esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this study was to investigate levetiracetam pharmacokinetics in critically ill adult intensive care patients and to identify pathophysiological factors affecting its kinetics. Fourteen critically ill patients in an intensive care unit were enrolled in the study and received intravenous levetiracetam. Blood samples were collected at specific time points to determine the levetiracetam pharmacokinetics. Patient characteristics such as renal function, demographics, disease severity, organ dysfunction, and biochemical laboratory tests were evaluated for their influence on the kinetics of levetiracetam. Estimated glomerular filtration rate (eGFR) had a statistically significant (\(p=0.001\)) effect on levetiracetam clearance. None of the other patient characteristics had a statistically significant effect on the pharmacokinetics. Simulations of dosing regimens revealed that even typically administered doses of levetiracetam may result in significantly increased concentrations and risk of drug toxicity in patients with impaired renal function. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score differed significantly among the three groups with different epileptic activity (\(p=0.034\)). The same groups also differed in terms of renal function (\(p=0.031\)). Renal dysfunction should be considered when designing levetiracetam dosage. Patients with a low APACHE II score had the lowest risk of experiencing epileptic seizures.  
levetiracetam; critically ill patients; population pharmacokinetic modeling; simulated dosage schemes; intensive care unit
## 3 Background
### Other Key Components Selection  
Although CubeSats are relatively small devices, they require many components to operate in space over an extended period. Alongside the thermal, propulsion and power system consideration must be taken to the communication, structure, computing, altitude determination and control. Although these systems are not specified design criteria in this paper scope, defining them is vital to ensure accurate calculation for the considered system as materials, weights and internal power dissipations are all considered.
## 4 Research Design and System Validation  
### TAM-Based Questionnaire  
Technology acceptance model (TAM) is a behavioral model developed by [PERSON] [20] in 1986 based on the rational theory. Its purpose is to find an effective behavioral model to explain the behaviors and attitudes of users' acceptance of new information systems in computer science. The model provides a theoretical basis for understanding the influences of external factors on users' beliefs, attitudes, and intentions, which further affect their use of technologies. The model is also believed to be widely employed to explain or predict the effects of using information technology.  
TAM constructs an acceptance model of users adopting information technology. It believes that cognitive usefulness and usability affect the attitude of using technology and further affect specific behavioral performance. It also argues that people's use of information technology is influenced by their behavioral intentions. In order to verify the practicality of the VR system, this study invites 30 subjects to experience the VR-based system, and then makes a comparison between the VR-based system and the general navigation system for pre-sale housing. TAM-based questionnaires are designed for experiment to understand costumers' attitudes, intention, preferences, and differences of usefulness and usability when using the two systems. A five-point scale (Likert scale) is adopted to determine the respondents' level of agreement, where a score of 5 is \"Strongly agree\". Under the TAM framework, this study makes adjustments and constructs the following four definitions. The questionnaire is shown in Appendix A.  
* Perceived usefulness refers to the degree to which a user believes the use of a system will promote his or her work performance or that it can save efforts. It is the subjective perception that individuals will increase the effectiveness of their work by using a certain information system.
* Perceptive ease of use refers to the degree to which the user perceives that technology is easy to use. If the system is easier to use, the user will be more confident in self-efficacy and self-control, and will have a more positive attitude towards the system.
* Users' attitude toward using information technology is influenced by both perceived usefulness and perceptive ease of use.
* The use of an information system is determined by behavioral intention, while behavioral intention to use is influenced by both individuals' attitudes towards the use of the technology and its perceived usefulness.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 3 Propagation Models  
### Ray Tracing  
Accurate radio channel characterization and coverage prediction methodology are required in order to efficiently plan a communication network. Ray tracing (RT) is a deterministic approach for radio propagation modeling, and it is widely used for detailed channel characterisation and for coverage prediction in various types of environments [41; 42]. Besides accurate modeling, RT offers a notable advantage: it is less dependent on the bandwidth and carrier frequency than stochastic models that are based on measurements. Unlike other empirical and semi-empirical channel models that rely on averaged channel measurements, the deterministic approach of RT utilizes geometrical optics (GO) and case-specific three-dimensional (3D) propagation environment [41]. RT also requires the information about the physical properties of the objects that are present in the environment, but, on the other hand, available ray tracing models are credible and accurate. Recent surveys on RT can be found from [43; 44; 45; 46]. The computational load and complexity of the RT model are higher when compared with other empirical and semi-empirical models. Furthermore, the computational load of the RT approach increases with the complexity of the simulation environment [47]. However, nowadays, RT methods have gained a lot of interest due to the availability of servers and clusters with high computational capabilities. In the literature, two broad classes of RT algorithms can be found based on their implementation i.e., image theory (IT) method [47] and shoot and bouncing ray (SBR) method [48], also known as ray launching (RL). The IT method is suited for a less complex environment, whereas the SBR method is more robust and better suited for complex environment [49]. Algorithm 1 shows the pseudocode of the ray tracing model.  
```
Input: 3D building map, Maximum number of reflections, Maximum number of diffractions, Frequency of operation, Tx and BD location in 3D, Antenna radiation pattern, Building type, planning margins Output: Path loss
1:procedureComputation of path loss
2: Load 3D building map in the tool
3: Compute images of the Tx against each building wall using Image Theory
4: Find the propagation paths i.e., LoS path, and other paths with given number of reflections and diffractions
5: Compute reflection and diffraction loss for reflected and diffracted paths, respectively.
6: Include the impact of Tx and BD antenna gain using 3 GPP antenna model given in Section 4.3.1.
7:ifBD is in \"Outdoor\" environmentthen
8: BPL is zero elseifBD is in \"Indoor\" environmentthen
9:ifBuilding type is \"Old\"then
10: Compute the BPL using Equation (18) given in Section 4.3.2
11:elseifBuilding type is \"New\"then
12: Compute the BPL using Equation (19) given in Section 4.3.2
13: Finally, the total loss is computed which includes other planning margins
14:return Path loss
```  
**Algorithm 1** 3D ray tracing model
## 4 Experiment Setup
### Evaluation Metrics  
Similar to [39], we measure the model's performance using key metrics, including accuracy, precision, recall, and the F1 score, to assess the effectiveness of traffic classification. Accuracy, denoted as \(A\), serves as an indicator of the model's performance, reflecting the proportion of correctly classified instances out of the total samples, and is computed following Equation (16). However, it is essential to understand that high accuracy alone may not provide a complete picture, especially in scenarios with imbalanced datasets. We evaluate model performance using accuracy, precision, recall, and the F1 score as defined in Equations (16)-(19).  
\[A=\frac{T_{p}+T_{N}}{T_{p}+F_{p}+T_{N}+F_{N}}, \tag{16}\]  
Precision and recall offer deeper insights into the model's performance on different classes of traffic. Precision, which measures the ratio of true positive predictions to the total predicted positives, is particularly crucial in assessing the model's reliability in identifying low-latency traffic without false alarms. In contrast, recall, which calculates the ratio of true positive predictions to all actual positives, highlights the model's ability to capture all relevant instances of low-latency traffic. The mathematical representations of these metrics are as follows:  
\[P=\frac{T_{p}}{T_{p}+F_{p}}, \tag{17}\]  
\[R=\frac{T_{p}}{T_{p}+F_{N}}, \tag{18}\]  
The \(F_{1}\) score, as a harmonic mean of precision and recall, provides a balanced metric that is especially useful when dealing with imbalanced classes. In the context of our study, where accurate identification of low-latency traffic is critical, the F1 score serves as a robust measure of the model's overall effectiveness and is defined as  
\[F_{1}=\frac{2 PR}{P+R}, \tag{19}\]  
By focusing on these metrics, we can better understand the strengths and weaknesses of our classification model. For instance, while accuracy gives us a broad view, precision and recall help us delve into specific aspects of model performance that are crucial for practical applications, such as minimizing false positives in low-latency traffic detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Traffic Scenario** & **Low-Latency** \\ \hline
3A & NO \\
3B & NO \\
3C & YES \\
2A + B & NO \\ A + 2B & NO \\
2A + C & YES \\ A + 2C & YES \\
2A + B + C & YES \\ A + 2B + C & YES \\ A + B + 2C & YES \\ \hline \hline \end{tabular}  
* A = File Transfer, B = Video Streaming, C = Low-Latency.  
\end{table}
Table 3: Complex traffic scenarios.  
This comprehensive evaluation ensures that our model is not only accurate but also reliable and efficient in real-world scenarios.
## 2 Materials and Methods
### Methodology
#### 2.3.5 Rockwell Hardness  
ASTM F3122-14 specifies ISO 6508-1:2016 [34] as a standard for the Rockwell hardness (HRB) of additive-manufactured parts. A cubic sample was sectioned along the growth direction; on the internal section of the sample, a 3 \(\times\) 3 line grid was drawn, following the instructions of ISO 6508-1. The procedure was repeated for three different samples to obtain 27 uniformly spaced indentations by means of an A-200 Galileo hardness tester.
## 1 Introduction
Three general procedures for measuring albedo can be found in the literature: Laboratory measurements, remotely sensed measurements, and field measurements. First, the laboratory measurements comprise the use of spectral radiometers and solar simulators to measure the spectral reflectivity of small samples; more extensive measurements can be carried out remotely by fixing the spectral radiometer on aircraft or satellites [33]. The second measurement is the remote sensing technique, which provides spectral data that can generate essential information. However, this technique might not be practical for field applications as it requires a careful estimation of atmospheric attenuation and has limited accuracy for small test surfaces in heterogeneous field environments [34]. The third procedure, taking field measurements of albedo, typically involves installing pyranometers over the test area at a height of less than half a meter, according to the ASTM E1918-06 standard [35]. This standard requires a target area of a minimum of 4 m in diameter, and the measurement should be performed in an open area [36]. [PERSON] et al. [37] proposed the use of a circular shield limiting the target area to 1 m and with a pyranometer installed at a height of 0.18 m. The shadows produced by the sensor and the surroundings were estimated by calculating the view factors of the mentioned surfaces. This last approach was applied in the present study. In this article, an optimized model approach is presented for the sun drying of paddy rice to predict crop temperature and mass evaporation. The objectives of this study were to (i) develop a dynamic model to simulate the sun drying of paddy rice, (ii) determine the relationship of albedo and the solar zenith angle, and (iii) evaluate the accuracy of the model at constant and variable albedos in different environmental conditions in the Philippines.
###### Abstract  
In Situ monitoring of the calorific value of coal has the advantage of reducing the amount of unburned carbon by injecting an appropriate amount of combustion air immediately to induce complete combustion. High sulfur concentrations cause severe environmental problems such as acid rain. In order to estimate the calorific value and measure the sulfur concentration, a new powerful technique for mixed coals was studied. Laser-induced breakdown spectroscopy (LIBS) does not require sample preparation. Several blended coals were used for the experiment to replicate the actual coal-fired power plant conditions. Two well-known data processing methods in near-infrared spectroscopy have been adopted to enhance the weak sulfur emission lines. The performance of the partial least square regression model was established by the parameters such as coefficient of determination, \(R^{2}\), relative error, and root mean square error (RMSE). The RMSE average was compared with the results of previous studies. As a result, the values from this study were smaller by 6.02% for the calibration line and by 4.5% for the validation line in near-infrared spectroscopy. The RMSE average values for calorific values were calculated to be less than 1%.  
laser-induced breakdown spectroscopy (LIBS); coal; sulfur; calorific value; multivariate data processing 2022
## 3 Development of Network-Level Pavement Maintenance Optimization Model
### Development of the Optimization Model  
#### 3.2.1 Determining the Minimum Maintenance Budget Funds  
In the whole life cycle of a road, we must keep the road performance and service level in a sustainable premise so that the road can provide better service throughout its life cycle. So, we will consider the entire life cycle of the road. The road performance and service level will not decline throughout the life of the road, that is, the decision makers of this year's road maintenance funds for the budget must refer to the previous year's road performance and service level so that this year's maintenance goals are not lower than the value of the same period last year in order to determine the Minimum Budget Funds B.  
The problem of minimum maintenance budget funding can be described as:  
\[MinB_{t}=\sum_{i=1}^{n}\sum_{k=1}^{11}\sum_{j=1}^{5}a_{i,k,j}L_{i}D_{i,k}b_{i,j} \tag{12}\]  
The constrains are as follows:  
\[\begin{array}{l}\sum_{j=1}^{5}a_{i,j,k}\leq 1,i=1,2,\ldots n;k=1,2,\ldots 11;\\ \sum_{i=1}^{n}\sum_{k=1}^{11}\sum_{j=1}^{5}a_{i,k,j}L_{i}D_{i,k}C_{k,j}\geq Z_ {l}-Z_{t-1}\\ a_{i,10,1}=a_{i,10,2}=a_{i,10,4}=a_{i,10,5}=0,a_{i,10,3}=1\\ a_{i,11,1}=a_{i,11,2}=a_{i,11,3}=a_{i,10,4}=0,a_{i,11,5}=1\\ a_{i,k,j}=\{0,1\}\end{array} \tag{13}\]  
where:  
\(B_{t}\)--the value of the decision objective function, indicating the budgeted funds for road maintenance in year t.  
\(a_{i,k,j}\)--the main decision variable, indicating that if the i-th road is in the k-th combination state of the road section using the j-th maintenance measure, \(a_{i,k,j}=1\), otherwise \(a_{i,k,j}=0\), (\(i=1\), \(2\),..., \(n\); \(k=1\), \(2\),..., \(11\): \(j=1\), \(2\), \(3\), \(4\), \(5\))  
\(L_{i}\)--the total mileage of the i-th road in the road network (km).  
\(D_{i,k}\)--the proportion of the i-th road in the road network that is in the k-th road performance combination state.  
\(b_{i,j}\)--the cost per kilometer of the i-th road in the road network to take the j-th maintenance measure (million CNY/km).  
\(C_{k,j}\)--the value of the road performance improvement if the j-th maintenance measure is applied when the road is in the k-th road performance combination state.  
\(Z_{t}\)--the PQI value of pavement in year t.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 5 Discussion
Despite the notable improvements achieved with our proposed method, several limitations remain, indicating the scope for further enhancement. A major limitation was the dependence on structured data availability. Our method involves the use of structured datasets such as BDD-100K for training, which might not be readily available for all autonomous driving domains. The requirement of labeled datasets restricted the applicability of our method to environments where annotated data were scarce or unavailable. To address this issue, future research should explore the integration of unsupervised or self-supervised learning techniques. Such integration can allow the model to learn from unlabeled data and thereby enhance its generalizability across diverse autonomous driving datasets. Another key challenge is real-time performance and latency considerations. While the use of LoRA effectively reduced the computational overhead, the real-time use of the model in autonomous vehicles would require additional optimization to meet latency constraints. Ensuring low-latency inference is important for real-world applications, especially in dynamic driving environments where rapid decision-making is necessary. Future research should explore techniques such as model compression and quantization along with edge-device deployment strategies to improve the inference speed while maintaining high accuracy. These optimizations would enhance the feasibility of our method for real-time autonomous driving systems, making it more adaptable to resource-constrained environments.
## 2 Materials and Methods
### Calcein-AM Assay and Binary Evaluation of the RPE Damage  
The viability of the RPE cells was tested by a calcein-AM assay directly after microsecond laser irradiation. Calcein-AM diffuses into the cell and is converted by intracellular esterases into calcein that fluorescents under blue light. Dead cells exhibit no esterase activity and therefore show no fluorescence. The RPE-choroid-sclera explants were incubated with 3 mM calcein-AM in PBS for 15 min. at room temperature. The viable-lethal analysis was conducted using a fluorescence microscope (Eclipse Ti-E, Nikon, Tokio, Japan) and a FITC-filter (excitation wavelength: 465-495 nm, dichroic for 505 nm, barrier filter at 515-555 nm). The cellular calcein-fluorescence assay had a binary outcome. In the case of visible SRT-induced damage, lesions were evaluated with 1 and in the case of no damage  
Figure 2: (**a**) Calcein-AM image of porcine RPE-choroid-sclera explant No. 2 after laser irradiation, showing the large LPC marker lesions (white triangle) and lesions in the probe region (white-dashed rectangle; 10 \(\times\) 10 lesions) where the microsecond treatment pattern was applied. The marker lesions served as orientation guide in the evaluation process of the experiments. (**b**) Close-up view of the explant showing a rectangular lesion in the RPE applied with microsecond laser irradiation with a spot of size 120 \(\times\) 120 μm\({}^{2}\). (**c**) When viewed _en face_, the RPE single-cellular layered hexagonal shape is revealed. Typically, the single porcine RPE cells measure about 14 μm in diameter, which corresponds to the size of RPE cells in the human fovea [33]. (**d**) The strongly pronounced rectangular lesion is directly related to the high-quality square top-hat laser spot with an IMF of 1.3.  
with 0. Visible damage was defined as a cluster of at least three non-fluorescing RPE cells in the exposure region, which appear dark in fluorescence microscopy. RPE damage exposure thresholds were determined via Probit-Plot analysis. The calculation was performed with Origin 2018 (OriginLab Corporation, Northampton, MA, USA) utilizing the Levenberg Marquardt iteration algorithm to fit with a \(\chi^{2}\)-tolerance-value of 1 \(\times\) 10\({}^{-9}\) within up to 400 iteration steps. The Probit-fit provides the irradiation threshold (ED\({}_{50}\)-value). An ED\({}_{50}\) irradiation value means that 50% of treatments at this irradiation level depict RPE lesions. ED\({}_{15}\) and ED\({}_{85}\) values were calculated to visualize the width of the fitted normal distribution in a logarithmic covariant basis [35]. Post-processing of the fluorescence microscopies was accomplished with the Fiji image processing package distribution of ImageJ [36].
## 3 ESTCube-Luna E-Sail Payload and Experiment Operations
### E-Sail Hardware
When the tether is deployed, the spin plane thrusters can be used to tilt the spacecraft body for communication sessions. options in the 8 kV to 40 kV range [49]. The baseline voltage of 20 kV can be provided by a 204 g module with dimensions of 119 mm \(\times\) 38 mm \(\times\) 25 mm. If we were to increase the voltage to 40 kV, then the mass of the high-voltage module would increase to 371 g (dimensions: 177 mm \(\times\) 41 mm \(\times\) 29 mm). Upon starting ESTCube-LuNa development, other options for high-voltage source will be considered. The high-voltage electronics will implement both voltage and current control, either to fix the energy of the electron beam or enable stable emission currents for long-term operation. In terms of the E-sail power management, the high-voltage level depends on the surrounding plasma and the available power to achieve the thrust performance independent of the solar wind density. By employing Equation (2) with the 20 kV tether charge and the 2 km tether length, the electron current gathered by the tether is 42 uA, which results in the power consumption of 0.84 W. As discussed in Section 5, 2 W is budgeted for the E-sail, which accounts for the efficiency of the high-voltage source and for variations in the solar wind density. If the solar wind is denser than normal and no more power is available than budgeted, then the high-voltage source will smoothly reduce the voltage to be less than the nominal 20 kV. In the case of ESTCube-LuNa's demonstration, providing a specific E-sail thrust value will not be possible, but we can run the system with maximum available power and maximum allowed voltage. The electron emitter will be based on carbon nanotube (CNT) technology, which has already been successfully tested in orbit on the 1-unit cubesat UWE-4 [41]. The ESTCube-LuNa electron emitter will feature a different geometry and spacing to accommodate the much higher operating voltage. Special care will be necessary to limit the energy of sparks at these high voltages in order to not damage both the emitter and the electronic circuit. The electron beam divergence half-angle is estimated to be 35\({}^{\circ}\).
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 5 Experiment Results and Analysis  
### Experiment on Non-Destructive Testing of Anchor Bolts  
#### 5.1.1 Anchor Bolt and Anchoring  
The experimental anchor bolts had a diameter of 20 mm and a length of 1500 mm. The anchoring section had a length of 1000 mm, and the performances of the anchor bolt and anchoring parameters are listed in Table 3. The anchor bolt protrudes 500 mm from one end of the rock mass for the installation, excitation, and measurement of the ultrasonic guided wave device. The length of the corrosion zone was set to 80 mm.