esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimised Twin Fluid Atomiser Design for High-Viscosity, Shear-Thinning Fluids
## 1 Introduction
The findings presented here may serve as a basis for the atomisation of other non-Newtonian fluids of similar rheological and physical material properties [17; 18; 19; 20; 21]. The importance of the efficient atomisation of such liquids is increasing [22; 23; 24; 25; 11]. Given the lack of systematic, parametric design studies for atomising high-viscosity, shear-thinning media, this work presents a detailed investigation to identify an efficient geometry for an external, single-stage, twin fluid atomiser. While this work is motivated by the challenge of atomising high-viscosity sewage sludge, the identified optimal nozzle configuration and its optimal operating parameters may also be applicable to other sprayable media of interest with comparable rheological and physical properties, thereby supporting a range of resource recovery processes. Given the complex interplay between fluid properties, however, caution must be exercised when generalising the findings to other fluids or the determined optimal operational settings. Although rheological similarity provides a useful initial indication of transferability, differences in chemical composition, heterogeneity, temperature sensitivity or shear-rate sensitivity may lead to deviations in spray behaviour. The paper is structured as follows: Section 2 describes the methodology and materials used, Section 3 presents the results and discusses implications, and Section 4 concludes.
## 4 Discussion  
### Small Crack Growth  
The fatigue life of the specimen is mainly made up of the crack initiation and propagation life. Specimen 1050 FC (Figure 9c) has the lowest CGR and overall fatigue life performance in the crack growth stage, which is not significantly different from the as-built specimen. This indicates that the crack initiation stage occupies most of the entire life stage. The fatigue crack initiation stage is mainly determined by the irreversibility of slip [34]. In local grains with good sliding directions, dislocations nucleate and slide. Plastic strain builds up with cycle loading until it becomes incompatible with adjacent grain sliding. As a result, little extrusions and protrusions occur, finally forming a small crack, and the local sliding deforms the surface grains, which are likewise susceptible to these protrusions and extrusions. Therefore, cracks tend to nucleate from surfaces or defects and the stress concentration of the applied load tends to accelerate the formation of small cracks. Favorable microstructures tend to reduce dislocation motion and resist crack initiation.  
The fatigue initiation life includes the small crack stage, and the length of the crack propagation is usually only a few hundred microns. At this time, the stress intensity factor  
Figure 12: Fracture surfaces of different specimens on Paris zone: (**a**) 850 FC, (**b**) 950 FC, (**c**) 1050 FC, (**d**) 850 WAC, (**e**) 950 WAC, (**f**) 1050 WAC.  
is very small, and a favorable structure can significantly improve the crack initiation life at this stage [35; 36]. After the initiation of a small crack, under the action of continuous cyclic loading, the small crack continues to propagate forward through slippage. The slip length determines microcracks' propagation [37]. For \(\rm T_{a}<T_{\beta}\), cracks tend to choose the most favorable direction for propagation to extend. Because grain boundaries are limited, the majority of small cracks propagate between them, and the crack propagation is zigzag. The secondary crack can share the stress at the tip of the main crack, and consume a lot of energy. This organizes further crack growth and extends the crack initiation life. Therefore, the smaller the \(\alpha\) grains, the denser the grain boundaries. Slipping becomes more difficult as the dislocations accumulate at grain boundaries. This improves crack propagation resistance while also extending crack initiation life. For \(\rm T_{a}>T_{\beta}\), the small crack's propagation path tends to extend along the \(\alpha\) colony boundary. When encountering another \(\alpha\) colony boundary, dislocations accumulate at the \(\alpha\) colony boundary and change the direction of expansion after accumulation to a certain extent. The size of the \(\alpha\) colony reflects the length of the slip to some extent. The larger the colony, the longer the slip length after each deflection, the faster the crack propagation rate, and the worse the resistance to crack initiation.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 3 The Proposed Model
### Overview  
The probabilistic model discussed in Sections 2.1 and 2.2 can be well applied with explicit feedback or implicit feedback. However, it cannot handle problems with explicit and implicit feedback simultaneously. In real-world CF applications, a user chooses an item based on their preference but cannot tell whether the item really suits him/her. Thus, in this study, we relate items with two types of latent factor vectors to distinguish between their behavior and true taste, namely the item rating latent factor vector and the item selecting latent factor vector. A user will select a certain item if the item selecting latent factor vector matches the user latent factor vector and will give a high rating on the item if the item rating latent factor vector matches the user latent factor vector.  
According to [11], real-world datasets contain both explicit and implicit feedback in the observed data and both of them are useful for predicting user preferences and modeling item characteristics. However, there is little research that uses both of them simultaneously in one recommendation model, which shows that the recommender system still has room for improvement. Thus, in this section, we propose a recommendation model based on probabilistic matrix factorization with both explicit and implicit feedback. As shown in Figure 2, we derive an implicit feedback matrix \(M\) and an explicit feedback matrix \(R\) from the original observed data. Then \(M\) is decomposed into the product of an item selecting latent factor matrix \(Z\) and a user latent factor matrix \(U\). Similarly, the explicit latent factor matrix \(R\) is decomposed into the product of an item rating latent factor matrix \(V\) and a user latent factor matrix \(U\). As mentioned in Equation (7), we also add an offset matrix \(Q\) to the user latent factor matrix to constrain user-specific feature vectors that has a strong effect on infrequent users.  
Figure 2: Observed data is divided into implicit and explicit feedback which are used for obtaining user latent matrix and different item latent matrix. ‘\(+\)’ denotes the positive implicit feedback.  
The main goal of our scheme is to separate rating prediction and item recommendation into two different tasks: once the model is obtained, we can predict the user ratings for the unknown items by calculating the product of \(U\) and \(V\); Further, we can provide a rank list for item recommendation by calculating the product of \(U\) and \(Z\).
## 4 Discussion
### Comparative System
However, this pattern does not hold true for the Sao Paulo metro due to its extensive railway network, which serves a larger population than the other three systems being compared. It's important to note that the data for Rio de Janeiro is from 2016, predating the data from the other systems. The relatively high emissions in Rio de Janeiro may be partially explained by the lack of available energy-saving technologies at that time. Greenhouse gas (GHG) emissions in railway systems can vary considerably due to various factors that affect the operation and infrastructure of these systems. One of the main influences is the type of energy used to power the trains, which has a significant impact on the total emissions of the railway system [35; 52]. In the case of the Pernambuco subway, it stands out from other systems compared in this article because it uses a combination of electric and diesel energy, with the latter being a fossil fuel with high GHG emissions. Furthermore, the number of passengers and freight transported via railway systems has a direct impact on GHG emissions [41]. Highly busy systems with high demand, such as Sao Paulo's, may show greater efficiency in terms of emissions per passenger-kilometer. However, this efficiency is not reflected when comparing Sao Paulo's system with other railway systems with lower passenger flow, such as Rio de Janeiro's, for example. This discrepancy can be partly explained by the energy efficiency of train engines, which directly influences GHG emissions. More modern and energy-efficient trains tend to emit fewer GHGs per kilometer traveled compared to older and less efficient trains. Additionally, operational factors such as the use of traffic control technologies, efficient operating practices, and proper maintenance also play a significant role in reducing GHG emissions in railway systems. However, to fully understand the specific differences between Sao Paulo's system and others, it is essential to conduct a detailed scientific investigation within that particular system. This would allow for the identification of factors that may be related to the high GHG emission rate in this specific system. It is crucial to emphasize that GHG emissions are influenced by a combination of several factors. Therefore, for a comprehensive assessment of GHG emissions in compared railway systems, it is necessary to consider all relevant variables and conduct a thorough investigation to analyze and compare each of them. The existence of this gap in the current article highlights the importance of future research that can fill this knowledge gap and enhance our understanding of the environmental impact of railway systems.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 4 Experiments
### Time Compensation during Signal Transmission  
Since the acoustic signal travels a long distance before it is detected by the test system, there exists a time delay between this signal and the instant when it was emitted by the noise source. Point \(x(t)\) is the position of the moving source at the instant \(t\). The signal as used for the data analysis has been generated at the source at point \(x(t_{0})\), which is located at a distance \(r\) from \(x(t)\), as was illustrated in  
Figure 8: Microphone array.  
Figure 9. Thus, the identified location at the instant time \(t\) is in fact the position of noise source at the moment \(t_{0}\). It is essential to compensate for the difference.  
This constellation, as graphically illustrated in Figure 8, can be expressed as:  
\[\left\{\begin{array}{l}R_{0}=\sqrt{x{\left({t_{0}}\right)}^{2}+y{\left({t_{0}} \right)}^{2}}\\ R=\sqrt{x{\left({t}\right)}^{2}+y{\left({t}\right)}^{2}}\\ r=\left|x{\left({t_{0}}\right)}-x{\left({t}\right)}\right|=vR_{0}/c\end{array}\right. \tag{37}\]  
To locate the noise source in actual conditions, point \(x{\left({t_{0}}\right)}\) moves with velocity \(v\) and sound velocity \(c\) can be calculated. Therefore, compensation \(r\) is available and needs be taken into consideration in the actual localization procedure.
# Pitfalls in the Detection of Hearing Loss via Otoacoustic Emissions
## 2 Materials and Methods
### Statistical Analysis  
The agreement between the Accu 1, Accu 2, and the ILO outcomes was calculated by dividing the number of concordant responses by the number of all responses. Additionally, [PERSON]'s kappa was used to evaluate agreement between devices. [PERSON]'s kappa represents the level of chance-corrected agreement [27], with its values ranging between -1 and +1. A value of 0 means there is no more agreement than can be expected by chance; a value below 0 means there is lower agreement than can be expected by chance. Kappa values above 0 were interpreted according to the classification given by [PERSON] and [PERSON] [28]: 0-0.2, slight agreement; 0.2-0.4, fair; 0.4-0.6, moderate; 0.6-0.8, substantial; 0.8-1, almost perfect agreement. A chi-square test was used to assess the relationship between the scores given by the two devices.  
From the available data, three groups of results emerged: refer-refer (when both devices gave a positive screening result); pass-pass (when both devices gave a negative result); and pass-refer (when the devices gave inconsistent results).  
Discriminant analysis was conducted to determine which variables (pure-tone hearing thresholds and SNR results) are best distinguished between the three groups (refer-refer; pass-pass; pass-refer). Discriminant analysis identifies the linear combination (functions) of predictors that contribute maximally to group separation. The number of discriminant functions is the number of groups minus 1 (i.e., for three groups, two discriminant functions are calculated). Discriminant coefficients act in a manner analogous to regression coefficients; the larger coefficient, the greater is the contribution of the given variable to the discrimination between groups. The centroids are the mean variate scores for each group. A combined-group plot shows the variate score for each object, grouped according to the condition to which the given object belongs to [29].  
A value of \(p\) < 0.05 was considered statistically significant. Statistical analysis was performed with IBM SPSS (Statistical Package for the Social Sciences) Statistics version 24 (Armonk, NY, USA: IBM Corp., 2016).
# Sentiment Analysis on Algerian Dialect with Transformers
## 3 Data Collection and Analysis
### Data Preprocessing  
The preprocessing step plays a crucial role in sentiment analysis, as it involves removing any irrelevant or noisy data before performing sentiment classification [32]. During model training and fine-tuning, we have experimented with various preprocessing tasks and have come to conclude that the following tasks improve the classification performance:  
* Removing HTML tags: When extracting comments from YouTube, some comments come with tags such as \(<\)/br\(>\) and more; removing them improves our primary metrics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Count** & **Negative Class** & **Count** & **Neutral Class** & **Count** & **Positive Class** & **Count** & **Entire Dataset** \\ \hline
3469 & \(\  
Replacing URLs, email addresses, and phone numbers: some comments include these, such as spam and promotional comments; instead of removing them, we have replaced them with a special token.
* Inserting spaces between emojs: Emojis are useful for detecting sentiment in sentences, so we did not remove them. Instead, we append and prepend white spaces between emojis, so that each is considered an individual token.
* Normalizing special Arabic characters: In this task, we have replaced several Arabic characters with other more common characters, such as converting \(\dagger\), \(\dagger\), and \(\dagger\) to \(\dagger\).  
As mentioned, we tried many preprocessing tasks, and only the above four tasks were useful. We tried many tasks, including removing emojis, stripping Arabic tashkeel and tatweet, converting Latin characters to Arabic characters and vice versa, and removing redundant punctuation.  
Surprisingly, all of these tasks did not improve the performance, and some made it even worse. Therefore, we did not use them during model training and evaluation. We will see more on this in the experiment results section.
###### Abstract  
Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring and early forest fire detection. However, they are susceptible to sensor malfunctions and network intrusions, which can compromise data integrity and lead to false alarms or missed detections. This study presents a hybrid anomaly detection framework that integrates a Transformer-based Autoencoder, Isolation Forest, and XGBoost to effectively classify normal sensor behavior, malfunctions, and intrusions. The Transformer Autoencoder models spatiotemporal dependencies in sensor data, while adaptive thresholding dynamically adjusts sensitivity to anomalies. Isolation Forest provides unsupervised anomaly validation, and XGBoost further refines classification, enhancing detection precision. Experimental evaluation using real-world sensor data demonstrates that our model achieves 95% accuracy, with high recall for intrusion detection, minimizing false negatives. The proposed approach improves the reliability of WSN-based fire monitoring by reducing false alarms, adapting to dynamic environmental conditions, and distinguishing between hardware failures and security threats.  
Keywords:forest fire prediction; wireless sensor networks (WSNs); anomaly detection; transformer autoencoder; hybrid machine learning; intrusion detection; adaptive thresholding; environmental monitoring; security +
Footnote †: journal:
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
## 3 Illustrative Example
### 2 DoF Unconstrained Ultralocal Control  
All the above responses obtained with a one degree-of-freedom (1 DoF) controller do not guarantee homogeneity of _iae_ values. This problem is a consequence of the zero of the particular closed-loop transfer functions, which may depend on the process linearization parameters, as for example for (57). Thus, the responses are not invariant against the change in the setpoint value. In addition to this fact, they also exhibited non-zero \(TV_{0}(y)\) values, indicating a possible overshoot of the output up to \(TV_{0}(y)/2\). In fact, the responses obtained may be completely satisfactory for most applications. However, to eliminate the overshoot in some critical applications, two-degree-of-freedom (2 DoF) controllers with pre-filters can be used. When using local models, the setting of the pre-filters (58) and (62) depends on the parameter \(a=1/T\) and thus also on the scheduling parameter \(\alpha\) (26) (see Table 2). This increases the complexity of the implementation. Since the 1 DoF ARC1 and the DOB1 did not improve performance in the previous experiments and can also cause stability problems due to destabilizing setpoint changes, the evaluation of the 2 DoF controllers focuses only on the ARC0 and DOB0 controllers with the pre-filters (44) and (51).  
Figure 12 shows the responses based on the fast and slow controllers (with the values \(T_{c}=0.1\) and \(T_{c}=0.5\)), using two settings of the disturbance reconstruction filter \(T_{f}=T_{c}\) and \(T_{f}=5T_{c}\), respectively. The quantitative evaluation of the responses in Figure 13 confirms that (with the exception of ARC0 with the value \(T_{f}=T_{c}\)) the step responses are obtained with zero deviations from the monotonicity of the process output \(TV_{0}(y)\approx 0\) and with zero deviations from the 1P process input signal \(TV_{1}(u)\approx 0\). These monotonic responses are homogeneous and achieve a slightly smaller value _iae_ than the sum of the time constants \(T_{c}+T_{p}\) (i.e. _iae_\(<1\)). At the same time, the _iae_ values achieved do not depend significantly on \(T_{c}\) and almost do not depend on \(w\). For larger \(T_{f}\) values, the normalized _iae_ values are similar throughout the entire range of the setpoint values \(w\) for both controllers considered. This shows that it then makes more sense to use simpler ARCs.  
Figure 12: Step responses of one-rank hydro system with a variable cross-section (63) and increasing setpoint value with 2 DoF gain-scheduled ARC0 and DOB0 controllers with pre-filters (44) and (51); \(T_{c}=0.1\) (left); and \(T_{c}=0.5\) (right); \(T_{f}=T_{c}\) and \(T_{f}=5T_{c}\).
## 5 Case Study and Analysis
### Data Construction  
Considering the impact of the above three variables on the frequency regulation performance of wind power, PSO is utilized in time-domain simulations to obtain the optimal control parameters for the combined inertial control under different scenarios. This approach aims to improve the minimum system frequency, reduce the absolute value of the maximum frequency deviation, and optimize the secondary frequency droop to achieve the best frequency regulation performance. The resulting optimal parameters are then combined with scenario data to generate comprehensive data sets. The algorithm's objective function and constraint are established as a first step. Designate the minimum system frequency \(f(t)_{\text{min}}\) as the optimization objective. The constraint is the system frequency constraint: \(f(t)_{\text{min}}\geq 49.8\), \(f(t)_{\text{max}}\leq 50.2\), less than or equal to 0.2 Hz in absolute value. The optimal control parameters combination (droop control parameter \(K_{\text{pf}}\) and virtual inertia control parameter \(K_{\text{df}}\)) is obtained through time-domain simulations by using PSO, and matched with these three variables in each scene to produce corresponding data sets.
## 4 Result of the Systematic Literature Review
### Publication Distribution among Journals and Conferences  
The selected papers that were based on SR strategies, applied to target search problems, have been published in a wide range and variety of journals, as well as conferences, which mainly focused on the engineering fields. From the 25 selected papers, 14 were published in journals, and the remaining 11 papers were published in conferences.  
There were 14 research papers that had been published across nine journals: this was led by _Lecture Notes in Computer Science_ (including subseries _Lecture Notes in Artificial  
Figure 3: The number of papers per year with a trend line.  
Intelligence and Lecture Notes in Bioinformatics) with four publications; the Journal of Robotics and Autonomous Systems and the journal Sensors, each with two publications; and the remaining six publications were each published in Studies in Computational Intelligence, the Iranian Journal of Science and Technology--Transaction of Electrical Engineering, IEEE/ACM Transaction on Computational Biology Bioinformatics, IEEE Access, Applied Soft Computing Journal, and Neurocomputing (Figure 4). Most of the journals covered the application of artificial intelligence and computational intelligence in the engineering domains.  
On the other hand, the research papers belonged to nine conferences. Both the Advances in Swarm Intelligence (ICSI) and IEEE Congress on Evolutionary Computation (CEC) were the top conferences, with two publications each (Figure 5). Each of the remaining conference papers were published in seven conferences which covered a wide range of domains; mostly the focus was on AI, computational intelligence, and controlling intelligent systems. It should be highlighted that only Information and Communication Technology (ICICT) focuses specifically on the communication and information domain, which is different from other conference domain focuses.
## 2 DPS Modeling of LEO-SIN  
LEO Satellite Internet Network (LEO-SIN) has some characteristics of large constellations with more than 1000 satellites, a high-frequency band with Ku, Ka, Q, and V, broadband with more than 100 MHz for each user. Therefore, the system model, channel model, and DPS model would be different from those of a GEO satellite mobile communication network and TMCN. This section introduces the system model, channel model, and DPS model of LEO-SIN.