esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The current study proposed and validated a multiple-step hybrid remote sensing method to estimate \(n_{\rm P}\) in the BS and YS from GOCI satellite measurements. This \(n_{\rm P}\) estimation model depends on three crucial steps, namely, (1) the TSM and \(b_{\rm bp}\) retrievals, (2) establishing close relationships between \(b_{\rm p}\) and \(c_{\rm p}\) with TSM, and (3) utilizing the [PERSON] et al. (2001) [2] model derived from Mie theory. The first two steps were calibrated and validated using a large quantity of in situ observed data from four cruises in the study area, whereas the third step was directly utilized, as it is widely recognized by the community. These steps jointly constructed our developed \(n_{\rm P}\) estimation model in this study, which led to reliable performances with reasonable uncertainties (2.55% and 2.32% for MRE, validated using in situ observations with the leave-one-out cross-validation method and satellite-derived results, respectively) and spatial distribution patterns. By means of 2825 GOCI satellite images collected during one year from December 2014 to November 2015, this \(n_{\rm P}\) estimation model documented \(n_{\rm P}\) distribution patterns in the BS and YS at different temporal scales for the first time, such as monthly, seasonal, and annual scales. Although this method is possibly region-specific and may need more validation for its application to other water areas, the developed method here provides a proof-of-concept template for \(n_{\rm P}\) remote sensing estimation in other coastal turbid water bodies.  
**Author Contributions:** [PERSON] designed this study and the algorithm; [PERSON] and [PERSON] contributed to the data analyses and drafted the manuscript; [PERSON] and [PERSON]. ([PERSON]) assisted with developing the research design and results interpretation; [PERSON] and [PERSON]. ([PERSON]) contributed to the interpretation of results and manuscript revision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (No. 2016 YFC1400901), the National Natural Science Foundation of China (No. 41876203, 41576172), the Jiangsu Six Talent Summit Project (No. JY-084), the Qing Lan Project (No. R2019Q07), the Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography (No. QNHX1812), the Startup Foundation for Introducing Talent of NUIST (2019R058), the Science and Technology Innovation Project for Overseas Talents in Nanjing (R2019 LZ05), and the CERES Oversea Joint Research Program, Chiba University (No. CI19-103). This work was also sponsored by the NSFC Open Research Cruise (Cruise No. NORC2018-01), funded by the Shiptime Sharing Project of NSFC.  
**Acknowledgments:** We acknowledge captains, officers, and crews of R/V Dongfanghong 2, Science 3, and Xiangyanghong 18 for providing excellent assistance during field sampling and measurements. We thank the Korea Ocean Satellite Center and NASA for providing satellite data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Quantum Computing for Healthcare: A Review
## 7 Open Issues and Future Research Directions
### Developing a Quantum Market Place  
One of the vital challenges in quantum computing implementations is the pricing and resource allocation of quantum services to the service subscribers. Similar to web services, a quantum computing marketplace could be developed, providing a platform for the subscribers to utilize a pay-per-use pricing model for the services. Users can subscribe to the services that they want and, based on the consumed services, the price should be determined. A quantum market place for healthcare systems may include suppliers and partners of quantum computing hardware who specialize in services such as automated diagnosis, drug discovery, healthcare monitoring, DNA sequencing, cardiomyopathyopathy analysis, and remote patient monitoring. Customers such as healthcare providers would subscribe to such services hosted in the cloud using a pay-per-use model. However, such a distributed quantum marketplace development requires a coordinated quantum strategy, which can be used to distribute quantum services and develop pricing models. Such a system also requires experts from different domains to have expertise in quantum systems and can develop financial models, services distributed mechanisms, and control strategies for quantum resource distribution. Recently, D-Wave announced plans to launch D-Wave's Leap quantum cloud service on the Amazon AWS cloud for the first time [207].
# Biosimilar Medicines: From Development Process to Marketing Authorization by the EMA and the FDA
## 5 Demonstration of Biosimilarity
### Quality Comparability Studies  
Quality comparability studies assess whether there are significant differences between the biosimilar and the reference medicine in terms of CQAs, such as structure, purity, biological activity and impurity profile [2]. This involves extensive laboratory tests and the use of sensitive techniques, such as SPR, ELISA and MS [7]. These studies are much more sensitive to detecting differences than clinical trials because of the high variability between the individuals who participate in them [2]. This step determines the need and type of non-clinical and clinical studies that should be carried out. In most cases, quality comparability studies are enough and there is no need to conduct non-clinical and clinical studies. However, if any variations that may impact the efficacy, safety or immunogenicity of the medicine are found, further studies are required [48; 52].  
Statistical Approaches to Evaluate Analytical Similarity  
The evaluation of analytical similarity in biosimilar medicines is founded on statistical methods, which permit objective assessment of whether the observed differences between the biosimilar product and the reference product are within the previously established limits considered as acceptable.  
The most frequently statistical tools used in this process are equivalence testing and confidence intervals. In most cases, they go together in the statistical analysis of biosimilar similarity, as a confidence interval is considered one of the critical elements in equivalence testing, helping ensure that the biosimilar CQAs fall within the appropriate range of values [38].  
However, the approaches between the EMA and FDA over this issue have a great discrepancy. Generally, the EMA is the more conservative, using narrower equivalence margins that reflect lesser tolerance for differences, even small ones. Consequently, the  
Figure 5: Comparison between the data required for the approval of a biosimilar medicine and its reference [2].  
EMA will generally require supplemental and more reassuring data, such as additional clinical studies, to provide sufficient proof that difference between the biosimilar and reference product does not matter for safety and efficacy and thus establishes similarity. In contrast, the FDA has adopted a relatively more flexible approach, as it adjusts the equivalence margins based on the variability of the reference product. This way, the FDA is more willing to accept small differences because of their focus on the totality of evidence available for the product [48, 49].
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 5 Analysis of Static Wind Reliability for Xiangshan Harbor Bridge
### Static Wind Reliability under Different Conditions  
During the whole life cycle of a bridge, various situations that are unfavorable to the wind stability of the bridge will inevitably occur, such as the steel wire breakage of stay cables or even a whole stay cable breaking. On basis of the above static wind reliability analysis, the same method is used for the static wind reliability under different mean values of steel wire breakage rates and the different stay cable broken positions.  
For different steel wire breakage rates of stay cables, the variation coefficient of the critical wind speed for static wind instability is 0.014. On this basis, supplementary deterministic analysis is carried out on the 0%, 5%, 10%, and 15% steel wire breakage rates, then the reliability index can be obtained similar to the method in Section 5.1, and the reliability index is transformed into the corresponding failure probability. They are given in Table 9. It can be seen from Table 9 that the reliability index gradually decreases with the increase of steel wire breakage rate, but in general, the static wind failure probability of Xiangshan Harbor Bridge is very low, so there is no static wind instability problem.  
For different stay cable broken positions, the variation coefficient of the critical wind speed for static wind instability is 0.034. On this basis, supplementary deterministic analysis is conducted when the broken stay cable is close to the main tower, at mid-span, and 1/4 of the main span. Similarly, the reliability index can be obtained and it can be transformed into the corresponding failure probability, which is listed in Table 10.
###### Abstract  
Simultaneous power transfer to multiple receiver (Rx) system is one of the key advantages of wireless power transfer (WPT) system using magnetic resonance. However, determining the optimal condition to uniformly transfer the power to a selected Rx at high efficiency is the challenging task under the dynamic environment. The cross-coupling and frequency splitting are the dominant issues present in the multiple Rx dynamic WPT system. The existing analysis is performed by considering any one issue present in the system; on the other hand, the cross coupling and frequency splitting issues are interrelated in dynamic Rx's, which requires a comprehensive design strategy by considering both the problems. This paper proposes an optimal design of multiple Rx WPT system, which can eliminate cross coupling, frequency splitting issues and increase the power transfer efficiency (PTE) of selected Rx. The cross-coupling rejection, uniform power transfer is performed by adding an additional relay coil and independent resonance frequency tuning with capacitive compensation to each Rx unit. The frequency splitting phenomena are eliminated using non-identical transmitter (Tx) and Rx coil structure which can maintain the coupling between the coil under the critical coupling limit. The mathematical analysis of the compensation capacitance calculation and optimal Tx coil size identification is performed for the four Rx WPT system. Finite element analysis and experimental investigation are carried out for the proposed design in static and dynamic conditions.  
wireless power transfer; frequency splitting; cross-coupling; multiple receivers; magnetic resonance 1
## 4 Results and Discussion  
We implemented our experiments in three phases; for the first phase, we used a sample of AWID3 dataset that includes a nominal two classes (Krack and Normal); the best accuracy that we achieved was for the decision tree with 95.1% without using FS, while the best accuracy after using ANOVA FS was 99.1% for the ensemble classifier. For the second phase, we used a sample with two nominal classes too (Kr00k and Normal); the best accuracy that we achieved was for the decision tree as well, with 81.8% without using FS. The accuracy increased to 96.7% for the neural network after applying ANOVA FS techniques. In the last phase, we used a multi-classes classifier, where the label includes three classes (Krack, Kr00k, and Nominal), and the accuracy results were determined to be low. However, after applying the FS techniques, the accuracy increased to 90.7% and 88.3% for the ensemble classifier and decision tree, respectively. Figure 8 summarizes the results for the three mentioned phases.  
When looking at the results, it becomes clear that the decision tree and ensemble classifier have a high performance in three experiments.  
It is worth mentioning that the wireless dataset including the AWID3 dataset has many challenges, such as:  
* High dimensionality: This refers to a high number of features in the dataset. So, it is important to transform the data from a high-dimensional space into a low-dimensional  
Figure 8: Comparison of the performance between the three phases.  
Figure 7: The performance of the learning algorithms—multiclass.  
space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension [50]. We solved this problem using the ANOVA FS techniques, where the performance of the algorithms shows clear improvement in accuracy results when we reduce the high dimensionality for the dataset.
* Overfitting of the dataset: This occurs when a statistical model fits exactly against its training data [51]. When performing the ML model to the data without solving the overfitting problem, the accuracy results will be almost 100% or 99.99% which is not a reliable performance. We solved this problem in the preprocessing step by getting rid of the features that copy the label, in addition to the importance of FS in solving this problem.
* Unbalanced data: Unbalanced refers to a classification data set with skewed class proportions. We solved this problem by taking almost the same number of instances for attack and benign in the three experiments.
## 3 Materials and Methods
### SHAP Algorithm for Feature Importance Calculation  
The SHAP algorithm can output the quantifiable importance of distinct features in orchid quality grading, facilitating the establishment of a standardized, data-driven paradigm for orchid assessment. This enhanced the reliability, transparency, and explainability of deep learning models. SHAP is grounded in the concept of game theory, particularly Shapley values, a method for assigning payouts to players depending on their contribution to the total payout. In the realm of machine learning, SHAP values interpret the prediction of an instance by computing the contribution of each feature to the prediction. The SHAP value is the average marginal contribution of a feature value across all possible combinations of features [20]. The core calculation process that encapsulated the computation of SHAP values for the prediction model \(f\) based on a feature set \(x\), as shown in Figure 3. The formula of SHAP algorithm was given by  
\[v(S\cup\{j\})=\int f\Big{(}\mathbb{x}_{S\cup\{j\}}^{(i)}\cup X_{C \setminus j}\Big{)}\text{d}\mathbb{P}_{X_{C\setminus j}}-\mathbb{E}[f(X)],C= F\setminus S \tag{2}\] \[v(S)=\int f\Big{(}\mathbb{x}_{S}^{(i)}\cup X_{C}\Big{)}\text{d} \mathbb{P}_{X_{C}}-\mathbb{E}[f(X)]\] (3) \[\phi_{j}^{(i)}=\sum_{S\subseteq F\setminus\{j\}}\frac{|S|!(|F|-| S|-1)!}{p!}[v(S\cup\{j\})-v(S)],\phi_{j}=\frac{1}{N}\sum_{i=1}^{N}\phi_{j}^{(i)} \tag{4}\]  
Figure 2: Architecture of multi-modal Transformers for _Rchb. f_. quality grading. Red numbers 9 and 0 are padding values of orchid feature indices and orchid feature values, respectively.  
where \(\phi_{j}^{(i)}\) is the SHAP value for feature \(j\), \(F\) is the set of all features, \(S\) is a subset of features excluding \(j\), \(|S|\) is the number of features in subset \(S\), \(|F|\) is the total number of features, and \(f\Big{(}x_{S}^{(i)}\cup X_{C}\Big{)}\) is the prediction model's output given the features in \(S\). \(f\Big{(}x_{S\cup\{j\}}^{(i)}\cup X_{C\setminus j}\Big{)}\) is the output of model when feature \(i\) is added to \(S\)[20]. In this study, by computing and averaging SHAP values for each feature across all samples, 7 features were ranked based on their importance in determining _Rchb. f._ grades, and the output of model was explainable. This ranking mined the key features affecting model grading decisions, providing valuable information for growers and consumers.
## 2 Materials and Methods
### Measurement Procedure
\begin{table}
\begin{tabular}{l l l l l} \hline  & & **Full Match** & **First Half** & **Second Half** \\ \hline  & F & 1.196 & 1.835 & 0.569 \\ \% HRmax & \(p\) & 0.327 & 0.141 & 0.686 \\  & n\({}^{2}\) & 0.104 & 0.152 & 0.053 \\  & F & 0.175 & 1.191 & 0.701 \\ TRIMP\({}_{\text{Edw}}\) & \(p\) & 0.250 & 0.329 & 0.596 \\  & n\({}^{2}\) & 0.017 & 0.104 & 0.064 \\  & F & 0.166 & 8.646 * & 0.149 \\ TD & \(p\) & 0.954 & \textless{}0.001 & 0.969 \\  & n\({}^{2}\) & 0.016 & 0.458 & 0.014 \\  & F & 1.689 & 6.554 * & 0.042 \\ D/min & \(p\) & 0.171 & \textless{}0.001 & 0.996 \\  & n\({}^{2}\) & 0.141 & 0.390 & 0.004 \\  & F & 4.766 * & 0.961 & 0.839 \\  & \(p\) & 0.003 & 0.439 & 0.509 \\  & n\({}^{2}\) & 0.317 & 0.086 & 0.076 \\  & F & 1.798 & 4.590 * & 0.699 \\ z1 & \(p\) & 0.148 & 0.004 & 0.597 \\  & n\({}^{2}\) & 0.149 & 0.309 & 0.064 \\  & F & 4.796 * & 3.317 * & 0.605 \\ z2 & \(p\) & 0.003 & 0.019 & 0.662 \\  & n\({}^{2}\) & 0.319 & 0.245 & 0.056 \\  & F & 6.490 * & 4.243 * & 1.810 \\ z3 & \(p\) & \textless{}0.001 & 0.006 & 0.145 \\  & n\({}^{2}\) & 0.388 & 0.293 & 0.150 \\  & F & 12.381 * & 3.166 * & 2.514 \\ z4 & \(p\) & \textless{}0.001 & 0.023 & 0.056 \\  & n\({}^{2}\) & 0.547 & 0.236 & 0.197 \\  & F & 4.043 * & 1.053 & 1.508 \\ z5 & \(p\) & 0.007 & 0.392 & 0.218 \\  & n\({}^{2}\) & 0.283 & 0.093 & 0.128 \\  & F & 2.894 * & 5.467 * & 0.877 \\ Acc2 & \(p\) & 0.034 & 0.001 & 0.486 \\  & n\({}^{2}\) & 0.224 & 0.348 & 0.079 \\  & F & 0.454 & 0.623 & 0.427 \\ Acc3 & \(p\) & 0.769 & 0.649 & 0.788 \\  & n\({}^{2}\) & 0.043 & 0.057 & 0.040 \\  & F & 4.586 * & 6.218 * & 1.313 \\ Dec2 & \(p\) & 0.004 & \textless{}0.001 & 0.281 \\  & n\({}^{2}\) & 0.314 & 0.378 & 0.114 \\  & F & 0.827 & 1.370 & 0.551 \\ Dec3 & \(p\) & 0.516 & 0.261 & 0.669 \\  & n\({}^{2}\) & 0.076 & 0.118 & 0.051 \\ \hline \end{tabular}  
* Denotes a significant difference (\(p\) < 0.05). % HRmax: percentage of maximum heart rate; TRIMP\({}_{\text{Edw}}\): Edward training impulse for internal load; TD: total distance; D/min: distance (m) per minute; speed zones: z1: 0.10–7.19 km/h, z2: 7.20–14.39 km/h, z3: 14.40–19.79 km/h, z4: 19.80–25.19 km/h, z5: 25.20 km/h, Acc2: 2.00–2.99, Acc3: \(>\)3.00 m/s\({}^{2}\), Dec2: \(-\)2.99, \(-\)2.00 m/s\({}^{2}\), Dec3: \(<\)\(-\)3.00.  
\end{table} 
Table 3: ANOVA results. Figure 4: Acceleration and deceleration differences between halves for each playing position. (**A**) position CD; (**B**) position SD; (**C**) position CM; (**D**) position SM; (**E**) position F. * Denotes a significant difference (\(p<0.05\)). Acc2: 2.00–2.99, Acc3: \(>\)3.00 m/s\({}^{2}\), Dec2: \(-\)2.99— \(-\)2.00 m/s\({}^{2}\), Dec3: \(<\)\(-\)3.00. Figure 3: Match running performance differences between halves for each playing position per speed zone. (**A**) Position CD; (**B**) position SD; (**C**) position CM; (**D**) position SM; (**E**) position F. * Denotes a significant difference (\(p<0.05\)). Speed zones: z1: 0.10–7.19 km/h, z2: 7.20–14.39 km/h, z3: 14.40–19.79 km/h, z4: 19.80–25.19 km/h, z5: \(>\)25.20 km/h. CD: central defenders, SD: side defenders, CM: central midfielders, SM: side midfielders, F: forwards.
## 4 Results  
### Data Processing  
After removing undesired frequencies coming from instrumental and environmental noise with a band-pass filter [140-190-500-700], we applied a gain function (Automatic Gain Function--AGC) to enhance the visibility of deeper reflections affected by signal attenuation. We subtracted the mean trace from the dataset to remove continuous flat reflection caused by the ground's surface (background removal). By limiting this last filter to the first 10 ns, the disruption of reflections from continuous flat layers below the surface was avoided. The existence of some diffraction hyperbolas made it possible to use the synthetic hyperbola method [49] to estimate that GPR wave velocities of 9-11 cm ns\({}^{-1}\) were consistently found across all profiles (Figure 3). We adopted a constant average velocity of 10 cm ns\({}^{-1}\) for time-to-depth conversion. Finally, we applied a static correction GPR profile using the topographies supplied by the GPS surveys. The low dips of the topographic surfaces made the correction by the antenna tilting useless.  
The GPR profiles are presented in color mode (grey tones), with a relative normalization of amplitudes and no linear/exponential graphical gains.
# Low-Cost Underwater Communication System: A Pilot Study
## 3 Conditions for Conducting the Experiment  
A working prototype of the modem was tested in a river port basin. It is a dead end of the Oda river; hence, it can be assumed that the water at this point is stagnant. A situation plan of the performed experiment is shown in Figure 4.  
During the day of the experiment, there were no boats around platforms. Therefore, the direct path of an ultrasound wave was not disrupted. The depth in this place varies between 2 and 4 m. The distance between the platforms is about 18.2 m according to Google Maps. The exact knowledge about the bottom is not available, but an assumption was made that it is unevenly rocky and covered with years of mud and vegetation.  
For this project, an off-the-shelf ultrasonic transducer was chosen. It is a quite popular model, TD0040, branded by Hurricane, a Chinese company. In Figure 5 the frequency characteristic of the sound pressure level (SPL) is shown. One can easily find it on the internet. Additional important parameters of this particular transducer are the beam width 50\({}^{\circ}\), high power, and IP67 housing. In the project, a small voltage which drives the transducer--only 15 [V]--does not utilize the available power of the transducer. Nevertheless, it is a pilot study, so the delivered power is high enough to test the modem.  
Figure 4: Map of the place of the experiment, taken from Google Maps. Coordinates: 51°08′06.0′′ N 16°59′57.9′′ E.  
Figure 5: A photo and frequency characteristic of the sound pressure level of the ultrasonic transducer used in the project.
###### Abstract  
Temperature is very important in the fabrication of products developed for different applications, considering the widespread use of additive manufacturing (AM) technology. Thermal properties seriously affect the mechanical properties of products. In this study, the effect of printing nozzle temperature changes on the dimensional and mechanical properties of samples fabricated with acrylonitrile butadiene styrene (ABS) material was investigated. This material can be preferred over foam material for drone and model aircraft areas due to its low density. A total of thirty-six tensile test samples (ISO527-type1A) were fabricated with fused filament fabrication (FFF), one of the AM methods, and their dimensional properties (length, width, thickness, and mass) were determined. During the fabrication process, the nozzle temperature of the 3D (three-dimensional) printer was increased from 220 \({}^{\circ}\)C to 270 \({}^{\circ}\)C in 10 \({}^{\circ}\)C increments. All samples were subjected to tensile testing, and stress-strain values were measured. Analysis of variance (ANOVA) was applied to examine the effect of nozzle temperature change on the findings obtained as a result of the experimental study. The printing nozzle temperature significantly affected both the mechanical strength and dimensional properties of the samples. The samples showed lower viscosity and less hardness at higher nozzle temperatures. The mass and density of the samples decreased with increasing temperature. The tensile strength value decreased by 41.52%.  
additive manufacturing; ABS; nozzle temperature; mechanical properties; ANOVA +
Footnote †: journal: Applied Sciences