esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Tool-First Validation  
In order to make a first validation about the correct working of the software, a comparison between the results obtained from both the tool and the WIT offline procedure, based on the same engine configuration, has been carried out. The configuration used for this comparison (Table 4) is about a Test Bed with an in-line four-stroke engine W6L50 DF, a two-stage ABB turbocharger and, as equipment, an Air Nozzle and emission machine with all the pollution detection modules. The analyzed operating point is at 110% of the engine nominal load in only gas mode. To perform this comparison, the pilot contribution to the combustion and the nitrogen and oxygen values in the fuel gas were not considered due to their negligible values. Once the Set Up Environment has been properly generated (Table 5) and the parsing has been performed, the software determined which functions had to be calculated, verifying whether the \begin{table}
\begin{tabular}{c c} \hline
**Engine** & W6L50 DF \\ \hline
**Turbocharger** & Two-stage ABB \\ \hline \multirow{6}{*}{**Test bed equipment**} & Air Nozzle \\  & Emission machine: \\  & - & CO, CO\({}_{2}\), O\({}_{2}\) module; \\  & - & THC module (Wet); \\  & - & NO\({}_{X}\) module (Wet). \\  & No Wastegate detected \\ \hline
**Load** & 109.4\% \\ \hline
**Fuel** & Only gas \\ \hline \end{tabular}
\end{table} 
Table 4: Engine configuration. Figure 4: Emission SubRoutine. Border Conditions were fulfilled or not. On the basis of the selected engine configuration, the software established 111 calculable functions out of 584. Tables 6-8 presents the output of the first validation of the tool determined by both Air Nozzle and Carbon Balance methods, compared with the same features evaluated by the WIT offline procedure. The root mean square error of all features evaluated with the tool and the WIT offline procedure is equal to 1.46%, which is a more than acceptable result given the extent of the analyzed quantities. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Stage** & **Unit** & **In-Live Tool** & **Offline WIT Procedure** & **Percentage Error** \\ \hline \hline ECR & & / & 10.546 & 10.550 & 0.038\% \\ \hline \hline \(BSFC\) & & g/kWh & 149.812 & 149.820 & 0.005\% \\ \hline \hline \(BSFC_{corr}\) & & g/kWh & 149.273 & 149.290 & 0.011\% \\ \hline \hline \(n_{298}\) & LP & rpm & 295.225 & 295.000 & \(-\)0.076\% \\ \hline \hline HP & rpm & 257.887 & 258.000 & 0.044\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the output values–Running parameters. BSFC: Brake-Specific Fuel Consumption, WIT: Wartsila Italia.
## 2 Materials and Methods
### Learning Algorithms
#### 2.3.3 Long Short-Term Memory (LSTM)  
LSTM is an advanced version of RNN which possesses extensive memory capable of dealing with long-term dependencies and memorizing information over an extensive period, with main components such as cell state holding information throughout the data processing [39]. LSTM has three logic gates that control how information flows through the system and can also overcome the weakness of RNNs [5]. LSTM is an extraordinary deep learning model with improved capabilities to overcome the challenging conditions faced by RNNs with three main functional areas: the forget gate, the input gate, and the output gate [37; 44; 45]. The advantages of LSTM included handling long sequences and being appropriate for processing data with long-range dependencies. The disadvantages of LSTM included computational complexity and being more intensive compared to other neural networks, being prone to overfitting when there are insufficient training data, and requiring several hyperparameter tunings.
# Fine-Tuning a Personalized OpenBioLLM Using Offline Reinforcement Learning
## 4 Experiment
### Dataset Construction  
The dataset utilized in this research was derived from the MIMIC-III database, and the preprocessing procedures were consistent with those outlined in [14]. Specifically, we focused on extracting data from the initial 72-h period following intubation, and we constructed trajectories using a 4-h time window. Consequently, each episode was limited to a maximum duration of 18 time steps. Among them, 19,780 patients who met the requirements for mechanical ventilation were screened out of 61,532 ICU hospitalization cases. These 19,780 patients received a total of 191,128 treatments. The observation value \(o\) at each time step consists of 38 patient-related clinical indicators, which are:  
* **Demographics:** Age, gender, weight, readmission to the ICU, Elixhauser score;
* **Vital Signs:** SOFA (Sequential Organ Failure Assessment), SIRS (Systemic Inflammatory Response Syndrome), GCS (Glasgow Coma Scale), heart rate, sysBP (Systolic blood pressure), diaBP (Diastolic blood pressure), meanBP (Mean blood pressure), shock index, resprate, temperature, SpO2 (Peripheral capillary oxygen saturation);
* **Lab Values:** potassium, sodium, chloride, glucose, bun (blood urea nitrogen), creatinine, magnesium, carbon dioxide, hemoglobin, WBC (white blood cell count), platelet, PTT (partial thromboplastin time), pt (prothrombin time), INR (international normalized ratio), ph, paco2 (partial pressure of carbon dioxide in arterial blood), base excess, bicarbonate;
* **Fluids:** urine output, IV total (total intravenous fluid given), cumulative fluid balance, total vasopressor usage.  
The treatment action consists of three actions denoted as \(a\): PEEP (Positive End Expiratory Pressure), FiO2 (Fraction of Inspired Oxygen), and TV (Tidal Volume). The final survival status of each patient is marked by \(d\), which is 1 if the patient is alive when discharged and 0 if the patient dies in the hospital. The trajectory data of each patient can be expressed as \(p^{i}=\{o^{i},a^{i};d^{i}\}\), where \(o^{i}=\{o^{i}_{1},\ldots,o^{i}_{T}\},a^{i}=\{a^{i}_{1},\ldots a^{i}_{T}\}\).
# Dental Solid Waste Analysis: A Scoping Review and Research Model Proposal
## 3 Results
### Data Items and Synthesis of the Results
To gain deeper insights from the collected data, it is noteworthy that all the studies employed longitudinal designs, utilizing continuous or repeated measures to track the generation rate of dental waste over extended periods. Moreover, sampling days range from 3 to 26. Specifically, in three studies, samples were collected between 31 May and 5 July 2002, 22 April and 5 July 2013, and June and September 2016, respectively. Another two studies collected samples for 1 year (April 2011-2012) twice monthly and four samples in each season of 2010, respectively. According to one study, samples were taken at a time interval that was not specified. It was calculated that 63, 84, 104, 120, 400, and 3816 samples were taken in total, respectively, in each study. Additionally, the number of dental-care-related structures participating in each survey was 38, 4, 21, 30, 20, and 159, respectively. [PERSON] et al. (2012) [20] collected their data from 28 dental practices in the public sector and 10 dental laboratories. It is important to note that 10 of the 28 dental clinics were involved in general dentistry and 8 were involved in dental specialties. In another study that was conducted by [PERSON] et al. (2009), [18] they utilized samples from four dental laboratories to compose their study. Another example is the study of [PERSON] et al. (2012), [19] who analyzed waste generation in 30 general and private dental practices. Moreover, [PERSON] et al. (2019) [23] conducted their investigation into 19 private and 1 public general dentistry \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Freq.** & **Percent** & **Cum.** \\ \hline Iran & 4 & 66.67 & 66.67 \\ Greece & 2 & 33.33 & 100.00 \\ Total & 6 & 100.00 & - \\ \hline
**Sector** & **Freq.** & **Percent** & **Cum.** \\ \hline Private sector & 2 & 33.33 & 33.33 \\ Public sector & 1 & 16.67 & 50.00 \\ Both & 3 & 50.00 & 100.00 \\ Total & 6 & 100.00 & - \\ \hline
**Funding** & **Freq.** & **Percent** & **Cum.** \\ \hline NO & 1 & 16.67 & 16.67 \\ YES & 3 & 50.00 & 66.67 \\ NO REPORTING & 2 & 33.33 & 100.00 \\ Total & 6 & 100.00 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive tables of the results of cross-sectional studies. practices, while [PERSON] et al. (2014) [21] included 123 private and 36 public dental practices in their study. Finally, [PERSON] et al. [22] collected data from 21 general dentistry practices in the public sector. Overall, the aim of all included research studies was to analyze the production rate and composition of DSW. To accomplish this, they collected the waste of the dental care centers under study within the time frame determined by each survey. Following this, qualified personnel followed safety and avoidance measures to divide the collected waste into fractions. For each fraction, its composition was categorized into infectious and toxic, toxic/chemicals/pharmaceuticals, and domestic.
### Machine Learning and Artificial Intelligence Applications
These advancements contribute to improving earthquake early warning systems, which aim to reduce the impact of seismic events on populations. The exploration of new data sources and methodologies continues to expand the field. [PERSON] et al. [24] applied ML to analyze hydrothermal data in the context of earthquake precursor detection, showcasing the versatility of ML in integrating diverse datasets for enhanced forecasting. Similarly, in the paper by [PERSON] et al. [25], the use of smartphone sensors for earthquake early warning was explored, demonstrating the potential of widely accessible technology in disaster preparedness. In parallel, ML techniques have become valuable tools for automatic earthquake detection in DL, involving large volumes of raw data. For instance, the Earthquake Transformer Model leverages DL to detect earthquakes and simultaneously identify seismic phases [26]. Additionally, systems based on batch normalization graph convolutional neural networks process raw waveform data from seismic stations, allowing for rapid alerts without human intervention [27]. These ML approaches are used to handle large volumes of real-time data.
# Nutritional Support for Liver Diseases
## 2 Nutritional Support for Liver Diseases
### Cirrhosis
#### 2.3.1 Dietotherapy  
So far, the effectiveness of various intervention therapies has been studied, which were parenteral nutrition (PN), enternal nutrition (EN), EN + intestinal probiotics, PN + EN, EN (without branched-chain amino acids BCAAs), late evening snack (LES), EN + LES, noLES. PN, EN, and PN+EN represent the routine therapy for cirrhosis, while EN+ intestinal probiotics, LES, and EN + LES have been at the cutting edge of therapy in recent years [119].  
In general, the optimal diet for a patient with cirrhosis should be high in protein and high in energy due to the catabolic effects of cirrhosis and the associated increased protein degradation and, thus, loss of muscle mass [6; 39]. As shown by [PERSON] et al. [118], a combination of dietary intervention (protein supply 1.2-2 g/kg/day) and physical activity (lasting \(\geq\)8 weeks, at least 3 days a week with 30-60 min of supervised aerobic and/or moderate-intensity resistance exercise) has the greatest potential for increasing muscle mass. In addition, physical training has a beneficial effect on skeletal muscles by reducing progression or even reversing muscle wasting [120] while improving physical fitness and fragility [121]. It is important that the intervention based on diet and physical activity is long-term [118].  
The introduction of a late evening snack that is rich in carbohydrates (50 g of complex carbohydrates) shortens fasting time and eliminates the adverse changes resulting from night-time catabolism [122], as well as improving nitrogen metabolism and, in the long term, leads to an increase in lean body mass and reverses sarcopenia [123; 124; 125]. On the other hand, greater frequency, such as 5-6 meals a day, shortens catabolism episodes during the day [126].  
In addition, for a complication called hepatic encephalopathy, it may be necessary to reduce the proportion of animal protein in favor of plant protein and dairy protein without affecting the overall protein content of the diet [6].  
It should be noted that sodium intake should be restricted in patients in the advanced stages of the disease, especially in the case of ascites [5].  
According to the ESPEN recommendations, parenteral nutrition (Grade GPP, strong consensus 100%) should be introduced in patients with moderate to severe cirrhosis who cannot eat food orally or cannot receive sufficient enteral nutrition. Additionally, parenteral nutrition should be given when fasting periods last longer than 72 h [(5)].  
Interestingly, a meta-analysis of 16 studies (7 case-control studies and 9 cohort studies) involving patients with NAFLD or HCV and abusing alcohol showed that coffee consumption at any level (from low to high) reduces the risk of the development of liver fibrosis and cirrhosis compared to non-coffee drinkers (OR 0.73 and 0.61, respectively) [(44)].
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 2 Heading Estimation Based on an AR Model
### AR Model  
The AR model not only satisfies the polynomial constraint of state variables, but also can reduce the noise by the criterion of minimizing the mean-square error (MMSE) using redundant observations [33]. In order to enhance the accuracy of heading estimation, we use the AR model to build KF.  
Here, we suppose that the current heading can be predicted by the previous \(S\) samples:  
\[att_{k}=\sum_{s=1}^{S}h_{s}att_{k-s} \tag{2}\]  
where \(att_{k}\) is the current heading and \(h_{s}\) is the coefficient matrix of the AR model.  
Without loss of generality, the current heading can be fitted by \(L\) polynomials,  
\[att_{k}=\sum_{l=1}^{L}a_{l}(t_{k})^{l} \tag{3}\]  
where \(a_{l}\) is the coefficient matrix of polynomials, \(t_{k}=kT\), and \(T\) is the sample interval.  
Substituting Equation (3) into Equation (2), we can obtain  
\[(k+1)^{l}=\sum_{s=1}^{S}h_{s}(k+1-s)^{l},l=0,1,...,L \tag{4}\]  
When \(l=~{}0\), we can obtain the equation as follows  
\[\sum_{s=1}^{S}h_{s}=1 \tag{5}\]  
We know from Equations (11)-(16) that KF based on the AR model needs to know \(\mu_{k}\). A cost function based on MMSE can be constructed as follows  
\[\begin{array}{l}\text{min}\mu_{k}^{T}P_{k-1,k-1}\mu^{T}\\ \text{subject to }A\mu_{k}=b\end{array} \tag{17}\]  
Equation (17) can be solved by the Lagrangian multiplication operator [34]  
\[L(\mu_{k},\lambda)=\mu_{k}^{T}P_{k-1,k-1}\mu_{k}-\lambda^{T}(A\mu_{k}-b) \tag{18}\]  
where \(\lambda\) is a Lagrange multiplier.  
Taking the partial derivatives with respect to \(\mu_{k}\) and \(\lambda\) for Equation (18) and setting them to zero. We can get  
\[\left\{\begin{array}{l}\frac{\partial L[\mu_{k},\lambda}{\partial\mu_{k}}=0 \\ \frac{\partial L[\mu_{k},\lambda}{\partial\lambda}=0\end{array}\right. \tag{19}\]  
We can obtain \(\mu_{k}\) by solving Equation (19),  
\[\mu_{k}=P_{k-1,k-1}^{-1}A^{T}\Big{(}AP_{k-1,k-1}^{-1}A^{T}\Big{)}^{-1}b \tag{20}\]  
We can see from Equation (20) that the solution of \(\mu_{k}\) uses the information polynomial and state variance. Thus, compared to traditional KF, KF based on the AR model can effectively reduce the noise.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 6 Hardware Configuration and Experiment  
The paper suggests the specific configuration for our designed underactuated quadrotor craft that is demonstrated in Figure 23.  
The quadrotor type proposed in this hardware configuration is completely customized and may carry variable payload from dull, difficult, and dirty situations. The prototype used in this paper is RC eye 650 proposed by RC Logger that can easily be purchased from Heli distributors. The proposed RC eye 650 consists of 6 degrees of freedom with only four Brushless DC (BLDC) motors. The four BLDC motors are distributed at same distance from the center of mass. There is a tank attached to it with some liquid. The idea behind attaching this tank is to utilize this drone for spraying disinfectants initially and secondly to check the case of smooth and non-smooth payload variation.  
The proposed underactuated unmanned aerial vehicle can spray the disinfectants from one place to another. Moreover, this difficult task can be performed easily due to use of pigeon eye view via zenmuse HD gimbal-based camera. The drone carries the additional battery so that the flight time can be extended up to 50 min more to spray approximately 3-L disinfectant solution. The underactuated quadrotor craft used in this prototype consists of the following mentioned components.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Control Designs** & **Chattering Effect** & \begin{tabular}{c} **Delay in Accelerations** \\ \end{tabular} & \begin{tabular}{c} **Estimation** \\ **Delay** \\ \end{tabular} & \begin{tabular}{c} **Steady-State** \\ **Error** \\ \end{tabular} & \
\begin{tabular}{c} **Overshoots** \\ **10-15\%** \\ \end{tabular} &
\begin{tabular}{c} **Settling** \\ **Time** \\ \end{tabular} \\ \hline
**SMC** & Yes & Yes & Yes & 4.0 & Yes & Huge \\ \hline
**F-SMC** & Yes & Yes & Yes & 4.0 & Yes & Huge \\ \hline
**SDF-SMC** & Yes & Yes & Yes & 22 & Yes & Huge \\ \hline
**MFSDF-SMC** & No & No & Yes & 0 & Reduced & Small \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance summary between SMC, F-SMC, SDF-SMC and MFSDF-SMC.
## 1 Introduction
The tracked human thumb motion is mapped to the robot thumb in a hybrid approach: the proposed algorithm maps the CMC joint motion to the base two joints, while a joint space mapping is used between the distal human MCP and IP joints to the last two joints. The algorithm is developed to implement a Cartesian space mapping and account for kinematic differences between the human CMC joint and the first two joints of the robot thumb. A framework is proposed to telemanipulate the robot thumb using the CMC tracking device in three stages: (1) device calibration and estimation of human thumb joint angles, (2) kinematic mapping algorithm of human to robot thumb, and (3) motion control. The personalization of the tracking device to accommodate different thumb sizes and the proposed mapping algorithm were evaluated by two volunteers. The volunteers were able to personalize and calibrate the CMC tracking device and then use the developed system to oppose a cylinder against the palm of the ARH by controlling only the base two ARH joints. The performance of the overall system following the proposed framework was evaluated by one of the volunteers. The system tracked the thumb motion of the volunteers and mapped the motion to control the robot thumb to perform prescribed poses and power and precision grasps. The manuscript is organized as follows. The background and related literature are discussed in Section 2. The kinematic models of the human and robot thumbs are presented in Section 3, and the tracking device development and functionality are discussed in Section 4. The developed framework is introduced in Section 5. The evaluation of the performance of the tracking device and the telemanipulation system following the proposed framework is presented in Section 6. The manuscript closes with conclusions and remarks for future research.
###### Abstract  
Microalgae has received overwhelming attention worldwide as a sustainable source for energy generation. However, the production of biofuel from microalgae biomass consists of several steps, of which lipid extraction is the most important one. Because of the nature of feedstock, extraction needs special attention. Three different methods were studied to extract algal oil from two different algae variant, _Chlorella_ sp. and _Spirulina_ sp. The highest percentage oil yield was obtained by ultrasonication (9.4% for _Chlorella_ sp., 6.6% for _Spirulina_ sp.) followed by the Soxhlet and solvent extraction processes. Ultrasonication and Soxhlet extraction processes were further optimized to maximize oil extraction as solvent extraction was not effective in extracting lipid. For ultrasonication, an amplitude of 90% recorded the highest percentage yield of oil for _Spirulina_ sp. and a 70% amplitude recorded the highest percentage yield of oil for _Chlorella_ sp. On the other hand, for Soxhlet extraction, a combination of chloroform, hexane, and methanol at a 1:1:1 ratio resulted in the highest yield of algal oil. Afterward, the crude algae oil from the ultrasonication process was transesterified for 5 h using an immobilized lipase (Novozyme 435) at 40 \({}^{\circ}\)C to convert triglycerides into fatty acid methyl ester and glycerol. Thus, ultrasonic-assisted lipid extraction was successful in producing biodiesel from both the species.  
microalgae; biofuel; enzymatic reaction; ultrasonication; biodiesel; immobilized lipase
# Research on Influence of Switching Angle on the Vibration of Switched Reluctance Motor
###### Abstract  
Switched Reluctance Motors (SRMs) have emerged as a viable competitor to other established electrical machines. Although SRMs have many advantages, such as a rare earth free nature, simple structure, high fault tolerance capability and low cost, vibration problems due to radial force variations is still a major issue faced by SRMs. Hence, aimed at the problem of vibration suppression for SRMs, this paper proposes a method that focuses on the influence of the change of the turn-on angle and turn-off angle on the vibration of the SRM under the switching angle control (SAC) strategy. Firstly, the influence of the turn-on and turn-off angles on the harmonic components of the current is analyzed in detail. Then, the vibration caused by the frequency of the harmonic components of the current and the natural frequency of the motor is mainly studied. The results show that the harmonic order affecting vibration is related to the rotational speed, and by analyzing the value of this harmonic order, the variation law of vibration with the switching angle can be obtained. When the turn-off angle is constant, the amplitudes of the current harmonic component and vibration first decrease and then increase with the increase of the turn-on angle. Additionally, when the turn-on angle is constant, the current harmonic and vibration show the tendency of periodic oscillation with the variation of the turn-off angle, and the oscillation period is related to the harmonic order. The combination of switching angles that minimizes the certain current harmonic component also minimizes vibration. The effectiveness of the variation law was verified on a 12/8 poles and 1.5 KW SRM drive system test bench, which provide a new perspective on vibration suppression of SRMs.  
SRM; vibration suppression; SAC