esa-sceva/satcom-chunk-collection huggingface dataset:
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 5 Experimental Evaluation
### Cosine Similarity Method
#### 5.1.1 Multiple Runs Algorithm  
Table 2 shows a summary of the main results, obtained using the multiple runs algorithm, but without the strategy of bucket category. Three different approaches were tried. In the first approach, a maximum of 11 runs was defined. Stop conditions were also defined, namely the algorithm would stop if 60% of the top-5 codes have a similarity below 0.197. If achieved a state in the run where it has a number of detected ICD-10 codes less than 12% of the total encoded so far, and the maximum number of 11 runs is not yet reached, the run is also stopped. This is performed to let prevail the codes found in the initial runs, which have a bigger similarity, thus a bigger probability of being correct, compared to codes in latter runs that are in less number. These values of percentages were defined empirically. More true positives may be found at a lower value of similarity, but at the same time, more possible false positives may be detected. On the other hand, if the value of the percentage is higher, it is likely that less true positives may be detected, but less false positives would be found. At the end it is a compromise between precision and recall.  
The second approach used the same configuration as the first approach, but instead of using up to 11 runs, it uses just five runs.  
The third approach uses the same configuration, but with up to 11 runs, and excludes any ICD-10 codes that have a similarity below 0.2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(S_{P}\) & \(S_{C}\) & **Prediction** & **Label** & **Result** \\ \hline \multirow{2}{*}{\(\geq T_{1}\)} & \multirow{2}{*}{-} & \multirow{2}{*}{Positive} & Positive & TP \\  & & & Negative & FP \\ \hline \multirow{2}{*}{\(<T_{1}\geq T_{2}\)} & \multirow{2}{*}{\(\geq T_{3}\)} & \multirow{2}{*}{Positive} & Positive & TP \\  & & & Negative & FP \\ \cline{1-1} \cline{3-5}  & & & Negative & FN \\ \hline \multirow{2}{*}{\(<T_{2}\)} & \multirow{2}{*}{-} & \multirow{2}{*}{Negative} & Positive & FN \\  & & & Negative & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the truth values resulting from applying the PLM-ICD-C rules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Micro-Avg** & **Precision** & **Recall** & **F1-Score** \\ \hline Approach 1 (11 runs) & 0.045 & 0.111 & 0.064 \\ Approach 2 (five runs) & 0.055 & 0.087 & 0.068 \\ Approach 3 (11 runs, similarity threshold 0.2) & 0.047 & 0.104 & 0.065 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metric (micro-average) results for the Cosine method, without bucket category concept.  
As the table shows, the values of precision, recall and F1 are very modest, showing that the Cosine similarity with just these aids is insufficient to provide useful coding suggestions.
## 2 Materials and Methods
### SIF and GPP Models  
Following an efficiency approach largely used for plant productivity and initially proposed by [PERSON] [45], SIF can be conceptualized as the product of absorbed photosynthetically active radiation (APAR), fluorescence yield \(Fyield(\lambda)\), and an escape factor \(\tau(\lambda)\) that accounts for the probability of emitted fluorescence to escape from the canopy and reach the sensor [20]. \(Fyield(\lambda)\) is the light-use efficiency for fluorescence, and represents the fraction of absorbed PAR photons that are re-emitted as fluorescence. The factor \(\tau(\lambda)\) can be considered as the effective transmittance of the canopy for fluorescence. Hence, SIF can be written as:  
\[\text{SIF}(\lambda)=\text{APAR}\times Fyield(\lambda)\times\tau(\lambda) \tag{11}\]  
On the other hand, GPP can be expressed as [45]:  
\[\text{GPP}=\text{APAR}\times\text{LUE} \tag{12}\]  
where LUE is the light use efficiency, which accounts for the fraction of absorbed photons that drive carbon fixation. SIF and GPP share a common factor, APAR. Any deviation from a strict proportional relationship between SIF and GPP is driven by the relationships between the other factors \(Fyield\), \(\tau\), and LUE.
## 5 Energy Consumption  
To determine the WSN lifetime in the optimization models, the energy consumption at each sensor location should be calculated. Generally, three statuses, including transmitting, receiving, and idle, of a sensor will cause energy consumption (see [3 ; 4 ]). One widely used energy model presented in [5 ] is adopted in our problem. The basic energy consumption models for transmitting, receiving, and idle can be expressed as  
\[\begin{cases}E_{t}=(\beta_{1}+\beta_{2}d^{\alpha})m_{t},\\ E_{r}=\beta_{3}m_{r},\\ E_{id}=\beta_{4}t_{id}Pm_{data},\end{cases} \tag{12}\]  
where \(\beta_{1}\) depends on the amount of energy spent in the electronics circuitry for transmitting each bit data, \(\beta_{2}\) is affected by the transmit amplifier efficiency, antenna gains and other system parameters, \(\beta_{3}\) is the amount of energy spent in the electronics circuitry for receiving each bit data, and \(\beta_{4}\) is the energy spent for each bit message in the idle state. Here \(\alpha\) represents the path loss exponent (\(2\leq\alpha\leq 4\)), which depends on the environment. The parameters \(m_{t}\), \(m_{r}\), and \(m_{data}\) are the lengths of the message transmitted in bits, the message received in bits, a sensing data packet in bits, respectively. In addition, \(t_{id}\) is the time period of the idle state and \(P\) is the dealing rate of the message sensed by sensors. Thus, we have the idle time as  
\[t_{id}=t-t_{t}-t_{r}=t-\frac{m_{t}}{Pm_{data}}-\frac{m_{r}}{Pm_{data}}, \tag{13}\]  
where \(t\) is the data gathering cycle, \(t_{t}\) is the message transmission time, and \(t_{r}\) is the message reception time. Hence, the total energy consumed in a data gathering cycle for a sensor is  
\[\begin{array}{ll}E&=(\beta_{1}+\beta_{2}d^{\alpha})m_{t}+\beta_{3}m_{r}+ \beta_{4}t_{id}Pm_{data}\\ &=[(\beta_{1}+\beta_{2}d^{\alpha})-\beta_{4}]m_{t}+(\beta_{3}-\beta_{4})m_{r} +\beta_{4}tPm_{data}.\end{array} \tag{14}\]
## 2 Scenario Description  
In the TR 38.901 [21], 3 GPP released its 3-D stochastic channel model for 5G mmWave massive MIMO communications in the range 0.5-100 GHz. A detailed procedure is provided to generate link level channel models. The model is applicable to a wide range of carrier frequencies, with particular attention to the mmWave range by taking care of weak propagation aspects such as atmosphere attenuation, and it supports large channel bandwidth (up to 10% of the carrier frequency). One purpose of this work is to present a simplified version (i.e., single cell network, time-invariant channel) of the system level channel model proposed in 3 GPP TR 38.901 developed in MATLAB. 3 GPP adopts different scenario settings, such as Urban Micro street canyon (UMi), Urban Macro (UMa), Indoor-Office, Rural Macro (RMa), and Indoor Factory (InF); for each scenario, a complete set of parameters is provided, such as intersite distance, path loss computation, large and small scale parameters, etc. In our work, we focus on a simplified single-cell UMi scenario. However, please note the provided MATLAB scripts [39] can also generate channel coefficients for a simplified single-cell UMa scenario. with a trisectorized single cell and static users, i.e., a time-invariant channel model. The spatial channel model could be described as a MIMO cluster delay line, i.e., each coefficient is the superposition of different clusters of rays. Each angular cluster has its own parameters, such as delay, power, azimuth angle spread of arrival/departure, zenith angle spread of arrival/departure, etc.
## 3 Results and Discussion
### Construction of a Dynamic Load Model for River Ice Impacted Bridge Piers
#### 3.4.3 Load Model Validation  
The dynamic load model of the river ice impacting on the bridge pier can be obtained by substituting the above parameter analysis results (17)-(21) into Equation (13). By comparing the discrete element simulation results of river ice with a porosity of 0.08, ice thickness of 0.5 m, ice speed of 2.5 m/s with the results of the load model, it can be found that the ice force time course calculated according to the two methods is relatively close to that shown in Figure 14. The comparison results of each parameter are shown in Table 4, in which the comparisons between the simulation results and the load model calculation results in each key parameter are listed in detail, and all of them show good consistency. It is evident that the river ice dynamic load model established in this paper more accurately  
Figure 13: Load time, unload time vs. vp: (**a**) load time fitting results; (**b**) unload time fitting results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Porosity** & **Velocity (m/s)** & **Ice Thickness (m)** & **Onset Time (s)** & **Load Time (s)** & **Unload Time (s)** \\ \hline \multirow{3}{*}{0} & \multirow{3}{*}{1} & 0.5 & 0.004 & 0.0109 & 0.0168 \\  & & 1 & 0.0009 & 0.0098 & 0.0188 \\  & & 1.5 & 0.0006 & 0.0117 & 0.0155 \\ \hline \multirow{3}{*}{0.05} & 1 & & 0.04 & 0.0104 & 0.0151 \\  & 2 & 0.5 & 0.036 & 0.0065 & 0.0113 \\  & 3 & & 0.032 & 0.005 & 0.0085 \\ \hline \multirow{3}{*}{0.1} & 1 & & 0.050 & 0.0084 & 0.0116 \\  & 2 & 0.5 & 0.044 & 0.0048 & 0.0089 \\  & 3 & & 0.048 & 0.003 & 0.0063 \\ \hline \multirow{3}{*}{0.15} & 1 & & 0.065 & 0.0065 & 0.0079 \\  & 2 & 0.5 & 0.062 & 0.0046 & 0.0055 \\  & 3 & & 0.060 & 0.0021 & 0.0039 \\ \hline \multirow{3}{*}{0.2} & 1 & & 0.085 & 0.0048 & 0.0041 \\  & 2 & 0.5 & 0.080 & 0.0029 & 0.0026 \\  & 3 & & 0.083 & 0.0015 & 0.0017 \\ \hline \multirow{3}{*}{0.25} & 1 & & 0.117 & 0.004 & 0.0028 \\  & 2 & 0.5 & 0.111 & 0.0013 & 0.0013 \\  & 3 & & 0.121 & 0.0006 & 0.0004 \\ \hline \multirow{3}{*}{0.3} & 1 & & 0.156 & 0.0034 & 0.0026 \\  & 2 & 0.5 & 0.159 & 0.0009 & 0.0013 \\  & 3 & & 0.153 & 0.0004 & 0.0002 \\ \hline \end{tabular}
\end{table}
Table 3: Time parameters.  
simulates the process of the impact of the flow of ice on the bridge abutment of the Heilongjiang Bridge. In practical application, the method of this paper can be referred to, according to the specific bridge project, the ice load model used for further correction and improvement.
## 3 Results
### Substrate Specificity of Y. lipolytica Extracellular Lipases in Crude and Purified Lipase Solutions
The purification of enzymes by ion-exchange chromatography was carried out on the supernatant from a shaken culture of the yeast strain _Y. lipolytica_ KKP 379. The strain was chosen because of its relatively high extracellular \"true\" lipase activity (Figure 5). A total of 80 protein-containing fractions were obtained, of which the hydrolytic activity was measured in 40 fractions in a _p_-nitrophenyl laurate hydrolysis reaction (Figure 6). Analyzing Figure 6, two peaks, characterized by hydrolytic activity, can be observed. The first was for fractions 15-17, for which the hydrolytic activity was approx. 0.250 U/mL, and the second was for fractions no. 59-63, where an activity of about 0.450 U/mL was noted. It can be concluded that the purified supernatant contained enzymes with different charges, and the obtained electropherogram showed bands corresponding to sizes of approx. 30 and 40 kDa (Supplementary Materials, Figure S1). The molecular weight of Lip2p lipase is 38 kDa [16], which may confirm the presence of this lipase in the supernatant of the KKP 379 strain and a second lower-molecular-weight enzyme, e.g., a protease, which is also secreted by this yeast, and has a molecular weight of 30 kDa [17]. Figure 5: The hydrolytic activity of lipases in crude enzyme extract (supernatant) from three _Y. lipolytica_ yeast strains towards different fatty acids esters of _p_-nitrophenyl. Means with the same letter (a–d) did not differ significantly (\(\alpha=0.05\)). In an effort to prove that it is a \"true\" lipase, we decided to conduct an additional experiment in which the specific activities of the extracellular lipase from _Y. lipolytica_ KKP 379 (fraction 60) towards different fatty acids esters of _p_-nitrophenol, i.e., butyric, caprylic, lauric, palmitic, stearic, and oleic acids esters, were determined (Figure 7). A purified fraction of the lipase exhibited high hydrolytic activity, and the highest specific activity was observed for butyric acid ester (0.93 U/mg). The elongation of the fatty acid side chain resulted in a decrease in activity; for the caprylic acid ester, the specific activity was 0.62 U/mg, then 0.29 U/mg for the laurate ester, and in the case of the palmitate and stearate esters, the obtained values were comparable, at 0.12 and 0.10 U/mg, respectively. Interestingly, the specific activity measured towards _p_-nitrophenyl oleate started increasing again, and the value was on the level of the C8 ester, at 0.56 U/mg. Figure 6: Hydrolytic activity (**a**) and protein concentration (**b**) of _Y. lipolytica_ KKP 379 protein fractions purified by ion-exchange chromatography.
## 4 Experiments and Results
### Classification Maps  
The classified wetland map produced by the Wet-ConViT model over the study area is depicted in Figure 5. Different land covers are classified into wetland and non-wetland categories on this map. In order to evaluate the performance of the proposed model, a zoom-in-area covering the North and East end of St. John's is considered for visual interpretation and comparison with classification maps of other state-of-the-art DL models (Figure 6).  
Overall, all models successfully identified the primary features and land cover types; however, each approach encountered specific challenges. The classification of marshland covers emerged as the most challenging task across the classified maps. In particular, CNN-based (i.e., Resnet50 and EfficientNet) classification maps underestimated this wetland category, whereas transformer-based models (i.e., ViT and Swin) represented an overestimation of this particular category. This overestimation issue was especially noticeable in the Swin model's results, where some regions saturated with lake water were incorrectly categorized as marshlands. Despite challenges in classifying water bodies within the transformer models, this category exhibited the highest degree of consistency across all predicted maps. An additional challenge observed with the transformer model was the classification of bog and fen wetland covers. In certain instances, these wetland types were erroneously identified as pasture, notably in the northern part of the study area.  
Figure 5: Proposed Wet-ConViT model classified map of the study area.  
Figure 6: (**a**) A small extent of the study area, alongside the associated land classification maps derived from (**b**) ResNet50, (**c**) EfficientNet, (**d**) ViT, (**e**) Swin, (**f**) CvT, (**g**) CoAtNet, (**h**) WMF, and (**i**) the proposed Wet-ConViT model.  
The category of urban land cover was effectively identified by most models, although some, like the CvT model, overestimated this category. The CvT model, while preserving many details in urban areas, occasionally misclassified grasslands and pastures as urban land. On the other hand, the hybrid CoAtNet model encountered overestimation challenges when classifying swamps, leading to the misclassification of numerous grassland and urban areas as swamps. The WMF model output map preserved details but showed an underestimation of bog and swamp land cover in the northern part of the study area. It also slightly overestimated forest land cover. As illustrated, the proposed model yielded a more homogenous land cover map with less noise while still preserving the key features of land cover classes.
_applied_  
_sciences_  
_Article_  
Numerical and Experimental Validation of a Supersonic Mixing Layer Facility  
**[PERSON]**  
Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Center,  
Mianyang 621000, China; jyd, [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (Y.W)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract:** The design of a supersonic-supersonic mixing layer facility was motivated by the need for a benchmark experimental platform to study the physical phenomena underlying supersonic mixing layers. The facility is an intermittent blowdown wind tunnel characterized by a two-stream design separated by a splitter plate in the middle of the nozzle. The splitter plate ends exactly at the start of the mixing layer test section. The Mach number of the primary stream is M\({}_{1}\) = 3 for all nozzles and the secondary streams are M\({}_{2}\) = 2, 2,5, and 2.9 to generate different convective Mach numbers of Mc = 0.25, 0.10, and 0.01, respectively. The facility was calibrated by pressure measurements to verify the Mach number and the pressure distribution in the streamwise direction. Large-eddy simulation (LES) was performed to illustrate a full view of the turbulent compressible mixing layer flow field and to compare this against the experimental data. Optical diagnosis measurements, i.e., femtosecond laser-induced electronic excitation tagging velocimetry (FLEET) for velocity measurement and focused laser differential interferometer (FLD) for the density fluctuation, were also performed on the facility.  
**Keywords:** supersonic mixing layer; wind tunnel; LES simulation; optical diagnosis
## 2 Materials and Methods  
### Experimental Protocol, Data Acquisition and Patients' Demographics  
The study was carried out with the number R-0032/12 from Local Ethical Committee of the Hospital Infantil Nino Jesus (Madrid, Spain). Public trial registered on 23 March 2017: ISRCTN18254257. Five children with CP participated in this study. Data were recorded with the ActiCap EEG cap (BrainProducts, Gilching, Germany ) and the BrainVision actiCHamp signal amplifier, at 256 Hz, and with an array of 32 electrodes (AFz, F3, F1, Fz, F2, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P3, P1, Pz, P2, P4). Patients were sitting on a reclining pallet at a fixed 50\({}^{\circ}\) inclination, and they interacted with a visual interface through VR glasses (Oculus Rift DK2. Facebook Technologies, LLC., Irvine, CA, USA).  
The interface consisted in a virtual environment designed in Unity 2019.3.0a6 (Unity Technologies, San Francisco, CA, USA) (Figure 1). Subjects followed a first-person avatar walking through a pre-defined path with a series of 22 obstacles, yielding a total session time of 18 min. When the avatar faced an obstacle, the walking stopped, and the patients should then relax, establishing a baseline period of about 4 s (times randomly varied between 3 s and 5 s in each obstacle to avoid stimulus habituation). Then, the obstacle was removed from their path, and participants were instructed to imagine the walking restarting, to resume it (motor imagery period; randomly varied between 3 s and 4 s). Patients were not actually controlling the interface, although they believed their neural signals were, indeed, commanding the virtual environment, whereas in fact the avatar's walking was restarted automatically.  
Two RS-EEG recordings were made immediately prior, and post, to the AO+MI-VR intervention, and are the focus of the data analyses of this study. As briefly mentioned in the Introduction, this should enable the investigation of FC patterns related to spontaneous baseline brain activity. These recordings' duration was not uniform, with the average recording time being of \((72\pm 15)\) s. Regardless, as explained below (Section 2.3), our approach takes relative thresholds for the data of each subject, which should minimize biases due to recording lengths.  
Patients' demographic data are shown in Table 1, with information concerning their topographical classification--diplegia or tetraplegia--whether they had a premature birth, participants' age and their score in the Gross Motor Functional Classification System (GMFCS) [37].
# An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN
## 5 Simulation Experiment
### Simulation Environment Settings
#### 5.1.2 Parameter settings  
According to the traffic characteristics, the traffic generator is used to simulate real network traffic. The average flow generation rate is 250 KB/s, the polling switch rate \(v=25\) KB/s, the average communication rate \(\theta=20\) KB/s, the polling controller rate \(\tau=10\) KB/s, and the threshold factor \(\delta_{j}\) is set to 0.8. The parameters of the firefly algorithm are set as follows: the value of the maximum attraction \(\alpha_{\max}\) is 1, the light absorption coefficient meets \(\lambda\in[0.01,100]\), and the range of step factor \(\vartheta\) is \(\vartheta\in[0,1]\). The higher the step factor, the stronger the global search ability, which will, however, reduce the convergence accuracy. The step factor is generally set to 0.4, and \(\varepsilon_{j}\) is a random number obeying uniform distribution on \([0,1]\). The maximum number of iterations is \(I_{iter}=500\), and the constant \(\zeta\) is set to 0.01.
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 3 Results and Discussion
### Irrigation Water Characterization
The assessment of sodium concentration as the sodium adsorption ratio (SAR) is important when considering water suitability for irrigation, as it affects soil salinity, hardness, and infiltration rate [58,59]. The higher the concentrations of Na relative to Ca and Mg, the higher the SAR will be.
## 3 Numerical Results and Analysis
### Magnetic Filter Field Simulation Result
\[\frac{1}{2}mv^{2}=\frac{1}{2}mv_{\perp}^{2}+\frac{1}{2}mv_{\parallel}^{2}= \text{constant} \tag{1}\]  
Figure 4: The filtering effect of the magnetic field on the electron beam with a temperature of 10 eV on the X\(-\)Z plane at 15 \(\upmu\)s.  
Figure 5: (a) The projection of all electrons on the X\(-\)Z plane; (b) The projection of electrons on the X\(-\)Z plane within the range of the Y-axis [\(-\)4 mm, 4 mm]. \(v_{\perp}\) and \(v_{\parallel}\) are the velocities of the electrons perpendicular to and parallel to the magnetic field, respectively, and \(v\) is the velocity of the electrons in the direction of the resultant force. Since this model has no applied electric field, does not consider the interaction between electrons, and ignores the influence of gravity, the Lorentz force does not work, so the total energy of the electrons is conserved. When the magnetic field intensity \(B\) increases, \(v_{\perp}\) increases and \(v_{\parallel}\) decreases. When \(B\) is strong enough, \(v_{\parallel}\) can decrease to zero (i.e., \(v=v_{\perp}\)) such that the longitudinal motion of some electrons in Figure 5b is suppressed, followed by reverse motion, which is continuously reflected in the chamber. Therefore, as shown in Figure 5, most of the electrons in the expansion region are still constrained by the magnetic filter field and move along the magnetic field lines to the chamber wall, while a few electrons are reflected by the strong magnetic field at both ends and move back and forth along the longitudinal direction of the chamber. With the effect of the magnetic filter field, the number of electrons guided to the chamber wall is greatly increased, and the number of electrons reaching the extraction surface is effectively reduced.