esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
An artificial neural network modelling approach was used to predict the specific energy (SPE) requirements of a power operated vertical axis rotor (RVA) type intra-row weeding tool. The depth of operation (DO), cone index (CI), and \(u/v\) ratio were considered as the model inputs. The DO had maximum influence on the SPE requirement of the RVA (F-stat: 363.50), followed by the CI (F-stat: 183.19) and the \(u/v\) ratio (F-stat: 3.72). A 3-5-1 neural network model was found capable of accurately predicting the specific energy requirement of RVA unit under varying operating conditions as indicated by high \(R^{2}\) (0.9735), low _RMSE_ (0.00614) and low _MAE_ (0.0225) for the training phase, as well as for the testing phase (\(R^{2}\): 0.9114, _RMSE_: 0.0197, _MAE_: 0.0479) on an independent trial dataset. In the soil bin conditions, the chosen factors of DO, CI, and \(u/v\) and their levels possibly cover all typical operating conditions which would contribute to suffice reliability of the ANN-based SPE estimations. Importantly, such an estimation approach will guide manufacturers and design engineers to develop matching implements for the tractors and operating conditions to ensure high performance efficiency and crop productivity. Other AI-based modelling techniques such as FIS (Fuzzy Inference System) and ANFIS (Adaptive Neuro-Fuzzy Inference System) will also be employed in future studies to improve the accuracies of SPE predictions. Extensive field trials on different soil types and vegetation conditions will be conducted hereafter.  
Conceptualization, S.P.K. and V.K.T.; methodology, S.P.K., V.K.T. and A.K.C.; software, S.P.K., A.K.C. and B.N.; validation, C.M.P. and C.R.C.; formal analysis, S.P.K.; A.K.C. and C.M.P.; investigation, S.P.K. and A.K.C.; resources, V.K.T.; data curation, S.P.K. and C.M.P.; writing--original draft preparation, S.P.K., C.R.C. and C.M.P.; writing--review and editing, C.R.M. and A.K.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Parameters** & **Training Phase** & **Testing Phase** \\ \hline \(R^{2}\) & 0.9735 & 0.91139 \\ _RMSE_ & 0.00614 & 0.01974 \\ _MAE_ & 0.02247 & 0.047913 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical measures of the ANN model performance in training and testing phases.  
Figure 10: Comparison of measured and ANN-predicted specific energy requirements.  
**Funding:** This research was supported by the All India Coordinated Research Project on Farm Implements and Machinery, ICAR, Government of India, New Delhi, India.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Acknowledgments:** The authors are thankful to DDG (Engg), and ADG (F. [PERSON]), ICAR, New Delhi; Project Coordinator, All India Coordinated Research Project on Farm Implements and Machinery for their financial assistance and valuable guidance to conduct this research work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
We have presented an efficient boundary element method with a multi-layer formulation and first-order non-reflecting boundary condition for the simulation of sound propagation in a shallow water environment. By using the pre-corrected fast Fourier transform method, the developed method, PFFT-BEM, which reduces the computational cost from O(\(N^{2}\)) to O(_Nlog N_), can be applied to the 3D full-field direct numerical simulation of sound propagation in environments with a sound frequency of up to O(100) Hz and a domain size of up to O(10) km. We found that the developed multi-layer numerical method is capable to simulate the sound propagation in inhomogeneous waveguide accurately. We also concluded that the first-order NRBC used in PFFT-BEM can efficiently eliminate the errors due to reflection from artificial numerical boundary. We demonstrated the usefulness of the developed PFFT-BEM for shallow water acoustic applications. First, the 3D direct numerical simulation capability of PFFT-BEM enable us to check the validity of classical approximation models. We show the evaluation of the 3D PE models for three examples: the ASA wedge, the 3D seamount, and the Gaussian canyon. Through these comparisons, we demonstrated and quantified the differences between 3D direct numerical simulation results by PFFT-BEM and 3D PE results due to the main assumptions used in the 3D PE. To show the usefulness of the PFFT-BEM in real applications, we compared the numerical results obtained by the 3D+T PFFT-BEM and the field measurement data from the SWARM\({}^{\prime}\) 95 internal wave experiment. The transmission loss obtained with the PFFT-BEM showed strong variability as the internal wave train moved. The magnitude and trend of these numerical variations agree fairly well with the field experiments. This indicates that the 3D+T PFFT-BEM is applicable for the direct simulation of sound propagation in a complex 3D shallow water environment. We concluded that the developed direct numerical method, PFFT-BEM, is very useful to quantify the accuracy of exiting numerical methods and simulate sound propagation in realistic multi-layer shallow water environments.  
Conceptualization, C.L.; methodology, C.L.; software, C.L.; validation, C.L.; formal analysis, C.L.; investigation, C.L.; resources, C.L.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, J.L.; All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the Office of Naval Research (N00014-16-1-2200) and was granted access to the HPC re-sources provided by the DOD HPC Modernization Program.  
The authors declare no conflict of interest.  
Figure 19: Comparison of field normalized acoustic pressure data and normalized acoustic pressure predicted by the PFFT-BEM.
# SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration
## 3 Student Counterfactual Analysis
### Analytical Objectives  
After regular meetings with professionals in different areas involved in university welfare, it became clear that they needed mechanisms to integrate different data types into the dropout analysis. In particular, during these meetings, they manifested their primary interest in a system to assist them in automatically recognizing features to guide their preventive/corrective actions. In contrast, the tool currently used gives only descriptive information, making it hard to analyze specific groups.  
The analytical objectives of our interactive system are aimed at aiding professionals to better understand the reasons why students drop out, while simulating possible scenarios by setting different values for actionable features. In summary, the main objectives are as follows:  
**AO1--Individual and student group characteristics analysis.** Identify how the characteristics of an instance or cluster affect the risk of dropout. Identify the values distribution of variables for dropout students.  
**AO2--Identify a group of students by indicators of similarity.** Automatic dropout detection should provide an indicator or a set of indicators of how feasibly a dropout can be prevented. Such indicators must support decision-making and generate new specialized preventive actions over specific groups of students with similar features.  
AO3--Find different scenarios that lead to dropout reduction in a set of students. Accessing an analysis of different scenarios of an individual or group of instances is a fundamental need. The user could compute and examine the various options and choose the best alternative, based on individual needs.  
**AO4--Customize and evaluate scenarios with feasibility and factibility metrics.** Despite the existence of multiple scenarios, the scenarios do not always match the user's needs. In many cases, users should guide the exploration based on their preferences/expertise, to evaluate the scenarios based on quantitative metrics (feasibility and simplicity).  
**AO5--Retrieving instances based on specific queries.** It could be helpful for university policymakers to know how chance in a scenario could impact a group of students. Thus, it is crucial to group the students based on criteria guided by the analyst's requirements.  
**AO6--Compare how the scenarios could impact different groups.** Education authorities sometimes are interested in knowing whether the action for a particular group of students could have the same effect on another group. It is necessary to evaluate the impact of a specific intervention in a group to obtain scenarios leading to dropout reduction.
# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
## 3 Results and Discussion  
The quality of the model depends on the structure and parameters of the model. We chose a grey-box first-principle structure as simple as possible to be able to use the model directly in a real-time control system of a house that has limited memory capacity and computing power. Such a model must be adjusted to a specific building, and some parts of the model might need to be described with more complex relationships. On the other hand, even the accuracy of the weather forecast is not ideal, and it will influence the predictions and decisions as well.  
Uncertainty in the weather forecast then leads to uncertainty in the energy demand prediction. We tested the prediction abilities of the model on weather forecast data between 22 July and 30 July 2020. The absolute mean for each 3 h forecast was around 0.1 and 0.2 EUR, which made 8 EUR uncertainty on the whole forecast horizon of 5 days. The non-absolute pricing error showed that in the period between 22 July and 30 July 2020, the energy demand predictions tended to be overestimated. The cumulated sum of the non-absolute means was \(-3.5\) EUR.  
Part of the work was focused on the possibilities of energy demand optimization via changing the desired indoor temperature. The cost function consists of the price for comfort and the price for heating and cooling. It was assumed that the user would choose the desired temperature and the times of the three regimes: presence, absence, and nighttime. The optimization would then find three different deltas (each used in different time frames) by which the desired temperatures should change to accommodate the compromise between price and comfort. The cost function used for the optimization was defined as a weighted sum of the absolute deviation from the desired temperatures and the price paid for electricity. The ranking optimization method was chosen, so the deviation from the desired temperature was weighted by the weight r, and the price was given a fixed weight of 1.  
The effects of changing the weight of comfort on the price and the temperature indoors are not very straightforward, as these also change with weather forecasts. The optimization was run for a range of different values of the comfort weight in order to make the possibilities of the compromise more comprehensible. These possibilities are depicted by a pricing range for different changes to the desired temperature. Such a range could then serve for a posteriori choosing of the comfort weight.  
The optimization did not take into account the possibilities of pre-heating or pre-cooling. This is a topic to be investigated in future works.
# A Mathematical Model for Reduction of Trim Loss in Cutting Reels at a Make-to-Order Paper Mill
## 3 Mathematical Model  
The mathematical model for minimizing trim loss while cutting master reels of flexible widths and stocked reels of smaller widths to fill a given order is characterized by the following indices, parameters, and decision variables.  
**Indices**  
\(a=1,2,\ldots,l\)  
\(b=1,2,\ldots,m\)  
stocked reels of width \(b\)  
\(c=1,2,\ldots,n\)  
feasible combination in master reels of width \(W_{a}\)  
\(d=1,2,\ldots,r\)  
feasible combination in stocked reels of width \(W_{b}\)  
\(i=1,2,\ldots,u\)  
required width \(i\)  
**Parameters**  
\(W_{a}\) width of master reels  
\(W_{b}\) width of stocked reels  
\(A_{ai}\) number of times a required width \(i\) appears in a feasible pattern of \(W_{a}\)  
\(B_{bi}\) number of times a required width \(i\) appears in a feasible pattern of \(W_{b}\)  
\(T_{ac}\) trim loss in cutting feasible pattern \(c\) of \(W_{a}\)  
\(T_{bl}\) trim loss in cutting feasible pattern \(d\) of \(W_{b}\)  
\(N_{b}\) available number stocked reels of \(W_{b}\)  
\(L_{i}\) length of smaller demanded widths \(i\)  
\(L_{a}\) length of master reels of width \(W_{a}\)  
\(L_{b}\) length of stocked reels of width \(W_{b}\)  
\(S_{i}\) shortages in an order for required width \(i\)  
\(E_{i}\) surpluses in an order for required width \(i\)  
\(D_{i}\) demand for reels of smaller width \(i\)  
\(w_{i}\) width of demanded reels \(i\)  
\(k_{i}\) available number of knives to cut an order \(i\)
## 5 Results and Discussion  
Our research includes a comprehensive comparative analysis of the TCN-Autoencoder model with other established methods in the field of time-series anomaly detection. For a robust comparison, we selected a set of models known for their performance in this field, including the GRU-Autoencoder, LSTM-Autoencoder, and Variational Autoencoder (VAE). First, we explain the details of these well-known models. Then, we engage in a discussion that compares the performance of these models with the TCN-Autoencoder model, aiming to highlight the performance of our model in detecting anomalies in time-series data.
# Non-Cannabinoid Metabolites of _Cannabis sativa_ L. with Therapeutic Potential
## 2 Non-Cannabinoid Compounds
### Tepenes and Their Derivatives, Tepenoids
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Monoterpenoids** & & **Sesquietrepenoids** & \\ \hline \(\alpha\)-Pinene & Eucalypt & Borneol & (-)-\(\beta\)-Elemene & Virridiflorol \\ \hline \multirow{2}{*}{Camphene} & Ocimene & Terpinen-4-ol & \(\beta\)- & \(\beta\)- \\  & & & Caryophyllene & (-)-Guaiol \\ \hline \multirow{2}{*}{Sabinene} & \(\gamma\)-Terpinene & -Terpineol & Aromadendrene & (+)-Cedrol \\ \hline \multirow{2}{*}{(-)-\(\beta\)-Pinene} & Sabinene & (+)- & Trans-\(\beta\)- & \(\beta\)- \\  & Hydrate & Dihydrocarvone & Farnesene & \(\beta\)-Eudesmol \\ \hline \multirow{2}{*}{\(\beta\)-Myrcene} & Terpinolene & Nerol & \(\alpha\)-Humulene & \(\alpha\)-Bisabolol \\ \hline \(\alpha\)-Phellandrene & Frenchone & Pulegone & Valencene & \\ \hline \(\Delta\) 3-Carene & Linalool & Carvone & Ledene & \\ \hline \(\alpha\)-Terpinene & Frenchol & Geraniol & Trans-Nerolidol & \\ \hline \multirow{2}{*}{p-Cymene} & (-)-Isopulegol & Geranyl Acetate & Caryophyllene & \\  & & & Oxide & \\ \hline \multirow{2}{*}{Limonene} & Camphor & & Globulol & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of mono- and sesquiterpenoids profiled in cannabis infloreses, leaves, stem bars, and roots for medicinal purposes [2]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Terpene** & **Biological Property** & **References** \\ \hline \multirow{3}{*}{Myrcene} & Potent analgesic & [19] \\  & Antioxidant; neuroprotective; anti-inflammatory & [20] \\  & Anticonvulsant & [21] \\ \hline \multirow{2}{*}{1,8-cineole} & Increases cerebral blood flow and enhances cortical activity & [22] \\ \hline \multirow{4}{*}{Limonene} & inhibits many species of bacteria and fungi—repellant & [23] \\  & Anti-inflammatory; antioxidant; antiviral; antidiabetic; & [20] \\  & anticancer & & [21] \\  & Antidepressant; anticonvulsant & [21] \\ \hline \multirow{4}{*}{\(\alpha\)-Pinene} & Antimicrobial; repellant & [23] \\  & Bronchodilator; anti-inflammatory, & \\ \cline{1-1}  & Memory improvement/enhancement; acetylcholinesterase & [21] \\ \cline{1-1}  & inhibitor & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Linalool \\ (Lavender scent) \\ \end{tabular} } & Anxiolytic; anti-inflammatory; antimicrobial; anticancer; & [20] \\  & neuroprotective; antidepressant & [24] \\ \cline{1-1}  & Anti-influenza & [21] \\ \cline{1-1}  & Sedative; induces apoptosis in cancer cells & [21] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The biological properties of some common terpen in cannabis. The Medical Cannabis Network also reports that a current study is being undertaken by researchers at the Israel Institute of Technology investigating the therapeutic efficacy of a cannabis terpene inhalant formulation in suppressing the immune system response against COVID-19.
# Color Point Cloud Registration Algorithm Based on [PERSON]
## 4 Experiment
### Point Cloud Registration
We choose Point-to-Plane ICP, colored ICP, and learning method DeepICP and DCP with pre-trained parameters as the benchmark. Because of the sensitivity of the initial transformation, we use FPFH and 5-Point RANSAC to coarse register the point cloud, then use the algorithm above to them. The source and target point cloud to be registered we selected in the experiment should be at least 30% overlapped thus there are enough correspondence points to calculate the transformation. To evaluate the performance on the dataset, we use Recall in addition to RMSE. The Recall of the registration is the ratio of the successful registration, which means that the transformation calculated should satisfy the Equation (23). Tables 1 and 2 show the recall of the algorithms when threshold \(\tau=0.5\) and the RMSE, respectively. It can be seen that [14] uses gray as color information which did not improve the accuracy of registration because of the influence of different lighting conditions. Scene 01 and Scene04 are even worse than Point-to-Plane ICP. Our algorithm converts color information from gray to hue, and it solves the impact of different light conditions to a certain part. From the data in Table 1, it can be seen that the recall of our algorithm is significantly improved. Table 2 shows that for each scenario, the RMSE of this algorithm is lower than that of algorithm [14], which fully demonstrates the higher accuracy of this algorithm. We also compare two deep-learning-based algorithm. We use the pre-trained model from their paper and test them on our dataset, and the results show that they do not perform well on our dataset, which is very different from their training dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scene** & **ICP(Point-to-Plane)** & **Colored ICP** & **DeepICP** & **DCP** & **Ours** \\ \hline Scene 01 & 0.30169 & 0.2565 & 0.22677 & 0.25655 & 0.31243 \\ Scene 02 & 0.46729 & 0.49933 & 0.39056 & 0.45560 & 0.57276 \\ Scene 03 & 0.40828 & 0.43826 & 0.43055 & 0.39081 & 0.43183 \\ Scene 04 & 0.72208 & 0.64827 & 0.59185 & 0.57669 & 0.69705 \\ Scene 05 & 0.39475 & 0.44788 & 0.46891 & 0.48991 & 0.47814 \\ Average & 0.45882 & 0.458047 & 0.42173 & 0.43391 & 0.498444 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recall (\(\tau=0.5\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scene** & **ICP(Point-to-Plane)** & **Colored ICP** & **DeepICP** & **DCP** & **Ours** \\ \hline Scene 01 & 0.011659 & 0.010644 & 0.011566 & 0.0133556 & 0.0083734 \\ Scene 02 & 0.009786 & 0.008685 & 0.010001 & 0.012001 & 0.0078229 \\ Scene 03 & 0.010901 & 0.009949 & 0.013001 & 0.009551 & 0.0090334 \\ Scene 04 & 0.009115 & 0.009015
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
## 1 Introduction
Besides antibodies, aptamers, and DNAzyme, another option is to use a peptide or an amino acid as a probe to bind with HMIs, such as cysteine [29]. The most widely reported peptide probe for HMI detection is glutathione (GSH) [30]. Because there are six possible coordination sites in GSH for binding with metal ions, it has distinct advantages in capturing HMIs when compared with cysteine, especially in case of lead ion (Pb\({}^{2+}\)) detection. Furthermore, it is highly stable, cost-effective, and easy to immobilize on a sensor. Therefore, GSH, as a promising probe for HMI detection, has received much attention in recent years. In this review, the chemical properties of GSH and its coordination with HMIs are briefly discussed first. Then, various GSH-based HMI detection strategies are reviewed and discussed. GSH-based HMI detection is categorized according to the signal transduction mechanisms, mainly optical methods and electrochemical methods. For optical detection methods, two formats have been reported, which are metal-nanoparticle-based platforms and semiconductor quantum-dot-based platforms. For electrochemical methods, the sensor structures can be mainly categorized into field effect transistors and working electrodes. Then, the performance of different HMI detection platforms is summarized, and future challenges are discussed.
###### Abstract  
The occurrence of rock bursts due to penetrating faults are frequent in China, thereby limiting the safe production of coal mines. Based on the engineering background of a 501 working face in a TB coal mine, this paper investigates stress and energy evolution during the excavation of this working face due to multiple penetrating faults. Utilizing both theoretical analysis and numerical simulations, this study reveals the rock burst mechanism within the triangular coal pillar influenced by the penetrating faults. Based on the evolution of stress within the triangular coal pillar, a stress index has been devised to categorize both the rock burst danger regions and the levels of rock burst risks associated with the triangular coal pillar. Furthermore, targeted stress relief measures are proposed for various energy accumulation areas within the triangular coal pillar. The results demonstrate that: (1) the superimposed tectonic stress resulting from the T6 and T5 penetrating faults exhibits asymmetric distribution and has an influence range of about 90 m in the triangular coal pillar, reaching a peak value of 11.21 MPa at a distance of 13 m from the fault plane; (2) affected by the barrier effect of penetrating faults, the abutment stress of the working face is concentrated in the triangular coal pillar, and the magnitude of the abutment stress is positively and negatively correlated with the fault plane barrier effect and the width of the triangular coal pillar, respectively; (3) the exponential increase in abutment stress and tectonic stress as the width of the triangular coal pillar decreases leads to a high concentration of static stress, which induces pillar burst under the disturbance of dynamic stress from fault activation; (4) the numerical simulation shows that when the working face is 150 m away from the fault, the static stress and accumulated energy in the triangle coal pillar begins to rise, reaching the peak at 50 m away from the fault, which is consistent with the theoretical analysis; (5) the constructed stress index indicates that the triangular coal pillar exhibits moderate rock burst risks when its width is between 73 to 200 m, and exhibits high rock burst risks when the width is within 0 to 73 m. The energy accumulation pattern of the triangular coal pillar reveals that separate stress relief measures should be implemented within the ranges of 50 to 150 m and 0 to 50 m, respectively, in order to enhance the effectiveness of stress relief. Blasting stress relief measures for the roof and coal are proposed, and the effectiveness of these measures is subsequently verified.  
triangle coal pillar; penetrating fault; stress evolution; rock burst +
Footnote †: journal: Applied Sciences