esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response of Reinforced Recycled Aggregate Concrete Pavement under Impact Loading
## 3 Experimental Results and Discussion
### Impact Force Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**RCP** & \(F_{\rm im,p}\) & \(F_{\rm in,p}\) & \(I_{\rm p}\) & \(V_{\rm r}\) & \(E_{\rm im}\) & \(E_{\rm ab}\) & \(E_{\rm ab}/E_{\rm im}\) \\
**Specimen** & **(kN)** & **(kN)** & **(kN.s)** & **(m/s)** & **(j)** & **(j)** & **(\%)** \\ \hline
6 MR & 206.1 & 85.9 & 1143.4 & 1.292 & 1960.0 & 1793.0 & 91.5 \\
7 MR & 175.9 & 110.7 & 1148.5 & 1.315 & 1960.0 & 1787.0 & 91.2 \\
8 MR & 150.9 & 116.1 & 1153.0 & 1.338 & 1960.0 & 1781.0 & 90.9 \\
7 MN & 200.2 & 105.1 & 1151.1 & 1.328 & 1960.0 & 1783.5 & 91.0 \\
7M1R & 220.5 & 111.7 & 1198.5 & 1.565 & 1960.0 & 1715.0 & 87.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact force characteristics of RCP specimen. Except for 7M1R, the energy dissipation ratio \(E_{\mathrm{ab}}/E_{\mathrm{im}}\) of all RCP specimens exceeds 90%, implying that the RCP specimens dissipate most impact energies through deformation and cracking. The energy dissipation ratio \(E_{\mathrm{ab}}/E_{\mathrm{im}}\) of 7 MR specimen was similar to that of 7 MN specimen. For 7M1R specimen, as shown in Figure 9, there was slight damage on the surface after impact, and approximately 87.4% of the impact energy was imparted to the specimen. For the less damaged specimen, more impact energy could be stored through the temporary elastic deformation of the specimen [27]. This stored energy would return to the drop-weight when the elastic deformation recovered, resulting in greater rebound speed \(v_{\mathrm{r}}\). On the contrary, severely damaged specimens had already entered their plastic stage and more impact energy was dissipated in the form of permanent deformation or crack damage. The 6 MR specimen was severely damaged and the energy consumption ratio \(E_{\mathrm{ab}}/E_{\mathrm{im}}\) reached 91.5%. As seen in Figure 9, the damage characteristics and crack patterns after the impact also confirmed this phenomenon.
# Predicting the [PERSON]'s Modulus of Concrete Using a Particle-Based Movable Cellular Automata Method
###### Abstract  
The elastic modulus is one of the fundamental parameters controlling the mechanical behaviour of concrete. In this study, the Movable Cellular Automata (MCA) method is applied to predict the [PERSON]'s modulus of concrete based on the properties of its components. Each automaton represents one component: cement paste, fine aggregate, or coarse aggregate.A parametric sensitivity analysis was performed using Grey System Theory (GST) on hypothetical concrete modeled with the MCA method. The analysis showed that the coarse aggregate type, coarse aggregate-to-total aggregate ratio, and water-to-cement ratio have the greatest impact on the [PERSON]'s modulus. To test the effectiveness of the MCA method in modelling concrete, results of numerical simulations were compared with experimental data available in the literature. The first numerical simulations were conducted for mortars containing cement paste and sand as well as for concretes produced by adding granite to them. Two approaches were used to perform the simulations; in the first approach, a sample contained automata representing cement paste, sand, and granite, while in the second the automata represented mortar and granite. High consistency was achieved, with results from both approaches differing by only 0.6%. Subsequent simulations focused on concretes with different water-to-cement ratios (0.45, 0.55, and 0.65), the origin of the basaltic aggregate, and various aggregate contents (60%, 54%, 48%, and 42%). Results showed high agreement between simulations and experimental data, confirmed by a high coefficient of determination R\({}^{2}\) of 0.84 and mean squared error of 2.43 GPa\({}^{2}\). Finally, simulations were performed for lightweight expanded clay aggregate concrete, resulting in an R\({}^{2}\) of 0.86 and mean squared error of 0.81 GPa\({}^{2}\), which demonstrates the effectiveness of the MCA method in predicting the static elastic modulus of concrete.  
uniaxial compression test; static and dynamic elastic modulus; cellular automata Article
## 3 Results and Discussion
### The Effect on the Planktonic Growth  
The inhibition of zinc sulfate heptahydrate (Figure 1) and zinc chloride (Figure 2) on planktonic bacterial growth provides insight into their dose-dependent antimicrobial action against _E. coli_ and _E. faecalis_. The results reveal differential bacterial responses, with the higher concentrations of zinc (10 mg/mL and 5 mg/mL) exhibiting the most significant inhibition, as indicated by reduced optical density (OD) values for all bacterial strains.  
Our findings align with those of previous research, which demonstrate the growth-inhibitory properties of zinc salts against bacteria. In one paper, it was noted that zinc aspartate  
Figure 1: Growth of _E. coli_ ATCC 14169 (blue), _E. coli_ ATCC 25922 (red), _E. faecalis_ ATCC 19433 (yellow), and _E. faecalis_ ATCC 29212 (green) in the presence of zinc sulfate heptahydrate. The data shown represent a single biological replicate per strain.  
Figure 2: Growth of _E. coli_ ATCC 14169 (blue), _E. coli_ ATCC 25922 (red), _E. faecalis_ ATCC 19433 (yellow), and _E. faecalis_ ATCC 29212 (green) in the presence of zinc chloride. The data shown represent a single biological replicate per strain.  
in high doses (2500 \(\upmu\)g/mL) had a bactericidal effect on _E. coli_ and _Staphylococcus aureus_ biofilms, which supports the observation that the antimicrobial property of zinc is dose-dependent [17].  
Surprisingly, zinc chloride was more inhibitory at lower concentrations (0.625-0.156 mg/mL) than zinc sulfate heptabhydrate, contrasting with another study's findings, where zinc sulfate was the most toxic zinc compound to _Bacillus subtilis_, _Lactococcus lactis_, and _Saccharomyces cerevisiae_, with higher concentrations required for significant inhibition [18]. [PERSON] et al. also found that _E. coli_ biofilms were less sensitive to zinc chloride at low concentrations but were effectively inhibited at high concentrations [14]. Previous studies show that genetic variation between _E. coli_ strains affects their resistance to zinc ions [19].  
_E. faecalis_ strains exhibited slow growth at low zinc concentrations, suggesting moderate resistance compared to _E. coli_. This finding concurs with research indicating that _E. faecalis_ exhibited intermediate susceptibility to ZnO nanoparticles, and planktonic growth inhibition depended on zinc ion release [20].  
Zinc chloride's higher antimicrobial activity in mid-range concentrations (1.25-0.312 mg/mL) also supports the role of higher Zn\({}^{2+}\) bioavailability in bacterial inhibition. Compared to zinc sulfate heptabhydrate, zinc chloride releases Zn\({}^{2+}\) more readily due to greater solubility, leading to higher uptake and toxicity against bacteria. The same was noted when researching zinc-based nanoparticles, where higher ionization led to greater antibacterial activity [21].
## 3 Results
### Impact of Opening Sizes
#### 3.1.1 Results for Building Models with Varying Windward Opening Sizes
Here, x/D = 0 represents the entrance point of the building, x/D = 0.5 represents the midpoint of the building, and x/D = 1 represents the exit point of the building. At x/D = 0, the highest normalized velocity value of 0.764274 is observed in WO1, which has the narrowest inlet opening, while the velocity values at this location decrease as the inlet opening increases, with the lowest velocity value of 0.305658 belonging to WO5. This demonstrates that narrow openings accelerate the flow at the entrance due to the nozzle  
Figure 12: Pressure coefficient distributions of buildings with different windward opening configurations. (**a**) WO1, (**b**) WO2, (**c**) WO3, (**d**) WO4, (**e**) WO5. effect, while wider openings result in slower but more widespread distribution of the flow throughout the volume. For all building models, there is a decrease in the velocity of the air entering the building from the windward opening, with a notable distribution in the velocity profiles at the building's midpoint (x/D = 0.5). The lowest velocity value is 0.0540731 in WO4, and the second lowest value is 0.136448 in WO5. In other models, the normalized velocity values at the midpoint are higher. This result illustrates that when the entrance opening exceeds a certain size, the airflow in the mid-section may weaken, meaning that very large entrance openings can create a stagnant zone in the mid-section. In the exit region (x/D = 1), WO5  
Figure 14: Comparison between the streamwise U/U\({}_{\text{ref}}\) along the centerline of the building models for varying inlet openings. Figure 13: Streamline distributions of buildings with different windward opening configurations. (a) WO1, (b) WO2, (c) WO3, (d) WO4, (e) WO5. achieved the highest normalized velocity at 0.832999. It was followed by WO4 at 0.800706 and WO3 at 0.648038. WO1 has the lowest exit velocity at 0.212390, and WO2 has the second lowest at 0.421573. This finding indicates that as the inlet opening increases, the amount of air evacuated from the building also increases, resulting in higher exit velocities despite the exit opening remaining constant.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 2 Progress of Traditional Aquaculture
### Problems in Aquaculture  
Water deterioration, which refers to oxygen depletion, harmful algal bloom, and eutrophication of water body in aquaculture, may lead to the failure of aquatic animals rearing and even cause serious environmental disasters [9; 22; 23]. As shown in Figure 1, the water deterioration mainly occurs in three aspects: (1) The addition of an excessive amount of traditional aquaculture feed, which consists of biomass rich in protein and lipid, may not be fully eaten by aquatic animals and the residual feed would be converted to soluble nutrients, driven by some bacterial activities, partly contributing to the eutrophication in water body. (2) The water deterioration is attributed to the wastes secreted by aquatic animals. In the aquaculture with high stocking density, this could be the main reason for water deterioration. The mechanisms of water-deterioration induced animals' diseases or death have been widely documented by previous studies [24; 25]. One of the key mechanisms is that elevated NH4\({}^{+}\) comes into cells and displaces K\({}^{+}\), causing the depolarization of neurons and the activation of the N-methyl-D-aspartate (NMDA) receptor, which leads to an influx of excessive Ca\({}^{2+}\) and subsequent cell death in the central nervous system [26]. (3) In the closed aquaculture system, eutrophication would accelerate the microbial reproduction and cause harmful algal bloom. A previous study discovered that some harmful algal bloom species, particularly cyanobacteria, could consume oxygen, at the same time, produce toxins [23]. As a result, owing to the oxygen depletion, bacterial reproduction, and toxin accumulation, survival and health of aquatic animals would be seriously threatened. Even if the water deterioration does not cause the failure of aquaculture, aquatic animals with diseases or toxins may cause serious food safety problems and negatively impact human health. Therefore, the negative effects of aquaculture problems on environmental protection and human health merit attention from both academia and the industry.
# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Federated Learning  
Federated learning (FL) is a decentralized learning approach used to ensure data privacy and decrease the exchange message between the server and clients [72]. As shown in Figure 4, the training process is divided into two steps: (i) local model training and (ii) global aggregation of updated parameters in the FL server. For more details, first, the central server specifies the hyperparameters of the global model and the training process. Then, it broadcasts the global model to several clients. Based on this model, each client trains the global model for a given number of epochs using its own data. Then, they send back the updated model to the FL server for the global aggregation. These steps are repeated until the global model is achieved for a selected number of rounds in order to achieve satisfactory performance. As a result, FL helps the final model to take advantage of the different clients without exchanging their data. Additionally, it may decrease the communication overhead by exchanging the model parameters instead of the clients' data.  
Since the traffic data has been increased, FL can be a promising solution for traffic management. For example, FL helps to detect the attack without communicating the packet/flow to a central entity [73]. It keeps the traffic where it was generated. As a result, FL has started to attract the attention of researchers for intrusion detection [74] and prediction [75].
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 2 Preliminaries
### Quantum Random Number Generator (QRNG)  
In contrast to traditional random number generators that depend on deterministic approaches, a QRNG produces truly random numbers by utilizing the principles of quantum physics. The intrinsic unpredictability of quantum mechanics assures that these observations yield random and unpredictable results [34; 35]. QRNGs derive randomness from the observation of quantum system states such as photon polarization [36; 37] or electron spin [38]. For example, when a photon travels through a polarizing beam splitter, its direction towards one of two outputs is determined randomly based on its polarization. This action forms the basis of QRNG operation. The photons that emerge from the outputs are detected, and the photon's destination, either output \"0\" or \"1\", is recorded. The measurement of photon polarization yields a random and unpredictable result because photon polarization is intrinsically random. This randomness is then used to produce a sequence of random numbers. Another method of generating random numbers through QRNGs involves measuring the spin of electrons or the states of other quantum systems. Such measurements also result in random outputs, which serve as a wellspring of genuine randomness. The Quantis generator from ID Quantique is predominantly based on physically random processes, with only minor non-random contributions [35]. An ultrafast QRNG based on quantum phase fluctuations is developed, which can achieve a high rate of over 6 Gbits/s [39], while the post-processing-free QRNG using gated single-photon detection can generate random bits at a rate of 4.01 Mbit/s [40]. While existing studies explore the use of QRNGs in cryptographic systems [41; 42; 43], few demonstrate real-world integration into trusted computing environments like TPMs, particularly for IoT use cases. Our work fills this gap by implementing a practical QTPM prototype and evaluating its impact on performance and entropy quality.
## 2 Materials and Methods
### Experiment Procedure and Measurement Methods  
The whole system was set up on a desk near a side wall of a general small office room (Republic of Korea) with a floor area of 53 m\({}^{2}\) and ceiling height of 2.5 m, and all windows and doors were closed during our tests to prevent daylight and occupancy disturbance (Figure A1). For the start-up of the experiments, the PBR columns were filled with the _Spirulina_ medium so that a gas-liquid interfacial area of each cylinder became approximately 78.5 cm\({}^{2}\), and they were incubated at -25 \({}^{\circ}\)C with 24 h continuous artificial backlights (3500-4800 lux) (Figure 3a). Plant-growth light emitting diode (LED) strips were used to create test light regimes illuminating the algal cylinder vessels: 7.2 W/12 V white LED, 28.8 W/12 V yellow LED, and 14.4 W/12 V purple lighting (two red (660 nm) and one blue (460 nm) LED combination) for the 4 L, 6 L, and 10 L container, respectively. After a 24 h incubation period, we evaluated IAQ by detecting the gas escaped from the air discharger column directly linked to PBRs through an air hole at the center of the chamber ceiling. To that end, a computer monitoring system with electrochemical gas sensors was equipped in the test environment: two SGP30 multi-pixel gas sensors (Adafruit, New York, NY, USA, range: 400-60,000 ppm eCO\({}_{2}\), resolution: 15 %Vol) for CO\({}_{2}\) and a SEN0322 oxygen sensor (DFRobot, Shanghai, China, range: 0-25 %Vol, resolution: 0.15 %Vol) for O\({}_{2}\) concentrations. The l2C (inter-integrated circuit) sensor modules were wired to a Raspberry Pi 4B board outside the chamber. Note that spatial CO\({}_{2}\) concentrations were estimated by averaging two SGP30 readings, and three small air outlets were built at the bottom of a chamber side so that each was connected to an airflow control pump and an inner pressure of the chamber space remained at the atmospheric pressure (1.013 \(\times\) 105 Pa) while in the inflow of the PBR-filtered air. The 4 L, 6 L, and 10 L volumes of PBRs were filled with a 2, 4, and 7 L medium with _S. maxima_ inoculated. Two different tests were carried out to identify CO\({}_{2}\) removal performance of the developed air purifier system: (i) measurement of the gas composition by activating entire PBRs simultaneously and (ii) measurement by operating only a single PBR container system, respectively (stopping all, other than target PBRs). The gas composition of CO\({}_{2}\) and O\({}_{2}\) was monitored at an interval of 15 s, considering the response time limits of the sensors. To test removal potential under high CO\({}_{2}\) concentration, high-grade CO\({}_{2}\) (10,000 and 6000 ppm) was forcibly injected using commercial CO\({}_{2}\) diffusers (Ista, Taiwan, 5500 mL of CO\({}_{2}\) at 1 atm, 25 \({}^{\circ}\)C). It was blown through the headspace of the chamber, maintaining the constant air exhaustion rate and prolongation of gas-liquid equilibration time (Figure 4).
## 4 Adaptive Hybrid GA-SAA Algorithm
### Neighborhood Searching Based on SAA  
Despite the recombination operations of GA, we further adopt neighborhood searching based on the framework of SAA to improve the randomness and diversity of solutions. In neighborhood searching, we propose two adaptive skipping strategies based on [PERSON] et al. [7] and [PERSON] et al. [24]. We need to randomly select a chromosome \(c\) from the successor population, evaluate the corresponding stop plan by the passenger flow assignment method to obtain the objective value \(Z(S)\), where \(S\) is the stop plan corresponding to \(c\), and execute the two strategies. The solution update depends on the Metropolis principle in SAA.  
The first skipping strategy is to skip stops of trains with the longest additional travel time in the specific segment according to the train load factor. Assume the train that passes the segment \(u=1,2,\cdots,N_{seg}\) with the longest additional travel time is \(l^{\prime}\) and its load factor is \(LF_{l^{\prime}}\). We adopt the skipping strategy to skip the stops of train \(l^{\prime}\) to mitigate the segment imbalance. If the load factor of train \(l^{\prime}\) is low, then it is unnecessary to stop at so many stations and some stops of train \(l^{\prime}\) should be skipped. This indicates that a lower load factor corresponds to a larger skipping probability. Let the base skipping probability be \(p_{skip}\); then, the skip probability for each stop of train \(l^{\prime}\) is  
\[p_{skip}^{l^{\prime}}=p_{skip}(1-LF_{l^{\prime}}) \tag{21}\]  
The second skipping strategy is to skip the stops at the stations where the corresponding passengers are all served. As for station \(v\in V\), if the corresponding passengers are all served, then skip the stop of the train that has the highest stop ratio with the probability \(p_{skip}\).  
After executing the two neighborhood searching strategies, we obtain the neighborhood solution \(c^{\prime}\) and the objective value \(Z(S^{\prime})\) of the corresponding stop plan. Let the current temperature be \(T\); then, we determine whether to update \(c\) by \(c^{\prime}\) according to the Metropolis principle in SAA: (1) if \(Z(S^{\prime})\leq Z(S)\), then update \(c\) by \(c^{\prime}\); or (2) if \(Z(S^{\prime})>Z(S)\) and \(\exp[(Z(S)-Z(S^{\prime}))/T]>Random(0,1)\), then update \(c\) by \(c^{\prime}\).
## 2 Systems Overview
### Wireless Communication Technologies for Implanted Devices  
The biomedical implanted devices can communicate with the exterior world by up-link, and using inductive wireless coupling or UHF links to transmit data from the body which is saved in sensors [19]. Most bio-devices are bi-directional systems with transmitted RF signal carriers either swallowed or surgically implanted in a patient. The communication between the two parts is possible even through different media such as biological tissues like fat, muscle, blood, bone, _etc._[20, 21]. The classification types of the most common passive and active wireless communications are shown in Figure 2.  
Figure 1: Architecture for data and power transmission system.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.5 Text Classification (TxC)  
The text classification (TxC) task is similar to TC, where input texts are categorized into different classes. However, the main difference from TC is that the input text is much shorter, which typically consists of a sentence or two. For the TxC task, we employed seven datasets, including Mawqif, iSarcasmEval, and ArSarcasmV2, which were described previously. All datasets in this group were written in DA, except the ASND dataset. Descriptions of the remaining four datasets are provided below:  
* Arabic Social Media News Dataset (ASND): ASND is an Arabic dataset collected from the official Aljazeera news accounts on Twitter, Facebook, and YouTube [67]. It contains about 10 k posts covering different categories such as business, economy, education, politics, spirituality, etc.
* Arabic Influencer Twitter Dataset (AITD): AITD is an Arabic dataset collected from 60 Arab influencers on Twitter [67]. For each influencer, the last 3200 tweets were collected and classified into one of ten categories.  
* Dangerous dataset: The Dangerous dataset is an Arabic dataset collected from various Twitter accounts. It includes samples of dangerous speech, such as offensive or hateful language, alongside nondangerous speech [68]. In total, the corpus contains approximately 110.3 million posts collected from 399,000 users.
* Adult content detection: Adult content detection is a collocation of Twitter posts of adult content [69]. In total, the corpora contain about 50 k posts.
## 4 Experimental Results and Analysis  
Our simulations are aimed at investigating the anti-aliasing performance and anti-noise performance of the proposed algorithm. To measure the degree of spectrum aliasing of the source signals, we define the degree of spectrum aliasing \(\lambda\):  
\[\lambda=\frac{B_{o}}{B}\times 100\% \tag{15}\]  
where \(B\) represents the source signal spectrum bandwidth, \(B_{o}\) and represents the maximum aliasing width between the source signal spectrum and the adjacent signal spectrum.  
Since the \"boundary effect\" of STFT has an impact on the separation performance [10], we selected the Kaiser window (\(\beta=6.865\)) as the window function. The evaluation criteria of separation performance were applied in our simulations, which include the source-to-distortion ratio (SDR), the source-to-interferences ratio (SIR), the source-to-artifacts ratio (SAR) [23], and the separated bit error rate (BER). In order to obtain bitstream information, the coherent demodulation method was used to demodulate the separated source signals. The mixed signals were separated by the SCA algorithm proposed in this paper and the literatures [8; 10]. The comparison algorithm in [8] belonged to the classic two-stage method, and [10] used the TF masking method to separate the source signal.