esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mineral Analysis  
Figure 8 shows the mineral analysis results of the specimens of different C/S molar ratios according to the use of silica fume and quartz powder. Intense quartz peaks appeared due to the influence of quartz used as aggregate, with the intensity increasing in proportion to the quantity of quartz powder mixture. In accordance with the previously reported results on ignition loss, the quartz powder not only tended to undergo hydration reactions in mineral analysis but also remained as residues after the reaction, which acted as fillers inside the matrix. The C-S-H hydrates appeared in the vicinity of 50\({}^{\circ}\), with non-significant differences in intensity among the specimens [29; 30].  
Figure 8: XRD patterns depending on C/S molar ratio, (**a**) QP, (**b**) SF.
# Numerical Simulation of KBKZ Integral Constitutive Equations in Hierarchical Grids
## 3 Numerical Method
### HiGTree/HiGFlow System
#### 3.1.1 Hierarchical Grids  
Equations (1) and (2) are approximated using finite differences in hierarchical Cartesian meshes. An illustrative representation of the mesh is given in Figure 1a and its structure of dependencies is illustrated in Figure 1b. In this data structure, each cell can be partitioned into distinct geometric shapes. Such generalization imposes difficulties in the numerical approximation in finite differences.  
For example, considering Figure 2, suppose that we are interested in approximating the second derivative in \(y\) direction centered on \(U_{c}\). Using second-order finite differences, we have:  
\[\frac{\partial^{2}U_{c}}{\partial y^{2}}\approx\frac{1}{\delta y}(U_{t}-2U_{c}+U _{b}); \tag{6}\]  
We can notice that, for this case, \(U_{b}\) does not match known values in the mesh (recalling that the components of the velocity field are computed in the facet centers), but it can be calculated by interpolation using values of neighboring cells as shown in the following equation:  
\[U_{b}=\sum_{k=1}^{V_{b}}w_{k}^{b}U_{k}\text{:} \tag{7}\]  
The number of neighbors \(V_{b}\) is defined according to the imposed precision. The weights \(w_{k}^{b}=w_{k}(\mathbf{x})\) are calculated using the moving least squares (MLS) method [23].
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 2 Related Works  
Ref. [1] analyzes network performance regarding delay, throughput, and signal-to-noise ratio (SNR), showing that tethered balloons can efficiently enhance emergency communication with minimal delays and strong throughput. The advantages include rapid deployment, low cost, and reliable service; however, challenges include coverage limitations and susceptibility to specific disaster types. In [4], the authors argue that aerial platforms represented by tethered balloons play an integral role in rescue in major disaster scenarios and simulate the performance of communication services. The authors of [9] state that tethered balloons are suitable for disasters except for storms and calculate their coverage and path loss at different distances. In [10], a multi-antenna tethered balloon was used as a repeater between a UAV high-altitude platform and a ground base station for an interference alignment technique. It is shown that there is an optimal balloon altitude that maximizes the overall rate of balloon-ground communication. A joint layered deployment of tethered balloons and UAVs is proposed in [11]. The optimal and greedy algorithms are employed to minimize the number of tethered balloons and UAVs deployed while providing effective coverage. Ref. [12] explores the relationship between coverage, propagation loss, and height using the Hata propagation model. A distributed deep Q-learning network optimization scheme based on resource allocation and joint UAV and tethered balloon placement control is discussed in [13] to maximize the total rate of this network. In [14], a UAV and tethered balloon-assisted allocation strategy is utilized to improve end-to-end throughput of the terrestrial infrastructure network. Ref. [15] proposed to take advantage of the low cost and flexible deployment of tethered balloons to deploy them in mountainous areas with extremely complex terrain and simulated their coverage quality and propagation loss using the Longley-Rice model. Ref. [16] uses a stochastic geometric approach and a user association strategy to optimize the location of tethered unmanned aerial vehicles (T-UAVs) to achieve cellular network coverage in areas where users congregate, concluding that T-UAVs trade-off mobility for an increase in coverage probability. Table 1 summarizes the contributions between this paper and the extant studies.
## 3 Results and Discussion: Environmental and Human Health Impact Assessment of Cooked Ham Production
### The Potential Impact of Nitrosamines on Human Health
The Global Fund for Cancer Research suggests that a reduction of 50 g meat product consumption per day could represent a 20% decrease in the number of colorectal cancer cases [85; 86]. In regard to the human health indicators developed in this study, the potential impacts of food safety in the use phase were not negligible. The main aim of this study was to evaluate the potential contribution of nitrosamines to the damage of human health throughout the ham production life cycle. The impact on morbidity associated with contaminants from food processes is not currently known. However, the meat production industry is exploring alternatives to reduce or replace nitrites while preserving the microbiological and sensory quality of the products. An innovative combination of biopreservation and high-pressure processing has been explored to reduce nitrites in ham and contribute to reducing the total potential human health impact by almost 8% and almost 20% for the potential human health impact resulting from nitrosamine toxicity. Another option is the use of plant extracts with active molecules capable of inactivating microorganisms and/or preventing the negative effects of contaminants [87]. An additional option is to improve animal nutrition in such a way as to provide substances with a beneficial effect on health or even vitamins to react against the formation of these carcinogenic compounds [88; 89]. Finally, a recent study has shown the influence of food processing conditions on the risk of cancer. A decrease in carcinogenesis in rats was linked to the anaerobic packaging of ham compared with unpackaged food and food exposure to air [90]. The innovative process of ham production combining high-pressure treatment and biopreservation could be a solution to not altering the concentration of residual nitrites in cooked ham [91; 92; 93; 94]. Several high-pressure-treated meat products are marketed with the claim \"nitrites/nitrates not added\". They contain natural preservatives, such as celery juice, that replace additives conventionally used in their preservation [95]. High-pressuremicrobial inactivation can allow for the production of sausages without nitrite use while maintaining food safety [92; 96]. This technology can also be used to improve the antimicrobial role of salt [97] or natural antimicrobials in ham [44; 45]. Some potential impacts on human health could be avoided by specific innovative treatments involving these new technologies.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### Photonic Convolutional Neural Network (PCNN)  
The main task of the convolution neural network is to complete convolution operations. The schemes in convolutional neural networks are MZI array [29] and MRR array [30; 31; 32; 33].
## 4 Simulation Results  
In this section, we compare the conventional and the proposed schemes in terms of BER before channel decoding and frame error rate (FER) after channel decoding. Each device transmits 20-byte data (160 bits) with 1/3-rate channel coding (convolutional coding with constraint length 7 and generator polynomial 171, 165, and 133), QPSK modulation, and eight times spreading with random phase sequences. We assume flat fading and consider channel estimation based on eight \((N=1)\), 16 \((N=2)\), and 32 \((N=4)\) resource elements for pilots in addition to ideal channel estimation. Open-loop power control of the transmitting devices may not be perfect and the received signal power is generated uniformly from -3 dB to +3 dB compared to the operating SNR. Successive interference cancellation is used in the decreasing order of the received power strengths.  
Figures 3 and 4 show the BER before channel decoding and the FER performances, respectively, with 12 concurrent accesses to the shared resource. The number of pilot symbols \(N\) increases the waste of resource and the accuracy of channel estimation. If the channel estimate is perfect, that is, if \(N\) is infinite, the NOMA system can achieve good performance. But, when \(N\) is small, the channel estimate is inaccurate and the performance of NOMA system degrades. When the channel estimation is perfect, there is no difference in the performances of the two schemes. With channel estimation errors, the performance is degraded but the performance degradation can be alleviated with the proposed scheme.  
Figure 4: FER with 12 active devices.  
Figure 3: BER before channel decoding with 12 active devices.  
Figures 5 and 6 show the BER before channel decoding and the FER performances, respectively with 20 active devices. As the number of active devices increases, the inaccuracy of the channel estimates increases and it is important to consider the effect of channel estimation errors.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 3 Methodology
### System Model  
Our trust assessment model is structured around two topological layers: intra-cluster topology and inter-cluster topology. The inter-cluster topology utilizes centralized trust management methods through RSUs and global cloud controllers, while the intra-clustertopology employs distributed trust management methods. The hierarchical trust assessment model conducts layered composite trust value assessments through dynamic vehicle nodes, cluster head layers, and RSU layers. Finally, anomalous vehicles are identified at the cloud layer based on the composite trust values. The detection model is depicted in Figure 2, where vehicles of the same color belong to the same cluster, and the red vehicle serves as the cluster head.
* Vehicle Node Layer: Each vehicle node computes its total trust value by integrating both direct trust (self-observed behavior) and indirect trust (recommendations from neighboring vehicles).
* Cluster Head Layer: The cluster head aggregates the total trust values of member vehicles within its cluster, computing an average total trust value for the cluster. This calculation incorporates association weights, quantifying the strength of relationships between individual vehicles and their cluster.
* RSU Layer: The RSU further refines the trust evaluation by aggregating trust data from multiple clusters within its coverage area. A global hybrid trust value is computed considering inter-cluster associations and vehicle trust assessments.
* Cloud Controller Layer: The cloud controller collects hybrid trust values from RSUs across different regions. A trust threshold range is dynamically computed using a consistency parameter.  
Vehicles are classified as trustworthy, malicious, and pending inspection. Vehicles flagged as malicious are identified as misbehaving nodes within the VANET.
# Experimental Evaluation of Gluam Made from Portuguese Eucalyptus
## 3 Results
### Bending Tests on Glulam Beams  
The bending test results obtained for glulam beams produced in this study with Portuguese eucalyptus are presented in Table 4.  
The glulam beams made from eucalyptus, on average, have a density of 812.70 kg/m\({}^{3}\) and a bending strength of 96.74 N/mm\({}^{2}\), which is a value three times bigger than the higher strength class for glulam made of softwoods, GL32 [7]. Moreover, the results obtained for the modulus of elasticity in bending ranges from 22,580 N/mm\({}^{2}\) to 26,300 N/mm\({}^{2}\), which are values approximately double the one of GL32 (32 N/mm\({}^{2}\) and 14,200 N/mm\({}^{2}\) for bending strength and global elasticity modulus, respectively). The force versus displacement curves are presented in Figure 6a and it is important to point out that all beam tests have reached failure modes typical for bending parallel to the grain, with a failure by pure tension and progress of the failure by shear (Figure 6b,c).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\rho\) & \(\omega\) & \(f_{m}\) & \(E_{mg}\) \\  & **(kg/m\({}^{3}\))** & **(\%)** & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline Mean & 812.70 & 11.07 & 96.74 & 24,180 \\ Maximum & 854.83 & 11.65 & 106.16 & 26,300 \\ Minimum & 777.19 & 10.79 & 89.36 & 22,580 \\ CoV (\%) & 3.68 & 3.13 & 7.37 & 6.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the bending tests on the glulam beams produced from eucalyptus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & & \(\rho\) & \(\omega\) & \(f_{c,0}\) & \(f_{m}\) \\ \cline{2-5}  & & **(kg/m\({}^{3}\))** & **(\%)** & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline \multirow{4}{*}{Compression parallel to the grain} & Mean & 802.43 & 9.65 & 72.84 & \\ \cline{2-5}  & Maximum & 845.42 & 10.87 & 86.35 & \\ \cline{2-5}  & Minimum & 691.53 & 8.74 & 52.5 & \\ \cline{2-5}  & CoV (\%) & 8.51 & 6.79 & 13.25\% & \\ \hline \multirow{4}{*}{Bending} & Mean & 809.5 & 8.86 & 151.34 \\ \cline{2-5}  & Maximum & 958.21 & 10.19 & 175.88 \\ \cline{1-1} \cline{2-5}  & Minimum & 615 & 6.58 & 118.65 \\ \cline{1-1} \cline{2-5}  & CoV (\%) & 12.33 & 9.24 & 11.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of physical and mechanical characterization on clear specimens.
## 1 Introduction  
The deep mixing method, an in situ soil admixture stabilization technique using cement and/or lime, was developed and put into practice in Japan and Nordic countries in the 1970s. Due to its wide applicability and high improvement effect, the method spread worldwide. More than two decades of practice have seen the equipment improved, stabilizing agents changed, and the applications diversified [1]. The quality of stabilized soil depends upon the type and condition of the soil, the type and amount of binder, and the production process. Quality control and quality assurance (QC/QA) practices have been accepted worldwide for more than five decades [2]. They are comprised of laboratory mix tests, field trial tests, monitoring and control of construction parameters during production, and verification by the engineering characteristics of stabilized soil.  
The purpose of deep mixing is not only to produce good-quality stabilized soil, but also to construct improved ground that guarantees the performance of the superstructure. The ground improved by the method is a composite system comprising stabilized soil columns and original soils [1]. Construction control parameters during production include the penetration and withdrawal speeds of the mixing shaft, rotation speed of the mixing blade, flow rate of the binder, and water/binder ratio (for the wet method). However, the field-stabilized soil strength has relatively large variability, even if the production adheres to the established mixing machine and procedure. One of the reasons behind the strength variability is the type of soil. Some cohesive soils are so \"sticky\" that the soil and binder mixture adheres to and rotates with the mixing blade without efficient mixing (entrained rotation phenomenon). Several approaches can be adopted for such soils: new type special cement, injecting chemical additives, and injecting air.  
In this manuscript, the recent development and future perspective of the QC/QA procedures are briefly discussed based on the Japanese experience/results with mechanical mixing technology by vertical shaft mixing tools with horizontal rotating circular mixing blade, as well as the countermeasures for problematic soils.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 2 Template Matching and Invariant Moments
\begin{split} M1&=\eta_{20}+\eta_{02}\\ M2&=(\eta_{20}-\eta_{02})^{2}+4\eta_{11}^{2}\\ M3&=(\eta_{30}-3\eta_{12})^{2}+(3\eta_{21}-\eta_{03}) ^{2}\\ M4&=(\eta_{30}+\eta_{12})^{2}+(\eta_{21}-\eta_{03}) ^{2}\\ M5&=(\eta_{30}-3\eta_{12})(\eta_{30}+\eta_{12})(( \eta_{30}+\eta_{12})^{2}-3(\eta_{21}-\eta_{03})^{2})+\\ (3\eta_{21}-\eta_{03})(\eta_{21}+\eta_{03})(3(\eta_{30}+\eta_{12})^{2}-( \eta_{21}+\eta_{03})^{2})\\ M6&=(\eta_{20}-\eta_{02})((\eta_{30}+\eta_{12})^{2}-( \eta_{21}+\eta_{03})^{2})+4\eta_{11}(\eta_{30}+\eta_{12})(\eta_{21}+\eta_{03} )\\ M7&=(3\eta_{21}\ -\eta_{03})(\eta_{30}+\eta_{12})(( \eta_{30}+\eta_{12})^{2}-3(\eta_{21}+\eta_{03})^{2})-\\ (\eta_{30}-3\eta_{12})(\eta_{21}+\eta_{03})(3(\eta_{30}+\eta_{12})^{2}-( \eta_{21}+\eta_{03})^{2})\end{split}  \tag{8}\]  
The seven invariant moments compose a set of characteristic quantities with rotation, scaling, and translation invariances.
## 4 Results
### Mesh Sensitivity Analysis
#### 4.1.2 Velocity and Cell Size  
Considering the pressure and mesh dependency findings only results from meshes D24 and D36 are presented for both numerical models with a turbulent intensity in the inlet of 5%. The calibration of this value is present in Section 4.4. The numerical outputs of the mean velocity in the shear stress region can be directly compared with the published experimental results from [PERSON] [12,26]. In Figure 6, the non-dimensional velocity patterns with respect to the impact velocity in the plunge pool, \(V_{j}\), expressed as functions of the ratios \(z/D_{i}\) and \(r/D_{i}\) where \(z\) represents the distance from the water surface to the bottom, are shown.  
Results evidence mesh independence for both solvers, and no significant differences were observed when comparing the outcomes of the two solvers. However, the numerical results did not accurately reproduce the velocity field observed in [PERSON]'s experimental data. Specifically, the numerical simulations failed to replicate the negative velocities in the experimental data outside the jet core.  
Figure 5: Dynamic pressure coefficient as a function of the number of cells in the jet diameter: (a) (\(r/D_{i}=0.00\)); (b) (\(r/D_{i}=0.69\)).
# Traits and Transports: The Effects of Personality on the Choice of Urban Transport Modes
###### Abstract  
We examine the influence of personality on car driving, usage of public transport and cycling. Personality is measured through the Big Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) and Environmental personality. Data were collected through a Web-based panel of adult citizen in the city of Gothenburg, Sweden (\(N=1068\)). Age, gender, income, children at home and residential area were used as control variables. Car driving is influenced by low degree of Openness, high degree of Conscientiousness, and low degree of Environmental personality. Usage of public transport is influenced by low degree of Conscientiousness, high degree of Agreeableness, and high degree of Environmental personality. Cycling is foremost influenced by a high degree of Environmental personality.  
urban travel habit; sustainable urban planning; transportation mode; personality Article  
Traits and Transports: The Effects of Personality on the Choice of Urban Transport Modes  
1  
<ccs2012>  
a multitrait approach (i.e., the Big Five personality traits), and a single-trait approach (Environmental personality).  
Compared to the other variables mentioned above, personality traits have been less studied in relation to usage of transportation modes, especially public transport and cycling. Regarding car use, personality traits have found to be associated with driving style and risky behavior [5], as well as choices of different car brands [6]. Almost all these studies conclude that assessing personality traits (such as Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) are important to understand car usage. Therefore, it is surprising that so few studies have relied on personality traits in modeling travel behavior in relation to different modes. Especially since travel behavior is relatively stable and less influenced by situational factors [7; 8; 9; 10; 11].  
Personality traits may be promising to consider as a determinant of travel behavior and choice of different modes. Car usage is interesting to study since it is the most common mode in Sweden and other Western countries. In order to change transport behavior from car driving toward more sustainable options, it might be of interest to study public transportation and cycling in relation to the same personality traits. The purpose of the present study is to examine to what extent travel behavior can be explained by personality traits-both the Big five personality framework and Environmental personality. For this we formulated three research questions:  
Research Question 1: How is frequency of car driving affected by the Big five personality traits and Environmental personality?  
Research Question 2: How is frequency of use of public transportation affected by the Big five personality traits and Environmental personality?  
Research Question 3: How is frequency of cycling affected by the Big five personality traits and Environmental personality?