esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Material Proportioning for Similar Modeling of Brittle Rocks
###### Abstract
(2021) selected the uniaxial compressive strength, the compressive strength-tensile strength ratio, and the impact energy index as evaluation indices. The optimal formulation for rockburst model materials was obtained by mixing quartz sand, gypsum, cement, glycerin, gelatin, and water. [PERSON] et al. (2022) introduced the rockburst tendency index and the brittleness evaluation index to analyze the relationships between the influencing factors and the various indices of the materials using an orthogonal design and a sensitivity analysis. [PERSON] et al. (2023) formulated rockburst simulation materials and analyzed the stress in and displacement patterns of the surrounding rock during excavation processes in rockburst models with various types of structural surfaces. [PERSON] et al. (2024) prepared white sandstone simulation materials with a strong rockburst tendency and discussed the internal energy changes in and rockburst mechanisms of the specimens at different temperatures. [PERSON] et al. (2025) prepared model materials with a rockburst tendency using quartz sand, gypsum, cement, and glycerin and analyzed the forms and characteristics of rockburst failure. [PERSON] et al. (2026) developed rockburst simulation material models and used the strain acquisition and digital microscopic imaging systems to analyze the deformation and failure evolution of the tunnel roof during continuous excavation. Although the aforementioned scholars have made significant progress in the selection and proportioning of rockburst-mimicking materials, few have provided insights into the variation patterns in the physical and mechanical parameters of these materials, making it difficult to accurately predict their mechanical properties. Based on the previous research, in this study, we selected refined iron powder, barite powder, quartz sand, gypsum powder, and a rosin-alcohol solution to formulate similar materials. The rockburst tendency index and the brittleness index were introduced, and the orthogonal design method was used to analyze the influence of the material proportions on the physical and mechanical parameters of the specimens. As a result, linear regression equations for the various physical and mechanical parameters were obtained. Additionally, uniaxial compression tests were conducted to analyze the brittle failure modes and acoustic emission characteristics of the specimens, which helped verify the feasibility of using materials to simulate brittle rocks. The results of this study will provide a scientific basis and reference for material proportioning in similar model tests of rockbursts in brittle rocks.
## 1 Introduction
* By using the bias compensation, this paper derives the identification model and achieves the unbiased parameter estimation for observability canonical state-space models with colored noise. * By employing the interactive identification, this paper explores the relationship between the noise parameters and variance and the bias correction term and realizes the simultaneous estimation of the system parameters, noise parameters and system states. The rest of this paper is organized as follows. Section 2 demonstrates the problem formulation about the observability canonical state-space system and derives the identification model. Section 3 develops a bias compensation-based parameter and state estimation algorithm. Section 4 provides two illustrative examples to show that the proposed algorithm is effective. Finally, Section 5 offers some concluding remarks.
## 3 System Design and Development  
### Overall System Architecture  
The developed system in this study, the Mixed Reality-based Remote Collaboration System (MR2 CS), consists of three unit systems: (1) Field Operator System (FOS), (2) Communication Server, and (3) Office Manager System (OMS). Figure 1 shows the overall architecture of MR2 CS. First, FOS has been developed based on HoloLens 2, MR smart glasses developed by Microsoft ([PERSON], WA, USA). A field operator on a construction site wears this head-mounted display (HMD) on his or her head and walks around the site to view the existing artifacts with BIM elements superimposed in the MR. The FOS user can receive instructions from a manager in the office. FOS sends Mixed Reality Capture (MRC), the combined image of real-world images of existing artifacts in the site and overlaid Virtual BIM elements, to OMS through Communication Server. Second, the Communication Serverwas built on WebRTC, a technology to find dynamic IP addresses of mobile devices, and helps information communication between FOS and OMS. Third, OMS displays MRC video in real time and communicates with a FOS user by a desktop or tablet PC in the office. An office manager can give instructions to a field operator by voice through OMS.  
The development environment of each unit system is shown in Table 1. FOS was developed in Visual Studio 2019. The communication server consists of an Application Server and a Relay Server. The Relay Server uses WebRTC to help transmit data without interruption of the dynamic Internet Protocol (IP) addresses of FOS and enables direct communication between FOS and OMS without the Relay Server. If direct data transmit channels of FOS and OMS are secured through WebRTC, MRC images can be shared more quickly and stably. The office manager can access OMS through any web browser and give instructions to FOS users by voice.
## 5 Role of IoT Technology in Education  
The Internet of Things has the potential to transform the education system by improving operational efficiency, enhancing learning experiences, and increasing safety and security. As technology continues to evolve, it is important for educational institutions to embrace IoT and leverage its benefits to stay competitive and provide the best possible education for their students.  
The advancement of IoT technology caused a paradigm shift in education by the emphasis shifts from knowledge and learning through memorization to a student-centered approach [27]. IoT aims to have a huge effect on the higher education learning process by giving students and teachers access to international tools and opportunities.  
IoT can also enhance the safety and security of educational institutions. Smart cameras and sensors can be installed to monitor the premises and alert authorities in case of any suspicious activity or emergencies. This can help prevent potential threats and ensure the safety of students, staff, and visitors.  
Furthermore, IoT can facilitate distance learning and virtual classrooms. With the help of IoT devices, students can attend classes remotely, participate in discussions, and collaborate with their peers in real-time. This can be especially beneficial for students who live in remote areas or have mobility issues [44].  
The integration of mobile learning applications for student evaluation has brought about an increase in the use of various IoT-based applications. These apps enable students to access resources, tackle assignments, and collaborate on projects with ease. Moreover, teachers can effectively demonstrate technical concepts and simulations through these apps.  
In the higher education sector, leveraging the power of IoT technology brings with it a host of security and privacy challenges. To ensure that these applications are secure and that valuable data are kept safe and private, creating standards for security and privacy is essential. In addition, collaborative methods must be employed in order to identify potential business risks associated with data breaches. With these measures in place, higher education institutions can confidently embrace the possibilities of IoT technology while protecting their data from potential threats [45].
###### Abstract  
This study investigates reliability modelling and optimal preventive maintenance polices for engineering systems subject to shocks, considering two types of self-exciting mechanisms. Under trigger mechanism 1, if the accumulative number of \(\delta\)--ineffective shocks reaches the trigger threshold \(k_{1}\) among the run of ineffective shocks, then the self-exciting mechanism will be activated, resulting in the accumulative number of effective shocks increasing by \(m_{1}\). Under trigger mechanism 2, if the consecutive number of \(\delta\)--ineffective shocks reaches a fixed threshold \(K_{2}\) among the run of ineffective shocks, then the self-exciting mechanism will be triggered and the accumulative number of effective shocks will increase by \(m_{2}\). The system breaks down when the accumulative number of effective shocks exceeds the fixed threshold. Based on the established shock models, the reliability indices are obtained through the finite Markov chain imbedding approach. According to the operation of the system under different monitoring conditions, two types of preventive maintenance strategies are considered; then, optimization models are established, and the optimal preventive maintenance thresholds are determined. Finally, the proposed models are illustrated by numerical examples.  
reliability; maintenance; shock models +
Footnote †: journal: Applied Sciences
# Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
## 4 Discussion
### Academic Groups and Social Capital  
See Table 16 and Figure 6. The Extraversion-Grade relationship was linked to the social science concept of _social capital_, which was used as a theoretical basis for our formation of Academic groups. [PERSON] et al. [58] obtained a classification accuracy of 60% for the expected grade category of a student (Fail, Pass, Good, Excellent) against their LMS behaviour. Among the features used by [PERSON] et al. [58] was the number of messages sent to a forum (Extraversion level).  
[PERSON] and [PERSON] [59] and other researchers do not illustrate the quantitative effect of social capital. However, the authors cite that 'an individual who creates and maintains social capital subsequently gains advantage from it [social capital]'. The quantification of the perceived effect of social capital was illustrated by the correlation between the Grade of a student in an Academic group, \(Gc_{i}(h_{i})\), and the average Grade of the group, \(\mathbb{E}[Gc_{i}]\). \(Gc_{i}(h_{i})\) responded with a statistically significant increase of 0.984 to a \(\mathbb{E}[Gc_{i}]\) increase of 1.  
Despite showing different insights and patterns, both the Discussion and Collaboration group methods showed that the _quality_ of a student's academic output (Grades) was associated with the quality of the academic output of their social capital. As stated in Section 1.2, a student may choose to _leverage_ their social capital (that is available to all students in a cohort) by becoming part of an Academic group.  
Our Academic group and Grade relationship, findings such as [PERSON] et al. [58]'s and the above statement by [PERSON] and [PERSON] [59] provide evidence for the positive relationship between student success and the accumulation of social capital. This section's work contributed to the theory that:  
_The quality of a student's social capital is the quality of their Academic group's performance._  
Academic Group Size Constraints  
Each Discussion and Collaboration group was constrained to a minimum size of three. When the sizes were reduced to two, all linear relationships between the student's Grade and the group's average Grade collapsed and were statistically insignificant. Furthermore, residuals, \(Gd_{i}(s_{i})-\hat{G}d_{i}(s_{i})\) (for Discussion-Grade relationships) and \(Gc_{i}(h_{i})~{}-~{}\hat{G}c(h)\) (for Collaboration group-Grade relationships) were not normally distributed for the group sizes of two.
## 4 Advanced Machine Health Diagnosis Framework
### Frequency-Domain Transformation  
The acquired time-domain signals are converted into the frequency domain using the Fast Fourier Transform (FFT) [18]. This process isolates the harmonic content of the signal, revealing fault-related frequency components that may indicate early stage degradation. Frequency resolution and noise filtering are adjusted according to the application requirements.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Characteristics of Underwater Acoustic Communication  
Due to the different transmission media, the underwater environment poses a more severe situation for routing protocol design than terrestrial wireless sensor networks. In the following paragraphs of this section, we will introduce these differences briefly.
## 4 Classification of PPDA Protocols
### Heterogeneous protocols
#### 4.3.2 Hybrid  
When a protocol uses more than one technique to achieve privacy-preserving data aggregation for WSNs, it belongs to the hybrid sub-group. PIA is only the hybrid-based protocol in this literature.  
**PIA:** To address Privacy-preserving Integrity-assured data Aggregation (PIA) for WSNs, recently, [PERSON] _et al._[39] proposed four distinct symmetric-key solutions. In their single aggregator model, an aggregator node is used as an intermediary between the user (_i.e._, a third party) and the sensor nodes that aggregates the sensor data and forwards the query response to the user, as shown in Figure 10. The problem is that the user wants to verify the integrity of the received aggregate value whereas the network owner does not want the user to access the original data. The proposed four solutions to the problem are as follows. The first scheme uses homomorphic encryption to hide the data. In this scheme, homomorphism and Message Authentication Code (MAC) [40] are combined to construct an authenticated encryption scheme for the aggregator model. However, it is limited to additive homomorphism and only supports aggregation functions such as add, average and standard deviation. The second scheme adopts an Order Preserving Encryption Scheme (OPES) [41] to preserve the privacy of the distribution of the data. For example, OPES preserves the order of the data such that any pair of plaintexts \(y_{1}\) and \(y_{2}\), where \(y_{1}<y_{2}\), encrypt to ciphertexts \(c_{1}\) and \(c_{2}\), where \(c_{1}<c_{2}\). However, this framework is useful for any aggregation function which can be approximated by uniform sampling [24, 42] and which relies only on the comparison operation. The third scheme directly uses the Secure Hierarchical In-networking Aggregation (SHIA) scheme [22] for adapting distributed integrity verification to the single aggregated model. Because the sensor nodes have access to all of the raw data, this scheme can support any aggregation function. However, the scheme has the communication overhead of _O(N)_ messages per sensor node, where \(N\) is the total number of sensor nodes in a network. To achieve data privacy and integrity assurance the third scheme is improved by introducing a logical aggregation tree within the aggregator node. This is the fourth scheme know the _improved scheme_ and each sensor node has the communication overhead of _O(log N)_. However, the scheme only supports decomposable functions, such as _Mean, Standard Deviation, Count_ and _Min/Max_.  
Figure 10: Data aggregation from sensors A, B and C with two aggregators X and Y.
## 2 Materials and Methods
### Reference Analytical Methods  
The reference measurements were carried out on the same samples used for the Raman spectral acquisition. PO\({}_{4}\), NH\({}_{3}\), NO\({}_{3}\) and nitrite (NO\({}_{2}\)) concentrations were determined by colorimetric methods implemented in a flow segmented analyzer (Skalar 5100, Skalar Analytical, Breda, The Netherlands). For the total P content, an acidic digestion of a mixed liquor sample was performed with 0.3 M H\({}_{2}\)SO\({}_{4}\) and 400 mg of K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) and analyzed using the flow segmented analyzer. Acetate and propionate (VFA) were determined by high-performance liquid chromatography (HPLC), using a VWR Hitachi Chromaster with a Biorad Aminex HPX-8/H 300 7.8 MM column and a DAD detector (0.01 N sulfuric acid was used as eluent with an elution rate of 0.5 mL/min). The total carbohydrates hydrolysable to glucose (i.e., bacterial glycogen and microalgae starch) were determined through an acidic digestion of lyophilized biomass [32]. PHAs were determined by GC according to the method described by [PERSON] et al. [33], using a Bruker 430-GC gas chromatograph equipped with an FID detector and a Restek column (60 m, 0.53 mm internal diameter,1 \(\upmu\)M df, crossbond). TSSs and VSSs were calculated according to APHA/AWWA/WEF standard methods [34]. Aqueous carbon dioxide was measured with a CO\({}_{2}\) Mettler Toledo sensor and the concentrations were corrected considering the pH of the reactor, taking into account the equations of CO\({}_{2}\) equilibrium in water and their respective constants according to Henry's Law (K = 0.0017 M; Ka\({}_{1}\) = 4.47 \(\times\) 10\({}^{-7}\) M; Ka\({}_{2}\) = 4.69 \(\times\) 10\({}^{-11}\) M) [35; 36].
## 3 Resources and Techniques
### Resources  
For an in-depth understanding of STPs, where a lack of uniformity in the models is observed as explained in Section 1, different resources need to be selected and accessible. This work takes the following resources into consideration:  
* A defined terminology in the field of STPs, and the critical factors and core features affecting STPs summarised in Table 2[2].
* Expert labelling for the case study of Spanish science and technology parks, developed and described by the [PERSON] et al. study [2], where three types of STPs are considered--science parks, technology parks, and hybrid parks--for which short definitions are presented in Table 1.
* A dataset built by [PERSON] et al. [2] in which the internal core features affecting the performance of STPs are presented. It presents a tabular format and covers a 16-year period (2004-2019), and 21 selected core features are considered, together with the STP name (STP identifier) and the year, resulting in a dataset with 23 columns. The rows of the dataset correspond to the STP name-year combination that constitutes each instance of the dataset, amounting to 603 instances in total.
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 3 A Periodic Watermark-Based Detection of Replay Attacks
### Construction of Periodic Watermark Signal  
In contrast to the approach presented in [PERSON] et al. [32], which injects watermarks of varying sizes at periodic intervals, this study introduces a method that continuously injects a periodic Gaussian random signal into the control signal. The watermark signal's period is denoted by \(T\), where \(T\in\mathbb{N}^{r}\). The series of periodic watermark signals is represented by \(\Delta o_{i}=[\Delta o_{0},\ \Delta o_{1},\ \cdots,\ \Delta o_{T-1}]\), with each \(\Delta o_{i}(i=0,\ \ldots,\ T-1)\) being independently drawn from a Gaussian white noise signal characterized by a zero mean and a covariance matrix \(\Sigma_{o}\). Assuming the system operates steadily at time \(t_{0}\), a signal is continuously  
Figure 6: A realization of a replay attack and periodic watermarking method.  
injected into the control variable. At any given time \(k\), this injected watermark signal \(\Delta o\) is defined as follows:  
\[\Delta o(k)=\Delta o(i),i=(k-t_{0})\bmod T \tag{13}\]  
where \((k-t_{0})\mod T\) represents the remainder of \(k\) divided by \(T\), and \(0\leq(k\mod T)\leq T-1\). After injecting the periodic watermark signal, the UMVs state equation can be expressed as:  
\[\left\{\begin{aligned} x^{r}(k+1)=&\sum_{p=1}^{3} \hbar_{p}(\varrho(k))(A_{dp}x^{r}(k)+B_{di}(u(k)+\Delta o(k))+\theta_{di}(k)) \\ y^{r}(k)=& Dx^{r}(k)+v(k)\end{aligned}\right. \tag{14}\]
# Drying Behaviour of 50 mm Thick Turkey Oak Lumber
## 4 Conclusions  
The drying tests revealed that Turkey oak lumber is indeed difficult to dry conventionally but not impossible. The following conclusions can be formulated:  
1. The drying quality very much depended on the wood quality and the growth defects. The presence of knots, eccentric pith, and inclined grain induced incipient inner stresses that were difficult to handle. Most pieces with such defects cracked and/or warped during drying.
2. Kilin-drying from a green state provided better results than air-drying, in terms of both quality and time, due to the possibility of rigorous air humidity control at high values during the first stage of drying.
3. The most important issue with the 50 mm thick lumber was the moisture gradient across the lumber thickness, which was high (10-18%) from the beginning of the process. Turkey oak is very sensitive to temperature, so as soon as the temperature rises (even under natural conditions, when shifting from winter to spring), if the air humidity is not high enough, the lumber will crack. By rigorously controlling the air humidity, kiln-drying created better conditions to keep the inner stresses under control. Maintaining the temperature at 30 \({}^{\circ}\)C and the air humidity at 80-85% until the surface layer (measurement at 8-10 mm depth inside wood) reached 24% helped to avoid cracks in all defect-free boards that were tested.
4. The gradual increase in the temperature up to 60 \({}^{\circ}\)C (maximum), by maintaining the drying gradient at 1.5-2.0, allowed good drying quality, with no cracks, of the defect-free boards, at an average drying rate of 0.45%/day.
5. Steaming prior to kiln-drying had a positive effect upon the drying quality. No influence upon the drying time was observed.  
Figure 12: Case-hardening samples of kiln-dried 50 mm thick Turkey oak lumber: (**a**) steamed board (gap opening = 2 mm) and (**b**) unsteamed board (gap opening = 3 mm).  
The number width influenced the drying quality: narrow boards (\(b\) = 15 cm), even if they contained both sapwood and heartwood, dried better than wide boards (\(b\) = 30 cm).  
End protection by means of paraffin-based sealer during air-drying did not help preventing cracks. Protection with a PVA adhesive + sawdust mixture applied on the kiln-dried boards provided much better results. No protected board cracked.  
Conceptualization, I.-T.D. and M.C.; methodology, I.-T.D. and M.C.; graphics, S.-VG.; validation, I.-T.D., S.-V.G. and M.C.; formal analysis, M.C.; investigation, I.-T.D.; resources, I.-T.D.; data curation, I.-T.D.; writing--original draft preparation, I.-T.D.; writing--review and editing, I.-T.D.; visualization, I.-T.D.; supervision, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded as part of the research programme of the Transilvania University of Brasov.  
Not applicable.  
The data presented in this study are available on request.  
The authors declare no conflict of interest.