esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The Monte Carlo method, based on probability and statistics, approximates solutions to problems by generating a large number of random or pseudo-random samples. It has been widely applied in various scenarios, such as laser signal propagation in rainy environments [17], underwater environments [18,19], and smoky environments [20,21]. Unlike these scenarios, MMW ground fuzes operate within the MMW frequency band, using radio waves for signal transmission and reception, and feature an ultra-wide beam. When using Monte Carlo method to simulate the echo signals of MMW fuzes under rainy condition, tailored adjustments were made, including (1) transmitting antenna's Gaussian emission pattern; (2) the target scattering model in MMV band with random sampling; and (3) precise incident angle calculation for accurate receive gain assessment. With these adaptive adjustments, the Monte Carlo method allows for the study of the impact of rainfall on MMW fuze detection and validates the accuracy of the simulation results through experimental verification. This study contributes to accurately assessing the impact of rainfall on the detection performance of MMV fuzes. The remainder of this paper is structured as follows: Section 2 discusses the attenuation and scattering characteristics of MMW signals under rainfall, establishing the foundation for the subsequent models. Section 3 provides a detailed derivation of the mathematical model for MMW fuze echo signals under rainy conditions. Section 4 outlines the simulation process of the fuze echo signal using the Monte Carlo method and the derived model. Section 5 analyzes the results of the simulations. Section 6 describes the verification of the simulation results through actual measurements. Section 7 concludes this paper with a summary of the findings.
## 3 Data Preparation
### Prediction Accuracy Evaluation  
To assess the performance and accuracy of the forecasting models, it is essential to select appropriate evaluation metrics, as some are scale-dependent while others are scale-independent [19]. Scale-dependent evaluation metrics, such as root mean squared error (_RMSE_) and mean absolute error (_MAE_), are commonly used. _RMSE_ quantifies the overall fit of the model by measuring how the errors are distributed. _MAE_, on the other hand, captures the average deviation between the predicted values and the actual values. These metrics are advantageous as they utilize the same scale as the original data, enabling straightforward comparisons.  
Additionally, representing the error in percentage form provides a clearer understanding of the model's performance. For this purpose, the mean absolute percentage error (_MAPE_) is utilized. _MAPE_ calculates the average of the absolute values of the relative errors, offering insights into the magnitude of the error relative to the actual values.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{\mathrm{N}}\left(y_{i}^{pred}-y_{i}^{act}\right) ^{2}}{\mathrm{N}}} \tag{12}\]  
\[MAE=\frac{\sum_{i=1}^{\mathrm{N}}\left|y_{i}^{pred}-y_{i}^{act}\right|}{ \mathrm{N}} \tag{13}\]  
Figure 7: Contrast between daily gas production of well MIP-3H pre- and post-smoothing.  
\[MAPE=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}}\left[\frac{\left|y_{i}^{pred}-y_{i }^{act}\right|}{y_{i}^{act}}\right]\times 100 \tag{14}\]  
In the above equations, \(N\) is the total number of observations, \(y_{i}^{act}\) is the observed value at the \(i\)th position, and \(y_{i}^{pred}\) is the predicted value at the \(i\)th position.  
The _RMSE_, _MAE_, and _MAPE_ metrics are utilized to measure the accuracy of the model in forecasting the time-series production of the well. A lower value for these errors, including _RMSE_, _MAE_, and _MAPE_, indicates better model performance and greater precision in the prediction of the production data. Conversely, higher values of these errors suggest a less efficient forecasting model.
## 2 Materials and Methods
### Computation Methods  
The amount of dye adsorbed onto MES/MMS was computed from Formula (1).  
\[Q_{S}=(C_{0}-C_{S})\times\frac{V}{m} \tag{1}\]  
\(Q_{S}\)--the mass of sorbed dye [mg/g];  
\(C_{0}\)--the initial concentration of the dye [mg/L];  
\(C_{S}\)--concentration of the dye after sorption [mg/L];  
\(V\)--the volume of the solution [L];  
\(m\)--sorbent mass [g].  
The data on dye sorption onto the tested sorbents were modeled using the pseudo-first-order model (2), pseudo-second-order model (3), and the intraparticle diffusion model (4).  
\[Q=q_{e}\times(1-e^{(-k_{1}\times t)}) \tag{2}\]\[Q=\frac{(k_{2}\times q_{e}{}^{2}\times t)}{(1+k_{2}\times q_{e}\times t)} \tag{3}\]  
\[Q=k_{id}\times t^{0.5} \tag{4}\]  
\(Q\)--the instantaneous value of sorbed dye [mg/g];  
\(q_{e}\)--the amount of dye sorbed at equilibrium state [mg/g];  
\(t\)--time of sorption [min];  
\(k_{1}\)--pseudo-first-order adsorption rate constant [1/min];  
\(k_{2}\)--pseudo-second-order adsorption rate constant [g/(mg\(\cdot\)min)];  
\(k_{id}\)--intraparticle diffusion model adsorption rate constant [mg/(g\(\cdot\)min\({}^{0.5}\))].  
Maximum sorption capacity data were modeled using three sorption isotherms: Langmuir 1 (5), Langmuir 2 (Langmuir double isotherm) (6), Freundlich (7), and Dubinin-Radushkevich (8).  
\[Q=\frac{(Q_{max}\times K_{C}\times C)}{(1+K_{C}\times C)} \tag{5}\]  
\[Q=\frac{(b_{1}\times K_{1}\times C)}{(1+K_{1}\times C)}+\frac{(b_{2}\times K_{ 2}\times C)}{(1+K_{2}\times C)} \tag{6}\]  
\[Q=K\times C^{\frac{1}{\alpha}} \tag{7}\]  
\[Q=Q_{max}\times\exp(-B\times\left(R\times T*\ln\left(1+\frac{1}{C}\right)^{2} \right)) \tag{8}\]  
\(Q\)--the mass of sorbed dye [mg/g];  
\(C\)--concentration of the dye left in the solution [mg/L];  
\(Q_{max}\)--maximum sorption capacity from the Langmuir equation [mg/g];  
\(b_{1}\)--maximum sorption capacity of the sorbent (type I active sites) [mg/g];  
\(b_{2}\)--maximum sorption capacity of the sorbent (type II active sites) [mg/g];  
\(K_{C}\)--constant from the Langmuir equation [L/mg];  
\(K_{1}\) and \(K_{2}\)--constants from the Langmuir 2 equation [L/mg];  
\(K\)--the sorption equilibrium constant in the Freundlich model;  
\(n\)--constant in the Freundlich model;  
\(B\)--constant in the Dubinin-Radushkevich model [mol\({}^{2}\)/\({}^{2}\)];  
\(R\)--universal gas constant--8.314 [J/mol\(*\)K];  
\(T\)--absolute temperature (K).
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
###### Abstract  
This work addresses the multitask-based trajectory-planning problem (MTTP) for space robotics, which is an emerging application of successively executing tasks in assembly of the International Space Station. The MTTP is transformed into a parameter-optimization problem, where piecewise continuous-sine functions are employed to depict the joint trajectories. An improved genetic algorithm (IGA) is developed to optimize the unknown parameters. In the IGA, each chromosome consists of three parts, namely the waypoint sequence, the sequence of the joint configurations, and a special value for the depiction of the joint trajectories. Numerical simulations, including comparisons with two other approaches, are developed to test IGA validity.  
space robotics; redundant; free-floating base; multiple tasks; trajectory planning; genetic algorithm +
Footnote †: journal: Applied Sciences  
_Article_  
Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 7 Results' Interpretation and Discussion
If we had the advantage of hindsight, our experimental design, which revolved around the mainstream computing architecture, would have been more relevant to the tested hardware wallet IoT device. In our current experiments, the ZVC software was installed on the NAND chip firmware with the following caveats:  
**First**, the obliteration of the attack, although adequate for the device tested, may not be complete, as the hardware supplier's firmware may remain a source of third-party permissions, and therefore vulnerable. **Second**, the restricted environment and limited processing power of a hardware wallet IoT device limit the extrapolation of the results to more complex mainstream computers. **Third**, although zeroing of the attack surface and maintaining sensitive data offline in a connected device may address most cyber-attacks, this cannot rule out the possibility of side-channel attacks if the connected device remains physically accessible to a hardware hacker and remains operational during the attack. **Fourthly**, although the experimental results led to the formulation of two new hypotheses (Section 6), the device we developed technically neither qualifies as quantum-resistant, nor as solid-state software-on-a-chip (3 SoC) as the 3 SoC hypothesis we formulated for future research defined in Section 9. **Finally**, the results of this research helped us fine-tune the definition of ZVC:  
_ZVC is a cybersecurity paradigm that proposes a new zero attack surface computer architecture that restricts all third-party applications exclusively to a web interface only, declining permissions for any utilization of computing resources by any non-native program and creates a switchable in-computer offline storage for securing sensitive data at the user's behest._
_Applied_  
_sciences_  
_Article_  
**Integration of an MES and AIV Using a LabVIEW**  
**Middleware Scheduler Suitable for Use in Industry**  
**4.0 Applications**  
**[PERSON] *\({}^{\text{\textregistered}}\)**, **[PERSON], [PERSON], [PERSON]\({}^{\text{\textregistered}}\) and [PERSON]\({}^{\text{\textregistered}}\)**  
Centre for Robotics and Intelligent Systems (CRIS), Department of Electronic and Computer Engineering,  
University of Limerick, Limerick V94 T9 PX, Ireland; [EMAIL_ADDRESS] (L.L.); [PERSON]ESS] (J.C.);  
[PERSON]ESS] (D.T.); [PERSON]ESS] (T.N.)  
* Correspondence: [EMAIL_ADDRESS]  
Received: 20 August 2020; Accepted: 9 October 2020; Published: 11 October 2020  
**Featured Application: This work provides details of an integration of a mobile Autonomous Intelligent Vehicle (AIV) with a Manufacturing Execution System (MES), which helps to minimize human intervention requirements in a manufacturing process. Reduction in human intervention in the manufacturing process is one of the key requirements of Industry 4.0. The proposed integration technique helps to ensure efficient production and provides optimum use of the production cells in a smart manufacturing environment.**  
**Abstract:** Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik '_RB-1_' robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV.  
**Keywords:** industry 4.0; smart manufacturing; AIVs; MES; LabVIEW; Turtlebot; RB-1; scheduler
## 4 Laboratory Experiment
### Experiment Procedure  
The procedure step of laboratory experiment mainly includes coil design, saline water configuration, bag installation, and coil arrangement. The detailed process is shown in Figure 13.  
Depending on the characteristics of vehicle-mounted transient electromagnetic detection, the size of coils in the laboratory experiment is also redesigned. The length of the transmitter coil and receiver coil is 50 mm and 250 mm, respectively, and the turn per coil is 5. The shapes of the transmitter coil and the receiver coil are fastened by four control points and a mesh disk, and both coils are placed on the plastic fixed plate. Both coils are connected to the transient electromagnetic instrument via an extension line, and the coil fixed plate with certain flexibility is reinforced by the plastic reinforcement line. The detailed layout of two coils is shown in Figure 13a.  
Figure 12: Indoor transient electromagnetic instrument.  
The saline water with a concentration of 5% at 25 degrees Celsius is pre-configured, as shown in Figure 13b. Water with a volume of 950 L is added to the cuboid box through a PVC pipe, and sodium chloride solid with a weight of 50 kg is also added. After the solid salt is fully dissolved, the resistance of the solution is measured by the U-type pipe and multimeter, and the resistivity of solution can be further converted. If the resistivity does not meet the requirements, the weight of sodium chloride or the volume of water can be adjusted appropriately to achieve the design value of solution.  
The customized plastic bag is laid horizontally on the ground, as shown in Figure 13c. The specified volume of saline water can be injected into the bag through the PVC pipe and control valve. The horizontal dimension of the bag is 3.0 m \(\times\) 3.0 m (length \(\times\) width), and the maximum vertical dimension of the bag can reach to 0.3 m (height) when the bag is fully filled with saline water. In this verification experiment, the bag was injected with appropriate saline water to achieve a thickness of 0.2 m.  
The coil fixed plate is arranged directly above the bag, as shown in Figure 13d. The coil fixed plate is hoisted on two wooden support frames by four nylon ropes. The distance between the coil fixed plate and the customized plastic bag can be adjusted by the nylon rope, and its maximum value can reach up to 4.0 m. In this verification experiment, the distance between the plate and bag was set as 3.0 m in order to reflect the short-distance characteristic of vehicle-mounted transient electromagnetic method.  
Through the above four steps, combined with the selection of transient electromagnetic instrument, the measurement of transient electromagnetic is carried out in the half-space medium. The final layout of laboratory experiment is illustrated in Figure 14.  
Figure 13: The detailed process of laboratory experiment: (**a**) transmitter coil and receiver coil; (**b**) configured saline water; (**c**) customized plastic bag; (**d**) relative position of coil.
## 5 Conclusions and Future Work  
Using image-based attributes, a deep-learning-based author identification method was suggested in this study. Line, imagesc, and pie charts were utilized to construct the image-based features from 1200 articles written by 50 authors from different generations, using a combination of internal and statistical features. An impressive result of 93.58 percent accuracy for the line chart was obtained. In order to evaluate the applicability of the suggested approach with articles from a variety of authors, experiments were also conducted on the partitioned datasets. Furthermore, to show the language independence of the proposed approach, experiments were conducted on an English (C50) dataset. The system was also tested with an incremental number of authors to simulate real-world scenarios. The system was successful in adapting to the changing datasets and a minimal deviation in the overall accuracy was noted in every step.  
In the future, the system will be extended to work with a larger number of articles from various authors of different genres. Experiments will be performed in other languages apart from Bangla and English to judge the language-independent nature of the proposed system. Furthermore, the proposed approach can be further implemented for the author's profiling problem. We also plan to use various techniques, such as clustering, ensemble learning, and extreme learning, to identify authors for respective articles. Furthermore, we would like to explore stylometric features [46], intra and interarticle-based features, graph-based [47], and word embeddings models, respectively, for author identification tasks.  
Conceptualization, A.D. and H.M.; methodology, A.D.; software, A.D. and H.M.; validation, A.D. and H.M.; formal analysis, A.D. and S.S.; investigation, A.D., H.M. and K.R.; resources, A.D.; data curation, A.D.; writing--original draft preparation, A.D., H.M. and K.R.; writing--review and editing, A.D. and S.S.; visualization, A.D. and M.O.S.; supervision, A.B., T.G. and K.R.; project administration, A.D., T.G. and K.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Accuracy (\%)** \\ \hline [PERSON] et al. (2019) [44] & 78.10 \\ [PERSON] et al. (2015) [45] & 68.76 \\ Our method & 93.52 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The results obtained on the C50 dataset using the proposed approach.  
**Acknowledgments:** Authors would like to thank [PERSON] and [PERSON], ex-students of the department of Computer Science, West Bengal State University for their help in dataset collection and development.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Problems, Challenges, and the Future of Phage Therapy
### Biological Challenges and Possible Solutions
Differences in cell wall composition, growth rate, or defense systems between strains may lead to variable phage susceptibility, necessitating highly personalized phage preparations and extensive in vitro testing prior to treatment [131]. The host immune response poses another barrier to therapeutic success. Depending on the patient's immunological status, phage type, dose, and route of administration, immune reactions may neutralize phages or provoke inflammatory or allergic responses. In some cases, increased anti-phage antibody titers have been associated with treatment failure and relapse, requiring a return to conventional antibiotics [145]. Understanding the interplay between phage biology and bacterial defense mechanisms will be crucial for designing robust, durable therapeutic strategies. Further research into host-pathogen-phage dynamics, supported by genomic and proteomic tools, is essential to unlock the full potential of phage therapy against drug-resistant mycobacterial infections. To address these limitations, innovative delivery strategies are being explored. Lipsosomal encapsulation enhances phage stability, enables intracellular delivery, and protects against immune clearance [146]. Surface ligand-functionalized liposomes may improve the targeted uptake by infected macrophages [147]. Aerosolized phage formulations offer a non-invasive method to deliver phages directly to the lungs--the primary site of TB infection [148]. Other vehicles, such as hydrogels and polymer-based carriers, provide a localized, sustained phage release at infection sites [149]. The genetic engineering of phages also shows promise. For example, the development of trehalose polyphleate (TPP)-independent mutants allowsthe infection of _Mycobacterium_ strains that have lost these essential glycolipids--a known resistance mechanism. [PERSON] et al. demonstrated that point mutations in tail spike proteins of BPs and Muddy phages can restore infectivity against TPP-deficient strains, offering a strategy to overcome phage resistance in clinical contexts [137]. Phage cocktails, composed of multiple strains with diverse host ranges and mechanisms of action, help suppress resistance emergence and are particularly effective in treating chronic infections with heterogeneous bacterial populations [11; 135; 150]. Moreover, phage-antibiotic combination therapy has shown synergistic effects in preclinical studies, enhancing bacterial clearance, inhibiting biofilm formation, and reducing required antibiotic doses [106; 118; 151]. The integration of these advanced delivery platforms and engineered phages into personalized therapeutic regimens could significantly enhance the clinical potential of mycobacteria therapy. However, overcoming the biological complexity of the host-phage-pathogen triad remains a prerequisite for widespread and consistent clinical success.