}\), where \(N\) is the number of signal frames, \(M\) is the length of each frame, and \(C_{in}\) is the number of input channels. The filter size is \(K\times K\), and the number of output channels is \(C_{out}\). The number of operations required for one output feature map is as follows:  
\[C_{reg}=K\times K\times C_{in}\times C_{out}\times N\times M. \tag{7}\]  
Depthwise separable convolution [26] breaks down a standard convolution into a depthwise convolution, followed by a \(1\times 1\) convolution called a pointwise convolution. Standard convolution filters the inputs and combines them into a new set of outputs in one step. Depthwise separable convolution splits this step into two layers, one for filtering and the other for combining. Specifically, depthwise convolution applies a single filter per input channel. Pointwise convolution is then used to create linear combinations of the outputs of the depth layer. Significant reductions in computational workload and model size can be achieved by representing convolution as a two-step process of filtering and combining.  
In depthwise convolution, each input channel is convolved with its own set of filters independently and the computational complexity is as follows:  
\[C_{depth}=K\times K\times C_{in}\times N\times M. \tag{8}\]  
The computational complexity for pointwise convolution is as follows:  
\[C_{point}=1\times 1\times C_{in}\times C_{out}\times N\times M. \tag{9}\]  
Therefore, the ratio of the computational complexity of depthwise separable convolution and regular convolution is as follows:  
\[\frac{C_{depth}+C_{point}}{C_{reg}} =\frac{(K\times K+C_{out})\times(C_{in}\times N\times M)}{K\times K \times C_{in}\times C_{out}\times N\times M}\] \[=\frac{1}{K^{2}}+\frac{1}{C_{out}}. \tag{10}\]  
It can be concluded that for the RIMformer with \(K=3\), the use of depthwise separable convolution requires less than \(\frac{1}{8}\) of the computational complexity of regular convolution.
# Duty Factor Reflects Lower Limb Kinematics of Running
## 3 Results
### Pelvis and Foot Segment Angles  
DF\({}_{\rm high}\) had a larger pelvis anteversion than DF\({}_{\rm low}\) at FS (DF groups main effect, \(p=0.026\); medium effect size, \(d=0.53\); Table 6 and Figure 3A). More pelvis retroversion during \(t_{a}\) and \(t_{b}\) (larger \(|\Delta\theta_{a}|\) and \(|\Delta\theta_{b}|\)) were observed for DF\({}_{\rm high}\) (DF groups main effect, \(p\leq 0.002\); medium effect size, \(d\geq 0.62\); Table 6 and Figure 3A). Running speed affected all pelvis anteversion-retroversion parameters, with an increase of the pelvis anteversion at FS, IA, MS, and TO, less retroversion during \(t_{a}\) and \(t_{b}\) (smaller \(|\Delta\theta_{a}|\) and \(|\Delta\theta_{b}|\)), and less anteversion during \(t_{p}\) with increasing running speed (smaller \(|\Delta\theta_{p}|\), speed main effect, \(p\leq 0.038\); Table 6). An interaction effect between DF groups and speeds was observed for \(\Delta\theta_{a}\), indicating less pronounced pelvis retroversion during \(t_{a}\) (smaller \(|\Delta\theta_{a}|\)) with the increase of running speed for both DF groups (\(p=0.001\); Table 6). DF\({}_{\rm high}\) had a more pronounced rearfoot angle at FS and IA, a smaller forefoot angle at MS, and a larger forefoot angle at TO than DF\({}_{\rm low}\) (DF groups main effect, \(p\leq 0.028\); \(\theta_{\rm FS}\) and \(\theta_{\rm IA}\): large effect size, \(d\geq 1.10\); \(\theta_{\rm MS}\) and \(\theta_{\rm TO}\): medium effect size, \(d\geq 0.60\); Table 7 and Figure 3B). More forefoot angle during \(t_{a}\), \(t_{b}\), and \(t_{p}\) (larger \(|\Delta\theta_{a}|\), \(|\Delta\theta_{b}|\), and \(|\Delta\theta_{p}|\)) were observed for DF\({}_{\rm high}\) (DF groups main effect, \(p<0.001\); large effect size, \(d\geq 1.04\); Table 7 and Figure 3B). Running speed affected all forefoot-rearfoot angle parameters, with an increase of the rearfoot angle at FS, an increase of the forefoot angle at IA, MS, and TO, and more forefoot angle during \(t_{a}\), \(t_{b}\), and \(t_{p}\) with increasing running speed (larger \(|\Delta\theta_{a}|\), \(|\Delta\theta_{b}|\), and \(|\Delta\theta_{p}|\); speed main effect, \(p\leq 0.001\); Table 7). The interaction effect indicated a slight increase of the forefoot angle at IA with the increase of running speed for both DF groups (\(p=0.001\); Table 7). Figure 3: Segment angles (\(\theta\)) during the running stance for the high (DF\({}_{\rm high}\)) and low (DF\({}_{\rm low}\)) duty factor running groups while running at 14 km/h for (**A**) pelvis (retroversion-anteversion) and (**B**) foot (forefoot–rearfoot) segment angle, expressed as a difference with respect to segment angle in the static upright stance. The running speed is representative of all speed. IA: impact attenuation, MS: mid-stance, rot: rotation.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 4 Prediction and Analysis
### Parameter Setting and Training
This means that the accuracy of the LSTM model was improved, and finally, the vehicle velocity prediction results of a single model and optimized model were compared. From Figures 12-14, it can be seen that when the vehicle velocity was steady (i.e., there is no sudden increase or decrease in acceleration), the prediction accuracy of the vehicle velocity was more accurate, as was the prediction accuracy of the QPSO-LSTM method. In summary, the QPSO-LSTM algorithm's vehicle velocity prediction performance was better than that of the single LSTM algorithm and the PSO-LSTM algorithm's vehicle velocity prediction effect. When the prediction horizon was 2 s, the trend and numerical size of the predicted vehicle velocity were almost consistent with the actual vehicle velocity, and the  
Figure 11: The change in the QPSO hyperparameters with iteration times for LSTM 3. (**a**) \(L_{\text{a3}}\) changes with the number of iterations, (**b**) \(L_{\text{b3}}\) changes with the number of iterations, (**c**) \(K_{\text{3}}\) changes with the number of iterations, (**d**) \(L_{\text{r3}}\) changes with the number of iterations. Figure 10: The fitness curve of QPSO with iteration times for LSTM 3.  
predicted vehicle velocity was more consistent with the actual vehicle velocity curve. Only at the turning points of the vehicle velocity did the prediction error increase. As shown in Table 7, for different prediction horizon lengths, the three prediction algorithms performed consistently, and the prediction accuracy indicators were better for shorter prediction horizons. For example, with a single LSTM prediction algorithm, as the prediction time horizon increases, the _RMSE_ becomes larger, ranging from 1.9662 km/h (type 1, 2 s), 1.8531 km/h (type 2, 2 s), and 2.8681 km/h (type 3, 2 s) to 9.6329 km/h (type 1, 10 s), 8.1622 km/h (type 2, 10 s), and 8.9326 km/h (type 3, 10 s). The _MAE_ also becomes larger, ranging from 1.5097 km/h (type 1, 2 s), 1.288 km/h (type 2, 2 s), and 2.0725 km/h (type 3, 2 s) to 7.0982 km/h (type 1, 10 s), 5.7281 km/h (type 2, 10 s), and 5.7073 km/h (type 3, 10 s). The R\({}^{2}\) becomes smaller, and the fit of the prediction model becomes worse, ranging from 0.9723 (type 1, 2 s), 0.98785 (type 2, 2 s), and 0.99134 (type 3, 2 s) to 0.32817 (type 1, 10 s), 0.78341 (type 2, 10 s), and 0.87092 (type 3, 10 s). The emergence of the same pattern was observed in both the PSO-LSTM and QPSO-LSTM prediction algorithms. In summary, from the prediction accuracy indicators _RMSE_, _MAE_, and \(R^{2}\), a  
Figure 12: Prediction results of driving condition type 1 by different methods with 2 s time horizon. Figure 13: Prediction results of driving condition type 2 by different methods with 2 s time horizon. time horizon setting of 5 s for prediction was considered to be more appropriate, and the vehicle velocity prediction model based on QPSO-LSTM was superior to the LSTM and PSO-LSTM prediction algorithms.
## 2 Experimental Part  
Sn, Ag, and Cu with purity of 99.95 wt. % were melt at 900 \({}^{\circ}\)C \(\pm\) 10 \({}^{\circ}\)C in a vacuum furnace (MZG-0.5, Chengdu Jituo Instrument Equipment Co., Ltd., Chengdu, China) firstly to obtain the SAC387 solder. Then, Nd was added into the molten SAC387 solder in the form of Sn-10 Nd alloy to avoid the loss of Nd, and the furnace temperature at this time was adjusted to 550 \(\pm\) 1 \({}^{\circ}\)C. Based on the previous researches [16], the addition of Nd was adjusted to be 0.05 wt. %. Finally, the molten alloy was cast into small bars and pieces in air for experimental use.  
Corresponding solder pieces (SAC387; SAC387-0.05 Nd) were soldered on Cu substrate in an automatic reflow furnace (T-962, Shenzhen Bangqi Chuangyuan Technology Co., Ltd., Shenzhen, China) to fabricate the solder joint specimens [17]. For interfacial microstructure observation, specimens were cross-sectioned perpendicularly to the solder/Cu interface. Aging treatment was carried out according to the standards of Department of Defense of the USA (MIL-STD-883) and IPC-Association Connecting Electronics Industries (IPC-SM-785) [18]; the aging temperature was set as 150 \({}^{\circ}\)C, and the aging time was extended to 1400 h (h). All samples were polished with 0.3 \(\upmu\)m Al\({}_{2}\)O\({}_{3}\) powder and then etched by 4% HNO\({}_{3}\)-alcohol for microstructure observation. The scanning electron microscope (SEM) with Energy-dispersive X-ray spectroscopy (EDS) (S-3000N/H, Hitachi Co., Ltd., Japan) was used to analyze the microstructure of solder joint.  
The shear force of solder joint was tested through micro-joint strength tester (STR-1000, Hitachi Co., Ltd., Tokyo, Japan) as shown in Figure 1. The shear samples were made by soldering 0805 ceramic resistors on printed circuit board (PCB) attached with Cu pad. Each shear test was repeated five times, and the average value was saved as the final result, and the fractograph of joint was studied through SEM.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 3 Materials and Methods
### Procedure of the Study  
Figure 3 shows the research procedure and technical methods, including the detailed steps for retrieving LST from the MERSI-II data. The following are the steps that this study takes as its main approach: We start by obtaining Level 1 radiance data from the FY-3D satellite data portal. Second, we estimate the LST and its required parameters of the TFSW algorithm. Third, we conducted a cross-validation analysis on the retrieved LST to determine the algorithm's applicability and usefulness. Finally, the application of the algorithm was applied for the detailed spatial and temporal analyses of LST for Pakistan.  
Figure 3: The framework depicting the phases required for LST estimation and spatiotemporal analysis.
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Contemporary Use of 3D-Printed Accessories in Orthodontics
#### 4.1.8 Brackets Placement Guides  
The effectiveness of orthodontic treatment with fixed appliances depends on precise bracket positioning [73]. Advanced direct and indirect bonding techniques are continuously investigated, highlighting the effectiveness of different technologies and techniques in achieving precise bracket placement [73; 74; 75; 76; 77; 78; 79; 80; 81]. Indirect bonding is an alternative to manually placing brackets. This approach involves planning and positioning the brackets on a model before transferring them to the oral cavity. Advances in scanning and manufacturing have significantly enhanced the accuracy of bracket placement [74]. Technological progress has opened new research opportunities in the development of indirect techniques. The use of individual 3D transfers is particularly advantageous in difficult clinical cases, enhancing the efficiency of the procedure, reducing the number of technical steps, and reducing the overall time spent in the orthodontic practice [74].  
[PERSON] et al. assessed the accuracy of indirect bonding using 3D printing guides versus double-layer guide plates. The study concluded that the 0.6 mm double-layer guide plate group was more accurate than the 0.8 mm group, and while the accuracy of 3D printed guides was comparable, there was no significant difference between the types of guides used [75]. [PERSON] et al. introduced a digital workflow creating a detailed 3D model of dental arches, enhancing the precision of indirect bonding and reducing errors in bracket positioning, aiming for easier and reproducible bracket placement for clinicians in the future [76]. A randomized clinical trial (RCT) of [PERSON] et al. found that CAD/CAM technology is reliable for indirect bracket bonding, with low rates of bracket loss compared to the previous literature [77]. A study by [PERSON] et al. showed significantly shorter chair times, demonstrating that while its accuracy was comparable to conventional methods, it provided the added advantage of reducing chair time, thereby improving the experience for both patients and professionals [78].