esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As concrete for infrastructure applications requires lower w/c ratios (<0.45) to withstand aggressive exposure, it is more brittle than conventional concrete with w/c > 0.5. To predict and mitigate cracking in cement-based structures, approaches such as linear elastic fracture mechanics (LEFM) and fracture energy methods have proven effective [28]. The fundamental fracture properties of concrete, including the critical stress intensity factor, fracture energy, and characteristic length, can be determined using simple three-point bend notched beam specimens. Previous tests [29] revealed that incorporating 10 wt.% zeolite and 5 wt.% limestone, along with chemical admixtures, demonstrated an increase in fracture properties. The authors suggested that this can be attributed to the consolidation of the concrete microstructure through the reaction between active silica in zeolite and calcium hydroxide, leading to the formation of larger quantities of calcium hydrosilicates. Furthermore, the absorption modification of hydrated products with chemical admixtures contributes to a higher strength and improved fracture properties in the modified concretes. A common method to enhance concrete fracture properties is to incorporate polypropylene (PP) fibers of varying shapes, lengths, and characteristics into concrete at specific volumes [30]. The authors of [31] tested the compressive strength, flexural strength, and mid-span deflection of fiber-reinforced engineered cementitious composites blended with silica fume and zeolite. It was found that despite a low zeolite dosage (3 wt.%), it provided a better performance in combination with PP fibers in a sulfate environment, as evidenced by the significant increases in the studied parameters. Another experimental study [32] discussed the properties of an eco-friendly fiber-reinforced self-consolidated concrete with 10 wt.% zeolite. The authors established that samples with 0.50 to 0.75 vol.% of polyolefin fibers showed the highest compressive and tensile strength. A previously conducted study [33] investigated the impact of PP fibers' addition on concrete's physical and mechanical properties. The experimental findings demonstratedthat incorporating PP fibers significantly enhanced the compressive and flexural strength as well as fracture energy. It was shown that the optimal combination of 0.675 and 3.5 kg/m\({}^{3}\) of micro- and macro-PP fibers, respectively, can synergistically increase the fracture parameters, benefiting both the pre-peak and post-peak stages. Although the influence of each individual technological factor (content of zeolitic tuff, volume of entrained air, and dosage of fibers) has been sufficiently studied, there is limited information in the scientific literature regarding their combined effect. In this study, a work-of-fracture method is applied to assess the effect of the optimal combination of zeolitic tuff, two types of fibers, and an air-entraining agent on the performance of concrete under load.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion  
### Simulation Setup  
The proposed protocol was implemented in MATLAB R2020b and compared with the CHEF and EEUC algorithms. It was evaluated in two scenarios, each with 100, 200, and 400 sensor nodes. In Scenario 1, the BS was positioned at the network's center, while in Scenario 2, it was located outside the network. Initial parameters are provided in Table 2. Figure 12(a),b illustrate the configurations for Scenario 1 and Scenario 2, respectively.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 2 All-Digital True Random Number Generators
### Digital TRNGs
#### 2.2.1 Transition Effect Ring Oscillator  
The Transition Effect Ring Oscillator (TERO) [18,22] acquires randomness by exploiting oscillatory metastability of latches. Its structure is depicted in Figure 1. Analysing the behaviour of the circuit for different input values, it can be observed that if \(\textit{rst}=1\), output ports of the latch, \(b1\) and \(b2\), are stuck at zero and a stable state is reached with \(a1=a2=\overline{\textit{ctrl}}\). If \(\textit{rst}=0\), AND gates act as bypass gates and the loop is composed by, depending on the inputs of XOR gates, two buffers and two inverters or four buffers. In both cases, a stable state is reached since inverting elements are always of even number.  
When \(\textit{ctrl}\) transitions from low to high or high to low, \(a1\) and \(a2\) are inverted and this generates an oscillation that runs inside the feedback loop until the latch stabilizes to a steady-state. This behaviour is called oscillatory metastability and has been widely addressed in [23]. Oscillatory metastability can be observed in latches with more than two gates and a feedback chain with delay elements. Taking into account intrinsic noise in semiconductors, the number of oscillation for each transition at ctrl input results to be a random variable.  
Entropy is accumulated by counting the number of oscillations and a single bit is produced for each ctrl transition: a logical 1 is generated if the number of oscillations at the output ports, \(b1\) and \(b2\), is odd; a _logical 0_ is generated if the number of oscillations at the output ports, \(b1\) and \(b2\), is even; For this purpose, an asynchronous counter placed at the output of the TERO structure may be used: just one T flip-flop is enough to discriminate between odds and even oscillations, leading to a rather compact entropy element. However, oscillatory metastability behaviour is not particularly fast: in [18] the author proposes a ctrl period equal to 4 \(\upmu\)s, which translates in a throughput of 250 kbps. Particular attention must be paid to the routing, especially in the balance between loop branches. As stated in [23], an asymmetry in propagation delays of the feedback branches helps to reduce the resolving time: this implies the possibility of reaching higher throughputs but also a probable loss of quality due to the reduced standard deviation of the random variable that models oscillations. These considerations strongly depend on the technology in which the TERO element will be built.
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 2 System Model and Problem Formulation  
Considering a narrow-band DFRC system, as shown in Figure 1, the system incorporates an \(N\)-antenna uniform linear array (ULA) and serves \(K\) single antenna communication users in the downlink while detecting targets simultaneously.
## 4 Conclusions, Limitations, Future Directions
### Future Directions  
Building on the promising results of this study, several future research directions are envisioned, as follows:  
1. Incorporation of additional building parameters. Future work should include more detailed characteristics of building components--particularly the thermal performance of windows, shading devices, and occupancy schedules--to better reflect real-world thermal dynamics;
2. Validation with real-world data. While this study relied on simulation data for model training and evaluation, validating the proposed framework using real-world measurements from monitored buildings would enhance its reliability and practical applicability;
3. Dynamic and real-time prediction. The integration of the framework into BEMS for real-time thermal load predicting and control represents a valuable extension, especially for smart buildings and grid-responsive operations;
4. Cross-regional generalization. Although the study focused on three Moroccan climates, extending the framework to other geographical regions with different climate patterns and building typologies will further test its adaptability and scalability;
5. Integration with multi-objective optimization. Future research may explore combining predictive models with optimization algorithms (e.g., genetic algorithms, NSGA-II) to support the design of buildings that balance energy efficiency, cost, and thermal comfort.
6. Use of deep learning and hybrid architectures. Further investigation into deep learning models (e.g., CNNs, LSTMs, Transformers) and hybrid architectures that can automatically learn temporal and spatial patterns in energy data may enhance predictive performance.  
Conceptualization, Y.E.M. and M.T.U.; methodology, Y.E.M.; software, Y.E.M.; formal analysis, Y.E.M. and M.T.U.; investigation, Y.E.M. and M.T.U.; resources, M.T.U.; writing--original draft preparation, Y.E.M. and M.T.U.; writing--review and editing, Y.E.M. and M.T.U.; visualization, Y.E.M.; supervision, Y.E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: (a) The dataset, models, or codes supporting this study's findings are available from the corresponding author upon a reasonable request. (b) All data, models and code generated or used during this study appear in the submitted article.
## 2 Physical, Chemical, and Environmental Properties of Phthalates
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PAE Congeners** & **Acronym** &
\begin{tabular}{c} **Molecular** \\ **Formula** \\ \end{tabular} & **CAS** & **R1** & **R2** & **Log K\({}_{ow}\)** \\ \hline dimethyl phthalate & DMP & C\({}_{10}\)H\({}_{10}\)O\({}_{4}\) & 131-11-3 & CH\({}_{3}\) & CH\({}_{3}\) & 1.60 \\ diethyl phthalate & DEP & C\({}_{12}\)H\({}_{14}\)O\({}_{4}\) & 84-66-2 & CH\({}_{2}\)CH\({}_{3}\) & CH\({}_{2}\)CH\({}_{3}\) & 2.47 \\ disobutyl phthalate & DiBP & C\({}_{16}\)H\({}_{22}\)O\({}_{4}\) & 84-69-5 & CH\({}_{2}\)CH(CH\({}_{3}\))\({}_{2}\) & CH\({}_{2}\)CH(CH\({}_{3}\))\({}_{2}\) & 4.11 \\ dibutyl phthalate & DnBP & C\({}_{16}\)H\({}_{22}\)O\({}_{4}\) & 84-74-2 & CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{3}\) & CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{3}\) & 4.50 \\ dimethylglycol & DMEP & C\({}_{14}\)H\({}_{18}\)O\({}_{6}\) & 117-82-8 & CH\({}_{2}\)CH\({}_{2}\)OCH\({}_{3}\) & CH\({}_{2}\)CH\({}_{2}\)OCH\({}_{3}\) & 1.11 * \\ phthalate & BBzP & C\({}_{19}\)H\({}_{20}\)O\({}_{4}\) & 85-68-7 & CH\({}_{2}\)C\({}_{6}\)H\({}_{5}\) & CH\({}_{2}\)C\({}_{6}\)H\({}_{5}\) & 4.73 \\ dicyclohexyl phthalate & DCHP & C\({}_{20}\)H\({}_{26}\)O\({}_{4}\) & 84-61-7 & CH(CH\({}_{2}\))\({}_{5}\) & CH(CH\({}_{2}\))\({}_{5}\) & 5.6 \\ di-n-pentyl phthalate & DnPP & C\({}_{18}\)H\({}_{26}\)O\({}_{4}\) & 131-18-0 & CH\({}_{2}\)(CH\({}_{2}\))\({}_{3}\)CH\({}_{3}\) & CH\({}_{2}\)(CH\({}_{2}\))\({}_{3}\)CH\({}_{3}\) & 5.62 \\ bis (2-n-butoxyethyl) & DBEP & C\({}_{20}\)H\({}_{30}\)O\({}_{6}\) & 117-83-9 & CH\({}_{2}\)CH\({}_{2}\)O(CH\({}_{2}\))\({}_{3}\)CH\({}_{3}\) & CH\({}_{2}\)CH\({}_{2}\)O(CH\({}_{2}\))\({}_{3}\)CH\({}_{3}\) & 4.06 * \\ phthalate & DPhP & C\({}_{24}\)H\({}_{38}\)O\({}_{4}\) & 84-62-8 & C\({}_{6}\)H\({}_{5}\) & C\({}_{6}\)H\({}_{5}\) & n.a. \\ di(2-ethylhexyl) & DEHP & C\({}_{20}\)H\({}_{14}\)O\({}_{4}\) & 117-81-7 & CH(CH\({}_{2}\))\({}_{5}\)(CH\({}_{3}\))\({}_{2}\) & CH(CH\({}_{2}\))\({}_{5}\)(CH\({}_{3}\))\({}_{2}\) & 7.60 \\ phthalate & DnOP & C\({}_{24}\)H\({}_{38}\)O\({}_{4}\) & 117-84-0 & (CH\({}_{2}\))\({}_{7}\)CH\({}_{3}\) & (CH\({}_{2}\))\({}_{7}\)CH\({}_{3}\) & 8.10 \\ disonomyl phthalate & DiNP & C\({}_{26}\)H\({}_{24}\)O\({}_{4}\) & 28553-12-0 & C\({}_{9}\)H\({}_{19}\) & C\({}_{9}\)H\({}_{19}\) & 8.8 \\ dinonyl phthalate & DnNP & C\({}_{26}\)H\({}_{24}\)O\({}_{4}\) & 84-76-4 & C\({}_{9}\)H\({}_{19}\) & C\({}_{9}\)H\({}_{19}\) & 9.52 * \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Most common phthalates with acronyms, molecular formulas, CAS, R1, and R2 chains and their log K\({}_{ow}\).
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 5 Model Maintenance Algorithm
### Initialization  
Before deployment in large scale sensor networks, locations of sensors are not pre-determined, and the network topology is unknown to any sensor. Sat this stage, the three matrices of each sensor are initialized to empty matrices. After deployment, sensors start the introduction phase by broadcasting hello messages for once to initialize their connection matrix \(C\).
## 3 Mechanical Parameters of Rock Mass Under the Influence of Freeze-Thaw Cycles
### Conventional Triaxial Compression Testing of Rocks  
Conventional triaxial compression tests were conducted on dacite, magnetite, and skarn specimens under confining pressures of 5 MPa, 10 MPa, and 15 MPa. The corresponding stress-strain curves for the three rocks under triaxial loading conditions are provided in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Lithology**} & **Number of Freeze-Thaw Cycles** & \begin{tabular}{c} **Uniaxial Compressive Strength** \\ _\(\boldsymbol{e_{1}}\)/MPa_ \\ \end{tabular} & \begin{tabular}{c} **Modulus of** \\ **Easticity** \\ _\(\boldsymbol{e_{1}}\)/MPa_ \\ \end{tabular} & \
\begin{tabular}{c} **Rock Density** \\ _\(\boldsymbol{e_{2}}\)/g/cm\({}^{3}\)_ \\ \end{tabular} &
\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} \\ \hline \multirow{4}{*}{Dacite} & 0 & 194.7 & 73.25 & 2.567 & 0.38 \\  & 30 & 161.9 & 52.10 & 2.566 & 0.28 \\  & 60 & 146.1 & 47.26 & 2.566 & 0.29 \\  & 90 & 136.8 & 36.32 & 2.565 & 0.22 \\ \hline \multirow{4}{*}{Magnetite} & 0 & 166.1 & 92 & 3.428 & 0.28 \\  & 30 & 124.3 & 76.17 & 3.428 & 0.21 \\  & 60 & 110.4 & 55.0 & 3.426 & 0.26 \\  & 90 & 90 & 49.2 & 3.426 & 0.20 \\ \hline \multirow{4}{*}{Skarn} & 0 & 189.3 & 105.6 & 3.239 & 0.25 \\  & 30 & 163.5 & 88.27 & 3.239 & 0.24 \\ \cline{1-1}  & 60 & 125.5 & 66.73 & 3.237 & 0.21 \\ \cline{1-1}  & 90 & 106.9 & 38.78 & 3.236 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock mechanical parameters of uniaxial compression under different freeze-thaw times.  
Figure 4: Failure modes of rocks under uniaxial compression and different numbers of freeze-thaw cycles. (**a**) Dacite; (**b**) magnetite; (**c**) skarn.  
As shown in Figure 5, the stress-strain curves of the three types of rocks under triaxial compression exhibit the following overall characteristics: the compaction stage is relatively short or almost absent, the linear elastic stage is prolonged, and a rapid stress drop occurs in the post-peak residual stage, demonstrating pronounced characteristics of hard rocks. With increasing confining pressure, the peak strength and peak axial strain at failure were observed to progressively increase, accompanied by enhanced ductility during failure. The mechanical parameters derived from triaxial compression tests for all three rock types are summarized in Table 2.
## 3 Pricing Data
### p-Norm  
This section describes the p-norm and how it can be used for pricing adjustment. The p-norm [14] is a set of norms. The norm varies according to \(p\). We can adjust the price according to the change of the \(p\) value.  
**Definition 6** (p-norm [16]).: _Let \(X\) represent a vector \((x_{1},x_{2},\ldots,x_{n})\); for a real number \(p\geq 1\), \(\|X\|_{p}=(\sum_{i=1}^{n}|x_{i}|^{p})^{\frac{1}{p}}\) defines the p-norm of \(X\)._  
When \(p=1\), \(\|X\|_{1}=\sum_{i=1}^{n}|x_{i}|\), it is the Manhattan norm of \(X\). When \(p=2\), \(\|X\|_{2}=(\sum_{i=1}^{n}x_{i}^{2})^{\frac{1}{2}}\), it is the Euclidean norm of \(X\). \(p=\infty\), \(\|X\|_{\infty}=max\{|x_{1}|,|x_{2}|,\ldots,|x_{n}|\}\). Similarly, by Definition 6, the p-norm can also be applied to the set.  
**Proposition 1** ([16]).: _If \(p\geq 1\) and \(a\geq 0\), \(\|X\|_{p+a}\leq\|X\|_{p}\)._  
Work of [PERSON], et al. [11] was proven in Proposition 1. The value of the p-norm is inversely proportional to \(p\), that is when \(p\) increases, the p-norm decreases.  
**Definition 7** ([16]).: _Let \(I,X\), and \(s_{I}\) represent a database instance, a set of source tuples, and a price setting function, respectively. \(X\subseteq I\). \(\|X\|_{p}=(\sum_{i=1}^{n}s_{I}(t_{i})^{p})^{\frac{1}{p}}\) defines the price of \(X\), where \(t_{i}\in X\)._  
The p-norm becomes very effective when the data seller offers different discounts to different buyers. For an ordinary customer, \(p\) is equal to one, and for an important customer, \(p\) is greater than one. As the customer importance increases, so does the \(p\) value. This allows the data seller to adjust the size of the \(p\) value to provide different discounts to different customers. Therefore, Proposition 1 and Definition 7 can be used to set different prices for source tuples.  
**Proposition 2**.: _Assume that there are two sets \(X_{1}\) and \(X_{2}\). If \(X_{1}\subseteq X_{2}\), then\(\|X_{1}\|_{p}\leq\|X_{2}\|_{p}\)._  
Proposition 2 was proven in [14]. This proposition indicates that the more elements a set contains, the greater its p-norm value.
## 3 Results  
### Identification of ITCs  
The identification of released ITCs in samples was done using reference compounds **2** and **4** and by comparison of mass spectra with those in NIST 08 database. As an example, the total ion chromatogram (TIC) spectrum of volatile ITCs released from SO flowers in water is reported in Figure 2, showing two main peaks of interest, one at a retention time 4.773 min and another at a retention time 6.085 min.  
Peaks at 4.590, 6.857, and 8.593 are spurious peaks identified as silyl derivatives from the injection system (Figure S5). The mass spectrum of the main volatile compound at 4.773 min is identical to those reported for IPITC in the NIST--National Institute of Standards and Technology library and in [PERSON] et al. [5]; therefore, the main volatile compound can be identified as compound **2**.  
For the peak at retention time 6.085 min, the database indicated two possible structures of the isothi cyanates with iso-butyl or sec-butyl side chain. The latter derives from glucocochlearin **3**, which was previously identified in our samples; a direct comparison with an authentic sample allowed identifying this peak as SBITC, compound **4**. No other ITCs could be identified in our samples; therefore, for the following experiments the total ITCs content was calculated by the sum of compounds **2** and **4**.
## 2 Materials and Methods
### Surgical Procedure  
The posterior dynamic stabilization system used in this study (Bioflex System; Biospine Co., Ltd., Seoul, Republic of Korea) consisted of Ti6 Al4V pedicle screws and a Nitinol spring rod. Nitinol is a nickel alloy, and Ti6 Al4V is a shape memory alloy that was invented in 1962 by the US Naval Ordnance Laboratory. The diameter of the straight part in the Nitinol spring rod is 4 mm, featuring a coiled shape with 1 or 2 turns designed to provide physiological stability during flexion, extension, and lateral bending (Figure 1A, Supplement Video S1). The anterior fixation system used in this study comprised a threaded round cage made of Ti6 Al4V (Figure 1B). The surgical procedure mirrored that of conventional posterior lumbar interbody fusion (PLIF) with a pedicle screw system. Under general endotracheal anesthesia, the patient was placed in a prone position. The procedure involved a midline incision, laminectomy at the target level, and disectomy. Pedicle screws were inserted into the pedicles bilaterally under fluoroscopic guidance. After meticulous preparation of the cartilaginous endplate, the cage was carefully inserted between the vertebral bodies by the threaded combination method without damaging the endplate. For the disc space, a threaded round titanium (Ti6 Al4V) cage with a size that could reach as far forward into the disc space as possible, where cortical bone is more abundant, was chosen to prevent subsidence [19]. Finally, the Bioflex Nitinol spring rod and set screws were inserted. Appropriate positioning was confirmed by X-ray imaging before finishing surgery.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Value** & **Percentage (\%)** \\ \hline Uppermost instrumented verteb & & \\ L2 & 1 & 1.1\% \\ L3 & 22 & 24.7\% \\ L4 & 44 & 49.4\% \\ L5 & 22 & 25\% \\ Lowermost instrumented verteb & & \\ L4 & 6 & 6.7\% \\ L5 & 46 & 51.7\% \\ S1 & 37 & 41.6\% \\ Surgery level & & 1.4 \(\pm\) 0.6 \\
1 level & 58 & 65.2\% \\
2 level & 26 & 29.5\% \\
3 level & 5 & 5.7\% \\ \hline \hline \end{tabular} For parametric distributions, the values are expressed as the mean \(\pm\) standard deviation. For non-parametric distributions, the values are represented as the median. The percentages were calculated based on the total number of patients.  
\end{table}
Table 1: _Cont._  
Figure 1: **(A)** Photographs depicting the components of the BioFlex dynamic stabilization system consisting of titanium screws and Nitinol rods. There are 2 types of rods: 1 features a single-turn coil,and the other has a dual-turn coil. The photograph in the upper right corner illustrates the unique design of the BioFlex screw head. The design incorporates 2 grooves, allowing for the insertion of 2 rods per segment in instances where stabilization extends beyond a single segment (adapted with permission from Bio-Spine Corp, Seoul, Republic of Korea.). (**B**) Photographs of a threaded round cage made of Ti6 Al4V.
## 2 Scope and Objectives  
As per the best of the authors' knowledge, only one study in early 2021, [PERSON] et al. [40], investigated the case of speaking from an infected instructor. [PERSON] et al. conducted experiments with an optical diagnosing technique. The integrated Schlieren imaging and digital inline holography systems were used to measure the exhaled gas flow field and the sizes/distribution of the generated particles through the normal breathing behavior of eight participants. They also conducted a CFD simulation (with coarse meshesof 80,000 and 160,000 grid points in a classroom 5 \(\times\) 10 \(\times\) 3 m) using the open-source OpenFOAM-6 CFD package to evaluate the risk of students inhaling from an infected instructor during speaking.  
The current research work is considered a step forward in studying the safety of the students during exam time. This study attempts to simulate exhaled droplet dispersion from an infected instructor to students during exam time in a real classroom at Imam Mohammad Ibn Saud University, Riyadh, under different real ventilation scenarios. The authors do not attempt to give a complete solution to the problem but rather provide initial guidelines to the minimum ventilation requirements and the student distribution in the classroom to minimize the transmission of the airborne infection from the instructor to students. Due to the urgency of the current situation and the lack of research on the transmission of infectious diseases in classrooms, this work attempts to provide an essential overview of challenges and required measures supplemented with a potential practical framework.  
The main objectives of the study in-hand are:  
* To numerically track the spreading of the aerosol particles with time-based on a calibrated CFD model with decent spatial resolution.
* To introduce some well-defined dimensionless parameters that could be used to assess the vulnerability to the exposure of the droplets of aerosols.
* To investigate the effect of adopting different ventilation schemes on the students' expected exposure to the aerosol droplets.
* To investigate the effect of keeping the entrance door opened or closed on the students' expected exposure to the aerosol droplets.
* To investigate the effect of droplet size on the students' expected exposure to the aerosol droplets.