esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 4 Evaluation
### Evaluation of Reference Pressure Extraction
It scored 99.4% for precision, 90.7% for recall and 94.8% for the F-score for the fusion of all sensors on the basis of the voting fusion. When the light sensor was not available or when the proximity sensor indication was true, it scored 99.1% for precision, 97.3% for recall and 98.2% for the F-score. Figure 14: Locations that correspond to the detection of the outdoor–indoor transition (OITransition). The figure includes nine different determined locations for the entrance to the building (red dots) [15]. As a result, we can conclude that the OITransition can be recognized and represents a robust means for the extraction of the reference pressure. Additionally, the GPS sensor scored the lowest false positives, while the light sensor scored the lowest false negatives. Furthermore, the fusion of the three sensors scored the lowest false positive rate, and the false positive rate dropped only by 0.3% when the light and magnetic field sensor were the only sensors that were fused.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 1 Introduction
### MoS\({}_{2}\) 2D Materials
MoS\({}_{2}\) mono-layers were also fabricated into ultrasensitive photodetectors [56; 57], and integrated circuits to perform the NOR logic operation [58], besides being employed in drug delivery [59], thermoelectrics [60; 61], piezotronics [62; 63], osmotic nanopower generators [64], and valleytronics [65]. Because of the novel properties and wide applications of 2H-MoS\({}_{2}\) 2D layers, MoS\({}_{2}\) 2D layers have been prepared by various physical or chemical methods [41; 44; 45; 46; 66; 67; 68; 69]. Individual MoS\({}_{2}\) layers were usually obtained by the micromechanical cleavage technique [70] because of weak interactions between sulfur layers and strong intralayer interactions of MoS\({}_{2}\) bulks. The mechanically cleaved MoS\({}_{2}\) layers are usually highly crystalline with low defect density. Massive MoS\({}_{2}\) layers have successfully been produced through the liquid-phase exfoliation [71; 72]. MoS\({}_{2}\) few-layers can also be fabricated from CVD. Interested readers can refer to the references herein. The MoS\({}_{2}\)-based devices will be used in harsh environments from basic science to industry, such as near nuclear plants/reactors and nuclear medicine imaging. Radiations may damage MoS\({}_{2}\) layers by breaking atomic bonds and ionizing atoms. To use the MoS\({}_{2}\) devices reliably under irradiation, it is desirable to explore the irradiation effects on MoS\({}_{2}\) layers. Figure 1: (**a**) Crystallographic structure of 2H-MoS\({}_{2}\) bulks and (**b**) Three-dimensional structure of 2H-MoS\({}_{2}\) mono-layers.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 3 Data
### Machine Learning  
We used supervised learning to find the mapping between input data (wind velocity and flow velocity) and output data (drifter location). The performance of the following two regression functions were examined.  
Support vector regression (SVR): While conventional classifiers minimize error rates during the training process, support vector machines (SVMs) construct a set of hyperplanes so that the distance from it to the nearest training data point is maximized. They were considered as alternatives to artificial neural networks in the 1990s since nonlinear classification became possible through the kernel trick [31]. Support vector regression uses SVM for regression with continuous values as the output [32].  
Gaussian process (GP): GP [33] is an ML model that predicts data as the average and variance of probability distributions. It predicts functions that can represent given data in the defined function distribution and estimates functions for experimental data based on the arbitrary training data.
## 5 Case Study: Experimental Results
### User Behavior Analysis
#### 5.3.1 Temporal Behavior of Community Users  
Users are active in different time slots; for example, some users connect to the community at night during weekdays, while others are online during working hours. This difference in active hours may affect user interactions. When a large number of articles are uploaded within a given time interval, it is difficult for users with different active hours to be involved in the same article, unless it is marked as a hot. article on the front page. On the other hand, if articles are exposed on the front page for several days due to a low article-upload rate, users are more likely to have a chance to participate in the same article, regardless of their active hours.  
Figure 6 shows the active times of users in terms of hour and day of the week for two online communities (R1 and PG datasets). We performed topic modeling with \(K=16\) for both datasets, but different scales are used in the figures. In \(r\)/politics, we find some groups of users have common active times. For example, users with Topics 2 and 10 are highly active at 18:00-22:00 GMT. Users with Topic 2 are inactive on the weekend, while those with Topic 10 are just less active then. Topic 11 is active during 00:00-05:00 on weekdays. We deduce that these users may live in a different time zone, because their active hours are shifted 3-4 h later than other topic users.  
\begin{table}
\begin{tabular}{c r r r r r r r} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Topics**} & \multicolumn{4}{c}{\(\theta=0\)} & \multicolumn{4}{c}{\(\theta=1\)} \\ \cline{3-8}  & & **Proposed** & **Spectral** & **Random** & **Proposed** & **Spectral** & **Random** \\ \hline \multirow{8}{*}{R1} & 2 & 0.066 & 0.034 & 0.001 & 0.106 & 0.000 & 0.000 \\  & 4 & 0.074 & 0.092 & \(-\)0.001 & 0.114 & 0.000 & \(-\)0.002 \\  & 8 & 0.060 & 0.093 & 0  
However, these differences in users' active hours are not observed in the PGR21 dataset. In this community, there is not a sufficient number of articles to push recent articles onto the next page of the article list. Newly uploaded articles can be exposed on the front page for several days, sufficient time for all users to participate, according to their interests. Consequently, the active hours are less important in separating user interactions than in the case of Reddit.
## 2 Experimental Apparatus and Method  
Bench test of oil-film bearing oil was conducted on large-scale mill oil-film bearing test rig. This test rig consists of a mechanical system, hydrodynamic lubrication system, hydrostatic lubrication system, thin-oil lubrication system used for the accelerator, hydraulic loading system, electrical control system, pneumatic control system, and data acquisition system. Its mechanical system is composed of a hydrostatic-dynamic oil-film bearing subsystem and experimental hydrodynamic oil-film bearing subsystem. The operating performances of oil-film bearing oil can be monitored on-line. The schematic diagram of oil-film bearing test rig is shown in Figure 1[22].  
Figure 1: Schematic diagram of oil-film bearing test rig.  
Several kinds of transducers have been installed in oil-film bearing test rig. Figure 2 is the distribution of different transducers on the test bearing. There are 3 pressure transducers (HYDAC-ETS300, HYDAC, Bremen, Germany) used for measuring oil-film pressure, 6 displacement transducers (BENTLY3300X, BENTLY, Exton, PA, USA) for oil-film thickness, 21 thermocouples (HYDAC-ETS300, HYDAC, Bremen, Germany) for oil-film temperature, and 1 torque transducer (ZRN503) (ZRN503, Beijing Ruineng Instrument Technology Co., Ltd., Beijing, China) for power or torque measurement in Figure 1. The transducers are distributed based on theoretical calculation results of oil-film pressure, oil-film thickness, and oil-film temperature, and the positions of transducers are determined in accordance with mesh subdivision of actual oil-film performance.  
Physical signals from these transducers will be translated into digital signal via PLC (Programmable Logic Controller), and the digital signal will be displayed on IPC (Industrial Personal Computer) (610L, Advantech Co., Ltd., Shanghai, China). So the operating performances of oil-film bearing (Taiyuan Heavy Industries Co., Ltd., Taiyuan, China) can be monitored on-line. The main structural parameters of test bearing are shown in Table 1. When the oil supply is sufficient and the system is preheated, the instruction from upper computer will be transmitted to PLC, thereafter, the DC (Direct Current) electromotor (24-250-42, Xi'an Motor Co., Ltd., Xi'an, China) and hydraulic loading system will be started. When the test rig operates at the preset speed and load, the transducers begin to collect data of the oil film. In order to analyze the operating performances of oil-film bearing oil, six typical load cases were set, as shown in Table 2.
## 6 Working Principle of Volumetric Verification
### Influence of Laser Tracker (LTs)' Measurement Characteristics on Verification Results  
As the active target cannot be used with LTs from different manufacturers, it is replaced by a traditional retroreflector, the Spherically Mounted Retroreflector, SMR-1.5\". The workspace of the MT to be verified is reduced because the angle of vision of the SMR is \(\pm\)30\({}^{\circ}\). Therefore, the characteristics of the new verification process are defined below:  
MT workspace: XFYZ: 0 mm \(\leqslant\) X \(\leqslant\) 5050 mm, 0 mm \(\leqslant\)\(Y\)\(\leqslant\) 2050 mm, 0 mm \(\leqslant\)\(Z\)\(\leqslant\) 750 mm, \(C\) = 0\({}^{\circ}\), and \(B\) = 0\({}^{\circ}\); MT workspace to be verified: 0 mm \(\leqslant\)\(X\)\(\leqslant\) 2000 mm, 0 mm \(\leqslant\)\(Y\)\(\leqslant\) 1000 mm, 0 mm \(\leqslant\)\(Z\)\(\leqslant\) 450 mm, \(C\) = 0\({}^{\circ}\), \(B\) = 0\({}^{\circ}\), \(X\)-axis interval = 250 mm, \(Y\)-axis interval = 250 mm, and \(Z\)-axis interval = 150 mm.  
To analyze the influence of the LT on verification, the same mesh of points was measured with three LTs from three different manufacturers with the measurement specifications presented in Table 3.  
Although the influence of geometric errors on verification points is the same for all LTs, the MT errors that each LT provides are different due to the different sources of error such as SMR errors, LT measurement characteristics, non-geometrical error, thermal variations, and so on. By measuring the same mesh of points simultaneously with all LTs, the influence of extra error in the measurement data is only the result of the LT characteristics and SMR errors.  
Table 4 presents the results of the identification process using the three LTs independently. The initial errors that each LT provides are different. LT A provides an average initial error of 33 \(\upmu\)m using IFM technology, which is smaller than the errors of 40 and 54 \(\upmu\)m provided by LTs C and B using ADM. Thus IFM may introduce less error than ADM technology. Similarly, LT C introduces less error than LT B using the same technology. If the final average error is analyzed, all LTs provide similar results. Therefore, the approximation functions obtained with LT B compensate for non-geometric errors.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Implications for DES Implementation in Healthcare Organizations  
Our systematic review identifies key factors influencing the successful implementation of DES in healthcare organizations. Case studies reveal that institutions carefully consider both technical infrastructure and organizational factors when adopting DES solutions.  
From a technical perspective, integration with existing hospital information systems, computational resources, network infrastructure, and data storage capabilities play a fundamental role in ensuring a seamless implementation. Equally important are organizational factors, such as staff expertise, resource availability, stakeholder engagement, and project timelines. Institutions that prioritized structured training programs and cross-departmental collaboration strategies often reported smoother transitions and higher adoption rates.  
A common approach among successful implementations involved phased rollouts, beginning with pilot projects before scaling to broader applications. Additionally, robust data collection and validation procedures emerged as critical elements, with organizations that established strong data foundations experiencing more reliable model outcomes. This observation aligns with the findings of [PERSON] and [PERSON] [5], who emphasized the importance of data quality in simulation success.  
Furthermore, stakeholder engagement proved to be a decisive factor across multiple studies. Hospitals that actively involved clinical staff in the simulation development process reported greater acceptance and practical utilization of DES tools, as documented by [PERSON] et al. [39]. These findings underscore the necessity of a multidisciplinary approach, where both technical and human factors are addressed to maximize the impact of DES in healthcare settings.
## 5 Numerical Analysis
### The Numerical Analysis Related to the Service Differentiation
#### 5.1.3 Service Differentiation and Service Cost Coefficient  
Given that \(\delta_{1}=0.6\), \(\delta_{2}=0.4\), \(\alpha=0.5\), \(\beta=0.4\), and \(\eta=[0.8,1.2]\), the impact of the service cost coefficient \(\eta\) on the service differentiation \(\Delta s\) under centralized and decentralized decision making is depicted in Figure 4.  
Figure 3: (**a**) The relationship between service differentiation and consumer’s price sensitivity. (**b**) The relationship between service differentiation and consumer’s service sensitivity.  
Figure 2: (**a**) The relationship between service differentiation and consumer preference in channel 1. (**b**) The relationship between service differentiation and consumer preference in channel 2.  
It can be observed that, under any strategy, the service differentiation \(\Delta s\) decreases as the service cost coefficient \(\eta\) increases. When the service cost coefficient rises, or when the cost-benefit efficiency of adopting advanced IT is relatively low, providing the same level of service requires more investment. Merchants, being rational and profit-maximizing, will inevitably reduce their emphasis on service differentiation.
# Continuous Control Set Predictive Current Control for Induction Machine
## 4 Stability and Controllability of the System
### Controllability Analysis  
Since there are parallels that can be drawn between the inner loop regulator and state feedback, it would be interesting to analyze the controllability of the system. This is done by deriving the controllability matrix (\(\mathbf{Co}\)), as shown in Equation (25), and calculating its rank. Like the stability analysis, the procedure will be conducted for the whole range of the possible rotor speed values: \(-157\;\text{rad}\,\text{s}^{-1}\leqslant\omega_{r}\leqslant 157\;\text{rad}\, \text{s}^{-1},\omega_{r}\in\mathbb{Z}\).  
\[\mathbf{Co}=\begin{bmatrix}\mathbf{B}&\mathbf{A}(\omega_{r})\mathbf{B}&\mathbf{A}^{2}(\omega_{r}) \mathbf{B}&...&\mathbf{A}^{n-1}(\omega_{r})\mathbf{B}\end{bmatrix} \tag{25}\]  
After calculating the rank of controllability matrix for each rotor speed value, it can be concluded that the system is fully controllable. For better visual representation of the controllability it is convenient to calculate the controllability gramian and plot its eigenvalues. Figure 3 shows the gramian eigenvalues for the aforementioned rotor speed range, and it can be concluded, that since all the eigenvalues have positive real part, gramian matrices are positive definite, which proves the controllability. Further information on the gramian matrix calculation can be found in [34].  
Figure 2: Closed loop discrete system poles for range of rotor speed values.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 4 Evaluation
### Overall Performance  
Under the optimal parameters, 100 independently repeated experiments were conducted, the average values of TP, FP, TN, and FN finally obtained are shown in Table 3, and the final average values of Pmalicious, Rmalicious, Fmalicious, Pbenign, Rbenign, and Fbenign are shown in Table 4.  
As can be seen from the above table, after the 16 most important features were selected, the final recognition results were significantly improved. By deploying the Fisher score, the average Pmalicious, Rmalicious, Pranicious, Pbenign, Rbenign, and Fbenign, from 85.52%, 89.57%, 87.50%, 89.05%, 84.83%, and 87.48%, were increased to 89.73%, 95.26%, 92.41%, 94.05%, 89.10%, and 91.51%. It can be seen from Table 4 that the recognition rate of web shell (Rmalicious) is 95.26%, and the recognition rate of normal script (Rbenign) is 89.10%. The final classification accuracy of the model is 92.18%. Considering that FN (the malicious web shells marked benign ones) in the web shell detection system is often more critical than FP (that is, the benign web scripts marked as web shells), in practical applications, the classification threshold can be adjusted. For example, on a web server with higher security, the classification threshold can be lowered so that the recognition rate of benign scripts can be appropriately reduced to improve the detection rate of web shells.  
To get the ROC curve, we set the threshold value 0 from 0 to 1; the step size is 0.01. Under different classification thresholds 0, the average values of TPR and FPR of both web shells and benign scripts were calculated, and the resulting ROC curves with/without feature selection are shown in Figure 7. The resulting AUC values are 0.9257 and 0.8940, respectively. From the usability point of view, the increase in FPR will greatly increase the workload of the web administrator (because the administrator needs to conduct a second screening of real web shells based on a large number of alarms). When it comes to security, the reduction in TPR will cause a greater potential threat to the web platform. Due to the high AUC (with feature selection), our method has sufficient optimization space to balance security and usability in a real-world scenario.
###### Abstract  
Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms--such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them--have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning.  
evacuation planning; multi-objective optimization; meta-heuristic algorithms; AMOSA; MOABC; MSPSO; NSGA-II
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### Training and Validation Method
In addition, because precision and recall depend on the threshold value, the critical success index (CSI) was used to determine the optimal threshold value and is defined as follows [42,43]:  
\[\mathrm{CSI}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}+\mathrm{FN}} \tag{6}\]
###### Abstract  
Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called _content reordering_, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish.  
abstractive summarization; monolingual models; multilingual models; transformer models; transfer learning +
Footnote †: journal: Applied Sciences  
17 September 2021  
Accepted: 20 October 2021  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 General Opportunities, Challenges, and Applications
### Opportunities
#### 3.1.2 Improved Model Personalization  
Today's consumers, equipped with a plethora of choices, desire tailored experiences. FL is perfectly positioned to respond to this demand. By continually refining models based on individual device data, FL crafts a learning model that is inextricably linked to user behavior. As these models evolve, they intuitively align with the users' preferences, resulting in experiences that are not only personalized but deeply resonant. This level of customization is paramount in ensuring user retention and satisfaction in an increasingly competitive market [21; 22].