esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Approach to Designing and Implementing a Recommendation System for Healthy Challenges
## 3 Results
In Figure 3, it is evident that experts responded positively in every single case to question 1. This indicates that none of the proposed challenges are considered harmful or counterproductive for the user; this positive unanimity is the result of the filtering phase of the algorithm, which does not allow an undesirable challenge to enter the user's recommendation. Similarly, in question 2, we obtained a 100% positive response rate, indicating that all the proposed recommendations have the support of the experts; again, because no challenge not compatible with the user can enter the recommendation, all the experts supported the proposed recommendation. Moving on to question 3, the positive and negative responses align with the changes that experts would suggest improving the recommendation. Figure 3: Distribution of experts’ responses regarding the proposed personalized recommendation. During this phase, intentionally adding unnecessary challenges for users resulted in a 43% occurrence of negative responses. Importantly, this trend aligns with cases where unnecessary challenges were purposely introduced, achieving an 87% correlation rate. This suggests that the existence of 87% of these contrived challenges, introduced to counteract expert bias, was indeed recognized. For example, if a user attained a rating of ten points in the ASC metric, it would not be necessary for that user to be presented with a challenge aimed at improving their ASC within their recommended activities. However, if such a challenge was deliberately included for the user, experts should be capable of identifying it. As an illustration, consider the profile of tester A shown in Figure 1, where the challenge \"Forget about sugar\" would not be needed since tester A already achieved a perfect score of 10 in the ASC dimension. It is worth noting that the 13% of fake challenges that went unnoticed can be attributed to the fact that these challenges were not considered problematic in any scenario. Instead, they were simply unnecessary and sometimes difficult to spot. To ensure the validity of the recommendations, an extra validation strategy was applied. Additionally, the 30 initial users received their characterization according to the presented model and the ranked set of recommended challenges tailored for their particular profile. A brief survey consisting of three questions was also submitted to validate the proposal. This outcome is presented in Figure 4. Most users find the challenges achievable and feel confident about successfully finishing them. However, almost half of the users view the challenges as overly tough. This could be due to the inclusion of an additional 10% effort in the model. While this choice could be changed, the authors firmly believe that pushing users beyond their comfort zone can yield significant benefits.
# The Aerodynamic Analysis of a Rotating Wind Turbine by Viscous-Coupled 3D Panel Method
###### Abstract  
In addition to the many typical failure mechanisms that afflict wind turbines, units in Taiwan are also susceptible to catastrophic failure from typhoon-induced extreme loads. A key component of the strategy to prevent such failures is a fast, accurate aerodynamic analysis tool through which a fuller understanding of aerodynamic loads acting on the units may be derived. To this end, a viscous-coupled 3D panel method is herewith proposed, which introduces a novel approach to simulating the severe flow separation so prevalent around wind turbine rotors. The validity of the current method's results was assessed by code-to-code comparison with RANS data for a commercial 2 MW wind turbine rotor. Along the outboard and inboard regions of the rotor, pressure distributions predicted by the current method showed excellent agreement with the RANS data, while pressure data along the midspan region were slightly more conservative. The power curve predicted by the current method was also more conservative than that predicted by the RANS solver, but correlated very well with that provided by the turbine manufacturer. Taking into account the high degree of comparability with the more sophisticated RANS solver, the excellent agreement with the official data, and the considerably reduced computational expense, the author believes the proposed method could be a powerful standalone tool for the design and analysis of wind turbine blades, or could be applied to the emerging field of wind farm layout design by providing accurate body force input to actuator line rotors within full Navier-Stokes models of multi-unit wind farms.  
CFD; panel method; boundary layer; flow separation; thick wake; actuator line model; wind farm +
Footnote †: journal: Applied Sciences
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 3 Data Acquisition and Preprocessing  
### Brief Introduction to Projects A-1 and B-2  
Project A, with a total length of 5.09 km, was constructed as a cable tunnel. The tunnel is divided into four sections according to the construction method: section A-1 as shield TBM (2.26 km), sections A-2 and A-4 (0.74, 1.63 km) as open cut, and section A-3 (0.46 km) as semi-shield TBM. The geological formation consists of predominantly Precambrian biotite banded gneiss and some intrusive rocks. In particular, small amounts of quartzite and limestone are interspersed in the metamorphic rock throughout the whole area. Section A-1, considered in this study, is located at a depth of between 45.0 and 51.5 m from the ground surface. Since the fractured zone is distributed along the tunnel alignment, an earth pressure balance (EPB) shield type was used to excavate the tunnel more efficiently.  
On the other hand, project B (tunnel length is 5.22 km) was built as a subsea tunnel to supply power. The tunnel depth is 60 m from the seabed and passes through strata of soft and hard rock. In section B-2, a slurry shield type was used to resist the high water inflow. Details of equipment specifications are summarized in Table 1.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 3 Methods
### Training Objective  
The training objective is to minimize the loss function which measures the discrepancy between the predicted and true values on the training or validation dataset. Some widely used loss functions are:  
* Mean Absolute Error (MAE): \[L_{mae}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y_{i}}|\]  
Figure 4: The overview sketch of transformer.  
Mean Squared Error (MSE): \[L_{mse}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}\] \[\bullet\] Root Mean Squared Error (RMSE): \[L_{mse}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}}\] \[\bullet\] Huber Loss: \[L_{luther}=\begin{cases}(y_{i}-\hat{y_{i}})^{2}&for\quad|y_{i}-\hat{y_{i}}|\leq \delta\\ 2\delta|y_{i}-\hat{y_{i}}|-\delta^{2}&otherwise\end{cases}\] where y is the real value, \(\hat{y}\) is the predicted value, n is the number of samples, \(\delta\) is a pre-defined threshold.  
Among the losses, MAE is the simplest one. It calculates the mean of absolute differences between the predicted and actual values. MDPE can be regarded as a normalized version of MAE, as large scale values will not have a key impact. Meanwhile, MAE and MDPE can be biased. MSE is unbiased and computationally efficient, but it is sensitive to outliers. RMSE is a less extreme loss function for large values compared to MSE but the gradient is less smooth near the extreme point. Huber loss function [53] is more smooth and robust, but with a greater training burden.
## 2 Material and Methods
### Statistical Analysis  
Normality of the data distribution was assessed using the Shapiro-Wilk test. Categorical variables were compared using [PERSON]'s exact test and the Chi-square test. Continuous variables were presented as either median (interquartile range, IQR) or mean (\(\pm\) standard deviation, SD) based on their distribution. Comparisons between groups were made using either a paired sample \(t\)-test for normally distributed data or the Mann-Whitney U test for non-normally distributed data. Specifically, variables with a normal distribution were reported as mean \(\pm\) SD, and analyses included the \(t\)-test and Pearson correlation. For variables that did not follow a normal distribution, the Mann-Whitney U test and Spearman correlation were applied.  
To assess significant differences within the MWGG and MWMG groups, \(t\)-tests were employed. The Pearson correlation coefficient (denoted as \"\(r\)\") was calculated to evaluate the linear relationship between continuous variables, with correlation values ranging between \(-1\) and \(1\). Additionally, we visually inspected the scatterplot to verify linearity. A \(p\)-value below 0.05 was considered statistically significant. All analyses were conducted using Stata version 16.0 (StataCorp, College Station, TX, USA).
## 3 Model of Image Motion
### Disturbances Rejection Strategy  
Disturbances affecting satellites can ultimately impact the accuracy of their orbital and attitude parameters. By enhancing the measurement precision of these parameters, it is possible to effectively mitigate the influence on the accuracy of IMC.  
For the disturbances affecting a pointing platform, a disturbance rejection control strategy is employed to enhance the disturbance rejection performance. The pointing platform utilizes a three-loop control method involving current, velocity, and position. The current loop employs PI control, with a linear control design that features a sufficiently high bandwidth to ensure a rapid response speed for the current. Within a velocity loop, sliding mode control (SMC) is used to improve the system's ability to reject disturbances and enhance the dynamic response performance of the system. The position loop control ensures the tracking accuracy of the system. The specific controller design for the velocity loop is as follows:  
The mechanical motion of the permanent magnet torque motor is described by  
\[J\dot{\omega}+B\omega=K_{t}i_{q}+d, \tag{10}\]  
where \(J\) is the mechanical inertia of the motor and load, \(B\) is the coefficient of viscous friction, \(d\) is the load disturbance, and \(Kt\) is the torque constant.  
Setting \(\omega_{r}\) as the reference speed, the speed error can be defined as  
\[e=\omega_{r}-\omega. \tag{11}\]We can define the sliding surface as  
\[s=e+c\int_{0}^{t}e(\tau)d\tau, \tag{12}\]  
where \(c\) is a positive constant.  
A sliding mode controller with the exponential reaching law can be designed as  
\[i_{q}=\frac{1}{K_{t}}(\dot{\omega}_{r}+\frac{B}{J}+ce+k_{1}sign(s)+k_{2}s), \tag{13}\]  
where \(k_{1},k_{2}\) are positive constants.  
The stability proof of the controller can be found in [16].  
The block diagram of the imaging control is shown in Figure 4. The camera first receives the orbit and attitude information of the satellite, as well as the position of the target star. Based on the above information, it performs IMC calculations and calculates the angle \(A_{t}\) and \(E_{t}\) that need to be compensated for pointing to the two-axis of the platform. The pointing platform achieves a high-precision, three loop control based on \(A_{t}\) and \(E_{t}\), thereby realizing a high-precision pointing of the platform and controlling the camera rotation.
## 2 Methodology
### Peridynamics Damage Criterion of Coal-Rock Mass
\tag{9}\]In the formula, \(c\) represents the microscopic elastic parameter of the substance, called the micromodulus, and \(e\) represents the elongation of the bond. Based on the principle of strain energy density equivalence, an expression for PD bond-type micromodulus \(c\) is established through traditional continuum mechanics:  
\[c=\left\{ \begin{array}{ll}\frac{3\Gamma}{\hbar\delta^{3}}&(\text{one dimension})\\ \frac{8\Gamma}{\hbar\delta^{3}(1-\
u)}&(\text{plane stress})\\ \frac{8\Gamma}{\hbar\delta^{3}(1-\
u-2\
u^{2})}&(\text{plane strain})\\ \frac{15\Gamma}{2\pi\hbar\delta^{3}(1-2\
u)}&(\text{three dimensions})\end{array} \right. \tag{10}\]  
In the formula, \(E\) is the elastic modulus of coal-rock mass, \(\
u\) is the Poisson ratio, \(h\) is the continuous thickness at the particle point, and \(\delta\) is the neighborhood radius. The criterion for the destruction of the key is an important theoretical basis for analyzing the generation and expansion of damage fractures in coal-rock mass through PD key-type analysis. It can be assumed that when the elongation rate \(|s|\) of the key reaches the critical elongation rate \(s0\), the key will break irreversibly. That is,  
\[|s|\geq s_{0} \tag{11}\]  
The elongation of the key is \(|s|\):  
\[|s|=\frac{e}{|\xi|}=\frac{|\xi+\eta|-|\xi|}{|\xi|} \tag{12}\]  
The critical elongation \(s_{0}\) can be obtained according to the critical energy release rate \(G_{0}\) of coal-rock mass:  
\[s_{0}=\frac{6G_{0}}{\pi\delta^{3}\xi|\{\xi\}-t(-\xi)|} \tag{13}\]  
At the same time, in order to describe and characterize the damage of coal-rock mass from microcracking (microscopic initiation) and fissure (microscopic expansion) to macrofracturing (macroscopic damage), a local damage concept of PD bond-type material point is established, that is, the ratio of the number of broken bonds to the total number of bonds within the near field. \[\text{D}(\text{x},t)=\frac{\int_{\text{H}_{\text{x}}}(1-\varphi(\text{x}^{ \prime},\text{x},t))dV_{\text{x}^{\prime}}}{\int_{\text{H}_{\text{x}}}dV_{ \text{x}^{\prime}}} \tag{14}\]  
In the formula,  
\[\varphi(\text{x}^{\prime},\text{x},t)=\left\{ \begin{array}{ll}1&(|s|<s_{0}) \\ 0&(|s|\geq s_{0})\end{array} \right. \tag{15}\]  
Equation (15) indicates that in the PD bond type, there are only two states of bond destruction and non-destruction for the same material point in the near field, and there is no intermediate state. Due to the extensive presence of discontinuous structures such as pores, joints, and fractures within coal rock bodies, the seepage flow exhibits non-uniformity and anisotropy [11; 12; 13]. The seepage flow foundation model primarily comprises an equivalent continuous medium based on representative units, a discrete fracture network based on fracture seepage, and a dual medium based on pore-fracture seepage.
# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 3 Applications and Implications of Generative AI and LLMs in Medicine
### Can We Trust Generative AI? Is It Clinically Safe and Reliable?  
The integration of generative AI in healthcare offers transformative potential, from streamlining clinical tasks and improving diagnostic accuracy to providing health insights and aiding in disease prediction. However, as rightly noted by [PERSON] et al. [6], LLMs are not yet ready for autonomous clinical decision-making. Significant concerns have been raised regarding LLM output quality variability, bias, ethical issues, and the need for robust regulatory frameworks, all of which must be satisfactorily addressed to ensure the safe and effective use of these technologies [6].  
A number of studies have shown that generative AI cannot reliably extract and summarize information from unstructured clinical notes, e.g., [31; 32], and guidance is being issued by concerned bodies for that purpose; for example, the Royal Australian College of General Practitioners (RACGP) guidance on using AI scribes in practice [33] and the Australian Health Practitioner Regulation Agency (AHPRA) and National Boards guidance on meeting professional obligations when using AI in healthcare [34]. The RACGP guidance reminds general practitioners that they are liable for errors within patient health records even if they were generated by an AI scribe and that they must obtain patient consent to use an AI scribe in the consultation. AHPRA's guidance clearly states that \"practitioners must apply human judgment to any output of AI\".  
In an effort to address the challenge of \"faithfulness hallucinations\" in AI-generated medical summaries, Mendel AI, a US-based startup specializing in healthcare AI, in collaboration with UMass Amherst, used Hypercube, a tool for the automated detection of LLM hallucinations, to mitigate the high costs and time associated with a completely manual review of LLM-generated medical record summaries. Hypercube integrates medical knowledge bases, symbolic reasoning, and NLP to detect hallucinations, allowing for an initial automated detection step before human expert review. If left undetected, these hallucinations can pose significant clinical risks to patients, and lead to misdiagnoses and inappropriate treatments [32].  
The remainder of this subsection explores the regulatory, ethical, and practical challenges associated with implementing generative AI in clinical settings and public health, emphasizing the importance of comprehensive standards, human oversight, and collaboration among stakeholders.
# Mechanical Performance of Cement Bound Granular Mixtures Using Recycled Aggregate and Coconut Fiber
## 3 Results and Discussion
### Unconfined Compressive Strength  
The results of the 7 days UCS are presented in Figure 3. For each CBGM, the average values and standard deviations shown in Figure 3 were determined from three tested specimens. All CBGM presented good UCS results, with average values higher than 5 MPa. Such strength levels allow them to easily comply with the UCS requirements set by most infrastructure agencies. The values largely exceed the minimum UCS set by the Portuguese infrastructure agency [44], a 28 days UCS higher than 1 MPa, and the minimum UCS indicated by the Australian Guide to Pavement Technology [56], a 28 days UCS higher than 2 MPa. Regarding UCS, some of the more demanding specifications are the Spanish PG-3 [57] that requires a 7 days UCS higher than 4.5 MPa and the South African Pavement Engineering Manual, SAPEM [58], which indicates a minimum 7 days UCS of 4 MPa and 6 MPa for the CSM-C2 and CSM-C1 classes, respectively.  
In the case of the CBGM with 2% cement, the use of CF caused a slight decrease in the UCS, 11% and 10% for the CBGM with NA and RA, respectively. On the other hand, in the CBGM with RA and 3% cement, the use of CF increased the UCS by 2%. These variations indicate that the CF reinforcement did not contribute to increasing the UCS. However, on a smaller scale, this was to be expected, as some studies about the use of CF in cement concrete noted that the presence of the CF could promote an increase in voids with a respective decrease in density, which in turn affects UCS.  
For the CBGM using RA, the increase in cement dosage from 2% to 3% increased UCS by 34% and 51% for the CBGM without CF and using CF, respectively.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 2 Preliminaries
### UV Network Coverage
#### 2.3.2 Evaluation Standard  
The evaluation criteria of UV network coverage are mainly coverage rate and k-connectivity. Coverage rate (\(C_{ov}\)) refers to the ratio of the total coverage of all nodes to the entire target area [15],  
\[C_{ov}=\frac{N_{cov}}{TA} \tag{6}\]  
where \(N_{cov}\) represents the coverage area of all nodes, and \(TA\) represents the target area.  
k-coverage means that any target is covered by at least k nodes in the network, and k-connectivity means that any node can communicate directly with at least k nodes in the network. In most cases, the coverage blind zone has a small impact on the overall performance of the network, and the interruption of the communication link causes more serious losses to the performance of the network. Thus the network needs to meet the requirements of 2-connectivity and 1-coverage.  
In this manuscript, the distribution of nodes in the target area of an arbitrary polygon is a uniform random distribution. In the case of ensuring the 2-connectivity of the network, the communication scope of all nodes can cover the entire target area. Therefore, this manuscript focuses on the 2-connectivity of the UV network.  
Figure 4: The Boolean coverage model.
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 5 Discussion
### Performance of Massive Deployment by Using Network Connections  
Similar to Equation (1) being defined in Section 5.1, the speedup for massive deployment in large-scale computers is defined as the ratio of time being required to deploy multiple computers in sequence to the time that is required to deploy multiple computers in the multicast mechanism. Figure 9 illustrates the speedup factor versus various configurations of machines, 1, 2, 4, 8, 16 and 32 in the test environment. The ideal speedup factor assumes no overhead in massive deployment. So, the speedup increases linearly as the number of client machines increases. However, for the real-world number, derived from the experiment in Section 4.2, the speedup is getting saturated as more computers are deployed using the multicast mechanism. From Figure 7b, the efficiency became better when deploying more than eight machines, because the average time differences are decreasing when compared with the cases for deploying one, two, and four machines. This finding is consistent with previous research result in the similar experiments by [PERSON] et al. (2017), which concluded that the total network bandwidth consumption of the deployment is significantly reduced if the machines are more than eight machines in general.  
Figure 8: Speedup for deploying USB flash drives.  
Figure 9: Speedup for deploying massive computers using multicast mechanism.
# A Sigfox Energy Consumption Model
## 5 Evaluation  
Based on the models presented in Section 4, this section evaluates Sigfox device current consumption and lifetime, as well as the energy cost of data delivery. This section is divided in five subsections. The first three focus on one of the aforementioned performance parameters, respectively.  
Section 5.4 analyzes the impact of critical parameters on the model. Finally, Section 5.5 studies the performance of Sigfox devices when using energy harvesting systems as power sources.  
The evaluation considers a wide range of \(T_{Period}\) values, including values (below 10 min) that are smaller than the minimum ones that stem from the maximum uplink and downlink message rate limitations intended to comply with spectrum access regulations. The purpose is illustrating the performance that can be achieved when such regulations are not in force (e.g., in some regions of the world).