esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Traditional spam filtering methods used during SMTP transactions, such as blacklists, whitelists (domains, IP addresses, and email addresses), or SPF logging, do not provide enough accuracy to reduce the threat of spam messages [8]. Blacklists have the disadvantage of not being updated as fast as spammers change their addresses or domains. In addition, legitimate email providers run the risk of having one of their domains or addresses on a blacklist. Moreover, one of the main problems with SPF is the incompatibility with email forwarders. SPF checks can fail when forwarding an email because SPF components authenticate the forwarding server instead of the original sending server [9]. In this study, we introduce a framework designed to use a wide variety of techniques or tools for spam filtering during SMTP transactions (proactive filtering). The goal is to create a new email filtering framework (LiSB) that ensures that the sender of the email knows if the email has not been delivered to the receiver. We have also developed some profile-based filters (modules) that can be run at SMTP time (thus, proactively). These modules are able to check features such as whether the behavior of a given email server changes over time (which is anomalous). We have also built other modules to check for inconsistencies in the information sent. Considering the disadvantages discussed above, this paper also proposes the use of different Machine Learning (ML) schemes for the development of highly reliable spam filters that operate during SMTP transactions. Although the execution of an ML-based filter during the SMTP transaction may seem a simple task, it is actually a major challenge because it is necessary to find a trade-off between the accuracy achieved by the filter and its complexity (number of features) to avoid an SMTP transaction timeout. With this issue in mind, we have also provided another module to execute simple content-based filters using ML schemes (in particular, Random Forests seems the most suitable one [10]). Finally, we analyzed the performance and computational requirements of our proposal. The framework can be easily extended by incorporating new techniques. The source code of the LiSB framework has been publicly shared in a GitHub repository ([[https://github.com/sing-group/LiSB](https://github.com/sing-group/LiSB)]([https://github.com/sing-group/LiSB](https://github.com/sing-group/LiSB)), accessed on 1 September 2022). The rest of the paper is organized as follows: Section 2 summarizes previous efforts and proposals on spam detection during SMTP transactions; Section 3 presents the architecture of the LiSB framework and the evaluation of some ML models that have been considered for use during SMTP transactions; and finally, Section 4 shows the main conclusions and outlines directions for future work.
# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
### Research Questions and Objectives  
In response to the gaps identified in existing literature, this study aims to address the following key research questions: which Industry 4.0 technologies exert the most significant influence on the sustainable development performance of manufacturing enterprises?  
How can these technologies be effectively integrated to maximize their collective impact on sustainability, spanning economic, environmental, and social dimensions?  
What strategies should manufacturing enterprises adopt to successfully implement these technologies in pursuit of sustainable development?  
The primary goal of this research is to cultivate a comprehensive understanding of the role that Industry 4.0 technologies play in advancing sustainable manufacturing. By applying the Fuzzy Integration-Rough Set Theory (FI-RST) model, this study will quantitatively evaluate the effectiveness of various Industry 4.0 technologies in enhancing the sustainability performance of manufacturing enterprises. The research seeks to pinpoint the key technologies that can act as pivotal levers for sustainability, offering actionable insights for industry practitioners and policymakers alike.  
The significance of this research lies in its potential to inform strategic decision making within the manufacturing sector, particularly in the realm of sustainable development. This case study not only bridges the gap between theoretical research and practical application but also paves the way for sustainable manufacturing growth amidst a fast-shifting industrial landscape.
# Contributions of TOR Signaling on Photosynthesis
## 2 TOR: A Key Regulator in Photosynthesis  
Extensive studies have demonstrated that TOR senses and integrates nutrient, hormone, light, energy, and other environmental cues to orchestrate growth and development. As a serine-threonine protein kinase, TOR mediates changes in protein phosphorylation and modulates a myriad of important cellular and metabolic processes, including nutrient assimilation, nutrient transport, ribosome biogenesis, cell division, cell expansion, polysome integrity, metabolism, autophagy, and development [37; 38; 31; 14; 39; 17]. Photosynthesis functions as one of the most important anabolic processes in plants. Recent research suggest that TOR plays an indispensable role in plant photosynthesis [40]. Plants with TOR dysfunctions showed severe defects in chloroplasts and photosynthesis [41; 42; 36; 43; 44]. Transcriptome analysis in TOR-inhibited plants revealed a broad regulation of plant photosynthesis-related genes [28; 30; 41; 45]. Intriguingly, activation of TOR leading to defects in chloroplast development compared with wild type, further supports the pivotal roles of TOR in regulating plant photosynthesis [46; 47]. In the present review, we summarize recent advances that address the functions of TOR in regulating plant photoautrotrophy and maintenance of photosynthesis (Figure 2).
## 7 Summary and Conclusions  
This paper delves into the current UWB IPS research. It starts with a detailed description of different wireless common technologies for IPSs, such as Wi-Fi, BLE, ZigBee, RFID and UWB, along with the research efforts in this regard. Then, it is followed by an evaluation of the advantages and disadvantages of localization algorithms for IPS. The paper also thoroughly surveys the unique characteristics of UWB technology and the challenges still faced by the IPS implementation. The state-of-the-art ML-based research efforts in solving the challenge associated with NLoS effects for UWB are also surveyed and discussed. Furthermore, k-NN, SVM, DT, NB, and NN techniques for ML-based UWB IPSs for indoor localization are discussed in detail as well as some of the obtained results of the UWB IPSs system development so that the ranging error can be reduced to less than 10 m. Finally, the paper identifies limitations and potential open problems for further research related to the successful deployment of ML-based localization techniques and future research directions in this regard.  
Conceptualization, Q.Z.A.; Methodology, F.C. and Q.Z.A.; Software, F.C.; Validation, Q.Z.A.; Formal analysis, F.C.; Investigation, Q.Z.A.; Writing--original draft, F.C. and Q.Z.A.; Writing--review & editing, P.I.L., P.S. and T.A.; Supervision, Q.Z.A. and P.L.L.; Funding acquisition, T.  
All authors have read and agreed to the published version of the manuscript.  
Part of this work is supported by the European Union through the Horizon 2020 Marie Sklodowska-Curie Research and Innovation Staff Exchange Programme \"Research Collaboration and Mobility for Beyond 5G Future Wireless Networks (RECOMBINE)\" under Grant 872857.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
2-Dimensional & 2D 3-Dimensional & 3D  Angle of Arrival & AoA  Angle of Departure & AoD  Bluetooth Low Energy & BLE  Burst Position Modulation & BPM  Channel Impulse Response & CIR  Channel State Information & CSI  Decision Tree & DT  Effective Radiated Power & ERP  Global Navigation Satellite Systems & GNSSs  Inertial Positioning System & IPS  Internet of Things & IoT  k-Nearest Neighbor & k-NN  Machine Learning & ML  Naive Bayes & NB  Neural Network & NN  Non-Line-of-Sight & NLoS  On-Off Keying & OOK  Phase of Arrival & PoA  Physical Layer & PHY  Physical Layer Header & PHR  Power Spectral Density & PSD  Pulse Amplitude Modulation & PAM  Pulse Position Modulation & PPM  Radio Frequency Identification & RFID  Received Signal Strength Indicators & RSSIs  Signal-to-Noise Ratio & SNR  Start of Frame Delimiter & SFD  Support Vector Machine & SVM  Synchronization Header & SHR  Time Difference of Arrival & TDoA  Time-of-Arrival & ToA  Time-of-Flight & ToF  Transfer Learning & TL  Two-Way Time of Arrival & TW-ToA  Ultra-Wideband & UWB &
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 1 Introduction
This study proposed improved spatial predictive analysis approach by combining the important information from spatial data relationships and the interrelation between geotechnical properties to construct machine learning models for three-dimensional geotechnical subsurface modeling. This study focuses on the subsurface geotechnical properties modeling of Saemangeum on shore solar farm area in Gunsan, Republic of Korea as the study area. This study proposes a different EDF modeling approach from the one proposed by [PERSON] et al. [20] by measuring the distance between the query points and known boreholes, which represents a direct spatial relationship between them. The target attributes to be predicted in this study are geotechnical properties and soil classification across the study area. Several popular algorithms (Support Vector Machine, Gaussian Process Regression, Artificial Neural Network, and \(k\)-Nearest Neighbor) are used to construct the machine learning models. Dimensionality reduction by using principal component analysis (PCA) and hyperparameter optimization are applied to all constructed machine learning models. The effect of different machine learning modeling approaches that employ EDF proposed by [PERSON] et al. [20], EDF proposed in this study, and raw XY coordinate as input features are analyzed by comparing the performance score of all constructed machine learning models. At the end of this study, machine learning-based prediction of geotechnical properties and soil classification are analyzed by visualizing the geotechnical subsurface model using the MATLAB R2021a program. Besides the geotechnical engineering, the spatial predictive modeling approach proposed in this study is also applicable in other fields. In geological engineering, this approach is applicable for geologic mapping where the information from geological bore log is commonly interpolated to generate the geologic map. In other fields such as environmental engineering, this approach is also applicable for three-dimensional predictive mapping of subsurface soil organic carbon and soil contamination.
## 4 Algorithm Tuning and Experimental Evaluation  
In this section, we report on our experiments aiming to:  
1. Determine the optimal value of the _bsf_ factor, to tune the proposed algorithm and;
2. Evaluate the accuracy of the rating prediction of the proposed algorithm, both when used independently and when combined with a state-of-the-art CF algorithm also aiming at rating prediction accuracy improvement.  
For the evaluation of the rating prediction quality, both the RMSE and MAE error metrics have been employed. Their quantification was accomplished using the standard \"hide one\" technique, where a rating from each user in the database is hidden and its value is tried to be predicted [60; 61; 62]. In our work, this experiment was executed twice, the first time a random rating was hidden for each user, while in the second experiment, his last rating was hidden (considering the ratings' timestamps in the rDB). Thesetwo experiments produced very close results (less than 1% difference observed), hence, we report on the results from the first experiment, for conciseness. The practice described above is the typical one when evaluating a rating prediction CF algorithm [31; 63; 64].  
Our experiments were executed on six datasets; four of these were obtained from Amazon [65; 66], the fifth was sourced from MovieLens [67; 68], while the last was sourced from NetFlix [69]. Regarding the four Amazon datasets, we used the 5-core ones, where each user and item have at least 5 ratings, to ensure that, unlike in the simple Amazon datasets where for some users and items only one rating exists in the rating database, at least 4 other ratings exist and hence the application of any CF algorithm can produce valid results. The four Amazon datasets are considered relatively sparse (their density is less than 0.1%), while the MovieLens and the NetFlix ones are considered relatively dense (their density is greater than 1%). We opted to use both sparse and dense datasets in order to confirm the applicability of the presented algorithm in every CF dataset, regardless of its density. Table 1 is a synopsis of the datasets utilised in this work.  
The aforementioned datasets are widely used in CF research [70; 71; 72; 73] and they contain each rating's timestamp (essential information for hiding each user's last rating), while at the same time they vary considering their item domain category (music, videogames and TV series, books and movies).
## 5 Layout of Points for the Extraction of Temperature  
The basic trend of the Nanjing Xinshengwei Yangtze River Bridge is north-south. When the sun shines directly on the east side of the main tower at sunrise in the morning, the temperature of the outer tower wall on the east side is higher than that on the west side of the main tower. With the change of time, the sun moves to the west side of the main tower in the afternoon, and the east side of the main tower is in a shadow area. The temperature of the outer tower wall on the west side increases while the temperature on the east side decreases. Under the influence of the temperature of the solar radiation, the main tower will produce a temperature field gradient, thus causing temperature deformation.  
Therefore, to determine the variation of the temperature field of the tower column under the most unfavorable high-temperature weather, the temperature difference between the two sides of the main tower caused by sunshine on one day on 14 August 2022, will be simulated. The structural detail around the 12 th column is complex, as the chamfer, transverse partition, hole and prestressed steel bundle exist in the connecting part between the beam and column of the main tower [23]. It is believed that the temperature field corresponding to the selected 12 th column is more sensitive to the solar radiation. If the 12 th column can maintain good structural performance under the most unfavorable temperature, the whole bridge tower is believed to be within the safe range. Therefore, the 12 th column was selected in the present study. The east, south and west tower walls are alternately replaced as the sunshine area, while the north tower walls are always the shadow area, and three temperature measuring points are set on the inner and outer walls. The layout of points (i.e., A1-A3, B1-B3, C1-C3) for the extraction of temperature is shown in Figure 10.  
Figure 10: The layout of points for the extraction of temperature (unit: cm).
# Optimizing Latent Factors and Collaborative Filtering for Students' Performance Prediction
## 4 Proposal for Improving The Prediction
### Efficient Tuning of Collaborative Filtering  
In order to show the influence of \(\beta\) and \(\lambda\) on the prediction accuracy, given a particular number of latent factors, we apply Direct Search (DS). DS is a very simple approach to obtain the optimal pair of values (\(\beta\), \(\lambda\)) among a set of pairs taken from predefined sets for both parameters. Each of \(\beta\)'s and \(\lambda\)'s values grow by incremental steps \(h_{\beta}\) and \(h_{\lambda}\), respectively, from the minimum \(\beta_{min}\) and \(\lambda_{min}\) to the maximum \(\beta_{max}\) and \(\lambda_{max}\) values, respectively. The search is implemented by two nested loops, where \(RMSE\) is calculated for each generated pair.  
This method has the disadvantage of limiting the number of evaluated pairs (\(\beta\), \(\lambda\)), since it is directly related to the computing effort that can be made. Because of this, better values could have been ignored and not considered in the search; however, DS allows one to find a good solution among the ones that have been generated, which may lie in the vicinity of the potentially optimal solutions.  
Figure 4 shows an example of DS, where a dataset composed of 107 students and eight tasks was processed, applying predictions for 56 unknown scores, considering the ranges \(0.1<\beta<200\) and \(0.001<\lambda<200\). The 209 and 227 values for \(\beta\) and \(\lambda\), respectively, were extracted at equal intervals from these ranges. In total, forty-seven-thousand four-hundred forty-three values of \(RMSE\) were calculated, obtaining a minimum value of 0.333 (yellow circle on the plot), which corresponded to \(\beta=1\) and \(\lambda=0.002\). The predictions considered \(K=64\) latent factors.  
This figure displays the \(GRMSE\)metric for each pair (\(\beta\),\(\lambda\)). This metric is the same \(RMSE\) after applying a linear function that highlights the minimum points, maintaining the proportion among all the points, in order to make minimums easier to detect on the plot. The evidence of several minimum points suggests that the prediction accuracy is very sensitive to the selected pair, so we would need to perform a deeper search. Nevertheless, DS wastes much computational effort when searching in wide areas without minimum values of \(RMSE\). This consideration led us to suggest more efficient optimization alternatives in order to find the value of \(K\) and the pair (\(\beta\),\(\lambda\)) for which \(RMSE\) is minimum.
## 4 Intelligent Detection and Recognition
### Programming Environment and Model Training  
The intelligent detection platform was established in the PyTorch environment on a Windows system in order to implement the YOLO and SSD object detection algorithms. Python 3.7 and the corresponding Anaconda were installed, followed by CUDA 10.0 and cuDNN 7.4.1.5 to configure the runtime environment. Finally, the vs. Code platform was installed via Anaconda to run the YOLO v3 and SSD object detection algorithms, enabling the intelligent detection and identification of subgrade defects in the GPR images. The computing environment's configuration is shown in Table 4.  
In Figure 17, the changes in the SSD's loss function and mean average precision (mAP) during training are illustrated. It can be observed that, when training the GPR subgrade defect dataset using the object detection algorithms, the training procedure was divided into 100 nodes. After processing each node, a loss value and an mAP value were generated. The trend graph of the loss during training showed a general downward trend with an increasing number of computational nodes, eventually converging.  
The SSD model's loss value eventually converged at 0.4876, with a minimum value of 0.4652, indicating a small difference and a steady decrease in the loss value. At the 50 th node, a sudden change was observed, with the 51 st loss value increasing before gradually decreasing again, which was considered a normal occurrence. The YOLO v3 model's loss value eventually converged at 4.7117, with a maximum value of 442.6985 and a minimum value of 4.5620, indicating a significant difference. After completing the first step of fitting, the loss value dropped sharply and then began to decrease steadily from the second step, approaching convergence. Both algorithms exhibited oscillations in their mAP curves, showing an overall upward trend and eventually converging. The mAP value of the SSD model converged at 75.07%, while that of the YOLO v3 model converged at 76.69%.
## 3 Results and Discussion
### Antibacterial Assessment of EGCG  
The agar dilution method was used to examine the minimum bactericidal concentration of _E. coli_ and _S. aureus_. Figure 3 illustrates the relationship between the colony growth of _E. coli_ and the EGCG concentration. When the EGCG concentration reached 200 \(\upmu\)g/mL, the number of bacteria decreased significantly. The growth of _S. aureus_ (Gram-positive) compared with the Gram-negative bacteria group is shown in Figure 4. The MIC to antibiotics with EGCG was 25 \(\upmu\)g/mL. The antibacterial concentration against _S. aureus_ was lower than that against _E. coli_. These results may have occurred because Gram-negative bacteria have cell wall (outer membrane) components [26]. In this study, a concentration of 200 \(\upmu\)g/mL (the highest antibiotic concentration) was chosen for the fabricated EGCG-MBG scaffold.  
Figure 2: (**A**)The XRD patterns and SEM images of the mesoporous bioglass (MBG) scaffolds before (**a**) and after (**b**) sintering at 700 \({}^{\circ}\)C. (**B**)The isotherm measurement and Barett-Joyner–Halenda (BJH) porous size distribution of the MBG scaffold.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 2 Three-Phased Induction Motor
### Braking of an Induction Motor  
Various braking types exist to brake an induction motor. They are very useful for many applications, such as electric traction and propulsion (trains, metros, ships, cars, trucks, etc.), machine tools, elevators, winches, pumps, domotics, and forklifts.  
Coasting stop:  
With the coasting stop, the stator electric supply is cut, and the rotor gradually stops thanks to friction forces.  
Controlled stop:  
The controlled stop consists of gradually reducing the stator voltage to zero, thus giving a decreasing set-point to the rotor rotational speed. Two main controlled stop techniques exist, the hyper-synchronous stop and the direct current injection.  
Hyper-synchronous braking:  
When the rotor speed is higher than the rotating field speed, the motor brakes. Coupled with a frequency converter, which gradually decreases the speed, the machine can be stopped. The braking torque is high, provided that a field corresponding to the optimum slip value of generator operation is generated. This method is particularly effective for rapidly braking an induction machine without any additional mechanical device.  
Stopping by injection of direct current:  
The direct current supply of the stator creates a fixed field in the machine, which opposes the movement. The control of the direct current intensity drives the braking, even if this braking method is less effective than the hyper-synchronous. Moreover, the current constraints are also very severe.  
Reverse current stop:  
The reverse current stop principle consists of reversing two phases for a short time. This is therefore equivalent to an unmanaged hyper-synchronous braking at a fixed frequency. The resistive torque is low and the current drawn is also very high. The risk of overheating the machine windings makes it advisable to provide additional resistors to reduce the intensity. Furthermore, since the decelerating torque remains negative throughout the braking phase, it is necessary to cut off the power supply just at the time when speed passes through zero. Otherwise, the rotation would be reversed.  
Electro-mechanical braking:  
This braking system consists of a disc brake attached to the shaft of the induction machine and whose jaws, initially clamped when switched off, are controlled by an electromagnet. After supplying the electromagnet, the jaws loosen, leaving the rotation free. Power failure causes braking. This device, also called \"failure brake\", is often provided as an emergency stop device.