esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### LVO Detection Using Automated Software  
An automated tool--e-CTA (a part of e-STROKE, version 10.1p3, Brainomix Ltd., Oxford, UK)--was used to analyze each patient's CTA raw data for the presence, side, and site of an intracranial LVO.  
The e-Stroke Suite image processing algorithms follow an AI approach, with a combination of traditional 3D graphics and statistical methods and machine learning classification techniques. The input DICOM data is first resampled to correct any gantry tilt and standardize the input resolution. Then, a fast proprietary registration approach is applied to re-align the data, removing any tilt and rotation. This ensures that the image is presented in a standard reference frame.  
The e-CTA uses a combination of machine learning and deep learning algorithms to identify LVOs. The e-CTA received CE mark certification in 2018.  
In all cases, processing time, defined as the time from data transmission to the reception of results, was recorded.
## 5 Integration of EOLSR and SERENA
### Benefits Resulting from the Integration
Bandwidth is spatially reused. No energy is lost in interferences. The use of EMPRs contributes to balance energy consumption. Routes selected by EOLSR tend to minimize the energy consumed by an end-to-end transmission.
###### Abstract  
In order to improve the performance of Terrain Referenced Navigation (TRN), an Interferometric Radar Altimeter (IRA) has been developed as a more accurate altimeter. The IRA outputs not only the relative distance (slant range, \(R\)) but also the cross-track angle (look angle, \(\theta\)) of the closest point on the zero Doppler line by using the principle of interferometry and two or more antennas. To perform TRN using the IRA, the 3D relative position of the closest point should be calculated. There is a formula to calculate the relative position of the closest point using the Euler angles. However, in an actual flight environment in which the influence of wind exists, the angle of attack, the side slip angle and \"the effective look angle\" should be used rather than the Euler angles. In this paper, a new formula for calculating the relative position of the closest point is proposed and mathematically derived. The proposed formula was verified with real data from actual flight. The flight test results show that the positions of the closest points calculated using the conventional method and the proposed method are different because of the wind effect. The TRN simulation results indicate that the proposed formula calculates the closest points more accurately than the conventional formula.  
terrain referenced navigation; interferometric radar altimeter; flight test +
Footnote †: 15 February 2019; Accepted: 5 April 2019; Published: 9 April 2019
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 3 Datasets Used
### Mimic-Iv  
MIMIC-IV was the source of EHR used in the present work. This dataset contains data of patients that were admitted to the Beth Israel Deaconess Medical Center emergency department or ICU between 2008 and 2019, annotated with ICD-9 or ICD-10 codes [25]. MIMIC-IV was released on 6 January 2023, and it is increasingly more popular for training and testing applications for automatic medical encoding.  
MIMIC-IV contains information for each patient (257,000 distinct patients), namely the hospital stay, laboratory measurements, medication, and vital signs. It is composed by 524,000 admission records [26].  
In this dataset, subject ID identifies a patient, while hadm ID identifies an admission of a patient to the hospital. For example, the following text is part of a MIMIC-IV EHR, with subject ID \"1000017\" and hadm ID \"22927623\": \"She describes feeling as though food gets stuck in her neck when she eats. She put herself on a pureed diet to address this over the last 10 days. When she has food stuck in the throat, she almost feels as though she cannot breath, but she denies trouble breathing at any other time. She does not have any history of food allergies or skin rashes\". This excerpt shows an example of a text that is text somehow vague concerning the diagnosis and negated terms (no food allergies or skin rashes). Those are common difficulties when coding.  
For the present work, the MIMIC-IV dataset was split into a training set composed by 73,381 discharge summaries (60%), a validation set consisting of 24,461 records (20%), and a test set consisting of 24,461 records (20%), following the partition method proposed in [22].  
For the sake of time, only a fraction of \(\frac{1}{6}\) of the test set (4077 out of the 24,462) samples were used in the experiments described below.
# Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
## 1 Introduction  
Informatization is the sign and key of agricultural modernization. Agricultural information can significantly change small scale of agricultural production, great temporal and spatial variation, low scale merit and other industrial weakness. Moreover, it plays an important role in the development of agriculture and the full realization of a well-off society [1; 2]. The Internet of Things (IoT) is defined as things connected to things in the Internet [3; 4; 5]. The agricultural Internet of Things is a new trend in world agricultural development, a new type of agriculture which combines the Internet of Things and agricultural production [6; 7; 8]. It will bring agriculture into the digital information age [9]. Agricultural Internet of Things is able to implement digital design, intelligent control, precise operation and scientific management for various agricultural elements. So it achieves a comprehensive perception, reliable transmission and intelligent processing, and ultimately achieves high yield, high efficiency, high quality, ecological and safety purposes [10].  
Citrus is largest fruit crop in the world. Its planting area and output are the first of the fruit trees. In addition, it is the world's third largest trade agricultural products. Citrus is produced in 135 countries and regions all over the world. Among these areas, China's citrus planting area and production is in the first place in the world, becoming the world's largest citrus producing country. In China, most citrus management methods are still in the relatively backward stage, monitoringof water, nutrients, and the temperature is still in the manual monitoring phase [11]. They result in inconvenient operation, time-consuming and laborious, low precision and the lack of information. Furthermore, the data acquisition will have a certain delay. So managers cannot achieve comprehensive and clear grasp of the orchard information [12]. Based on this, the paper applies the Internet of Things technology and single-point multi-layer detection method to the soil moisture, temperature and nutrient monitoring, sets up citrus orchard fertilization irrigation expert knowledge base and makes expert decision according to the soil condition in real time to guide the actual production process of citrus. The emergence of wireless sensor, data fusion and internet technology, can achieve the remote automatic monitoring on the citrus water, nutrient and temperature of growth environment. And through the model analysis and data processing, expert decision-making system can give effective measures of citrus management. Practice has proved that single-point multi-layer detection technology can effectively expand the detection range of citrus growth environment, and it is helpful to construct a more accurate expert knowledge base. At the same time, the system can effectively guide fruit growers to scientific management of citrus orchards and significantly improve the yield of citrus.
## 2 Structure and Working Principle of the Machine
### Working Principle  
This machine can complete the ditching and ridging, seedbed preparation, seedling casting, seedling covering, film mulching, film edge and seedling head covering, and other operations at one time. Before the operation, the seedlings are placed on the seeding device manually. The head of the seedlings should be aligned with the high position of the seedbed when placed. During operation, the whole machine is connected to the three-point suspension frame of the tractor through the suspension device. Under the pull of the tractor, the ditch shovel completes the work of ditching and ridging, then, the seedbed preparation device completes the ridge direction scraping, the extrusion of the seedling covering belt, and the forming of the seedbed with the progress of the whole machine. The earth wheel transmits the power to the seedling-casting device through the chain drive, and then the seedling-casting device completes the transmission and casting of the seedlings. The tractor passes the power through the gearbox to the rotary tillage soil-covering device which cuts  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Values** \\ \hline Machine dimensions & 2297 \(\times\) 2107 \(\times\) 1655 \\ (length \(\times\) width \(\times\) height)/(mm \(\times\) mm \(\times\) mm) & 2297 \(\times\) 2107 \(\times\) 1655 \\ Auxiliary power/kw & 44.1 \\ Overall quality/kg & 512 \\ Seedling placement method & Manual seedling placement \\ Seedling outcrop distance/mm & 0–40 \\ Plant spacing/mm & 44 \\ Field capacity/(hm\({}^{2}\)-h\({}^{-1}\)) & 0.45\(\sim\)0.65 \\ Number of seeding rows & 4 \\ Angle of inclined planting/\({}^{\circ}\) & 15 \\ Covering thickness of seedlings/mm & 40-80 \\ Number of plastic films & 2 \\ The width of plastic film/mm & 620 \\  & Three-point \\ \multicolumn{2}{c}{Suspension mode} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table}
Table 2: Main technical parameters of operating machine.  
Figure 2: Overall structure diagram. 1. Hanging device. 2. Seat. 3. Seeding box. 4. Seeding-casting device. 5. Frame. 6. Seeding head burial and film edge soil-covering device. 7. Spreading film roll. 8. Film-hanging frame. 9. Soil reflection device. 10. Gearbox. 11. Rotary tillage soil-covering device. 12. Ground wheel. 13. Power transmission device. 14. Seedbed preparation device. 15. Ditching shovel.  
the soil at high speed and the cut soil is thrown to both sides of the seedbed to cover the seedlings. The film installed on the film-hanging frame can be uniformly coated with the aid of the film-spreading roller. Finally, the covering disk covers the film edge with soil and backfills the ridge. The seedling-casting device can ensure the plant spacing is not affected by the advance speed of the whole machine by making the ground wheel drive the chain so that the seedling spacing is stable.
# A Control-Oriented Linear Parameter-Varying Model of a Commercial Vehicle Air Brake System
## 2 Nonlinear Mathematical Modeling  
This section presents the governing equations of the main valve component from the control voltage inputs to the output brake chamber pressure. The air brake system consists of two subsystems: pneumatic and mechanical subsystems. The pneumatic subsystem includes an air compressor, storage reservoir, brake line, quick release valve, relay valve, brake chamber and so on (see Figure 1); and the mechanical subsystem includes mainly push rod, slack regulator, brake pad, and so on (see [6] for details). There is a certain time lag (delay) from the time when a driver presses the pedal to the time that pressure starts building up in the brake chamber. To reduce the brake response time (or vehicle brake stopping distance), the treadle valves in the existing pneumatic system is replaced by the electro-pneumatic proportional valves; see Figure 1.  
The electro-pneumatic proportional valve, such as the ITV series from SMC corporation [27], consists of two solenoid valves (supply and exhaust), pilot chamber, and a spool valve; see Figure 2. When a control voltage is applied to the supply solenoid valve, the air supply solenoid valve opens, air in the supply port passes through the air supply solenoid valve and flows into the pilot chamber with the exhaust solenoid valve off. The pressurized air in the pilot chamber presses the diaphragm and piston, and the piston rod (spool) moves down. As a result, the air supply valve opens, and the compressed air flows from the supply port to the output port. When the input voltage to the supply solenoid is set to zero, the air supply valve closes, airflow stops, and output pressure remains unchanged after supply, and output flow is stabilized. After the exhaust solenoid valve is turned on, the exhaust valve opens, the compressed air in the pilot chamber releases to the atmosphere, leading to decrement of output pressure. The valve control circuit also measures output pressure via a pressure sensor to regulate the output pressure to be proportional to the reference voltage.  
Utilizing the principle of momentum, governing equations of fluid dynamics, valve motion and chamber pressure dynamics, a detailed nonlinear dynamic model of target pneumatic proportional  
Figure 1: Pneumatic brake system scheme.  
Figure 2: Sectional view of proportional valve.  
value is developed component-by-component based on the internal valve structure. There are four main subsystems for the valve model: solenoid valve, pilot chamber, valve spool, and brake chamber. The following assumptions are made for the proposed model:  
1. leakage in the chamber is negligible;
2. the orifice flow process is isentropic;
3. air is treated as ideal gas; and
4. coulomb friction effect is ignored.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Herbage and Forage
Tea waste was evaluated as a low-cost adsorbent for the effective recovery of Zn(II) from synthetic wastewater, following the pseudo-second order kinetic model and reaching 99.5% removal with three grams of tea waste per 100 mL of metal standard solution [92]. The residues of sunflower (specifically stalks and leaves) displayed good activity as a bioadsorbent, removing Zn, Ni, Cu, Cd, and Fe from aqueous solutions. The maximum sorption capacity observed for the five metals was about 50.8 \(\pm\) 22.3 mg/g on average; the adsorption behavior for these metals was used the pseudo-second order model, showing that it is a competitive system [89]. [PERSON] et al. [86] employed SCB to remove Fe(III) in different water samples through the batch equilibrium technique at an optimum pH of 3. The average recovery efficiencies for this metal were 95-97.4% in Nile River water, 95-98.4% in groundwater, 95-98% in drinking tap water, 95% \(\pm\) 0.2% in natural drinking water, and 97.2-98.2% in distilled water. The adsorption reaction was adjusted to pH 5 for the maximum uptake of Co(II) (15.5 umol/g), pH 6 for Cd(II) (70.0 umol/g) and Cu(II) (86.0 umol/g), and pH 7 for Zn(II) (81.0 umol/g). [PERSON] and [PERSON] [90] reported the sequestration of Cu using black tea waste in a pH range of 5-6, for a removal efficiency and maximum adsorption capacity of 77% and 48 mg/g. The adsorption kinetics showed
## 3 Results and Discussion
### Analysis of Variance (ANOVA)  
The statistical estimators R\({}^{2}\), adjusted R\({}^{2}\), and the \(p\)-value were used to evaluate the validity of the mathematical models. The results for turbidity, COD, phosphate, and UV 254 removal are presented in Table 3.  
The correlation coefficient (R\({}^{2}\)) quantifies the relationship between the experimental data and the predicted responses [47]. A comparison between the experimental results and the model-predicted values (Equations (2)-(5)) showed a strong correlation, with R\({}^{2}\) values of 0.9721, 0.9841, 0.9702, and 0.9862 for turbidity removal, COD removal, phosphate removal, and UV 254 removal, respectively.  
The adjusted R\({}^{2}\) also assesses the best fit but is more appropriate for comparing models with different numbers of independent variables. It accounts for the sample size and the number of terms in the model by incorporating degrees of freedom into its calculation. When a model includes many terms and the sample size is relatively small, Adj-R\({}^{2}\) may be noticeably lower than R\({}^{2}\)[47]. In this study, the adjusted R\({}^{2}\) values (0.9218, 0.9556, 0.9165, and 0.9615) for turbidity, COD, phosphate, and UV 254 removal were very close to the corresponding R\({}^{2}\) values, confirming the reliability of the models.  
Additionally, \(p\)-values were used to determine the significance of each coefficient, which is essential for understanding the interactions between variables [47]. A \(p\)-value <0.05 signifies statistical significance, confirming the validity and acceptability of the quadratic model [47; 48]. As shown in Table 3, all models had \(p\)-values below 0.005, confirming their significance.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Remote Sensing Images  
We acquired the MODIS MOD09A1 product to obtain 8-day composite surface reflectance data with a spatial resolution of 500 m and the Landsat Level-2 product to acquire surface reflectance at 30-m spatial resolution. All available data for the study area between day of year (DOY) 137 (early May) and DOY 305 (late October) from 2013 to 2017 were collected (Figure 2). Remote images with the experimental sites fully covered by the cloud were excluded from analysis. The MOD09A1 product provides bands 1-7, of which band 3, band 4, band 1, band 2, and band 6 were selected as blue, green, red, NIR, and SWIR bands, respectively, while the Operational Land Imager (OLI) on Landsat 8 provides nine spectral bands, of which band 2, band 3, band 4, band 5, and band 6 were selected as blue, green, red, near infrared (NIR), and shortwave infrared (SWIR) bands, respectively (Table 2). Basic processing, including radiometric correction and geometric correction, was implemented in both products mentioned above to provide application-ready datasets. The Landsat 8 image on DOY 205 in 2016 was used to obtain the spatial distribution of rice using a supervised classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experimental** & **Geographic** & & & & & **Stress** \\
**Site** & **Coordinates** & & & & & **Level** \\ \hline A & 27\({}^{\circ}\)47\({}^{\prime}\) N 113\({}^{\circ}\)10\({}^{\prime}\) E & 0.84 & 0.35 & 78.33 & 10.23 & Nonstress \\ B & 27\({}^{\circ}\)40\({}^{\prime}\) N 113\({}^{\circ}\)10\({}^{\prime}\) E & 2.31 & 0.24 & 91.05 & 17.34 & Moderate stress \\ C & 27\({}^{\circ}\)50\({}^{\prime}\) N 113\({}^{\circ}\)2\({}^{\prime}\) E & 3.28 & 0.51 & 120.75 & 18.15 & Severe stress \\ D & 27\({}^{\circ}\)58\({}^{\prime}\) N 113\({}^{\circ}\)2\({}^{\prime}\) E & 3.57 & 0.50 & 130 & 14.7 & Severe stress \\ \hline Background value & & 1.43 & 0.20 & 82.78 & 19.11 & \\ \hline National secondary & & & & & & \\ standard & & 0.3\(\sim\)1.0 & 0.3\(\sim\)1.0 & 250\(\sim\)350 & 20\(\sim\)30 & \\ \hline \hline \end{tabular} Note: Heavy metal concentrations are given in mg \(\cdot\) kg\({}^{-1}\). The background data were acquired from the Hunan Institute of Geophysical and Geochemical Exploration, China. The heavy metals content was measured by the Analysis and Test Center, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS).  
\end{table}
Table 1: The average condition of heavy metal contamination in the soil of the experimental sites.
# Low-Profile Harmonic Transponder for IoT Applications
## 3 Diode Conversion Loss  
Several published papers have addressed the diode CL as a constant parameter. However, the diode CL depends on several parameters, namely, matching, frequency, and input power. Therefore, in this section, the diode CL is evaluated at different power levels, utilizing the previously developed circuit model. The CL simulation of the matched diode is performed for the developed circuit model of the introduced transponder, in which the CL illustrates that a matched diode would outperform the unmatched one by approximately \(20\,\mathrm{dB}\) if the available RF power is less than \(-20\,\mathrm{dB}\), as will be explained.  
The diode biasing circuit is proposed as an alternative solution to minimize the diode CL, and the circuit parameters are designed to serve that purpose. Therefore, DC blocking capacitors can be introduced at the diode input and output along with an RF choking and DC return inductors, introduced before and after the diode, respectively. The voltage drop resistance after the battery can be utilized to maintain the optimum \(100\,\mathrm{mV}\) required biasing the diode. A \(2.5\,\mathrm{V}\) rechargeable battery (ML414H) is utilized to prove the concept. However, the optimum DC-biasing voltage value for maximizing the diode-generated second harmonic components is simulated to be \(100\,\mathrm{mV}\). Therefore, the role of the biasing circuit is also to maintain that value from the available fixed DC supply, allowing for the harvesting and maintenance of the required low DC voltage value.  
The proposed biasing circuit schematic and the corresponding implementation are explained in Figures 6 and 7, respectively. The conversion loss of the biased diode is measured after calibrating the cables and filters losses, and the measured results verify that the biased diode CL is higher than the CL of a matched diode if the available input power is greater than \(-20\,\mathrm{dBm}\) as illustrated in Figure 8. However, a matched diode CL is lower than a biased one for an input power smaller than \(-20\,\mathrm{dBm}\) as explained in Figure 8.  
The diode biasing solution would have two advantages. First, the biasing circuit can be directly connected to any standard \(50\,\mathrm{\Omega}\) antennas without matching considerations. Second, the circuit would operate over a wider bandwidth, compared to the matching solution. Moreover, the optimum biasing value, which is \(\approx\)\(100\,\mathrm{mV}\), can be harvested from the incident wave at the expense of having an extra harvesting circuit, as presented in [19]; thus, a completely passive solution is achievable. However, the transponder will be heavier and larger in size than the introduced narrow-band matching solution.  
Figure 5: EM simulation of the reflection coefficient over the complete frequency span at the fundamental and harmonic impedances of the diode.
## 3 Results and Discussion
### Effects of VIDD on Vehicle Speeds in Middle of Long Downhill Tunnel
#### 3.2.2 Effect on Average Vehicle Speed  
The average speeds and standard deviations of vehicles in the middle of the tunnel controlled by different types of VIDD are shown in Figure 17, and the results can be used to analyze the speed-controlling performance of the latter quantitatively. From Figure 17, except for the 55\({}^{\circ}\) slash-line VIDD, all the other types decreased the average speed and standard deviation in the middle of the tunnel. With the 90\({}^{\circ}\) fold-line VIDD, the average speed and standard deviation were 18.35% and 24.8% lower, respectively, than those with no VIDD, meaning that this type offers the best deceleration effect.