esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this experimental study, an abrasive waterjet machining application was investigated for contour cutting of AISI 304L. The impact of traverse speed and material thickness  
Figure 8: ANOVA results of KTA and MRR for AWJ contour cutting of AISI 304L with varied thickness. on kerf geometries and material removal rate was examined, enabling the application to achieve precise and higher efficiency in cutting. AWJM exhibits similar behaviour in cutting curvature and straight line profiles of AISI 304L workpieces, in terms of kerf geometries and rate of material removal responses; thus, a minimum kerf taper angle value of 0.825\({}^{\circ}\) and maximum MRR of 769.50 mm\({}^{3}\)/min were obtained from machining of curvature profile, whereas a minimum of 0.916\({}^{\circ}\) KTA and maximum of 751.5 mm\({}^{3}\)/min occurred when cutting straight line profiles. The cutting performance of AWJM was found to achieve better kerf geometries at a lower rate of traverse speed. However, a higher traverse speed was shown to be more effective in achieving a higher MRR. It was also observed that a traverse speed of 90 mm/min provided the lowest KTA values of 0.825\({}^{\circ}\) or 4 mm, 1.092\({}^{\circ}\) for 8 mm, and 1.235\({}^{\circ}\) for 12 mm material thickness. A higher traverse speed rate of 150 mm/min obtained the maximum values of MRR 346.5 mm\({}^{3}\)/min for 4 mm, 609.0 mm\({}^{3}\)/min for 8 mm, and 769.5 mm\({}^{3}\)/min for 12 mm thickness of AISI 304L material. Both traverse speed and material thickness were shown to impact the quality of cut regardless of the cutting profile; however, the material thickness was more influential than traverse speed. Using analysis of variance, the material thickness generated a contribution ranging from 69-91% in kerf taper angle and 62-69% for material removal rate, whereas traverse speed was revealed to obtain a percentage contribution ranging from 5-18% in kerf taper angle and 27-36% for MRR. Conceptualization, M.T.-R., A.V., and J.M.L. ; methodology, investigation and writing original draft, J.M.L. ; review and supervision, M.T.-R., A.V., and M.A. ; editing, and project administration, M.A. and J.M.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable for studies not involving humans. The authors would like to thank the School of Engineering, Edith Cowan University, Australia, for providing and administering the needed requirements in accomplishing this research and open access funding support. The authors declare no conflict of interest. The following abbreviations and nomenclatures are used in this paper:  
\(h_{t}\): Depth of penetration  
\(V_{f}\): Traverse speed  
\(W\): Kerf width  
\(W_{t}\): Kerf top width  
\(W_{b}\): Kerf bottom width. \(t\): Thickness of the material  
AISI: Austenitic stainless steel  
ANOVA: Analysis of variance  
AWJ: Abrasive waterjet  
AWJM: Abrasive waterjet machining  
KTA: Kerf taper angle  
MRR: Material removal rate
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 4 Experimental Results
In general, it was seen that particle swarm optimization (PSO) contributed positively to the performance of the models and was especially effective in complex models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Opt. Model** & \(\boldsymbol{t}\)**Test \(\boldsymbol{p}\)**Value** \\ \hline SVR & PSO + SVR & 0.0559 \\ RF & PSO + RF & 0.5988 \\ XGBoost & PSO + XGBoost & 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of the change between machine learning models and optimized models with \(t\) test. Figure 4: Predictive performance of machine learning + PSO models. Figure 5 illustrates the prediction performance of three different machine learning models (SVR, RF, and XGBoost) optimized by particle swarm optimization on 166 test data points. In the graphs, \"actual values\" and \"model predictions\" are shown in time series formats on the same axis, where the horizontal axis shows the data points (test data: 166 pieces) and the vertical axis shows the 'values (actual and predicted values). In the PSO + SVR model, the actual values and predictions overlapped to a large extent and the overall accuracy of the model was observed to be high. However, it was observed that the model reacted with a delay or showed slight deviations in some sudden changes. The PSO + RF model followed the actual values quite successfully. In terms of modeling fluctuations, it exhibited a more stable and consistent structure compared with the SVR model. The PSO + XGBoost model also produced forecasts quite close to the actual values and was especially successful in capturing sudden changes. It was observed that the model followed the general trend well and the forecast error was low. While the XGBoost + PSO and SVR + PSO models produced the most successful results, the RF + PSO model also exhibited reasonable performance.
## 4 Theoretical Approach to Query Result Merging
### The Query Reduction Principle  
In this section, we explore the properties of queries and define query result merging. Following is a definition of a well-defined query.  
**Definition 1**.: Well-Defined Query  
Let us denote that condition \(C\) \"refers\" to a set of attributes \(A\) when all the data fields involved in condition \(C\) is contained in set \(A\).  
Query:  
\[q=\Pi_{A_{n}}\left(\sigma_{C_{n}}\left(\cdots\left(\Pi_{A_{1}}\left(\sigma_{C_ {1}}(S)\right)\right)\right)\right)\]  
is well-defined if condition \(C_{i}\) refers to a subset of attribute set \(A_{i\cdot l}\) for every positive integer \(l<i\leq n\).  
This definition is reasonable since selection \(\sigma_{Cj}(...)\) would be examining non-existent attributes if \(C_{i}\) does not refer to the attributes in \(A_{i\cdot l}\) because projection \(\Pi_{A_{l-1}}(...)\) would leave out the attributes referred in \(C_{i}\). Otherwise, all queries can be answered. The following theorem shows how all queries can be reduced to a simple form. Without loss of precision, we ignore the query temporal information for now.  
**Theorem 1**.: Query Reduction Principle  
Every well-defined query:  
\[q=\Pi_{A_{n}}\left(\sigma_{C_{n}}\left(\cdots\left(\Pi_{A_{1}}\left(\sigma_{C _{1}}(S)\right)\right)\right)\right)\]  
can be reduced to the form:  
\[q=\Pi_{A}(\sigma_{C}(S))\]  
where \(A=A_{n}\) and \(C=\sum\limits_{i=1}^{n}C_{i}\)  
**Definition 2**.: Query Result Merging  
Let \(\Sigma=\{q_{i}\}\) be a set of queries, where:  
\[q_{i}=\Pi_{A_{i}}(\sigma_{C_{t}}(S))\]  
Also, given a current relation \(S\), let \(\Sigma_{S}\) be a set of queries in \(\Sigma\) whose conditions are satisfied by the tuple in \(S\). Then the relation defined by:  
\[M_{\Sigma}(S)=\Pi_{\bigcup_{q_{i}\in\Sigma_{S}}A_{i}}(S)\ (A_{i}\text{: the set of projection attributes of query }q_{i}).\]  
is called the merged query result for the query set \(\Sigma\) and the process of producing the merged query result relation is called \"query result merging\".  
Logically, it is reasonable to call this \"query result merging\" because the process extracts only the data fields requested by the queries whose conditions are satisfied by the current relation \(S\). Therefore, the resulting relation fully contains the requested data in the most compact manner.  
Unlike in [28], where query conditions and requested data attribute sets were merged, we do not attempt to merge queries. Merging queries into a single query is dangerous because when query conditions and projection attributes are merged, the merged query would indistinctively request all data attributes, even for the queries whose conditions are not satisfied. Therefore, instead of incurring unnecessary data transmissions by query merging, we apply a more robust concept of query result merging. The following section explains the complete query management framework for underwater sensor networks in detail.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 5 High-Speed Scenario  
High-speed mobility significantly affects the network QoS, and the typical scenario is the high-speed railway. This scenario is quite different from scenarios discussed in Sections 3 and 4 due to its private networks and the quite high speed of the train. In this section, we analyze the different refinement policies to enhance QoS in terms of frequency bands, switching between different networks, idle detection, and location division.
## 5 Discussion
### Objective Function
#### 5.3.2 Hierarchical Optimization Method  
* Advantages: This method's prioritization strategy guarantees that the most critical objectives are addressed first, which is paramount in scenarios where failing to meet primary objectives could negate the relevance of secondary objectives. In environments where some objectives cannot be compromised, such as safety-critical systems, environmental conservation, or healthcare, prioritizing these ensures alignment with the scenario's overarching goals and ethical considerations. This approach ensures that the optimization process respects the most critical requirements.
* Disadvantages: There is a substantial risk that lower-priority objectives might not be considered sufficiently if high-priority objectives significantly consume resources. It could yield solutions that, albeit satisfying primary goals, are sub-optimal in the context of the broader problem. The potential depletion of resources on high-priority objectives might marginalize secondary goals, undermining the comprehensive quality of the solution. Furthermore, optimizing objectives sequentially by importance can introduce challenges when objectives are highly interdependent. Enhancing one objective might impair another. This approach complicates pursuing a balanced and globally optimal solution.
# Cloud Computing for Big Data Analysis
###### Abstract  
We present a novel approach to the Internet of Things, large amounts of digital data are generated and collected from different sources, such as sensors, cameras, in-vehicle infotainment, smart meters, mobile devices, applications, and web services. The large volume of data produced daily, coupled with the speed with which such data are generated and its heterogeneity, have led to interesting new technological challenges in the collection, storage, and analysis of this data. Those data volumes, commonly referred to as big data, can be exploited to extract useful information and produce helpful knowledge for science, industry, and public services [1, 2]. Novel technologies, architectures, and algorithms have been developing to capture and analyze big data [3]. For example, in scientific and business fields, researchers and data scientists are analyzing big data to extract information and knowledge useful for making new discoveries and supporting decision processes [4].  
Many researchers focused their studies on the development of applications for big data analysis in various application fields, including trend discovery, social media analytics, pattern mining, sentiment analysis, and opinion mining. For example, from the analysis of large amounts of user data, we can understand human dynamics and behaviors, including the following: (\(i\)) the main tourist attractions and also the mobility patterns within a city [5]; (\(ii\)) the areas of a city where it is necessary to improve the means of transport [6] or where it is more suitable to open new businesses [7]; (\(iii\)) the behavior purchase of users while browsing an ecommerce [8]; (\(iv\)) the behavior of fans following important sporting events [9]; and (\(v\)) the political orientation of citizens and then estimates the outcome of a political event [10].  
In this context, cloud computing is a valid and cost-effective solution for supporting big data storage and executing data analytic applications. Cloud computing can be defined as a distributed computing paradigm in which all resources, dynamically scalable and often virtualized, are provided as services over the Internet. As defined by NIST (National Institute of Standards and Technology) [11], cloud computing can be described as follows: \"a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction\". From the NIST definition, we can identify five essential characteristics of cloud computing systems, which are on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Due to elastic resource allocation and high computing power, cloud computing represents a compelling solution for big data analytics, allowing faster data analysis and resulting in more timely results and greater data value.
## 2 Experimental
### Preparation of the Test Solution  
The stock solution of Cu\({}^{2+}\) (1000 mg/L) was prepared. The stock solution was diluted with distilled water for the initial solutions with two different concentrations (10 and 50 mg/L).  
Experiments on the adsorption of Cu\({}^{2+}\) from aqueous solutions were conducted at both 10 and 50 mg/L concentrations at 298 K and an initial pH of 7. The effects of the AgNPs dosage (0.05, 0.1, 0.15 and 0.2 g) and the pH (2, 5, 7, 9, 12) using 0.2 g of AgNPs at 298 K, respectively, on the Cu\({}^{2+}\) adsorption were investigated. The pH adjustments were adjusted using 1 M of NaOH and 1 M of HCl. The contact time effect on the Cu\({}^{2+}\) adsorption was studied for 150 min. The effect of the Cu\({}^{2+}\) initial concentration (from 10 to 50 mg/L) on the Cu\({}^{2+}\) adsorption using the AgNPs (0.2 g) was studied at an initial pH of 7, and at temperatures of 298 K and 323 K. The mixtures were shaken at 100 rpm for 3 h using an orbital \"rotaterm\" and a linear shaker, and were then filtered. The concentrations of Cu\({}^{2+}\) were determined via plasma mass spectrometry (inductively coupled plasma, ICP).  
The amount of the Cu\({}^{2+}\) absorbed (\(q_{e}\) (mg/g)) was calculated using the following equation:  
\[q_{e}\,=\,\frac{(C_{0}-C_{e})\ V}{m}, \tag{1}\]  
where \(C_{0}\) and \(C_{e}\) (mg/L) are the concentrations of initial and equilibrium, respectively, \(V\) (L) is the solution volume, and \(m\) is the adsorbent mass (g).  
The adsorption percentage of Cu\({}^{2+}\) (% Ads(Cu\({}^{2+}\))) was calculated as follows:  
\[\%\ \text{Ads}_{(\text{Cu}^{2+})}\,=\,\frac{(C_{0}-C_{e})}{C_{0}}\,\times 100. \tag{2}\]  
To describe the adsorbent behavior, the models of four kinetic and two adsorption isotherm were used and, to describe the adsorption type, the thermodynamic parameters (\(\Delta G^{\circ}\), \(\Delta H^{\circ}\) and \(\Delta S^{\circ}\)) were calculated.
## 4 Results
### Numerical Examples
This result indicates that, from the point of view of costs, it can be advantageous to perform more severe maintenance actions in advance. These results were obtained considering all \(C_{PM},C_{CM}\) combinations, varying from USD 200 to USD 10,000 (increments of USD 200) and from USD 500 to USD 50,000 (increments of USD 500), respectively. The average values of the parameters defined in Table 8 were used. Another set of experiments considered different values of the parameter \(\beta\), which is related to the expected number of failures over time. Higher values of this param  
Figure 5: Optimal times and intervention levels of preventive maintenance versus CPM and CCM. For most \(C_{\text{CM}}\) and \(C_{\text{PM}}\) combinations, the optimal intervention level was 20% in this case. (**a**) Optimal times for preventive maintenance. (**b**) Optimal intervention levels for preventive maintenance. Figure 6: Effects of maintenance costs on the preventive maintenance time. eter increase the cost with corrective maintenance, as the expected number of failures grows over time. In these cases, more frequent preventive maintenance may be required. The experiments consider \(\beta\) values ranging from 2.5 to 5.0, with steps of 0.5, as presented in Table 14 for the first case example, allowing us to evaluate the assertiveness of the PM optimization. In the table, the average results are presented regarding the most frequent number of cycles observed in 10 replications (mode), including a column with the observed frequency, named \(\hat{\rho}\). Obviously, with the increase in the parameter \(\beta\), which is directly proportional to the expected number of failures, the total costs increased proportionally to the number of maintenance actions. The difficulty of the problem also seemed to grow with the parameter \(\beta\), which explains the increased variability of the results (\(\hat{p}\) from 1.0 to 0.3). This limitation can be circumvented by adjusting the AG parameters, such as the size of the population and the number of generations. Finally, the results of the intervention level should be highlighted. As observed before, in the results shown in Figures 5 and 6, the solutions in Table 14 consistently indicate a greater intervention level in the first maintenance activity, reducing this intervention level in the final maintenance activity. Such a result is largely due to the form of parameterization of the improvement factor, which generated a lower increase in the improvement values as the intervention level increased, as shown in Figure 7.
## 6 Discussion  
Although ISO26262 Part 3 has stated in its scope of application that the hazards addressed in this document refer to the hazards caused by malfunctioning behaviors of items and their interaction, the HARA process only focuses on hazards caused by malfunctioning behaviors, and if every other system in the vehicle is sufficiently independent, they are assumed to be functioning correctly. However, it would be a bit arbitrary to conclude that HARA is only aimed at the hazards caused by item failures, because malfunctioning behavior refers to failure or unintended behaviors related to the design intent, but ISO26262 does not specify what \"unintended behavior\" exactly includes. While for STPA, accidents result from inadequate control of component failures, dysfunctional interaction of components, external disturbance, etc. In addition, STPA does not deal with risk assessment and only focuses on the causes of inadequate control or enforcement of safety constraints leading to accidents. In order to meet all the requirements of ISO26262 in the concept phase, we combine STPA and FMEA to form a new analysis method STPAFT. STPA and FMEA can complement each other, so STPAFT not only has the advantages of STPA in hazard analysis, but also can evaluate risk. Through the focus of FMEA on low-level components, STPAFT can obtain more detailed causal factors, which is very helpful for the functional safety requirements derivation in the concept stage of ISO26262. Another point worthy of note is that ISO26262 does not seem to make systematic requirements for the definition of an item. Therefore, we can use information of STPA on safety control structure to clearly describe an item in terms of system function and system boundary. In the area of the rapidly developing automotive domain, the increasing implementation of advanced functions and intelligence in vehicles greatly improves the degree of vehicle automation. In order to ensure functional safety, factors leading to hazards other than component failure must be taken into account. Therefore, it may be a trend to integrate STPA into the functional safety standards.
# Bounce: A High Performance Satellite-Based Blockchain System
## 9 Conclusions  
_Bounce_ is a satellite-based blockchain protocol designed for fault tolerant, high throughput, low latency, forkless operation, and low energy consumption. We use satellites because they are difficult to capture and even if they could be captured, the Bounce Units we embed in them are easy to make tamper-resistant.  
For that reason, our failure assumptions about them are that they don't suffer from Byzantine failures. Other components may, but the other components mostly act as relays, so even if only a few of them work correctly, the system makes safe progress.  
Our experiments show that _Bounce_ can process more than five million transactions per two second slot time with 3 to 10 s response times and a high degree of fault tolerance. The throughput numbers will increase as more capable terrestrial hardware is brought to bear, enabling the construction of larger Merkle Trees. Because the size of Merkle roots is independent of the size of the corresponding Merkle Tree, these throughput improvements can take place without requiring any changes to the protocol on the satellites.  
While real-world deployment may present additional practical challenges, _Bounce_ provides a foundation for future research and development of high-performance, globally accessible blockchain systems.  
Conception: D.E.S.; Design: X.L., T.K. and D.E.S.; Implementation: X.L. and T.K.; Experimentation: X.L. and T.K.; Interpretation: X.L., T.K. and D.E.S.; Writing: X.L., T.K. and D.E.S. All authors have read and agreed to the published version of the manuscript.  
U.S. National Science Foundation 1840761 A002.  
The experimental code (last updated on 7 March 2025) is openly available in the _Bounce_ repository at [[https://github.com/lapisliu/](https://github.com/lapisliu/)]([https://github.com/lapisliu/](https://github.com/lapisliu/)){project}relax\itshapeBouncel-core/tree/main (once published).  
The authors declare no conflicts of interest.
# Age and Gender Impact on Password Hygiene
## 1 Introduction
### Contribution  
Our paper presents research on password hygiene based on a set of leaked data of Lithuanian users. The analysed dataset exceeded 100,000 records. According to Eurobarometer data [23], Lithuanian users represent an average European user from a cyber hygiene perspective; e.g., 65% of Lithuanian users changed their passwords for online services during the previous 12 months, while the European average is 67%. Nowadays, security breaches and data leaks are commonplace. In social media and advertisements of security companies, lists of top passwords are presented every half a year. In addition, yearly reports of the National Cyber Security Centre of Lithuania show rising numbers of cyber security incidents. Information technology (IT) as a mandatory subject for high school pupils in Lithuania was introduced in 1986. At that time, schools did not have a sufficient amount of computers, and teachers were struggling to ensure the development of practical skills. In 1995, the school graduation IT exam was introduced. The national association of IT teachers was established in 2000. In 2005, the IT subject was already taught from the 5 th grade of secondary school. Therefore, we assume the varying exposure to IT of different generations to be visible in their password hygiene measurements.  
The work aims to investigate password strength and identify the impact of age and gender properties using an extensive password dataset. We raise the following research questions in the context of the leaked dataset:  
* Do older users have weaker passwords considering their lack of general IT education compared to younger generations?
* Has gender no impact on password strength, as previous studies did not find conclusive evidence of any significant differences?
* What is the password hygiene in general?  
Our applied research has practical implications for service developers and providers. They should know and understand the distribution of security-related behaviour of their users. Corresponding technical measures should be adapted to ensure the security of systems and the privacy of their customers.  
The paper is structured as follows. Section 2 covers the sample data and methodology of how leaked passwords were recovered. Section 3 provides statistical insights about the password strength from the perspective of age and gender differences. Section 4 discusses the implications of our findings and similarities with other research. Section 5 concludes the paper and offers directions for future work.