esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aims to describe the numerical modeling and nonlinear finite element analysis of typical spinal braces as a first step towards optimizing their topology for 3D printing. Numerical simulation was carried out in Abaqus CAE software Version 2021, utilizing a CAD (Meshmixer Version 3.5.474) scan of an actual spinal brace. Boundary conditions were defined by means of contact surfaces between the human body and the supporting pads located in the interior of the brace. The process of tightening the straps on the rear face of the brace was simulated via appropriate imposed displacements. The response is described through the deformations and developing stresses of the brace and the contact pressures in the areas of interaction with the human body. Parametric analysis indicated that increasing the cross-sectional thickness or elastic modulus of the brace material results in higher maximum von Mises stresses and lower displacements. The comparison between 3D-printed and conventional braces highlighted the potential of 3D-printing technology to achieve comparable performance with customized designs, leveraging the constitutive properties of 3D-printed material obtained from tension tests. The tension tests demonstrated that the 3D-printed material achieved higher values of modulus of elasticity compared to traditional brace materials. Finally, the topology optimization criteria to be applied for the design of spinal braces in the next step of this ongoing research are briefly described.  
Keywords:spinal brace; finite element analysis; nonlinear analysis; contact nonlinearity; laser scanning; topology optimization; 3D printing +
Footnote †: journal: Applied
## 4 Common Latitude Power Series Expansions with Geocentric Latitude as Variable
### Power Series Expansion of Rectifying Latitude with Geocentric Latitude as Variable  
According to the map projection theory, the meridian arc length \(X\) on an ellipsoid from the equator to geocentric latitude \(\phi\) is  
\[X=\int_{0}^{\phi}M(\phi)d\phi=a\sqrt{1-e^{2}}\int_{0}^{\phi}\sqrt{\frac{1-(2- e^{2})e^{2}\cos^{2}\phi}{\left(1-e^{2}\cos^{2}\phi\right)^{3}}}d\phi. \tag{21}\]  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Expand to \(e^{10}\) Based on the First**} & \multicolumn{1}{c}{**Expand to \(n^{5}\) Based on the Third**} \\ \multicolumn{1}{c}{**Eccentricity \(e\)**} & \multicolumn{1}{c}{**Flattening \(n\)**} \\ \hline \(\left\{\begin{array}{l}b_{2}=\frac{1}{8}e^{2}+\frac{7}{70}e^{4}+\frac{281}{ 804}e^{6}  
Similarly, given that an arc with the angle \(\psi\) and the radius \(R=a(1-e^{2})k_{0}\) is numerically equivalent to the meridian arc length, the rectifying latitude can be expressed as  
\[\psi=\frac{X}{R}=\frac{X}{a(1-e^{2})k_{0}}, \tag{22}\]  
\[k_{0}=1+\frac{3}{4}e^{2}+\frac{45}{64}e^{4}+\frac{175}{256}e^{6}+\frac{11025}{16 384}e^{8}+\frac{43659}{65536}e^{10}, \tag{23}\]  
where \(\psi\) is the rectifying latitude. The manual differentiation process is bypassed and the power series expansion formula for rectifying the latitude with geocentric latitude as the variable is  
\[\psi(\phi)=\phi+a_{2}\sin 2\phi+a_{4}\sin 4\phi+a_{6}\sin 6\phi+a_{8}\sin 8 \phi+a_{10}\sin 10\phi. \tag{24}\]  
The coefficients in Equation (24) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 7.
## 2 Materials and Methods
### Statistical Evaluation  
The data were presented as mean \(\pm\) SEM, with results of multiple groups compared using one-way ANOVA. A \(p\) value of 0.05 or below was used to determine statistical significance for all analyses, which were conducted using the GraphPad prism 6 program (GraphPad, San Diego, CA, USA).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Gene ID** & **Gene** & **Primer** \\ \hline _NM\_007392_ & _\(\alpha\)-SMA_ & Forward: 5\({}^{\prime}\)-TGCTGACAGAGGCACCACTGA-3\({}^{\prime}\) \\  & & Reverse: 5\({}^{\prime}\)-CAGTTGTACGACCAGGCATAG-3\({}^{\prime}\) \\ \hline _NM\_031512_ & _IL-1\(\beta\)_ & Forward: 5\({}^{\prime}\)-AGGACCACCTTCTT-3\({}^{\prime}\) \\ \hline _NM\_012589_ & _IL-6_ & Forward: 5\({}^{\prime}\)-TAGTCCTTACCCCAACT-3\({}^{\prime}\) \\  & & Reverse: 5\({}^{\prime}\)-TTGCTCCTTAGCCACTCCT-3\({}^{\prime}\) \\ \hline _NM\_001278601_ & _TNF-\(\alpha\)_ & Forward: 5\({}^{\prime}\)- CATGAGCACAGAAGCATGA-3\({}^{\prime}\) \\  & & Reverse: 5\({}^{\prime}\)- AAGCAGGAATGAGAAGGC -3\({}^{\prime}\) \\ \hline _NM\_011333_ & _MCP-1_ & Forward: 5\({}^{\prime}\)-GCATCCGCGTGCTC-3\({}^{\prime}\) \\  & & Reverse: 5\({}^{\prime}\)-CTCCAGCCTACTTCATITGGATC-3\({}^{\prime}\) \\ \hline _NM\_013599_ & _MMP-9_ & Forward: 5\({}^{\prime}\)-GTTTTGTACTATTGCCTGGA-3\({}^{\prime}\) \\  & & Reverse: 5\({}^{\prime}\)- ACCCAACTTTACCAGATCC-3\({}^{\prime}\) \\ \hline _NM\_010810_ & _MMP-7_ & Forward: 5\({}^{\prime}\)- AGGTGTGGGAGTGCCAGATGTG -3\({}^{\prime}\) \\  & & Reverse: 5\({}^{\prime}\)- CCACTACGACGGAAGTC-3\({}^{\prime}\) \\ \hline _NM\_011577.2_ & _TGF-\(\beta\)_ & Forward: 5-TTGCTTCAGGTCCACAGAGA-3\({}^{\prime}\) \\  & & Reverse: 5-TGGTGTTAGAGGGCAAGGAC-3\({}^{\prime}\) \\ \hline _NM\_017008.3_ & _GAPDH_ & Forward: 5-ACAGCAACAGGGTGGTGAC-3\({}^{\prime}\) \\  & & Reverse: 5-TTGAGGGTGCAGCCAACTT-3\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used to amplify target genes.
## 4 Understanding Centralised Identity Systems
### LDAP: The Directory-Based Approach
#### 4.5.4 Addressing Vulnerabilities Using LDAPS (SSL/TLS)  
One way to mitigate LDAP's security vulnerabilities is through the use of LDAPS, which encrypts LDAP traffic via SSL/TLS. This protects sensitive information from tampering or eavesdropping. The LDAPS process starts with an SSL/TLS handshake, initiated when the client connects to the LDAP server on port 636. The server responds by sending its digital certificate, which the client verifies. Once the certificate is validated, an encrypted session is established, ensuring that all subsequent LDAP operations, including the bind request, are conducted securely over this channel. Additionally, strong access control policies, regular security audits, and LDAP activity monitoring can further safeguard the integrity of both system and user data [28; 29].
## 2 Materials and Methods
### UPLC-MS Method for Identfication of Polyphenols  
Extraction of polyphenols was determined using the method described by [PERSON] et al. [11] with own modifications. The samples of HMP (5.00 \(\pm\) 0.01 g) extracted with 5 mL of methanol (80%, Sigma-Aldrich, Steinheim, Germany) then acidified with HCl. Next, the samples were shook for 30 min at room temperature and centrifuged (5000 rpm, 10 min.). After the addition of hexane (10 mL) to supernatant, the hydrophilic layer was obtained and kept in Eppendorf vials at 18 \({}^{\circ}\)C until analysis. Before the identification the sample diluted with 0.1% formic acid (1:1 \(v/v\), Sigma-Aldrich Steinheim, Germany). For the identification of polyphenols, the protocol proposed by [PERSON] [12] was used. The identification of polyphenols in HMP extracts was carried out using an ACQUITY Ultra Performance LC system equipped with a photodiode array detector with a binary solvent manager (Waters Corporation, Milford, MA, USA) with a mass detector G2 Q-Tof micromass spectrometer (Waters, Manchester, UK) equipped with an electrospray ionization (ESI) source operating in negative mode. For the separation of single polyphenols, an UPLC BEH C18 column (1.7 mm, 2.1 \(\times\) 100 mm, Waters) was used. The mobile phase consisted of 0.1% formic acid, _v/v_ (solvent A) and 100% acetonitrile (solvent B).  
The single components of polyphenols were characterized based on the retention time and the accurate molecular masses. The data obtained from UPLC-MS were analyzed in the MassLynx 4.0 ChromaLynx (Application Manager software, Waters).  
Phenolic acids were monitored at 320 nm and flavonoid glycosides at 360 nm. The PDA spectra were measured over the wavelength range of 200-600 nm in steps of 2 nm. The retention times and spectra were compared to those of the authentic standards (chlorogenic acid, quinic acid and apigenin di-glucoside were purchased from Extrasynthese, Lyon, France).
## Nomenclature  
\begin{tabular}{l l} \(A\) & area (m\({}^{2}\)), absorber \\ \(b\) & joint length (m) \\ \(Cp\) & heat capacity (kJ/kg K) \\ \(D\) & diameter (m) \\ \(F\) & standard fin efficiency \\ \(F_{R}\) & heat removal factor \\ \(Fr\) & solar collector efficiency factor \\ \(h\) & heat transfer coefficient (W/m\({}^{2}\) K) \\ \(l\) & solar radiation (W/m\({}^{2}\)) \\ \(k\) & thermal conductivity (W/m K) \\ \(\dot{m}\) & mass flow rate (kg/s) \\ \(Nu\) & Nusselt number \\ \(Pr\) & Prandtl number \\ \(\dot{Q}\) & heat flux (W) \\ \(U\) & global heat transfer coefficient (W/m K) \\ \(Re\) & Reynolds number \\ \(T\) & temperature (K) \\ \(W\) & distance between fins (m) \\ _Subscripts_ \\ \(a\) & ambient \\ \(b\) & joint between fins and pipes, lower loss \\ \(c\) & absorber \\ \(e\) & evaporator, border surface \\ \(g\) & glass \\ \(i\) & inlet, internal \\ \(o\) & external \\ \(p\) & flat plate \\ \(u\) & useful \\ _Greek symbols_ \\ \(\alpha\) & absorptance \\ \(\delta_{C}\) & absorber thickness (m) \\ \(\eta\) & efficiency \\ \(\mu\) & viscosity (Pa*s) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\sigma\) & Boltzmann constant (W/m\({}^{2}\) K\({}^{4}\)) \\ \(\tau_{0}\) & transmittance \\ \end{tabular}
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 3 Design of Proposed Optimal Producer Mobility Support Solution
### Interest and Mobility Interest Packet Transmission  
Normally in NDN, if a content consumer sends an interest packet requesting data, when the Interest packet reaches a content producer, the data is sent back immediately, by using NDN routing and forwarding plan E [48]. The interest packet can be dropped if the data and producer are not found due to the change of location. A new routing prefix is needed to locate the mobile producer as a result of the location change due to the movement. In the OPMSS, when the producer moves, the procedure called process handoff will take place by updating the intermediate routers about the new routing prefix by using the proposed _MI_ packets and broadcasting strategy, as explained in the previous section. Once the new routing prefix is updated among the intermediate routers, the NDN routing plane uses the best route strategy to create an optimal path for the data packet to reach the consumer from the new PoA of the mobile producer, once the handoff is completed. A pending Interest packet can then be forwarded by the consumer via the optimal route to the new location of the mobile producer. The interest packet and data exchange processes take place with the help of three data structure mechanisms--the content store (CS), the PIT, and the FIB table. The PIT is used to record the name prefixes of all interest packets and incoming faces, the CS table records the name prefixes and corresponding data, and the FIB table records the name prefixes and outgoing faces. The processes of interest packets transmission is illustrated in the flowchart of Figure 4.
# Popular Biochar as an Alternative Substrate for Curly Endive Cultivated in a Soilless System
## 1 Introduction  
One of the most used substrate constituents for soilless vegetable cultivation is peat [1]. Nevertheless, peat is a restricted resource with a huge demand, and its extraction determines deleterious environmental impacts [2]. Diverse organic resources might occupy an imperative function in lessening the C footprint of the horticulture production by completely or partially replacing peat-based substrates [3]. Biochar is a C-rich organic material, which is produced by thermal decomposition (pyrolysis) of plant-derived biomass in partial or total absence of oxygen [4]. As reported by [PERSON] et al. [5], the biomass-based materials are heated to temperature usually between 300 and 1000 \({}^{\circ}\)C. [PERSON] et al. [6] showed that heating time did not modify the properties of biochar, conversely, the pyrolysis temperature has a considerable impact on its physicochemical traits. Thus, compared to other straight biomass-based materials, biochar should go through a series of high temperature cycles to avoid any potential shrinkage of the material. Biochar has also been used as soil amendment inorder to enhance plant growth and yield via increasing water holding capacity, reducing bulk density, increasing cation exchange capacity and soil microbial biomass [7]. Besides, [PERSON] et al. [8] found that biochar suppress N\({}_{2}\)O emissions and ambient CH\({}_{4}\) oxidation in laboratory incubations. [PERSON] et al. [9], found that addition of biochar improves N fertilizer use efficiency in radish plants (_Raphanus sativus_ var. Long Scarlet) grown in hard-setting Alfisol (Chromosol). Nevertheless, [PERSON] et al. [10] report that the use of biochar as a substrate or substrate component is unfavorable due to its high alkalinity and content of leachable polyaromatic hydrocarbons, which are potentially toxic to plants. Biochar can be produced in huge quantities from materials originated from decomposed organic matter, including but not limited to wood and wood materials, bark, grasses and agricultural and industrial by-products [11].  
Curly endive (_Cichorium endivia_ L. var. _crispum_) is an extensively cultivated leafy green vegetable belonging to the _Asteraceae_ family. Curly endive achieved consumers' interest due to its notable quantity of nutritional and functional compounds such as ascorbic acid, phenolics, glucosinolates and sesquiterpene lactones and minerals which are fundamental for humans health and development [12; 13; 14].  
To our acquaintance, there are no studies regarding the relationship between the pyrolysis temperatures used for biochar production and its use as peat substitute for curly endive production in soilless cultivation. Thus, the aim of our work was to test the main physicochemical traits of various growing media constituted with decreasing content of peat and increasing percentages of poplar wood biochar. The biological results of these substrates on yield, nutritional and functional properties of curly endive plants cultivated in a protected environment were also tested.
# Numerical Modeling of Single Pile Behaviors Due to Groundwater Level Rising
## 3 Numerical Modeling
### Geometric Model and Boundary Conditions  
The model mesh covered a 100 m depth by 30 m width domain, much larger than the required distance of six times the pile diameter at the side and the pile base. The boundary condition for the mesh was constrained from moving in the horizontal direction for both side boundaries, and the bottom boundary was fixed in all directions. The mesh was used was symmetric across the axes and used 15-node triangular elements. PLAXIS 2D automatically generated the mesh. The mesh varied from coarse far from the pile to very fine close to the pile, see Figure 3b. The zone labeled 'A' extended to 2.5 m from the pile with coarseness factor 0.10, 'B' to 5 m, coarseness factor 0.20, 'C' to 10 m, coarseness factor 0.30, and 'D' to 30 m, coarseness factor 0.50. The total number of elements was 8313.  
The five bored pile models are set out in Table 1 and an example of piled numerical modeling is shown in Figure 3. Each pile has a six character reference, e.g., TiD\(d\), where \(t\) is the pile tip depth below ground level (e.g., 22 m) and \(d\) is the pile diameter (e.g., 0.6 m)--see examples in Figure 3a.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Piles** & **Type of Pile** & \
\begin{tabular}{c} **Length** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Diameter** \\ **(m)** \\ \end{tabular} & **Soil at Pile Base** \\ \hline T22D0.6 & Bored & 22.0 & 0.6 & Stiff clay \\ T27D0.6 & Bored & 27.0 & 0.6 & 1 st Sand \\ T39D1.0 & Bored & 39.0 & 1.0 & Stiff clay \\ T55D1.0 & Bored & 55.0 & 1.0 & 2 nd Sand \\ T55D1.5 & Bored & 55.0 & 1.5 & 2 nd Sand \\ \hline \end{tabular}
\end{table}
Table 1: Model pile dimensions and soil at pile base.
## 3 Results
### Impact of Plant-Based Coatings on Previously Reported Volatile Compounds of \"Rocha\" Pear  
In a first approach, the impact of the coatings on headspace volatile compounds previously identified in \"Rocha\" pear (Table 1) was assessed by cluster analysis. This analysis was performed at three sampling times (9, 32, and 134 days) for both storage conditions. The results obtained from the HCA analysis are presented in Figure 3 with a heatmap visualization of relative amounts of the analytes for the last sampling point (134 days). Heatmaps for all sampling points are provided in hte Supplementary Materials for NA (Figure S1) and DCA (Figure S2) conditions.  
As seen in Figure 3, at nine days after coating application, when storage conditions have yet exerted little influence on pear physiology, an almost complete clustering of replicates according to coating condition is visible in the dendrogram. CT2 was shown to be the most distinctive condition because it was placed in a different branch with a higher Euclidean distance. The branch containing the remaining coating conditions was divided among CT1 samples and the two remaining conditions (CTR and PCT). CTR and PCT were found to be the most similar, which is probably because no plant extract was incorporated in them.  
In subsequent times of analysis, a different clustering behavior was observed between the two storage conditions. In the case of NA, dendrograms obtained do not exhibit a sample clustering that can be associated with the coating conditions. In this case, the data suggest that the coating application and the kind of plant extract incorporated in the coating do not have a relevant impact on the release of the selected analytes after 32 days of storage. Differences observed in this case are only attributed to sample variability.  
Regarding storage in DCA conditions, a different behavior is observed. In this case, coated samples become increasingly distinct over time relative to the control. This is particularly evident at the end of 134 days, when all control samples are clustered in one of the two main branches of the dendrogram. On the one hand, the other main branch contains samples of coated conditions (PCT, CT1, and CT2) without any clustering according to the type of coating applied. These samples exhibit a low Euclidean distance, which indicates a high similarity between them and suggests that plant extract incorporation on coatings does not have a relevant impact on the release of the analyzed compounds to the pear headspace. On the other hand, coating application by itself seems to have an impact on the release of such compounds since CTR samples are clustered at a higher distance from the remaining samples. Through the heatmap visualization, it is possible to verify that this difference is explained mainly by a reduction of the release of esters, such as butyl acetate and hexyl acetate, which are the most abundant compounds released by \"Rocha\" pears. In addition, 2-methyl-1-propanol, 2-methyl-1-butanol, 3-hydroxy-2-butanone, and limonene release are increased in coated pears.
## 3 Simulation and Analysis
### Two Satellites under Asynchronous Scenarios  
Clock offset is another necessary factor to be considered in a practical system. When the clock offset retains the level of ns, the ranging error will have a bias of at least ten meters. Next, we take the clock offset into our consideration, and give a schematic of the clock offset in a transmission data frame in Figure 11. As introduced in Section 2.1, the transmission data frame period is 1 s, and each subframe lasts for 0.2 s. MS and SS transmit the ranging signal and receive the signal of the other satellite simultaneously. We denote that the distance measurements at MS as \(\mathrm{d_{S,M}}\), which is derived from the ranging signal transmitted from SS. Similarly, the distances measurements at SS can be recorded as \(\mathrm{d_{M,S}}\). The local ranging results are depicted as the curves in Figure 11.  
For the downlink, we denote the transmission data frame start time of SS to MS is \(t_{0}\), then the end time of the first subframe and the first frame is recorded as \(t_{0}+200\) ms and \(t_{0}+1000\) ms, respectively. When the ith subframe is transmitted from SS to MS, the average distance \(\mathrm{d_{S,M}^{(i)}}\) (the mean of ranging results in previous 200 ms) is inserted into its duty segment. A similar process is in uplink simultaneously. Through such a bidirectional process, MS (SS) can receive the average ranging results  
Figure 11: Measured distance exchanging model.  
Figure 10: BER of downlink under different PWDF.  
from SS (MS) and obtain its own local ranging results at the same time. Then, the estimated distances and clock offsets can be calculated from Equations (7) and (8).  
As described in Section 2.2, we eliminate clock offset as a by-product according to the DOWR algorithm by exchanging timestamps in the two links. The simulation result of the clock offset is shown in Figure 12. The blue line represents the true clock offset which is set as a constant \(10^{-9}\) s, and the red asterisk is the calculated clock offset through simultaneous ranging and communication algorithm. In the first two seconds, the tracking loop adjusts to a steady state, thus the calculated clock offset and the true clock offset have an obvious derivation. After the first two seconds, the tracking loop steps into a stable condition, then the calculated clock offset gradually approaches the true value. Through 50 Monte Carlo experiments, the time synchronization precision can be calculated as 0.07 ns. In addition, using Equation (7) we can also calculate the ranging results that are not affected by the clock offset.
## 2 Related Works
AttConvLSTM [24] proposes integrating the attention mechanism to emphasize the impact of specific parts on traffic flow predictions. ASTGCN [25] incorporates CNN in the time series component and leverages the GAT model for spatial correlation extraction, resulting in improved outcomes across multiple traffic datasets. Building upon ASTGCN, DGCN [26] constructs a dynamic Laplace matrix of the graph using the spatial attention mechanism for each input sequence data. A3 TGCN [4], an extension of TGCN, achieves significant prediction performance enhancement by incorporating attention to adjust the significance of time points in the time series. GMAN [27] employs an encoder-decoder architecture to model the influence of spatial-temporal factors on traffic conditions. The model comprises multiple spatial-temporal attention blocks that consist of a spatial attention mechanism, a temporal attention mechanism, and a gated fusion mechanism. Similarly to GMAN, STTN [5] contains stacked spatial-temporal blocks and prediction layers. Each spatial-temporal block involves a spatial transformer and a temporal transformer to jointly extract spatial-temporal correlations. ASTGNN [28] and traffic transformer [29] adopt the transformer architecture and utilize self-attention to model complex spatial-temporal correlations. Both models employ encoder-decoder structures to form a deep model of hierarchical features. ASTTN [6] provides a comprehensive analysis of local spatial-temporal attention mechanisms and full spatial-temporal attention mechanisms in the construction of the spatial-temporal transformer on a dynamic graph. In conclusion, the self-attention mechanism is proficient in encapsulating the dynamic spatial-temporal correlations inherent in traffic conditions. Moreover, the hierarchical local self-attention mechanism enhances the accuracy of capturing local spatial dependencies and short-term temporal correlations. It also efficiently amalgamates local and global spatial dependencies, as well as short-term and long-term temporal correlations. This serves as the foundation for the current study.