esa-sceva/satcom-chunk-collection huggingface dataset:
# Oil Press-Cakes and Meals Valorization through Circular Economy Approaches: A Review
## 3 Oilseed Production Process and by-Products Physiochemical Properties
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Type** & **Moisture \%** & **Dry Matter \%** & **Crade Protein \%** & **Crude Lipid \%** & **Ash \%** & **Crude Fiber \%** & **Carohydrates \%** & **Reference** \\ \hline \multicolumn{8}{c}{**SAFFLOWER**} \\ \hline CPC & 6.8 & 93.2 & 23.25 & 9.14 & 5.10 & 34.41 & 55.71 & [55] \\ \hline \multicolumn{8}{c}{**SOYBEAN**} \\ \hline SEC & 88.7 & 11.3 & 46-52.4 & 0.55-8 & 2-6.96 & 4.4-75.44 & 42.8-47.05 & [59,90,91] \\ CPC & 8.4-9.66 & 90.34-91.6 & 43.3-45.5 & 9.3-15.55 & 5.71-59.1 & 4.95-11.28 & 14.98-21.76 & [44,92,93] \\ HPC & 89.9 & 10.1 & 39.1 & 9.2 & 6.9 & N.a. & N.a & [48] \\ M & 12 & 88-95.79 & 44-49 & 0.5-0.65 & 421-7.5 & 3.5-7 & 35.85-47.79 & [70,94] \\ \hline \multicolumn{8}{c}{**GROUNDUNT**} \\ \hline CPC & 8.22-4.57 & 91.78-95.43 & 41.73-60 & 7-22.59 & 1.5-5.76 & 4.33-8.55 & 14.1-36.6 & [55,67,95,96] \\ \hline \multicolumn{8}{c}{**SUNFLLOWER**} \\ \hline SEC & 2.56-10 & 90-97.44 & 31.9-43.38 & 1-23.6 & 6.4-7.83 & 13.07-28.85 & 25.99 & [25,90,97,98] \\ CPC & 2.5-11 & 89-97.5 & 19.93-44.9 & 7-16.6 & 4.69-8 & 17.4-33.4 & 15-28.2 & [44,77,95,96,99,101] \\ \hline \multicolumn{8}{c}{**FLAXSEED**} \\ \hline CPC & 6.89-9.27 & 90.73-93.1 & 14.4-41.97 & 6.11-21.4 & 4.7-6.27 & 6.29-12.9 & 16.26-52.45 & [24,-26,44,47,52,68,76,102,-105] \\ HPC & 2.65-6.14 & 93.86-97.35 & 28.13-59.62 & 1.85-21.0 & 5.26-9.40 & 9.35-34.77 & 21.27-25.6 & [53,105] \\ M & 5.75-9.37 & 90.63-94.25 & 19.95-34.13 & 1.87-32.2 & 2.02-57.2 & 9.38 & 48.9 & [103] \\ \hline \multicolumn{8}{c}{**PUMPKIN**} \\ \hline CPC & 5-8.2 & 91.8-96.5 & 29.38-53.98 & 5.92-36.22 & 4.20-8.7 & 3.89-7.1 & 15.88-19.73 & [24,47,55,77,92,106] \\ M & 8.01 & 91.99 & 60.94 & 0.94 & 9.93 & N.a. & 28.19 & [107] \\ \hline \multicolumn{8}{c}{**HEMP**} \\ \hline CPC & 6.35-13.61 & 86.39-93.65 & 23.25-33.45 & 0.51-14.02 & 3.30-9.78 & 17.41-60.38 & 2.80-48.54 & [47,53,55,99,106,108] \\ HPC & 2.15 & 97.85 & 52.48 & 1.54 & 6.52 & 18 & 19.31 & [52] \\ M & 9.37 & 90.63 & 32.07 & 0.97 & 9.65 & N.a. & 57.31 & [107] \\ \hline \multicolumn{8}{c}{**COTTONSEED**} \\ \hline CPC & N.a & N.a & 24.79 & 8.91 & 6.2 & 7 & 32.2 & [96] \\ \hline \multicolumn{8}{c}{\({}^{\dagger}\) SEC-solvent extracted cake; \({}^{2}\) CPC- cold-pressed cake; \({}^{3}\) HPC- expeller cake, \({}^{4}\) M-meal; \({}^{5}\) Na- not analyzed} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 4 Development of CLSM
### Compressive Strength, Flowability, and Bleeding  
In this section, the optimal mixture ratio between the binder and fly ash was investigated in order to develop the CLSM, satisfying the target fluidity, strength, and bleeding.  
Figure 3: Compressive strength of specimens according to the ratio of base materials.  
[PERSON] et al. [9] reported that CLSMs typically consist of cement (Type 1 Portland), fly ash (Class C or F), a fine aggregate, and water. In this study, fly ash was selected as the filler, and two types of additives were supplemented to reduce the settlement of the mixtures. These materials were Na-montmorillonite as the settlement inhibitor and potassium carbonate (K\({}_{2}\)CO\({}_{3}\)) as the accelerator. As mentioned in Section 3, CSA-9 in Figure 3 was selected as the binder to develop the new CLSM. The water-material ratio was set to be 1:1 (i.e., W/M is equal to 100%).  
Figure 4 shows the compressive strength at 4 h, 8 h, 1 d, 3 d, 7 d, and 28 d, the flowability after mixing, and bleeding at 2 h of each CLSM specimen. Detailed information related to the composition of the CLSM is shown in Table 5. The compressive strength (\(q_{c}\)) typically increased as the proportion of the binder increased, and the \(q_{c}\) of the entire specimen satisfied the target strength (i.e., 28-day compressive strength had to be less than 1.0 MPa in Table 1). The \(q_{c}\) of CLSM-5 and CLSM-1 showed the highest and lowest compressive strengths (i.e., 0.784 MPa and 0.099 MPa) because the proportion of binder was the highest and lowest, respectively (Figure 4a). As shown in Figure 4b, the flowability of the specimens satisfied the target value (i.e., more than 200 mm spread in Table 1). The flowability increased as the amount of binder reduced. Therefore, the flowability of CLSM-1 and CLSM-5 was the highest and lowest, respectively, contrary to the results of the compressive strength.  
binder could prevent bleeding in spite of a high water content (i.e., water content was more than 100%). Furthermore, the incorporation of finer particles had the potential to reduce the required binder content and/or increase the water content within the CLSM mixture. This could increase the flowability of the CLSM and decrease the bleeding, as well as secure a sufficient compressive strength.
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 2 System Model  
In this study, we consider only the uplink phase of a user-centric cell-free massive MIMO system with limited fronthaul capacity, in which \(L\) APs equipped with \(N\) antennas are distributed across the service area, and \(K\) UEs equipped with single antennas are present. As illustrated in Figure 1, all APs are connected to a single CPU via fronthaul links, and the APs form subsets to support UEs in a user-centric manner. Additionally, all APs employ a low-resolution ADC architecture.  
We assume that the number of UEs each AP can support is limited by the number of orthogonal pilots, \(\tau_{p}\). Specifically, we consider scenarios where \(L\ll K\leq L\times\tau_{p}\). This limitation ensures that each AP can support up to \(\tau_{p}\) UEs without causing overserving APs and severe pilot contamination.  
We acknowledge that realistic massive access scenarios, such as those in IoT environments, often involve hundreds or thousands of UEs per AP. These scenarios use multiple access techniques to allocate different frequency and time resources to each UE. However, our study focuses on scenarios where UEs share the same frequency and time resources. By doing so, we aim to highlight the impact of pilot contamination in dense network environments where multiple access techniques are not employed.  
Remark 1: _In practical deployments, a combination of multiple access techniques and increased AP density would be necessary to support a larger number of UEs. Multiple access techniques such as time division multiple access (TDMA) or orthogonal frequency division multiple access (OFDMA)  
Figure 1: Cell-free massive MIMO system model.  
can be used to allocate different time/frequency resources to UEs, thus accommodating more UEs per AP without causing pilot contamination. Additionally, increasing the number of APs proportionally can help maintain system performance in dense environments. While our current study focuses on the constraints imposed by orthogonal pilots, we recognize the need for realistic scenarios where the number of UEs may exceed \(L\times\tau_{p}\). In such cases, network operators would typically deploy more APs or employ multiple access techniques to handle the increased load, ensuring the proposed algorithm can still function effectively._
## 5 Comparison of Different Topologies
### Topology Comparison Based on Data Latency  
The WSN traffic, which is characterized by the interaction with the environment or generated in response to certain events, is likely to be very different from human-driven forms of networks. A typical consequence is that WSNs are expected to exhibit very low data rates over a large time scale, but can have very burst traffic upon the occurrence of the certain events.  
A single-hop-to-sink structure would have the least data latency because there is no delay due to buffering at routers along the path. However, it is not scalable and there may be more loss due to collisions as the network density increases. Flat topology networks have higher data latency than the single-hop-to-sink structure but lower data loss because keeping the transmission power lower reduces the packet collision rates. Depending on the number of nodes and the distance between them, flat topology network may endure increased latency as message moves along multi-hop route to the base station. In addition, a flat topology network can cause the nodes closer to the base station to overloads with the increased node density. Such overload might cause latency in communication, and the worst case creates a black hole of overloaded (or dead) nodes around the base station.  
In hierarchical tree topology, as the data moves from the lower level to a higher level, it moves a greater distance, thus reducing the travel time and data latency. However, as the distance cluster levels increases, the energy dissipation, which is proportional to square of distance, increases. In [71], [PERSON] _et al._ propose a metric by \"energy \(\times\) delay\" and presents a chain-based scheme called PEGASIS that attempt to balance the energy and delay cost for data gathering from WSN. Clustering is a design approach to minimize energy consumption and to minimize data latency. In clustered hierarchical topology, only CH (along the hierarchy) performs aggregation, while in chain topology intermediate nodes perform aggregation. As a result, clustered hierarchical architecture has lower latency than chain topology. Nevertheless, individual packet latency may not be important criteria due to the inherent redundancy (caused by spatial and temporal correlation in the sensed data) in the transmitted packets.
###### Abstract  
In this study, different volume fractions of silicon-carbide-reinforced AA2024 matrix composites were successfully fabricated using stir-casting (SC) and die-casting (DC) processes. The microstructural difference and physical properties of the composites during the manufacturing process were investigated in detail. The microstructural analysis found that the composite produced by the SC process had some reinforcement clusters and pores; however, defects and clusters significantly decreased after the DC process. In particular, the degree of reinforcement dispersion was quantitatively analyzed and compared before and after the DC process using the dispersion-analysis method. As a result of quantitative evaluation, the degree of dispersion was improved 2.5, 4.6, and 4.0 times with 3 vol.%, 6 vol.%, and 9 vol.% SiC-reinforced composite after the DC process, respectively. The electron backscatter diffraction (EBSD) analysis showed that the grain size of the 9 vol.% SiC-reinforced DC composite (17.67 \(\upmu\)m) was 75% smaller than that of the SC composite (68.06 \(\upmu\)m). The average tensile strength and hardness of the 9 vol.% SiC-reinforced DC composite were 2 times higher than those of the AA2024 matrix. The superior mechanical properties of the DC-processed composite can be attributed to the increase in dispersivity of the SiC particles and to decreases in defects and grain size during the DC process.  
Al matrix composites (AMCs); stir-casting (SC); die-casting (DC); dispersion mechanism; mechanical properties +
Footnote †: journal: Applied Sciences
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 4 Preliminaries of the Security Protocol
### The Ring-LWE Encryption Scheme
#### 4.1.1 The Ring-LWE Problem  
Two polynomial \(a\) and \(s\) are chosen uniformly from the polynomial ring \(R_{q}=Z_{q}[x]/\langle f\rangle\), where \(f\) is an irreducible polynomial of degree \(n-1\). An error polynomial \(e\) of degree \(n\) is sampled from am error distribution \(\mathcal{X}\), which is a discrete Gaussian distribution \(\mathcal{X}_{e}\) with standard deviation \(\sigma\). The Ring-LWE distribution \(A_{s,\mathcal{X}}\) over \(R_{q}\times R_{q}\) consists of tuples \((a,t)\), where \(t=a\cdot s+e\in R_{q}\). Given polynomial pairs \((a,t)\) from \(A_{s,\mathcal{X}}\), it is very difficult to find \(s\). This problem is known as the search ring-LWE problem [30]. This paper utilizes the efficient version of the ring-LWE based crypto-system [31] that minimizes the computation overhead of the encryption scheme.
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 1 Introduction
Although a few scholars have employed the DEM for numerical simula  
Figure 1: Schematic diagram of helical anchor foundation installation. tions, the need to recalibrate parameters with each model adjustment is time-consuming. To address these challenges, this paper introduces a novel approach that couples Smoothed Particle Hydrodynamics (SPH) with the finite element method (FEM) to simulate the installation and extraction of helical anchors in sandy soil. Section 2 details the establishment and material parameters of the SPH-FEM coupled simulation used in this study. Section 3 utilizes this model to investigate the disturbance to soil stress and displacement caused by the installation process and to study the impact of the \(AR\) on the installation torque, compressive installation force, uplift bearing capacity, and the torque correlation factor \(K_{\text{t}}\) of helical anchors.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 5 Providing Information Based on Prospect Theory  
### Outline of the Technology for Providing Sightsseeing Information  
In this section, we explain our idea to provide appropriate information to a tourist. Original Prospect Theory is used to describe the value or impression according to an input such as earned money. However, in our sightsseeing support system, the value of information varies according to the situation. For example, the information regarding the restaurant is valuable if it is provided when a tourist is hungry; whereas, the information is not valuable just after lunch. Therefore, it is important to change the value of the information in real-time, which can be carried out in two ways: The first way is to change the value of the information itself, and the other is to change the baseline of the evaluation. In the application, according to the limited calculation and storage resources, it is difficult to calculate the value while considering additional surrounding information. Therefore, we explained value as the difference between the predefined information value and the baseline of the evaluation. If a tourist is satisfied with the provided information, the baseline is up, and if they receive the information again, the value is slightly lower than the original value. According to the psychological state, if the information is not useful for a tourist, the value becomes poor, and the dissatisfaction level and mental damage increase. We tried to reflect the difference between positive and negative mental reaction in the information value evaluation algorithm that is based on the Prospect Theory.
## 1 Introduction
It is an assembly of nanomagnets (NMs), e.g., magnetite, embedded in a solid matrix (polymer, silica, etc) or floating in a fluid (ferrofluid), the base fluid (bf), which could be water. This system is in contact with its environment that may be the container of the sample (a tube, a box), air or a tissue. In the sequel, the subscript \"ff\" will stand for ferrofluid; \"bf\" for base fluid and \"Fe\" for iron (or magnetic substance of the inclusions). In the equations below, the quantities \(\rho,c_{v},\kappa,\mathcal{P}\) and \(L\) refer to the whole sample (NM+host) and should then carry the subscript \"ff\". However, to keep the equations simpler, we omit the subscript. We will introduce the density of the magnetic substance within the particles \(m_{\rm Fe}/V_{\rm Fe}=\rho_{\rm Fe}\) and the volume fraction of the ferrofluid \(\phi\equiv V_{\rm Fe}/V_{\rm ff}\). In the general scheme for the measurement of the _specific absorption rate_ (SAR, denoted as \(\mathfrak{S}\)), the sample is initially set to the same temperature as its environment, \(T_{0}\). Then, the AC magnetic field (AMF) is switched on at \(t=0\). The latter excites the NMs which then release heat within the sample, i.e., they heat up the matrix/fluid (ferrofluid). The heat thus generated then diffuses across the sample and the heat flux reaches the sample's limits, or its interface with the environment, to which heat is transferred by conduction, convection or radiation. If we assume that the NMs are homogeneously distributed through the sample, the heat power by unit volume is \(\mathcal{P}/V_{\rm ff}\) (that is in \({\rm W}/{\rm m}^{5}\)). Figure 1: Sketch of the model system: a sample composed of nanomagnets (NMs) as heat sources, and the embedding fluid (base fluid), coupled to its environment (air). Source: Ref. [39].
## 3 Influence of Inherent Anisotropy  
### Macroscopic Results  
Figure 6 shows the stress path of each specimen during \(K_{0}\)-loading. The number of the legend represents the bedding angle \(\delta\) of specimen, and for the isotropic specimen, it is labeled by \"iso\". Consistent with existing simulation results [6,25] and discussion [14], all stress path lines are slightly bent and then gradually changed into straight lines. Meanwhile, for the specimens with \(\delta\) = 60\({}^{\circ}\), 45\({}^{\circ}\), and 30\({}^{\circ}\), their stress path lines are close with each other and higher than the line representing the isotropic specimen when the vertical stress is relatively large. In contrast, for the specimens with \(\delta\) = 90\({}^{\circ}\) and 0\({}^{\circ}\), the lines are similar but lower than the \"iso\" line. This clearly shows the effect of initial anisotropy on the evolution of horizontal stress with vertical stress.  
The evolution of two definitions of \(K_{0}\) values along with stress variations are shown in Figure 7. Consistent with previous expectations, \(K_{0}\) decreases with the increase of vertical stress, while \(K_{0}^{\prime}\) rise to a peak and then decrease [10]. When the vertical stress is high enough, both \(K_{0}^{\prime}\) and \(K_{0}\) tend to be stable. However, the value of \(K_{0}^{\prime}\) fluctuates while the value of \(K_{0}\) is stable. Therefore, the stable values of \(K_{0}\) at high pressure are treated as the representative \(K_{0}\) values of the specimen in the following paragraphs. To assess the effect of initial anisotropy on \(K_{0}\), the change of the representative \(K_{0}\) values with \(\delta\) is plotted in Figure 8. \(K_{0}\) increases first and then decreases with the change of \(\delta\). The \(K_{0}\) value reaches a maximum of 0.343 when \(\delta\) = 45\({}^{\circ}\) and a minimum of 0.308 when \(\delta\) = 90\({}^{\circ}\). From the above result, the initial anisotropy of the specimen does have a certain effect on the \(K_{0}\) value, consistent with some previous studies [23]. It's worth noting that the difference between the maximum and the minimum is only about 10%. Additionally, due to the particularity of the sample preparation method in this simulation, the anisotropy degree of the specimens in this simulation is much higher than that of the naturally generated soil anisotropy (mainly due to gravity deposition) [35]. Therefore, it can be inferred that the difference of \(K_{0}\) caused by anisotropic principal direction difference in actual engineering or laboratory test is much smaller than that of 10%. The initial fabric anisotropy is not a main influencing factor on \(K_{0}\) in engineering practice.  
Figure 6: Stress path of \(K_{0}\)-loading on specimens with different initial anisotropy.
## 3 Optimization Design
### Design Parameter Analysis  
Before undertaking multi-objective optimization, this section conducts a sensitivity analysis of the structural parameters to determine their impact on the final performance. This analysis helps identify which design parameters significantly influence performance and allows for an expanded range of choices for these parameters. The results of this sensitivity analysis, based on the previously described mathematical model, are illustrated in Figure 6. The analysis reveals that the driving force required by the mechanism is directly proportional to thickness \(t\) and width \(b\), and inversely proportional to radius \(r\). The parameters that significantly affect the driving force, in descending order of impact, are \(t\), \(r\), and \(b\). Similarly, the parameters that most affect the inherent frequency, listed in descending order, are \(t\), \(r\), and \(b\). It is evident that \(t\) has the most substantial effect on both the driving force and the inherent frequency. Therefore, the selected parameter optimization range for t should be broader: 0.2 mm \(<t<\) 0.5 mm. For \(r\), the optimization range should be 2 mm \(<r<\) 5 mm. Lastly, for \(b\), the optimization range should be 10 mm \(<b<\) 20 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Density/(kg/m3)** & **Elasticity** & **Poisson’s Ratio** & **Yield Strength/(MPa)** \\  & **Modulus/(MPa)** & & & \\ \hline
8300 & 133 & 0.35 & 750 \\ \hline \end{tabular}
\end{table}
Table 1: Performance parameters of beryllium bronze [30].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Length/mm** & **Width/mm** & **Height/mm** & \(I_{1}\)**/mm** & \(I_{2}\)**/mm** & \(I_{3}\)**/mm** & \(I_{4}\)**/mm** \\ \hline
120 & 120 & 50 & 40 & 97 & 40 & 121 \\ \hline \end{tabular}
\end{table}
Table 2: Performance parameters of beryllium bronze [31].  
Figure 6: _Cont._