esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mechanical Properties
### Solid Solution Strengthening  
The solidification and solid solution treatment determine the microstructure and mechanical properties of the Mg alloy components. The addition of different elements can alter the lattice characteristics of Mg alloys and increase the distortion energy. It is widely recognized that Mg-RE alloys can obtain higher performance than traditional commercial Mg alloys, and RE elements have a relatively high solid solubility and can replace the Mg atoms [90]. The effect of solid solution treatment can be expressed as the following equation [91]:  
\[\sigma=\alpha Gb\rho^{\frac{1}{2}} \tag{3}\]  
where \(\alpha\) is a constant, \(G\) is the shear modulus, \(b\) is the Burgers vector and \(\rho\) is the dislocation density.  
[PERSON] et al. [92] investigated the effect of solid solution treatment on mechanical properties of Mg-11 Li-12 Zn alloys, and the results showed that the large massive MgLiZn and MgLiZn phases dissolved in the matrix. The yield strength increased from 136 MPa to 260 MPa. [PERSON] et al. [93] found that the AlLi and Mg\({}_{2}\)LiZn\({}_{3}\) phases of Mg-9 Li-3 Al-2 Zn-1 Sn disappeared after the solid solution, and the microhardness increased due to the numerous existing \(\beta\)-Li phases, whose hardness is higher than the \(\alpha\)-Mg matrix. However, the elongation decreased to 1.4%, and the lattice distortion and the presence of coarse Sn-rich phases caused it. [PERSON] et al. [94] analyzed the effect of RE solute atoms (Gd, Y) in a Mg-1.4 Gd-1.2Y-0.4 Zn alloy on strength enhancement by the solid solution strengthening mechanism, and the contribution of solid-sourced Gd, Y and Zn atoms in the extruded Mg-1.4 Gd-1.2Y-0.4 Zn alloy was calculated to be 148 MPa. In the observation of [PERSON] et al. [95], it can be concluded that one at% solute atom dissolved in matrix can increase 54 MPa in YS for Gd, 37 MPa for Y and 2 MPa for Zn. Moreover, the grain boundary strengthening, dislocation strengthening, and precipitation strengthening were further discussed in the paper. Similar results were obtained by [PERSON] et al. [96] in the research of the mechanical properties of wrought Mg alloys, and the contribution of soluted RE atoms to strength was calculated to be 70 MPa.
# Performance of CRTS-II Ballastless Track-Bridge Structural System Rebars under Fatigue Loading Test
## 2 Experimental Program  
### Design and Construction of the Specim  
The prototype beam is the Chinese standard 32 m pre-stressed concrete simple-supported box girder. The CRTS II slab ballastless track system was selected for the rail system. The ballastless track system from top to bottom is mainly composed of the following structures: 60 kg/m steel rail, elastic fasteners, a 200 mm thick prefabricated track slab, a 30 mm thick cement emulsified asphalt mortar filling layer, a 190 mm thick (straight line section) continuous base plate, a sliding layer, lateral stoppers and other parts. There are T-shaped wide and narrow joints between adjacent track slabs, and concrete is poured at the wide and narrow joints. There are 6 fine-rolled threaded steel bars in the track slabs. The track slabs are connected by tension locks set at the wide and narrow joints. Therefore, the track slabs and the track bed on the bridge are all longitudinally continuous structures.  
The similarity principle was used to design the test specimen. The similarities between the test specimen and the prototype beam are as follows: (1) the similarity constants of structural size is 1:4, which satisfies the geometric similarity condition; (2) the top and bottom concrete stresses of the box beam are equal under self-weight, pre-stress and the train load, and the similar constant of stress is 1; (3) the concrete, emulsified asphalt mortar, steel and other materials are all consistent, and the similar constants of material line expansion coefficient and elastic modulus is 1; and (4) the specimen is a three-span continuous structure, and the constraint condition is consistent with that of the prototype.  
Based on the above design criteria, a 1:4 scale model was used for the test according to the similarity principle, test accuracy requirements and the possibility of experimental conditions. The beam length is 8150 mm, the beam height is 760 mm, and the beam top width is 3160 mm. The thickness of the web of the main beam is 160 mm, the thickness of the top plate is 110 mm, and the thickness of the bottom plate is 150 mm. According to the design results of the overall structure, the reinforcement ratio of the model beam and the prototype beam are approximately equal. HRB400-grade steel is used for the steel rebar; the diameter of the longitudinal rebar is 12 mm, and the diameter of the transverse rebar is 8 mm. The prestressing tendons were arranged in the same curve as in the prototype beam. The specimen was equipped with 7 1 \(\times\) 7 \(\Phi^{5}\) 15.2 bonded steel strands, pre-stressed by the post-tensioning method. Solid concrete blocks were poured at both ends of the beam. The bottom of the concrete block was connected to the ground with anchor rods, and the upper part was set with anchoring steel bars to connect to the track bed. Figure 1a-c shows, respectively, the schematic diagram of the three-span, simple-supported girder bridge, the experimental site picture, and the box girder cross-sectional dimensions.
## 3 Results
### NPP in Forest versus Non-Forest Areas  
About 22% of India's total geographic area is classified as forests, while the rest is classified as non-forest, which includes croplands. While total NPP growth rate is estimated at 3.9% per decade, forest NPP is estimated to increase at a rate of 3.5% per decade and non-forest NPP at a faster rate of 4.4% per decade. Lands used for agricultural activities, including grazing, account for 64% of India's total geographic area, while only 7.4% of the land area is used for non-agricultural activities, and the other 6.3% remains barren [42].Thus most of the non-forest NPP comes from agriculture and related activities which are heavily managed by humans. Two of the key agriculture management indicators, irrigated area and fertilizer use, are found to be strongly correlated with NPP growth in non-forest areas (Figure 7)--suggesting that observed NPP growth over the non-forested areas are also driven by anthropogenic interventions, such as increases in irrigation area (r = 0.77, R\({}^{2}\) = 0.59) and fertilizer use(r = 0.73, R\({}^{2}\) = 0.53), in addition to CO\({}_{2}\) fertilization.  
Figure 6: _Cont._  
Forested areas are also heavily managed, albeit not as intensely as the non-forest areas. India has been implementing an aggressive afforestation and reforestation program especially since the early 1980s, when large-scale afforestation and reforestation under the social forestry program and later under the Joint Forest Management initiative (1990s) were initiated. The cumulative area planted during the period 1980 to 2005 is estimated to be about 34 million ha (Mha) at an average annual rate of 1000.  
of 1.32 Mha [28]. Despite the rapid rate of planting, the overall forest area largely remained stable and increased only marginally from 64.20 Mha in 1987 to 67.71 Mha in 2005 [43,44] because the new plantation programs were taken up in already-existing forests which were either less dense or degraded. This suggests that most of the new plantations were aimed at restoring the degraded forests and increasing the productivity of the existing forests. Therefore, the NPP increase in forest areas is strongly correlated (r = 0.75, R\({}^{2}\) = 0.56) with the cumulative new plantation area (Figure 7(e)).
## 2 Bayesian Functional Mapping
### Likelihood  
Although only phenotypic values and marker genotypes are observable, the probability distribution of the QTL genotypes in a mapping population can be expressed in terms of the location of the putative QTL, the marker genotypes, and the distance between the markers. Let \(\mathbf{y}_{i}\) be the phenotypic value of progeny \(i\) at different time points, \(\mathbf{M}_{i}=\{M_{ik}\}_{k=1}^{m}\) be the \(m\)-marker genotype of progeny \(i\), \(\lambda\) be the QTL position measured by the distance of the QTL from the first marker of an ordered linkage group, and \(\mathbf{D}=\{D_{k}\}_{k=1}^{m}\) be the distances between markers \(1\) and \(k\). Suppose this QTL is located between markers \(k\) and \(k\) + 1. Then, we use \(\omega_{j|i}\) to denote the distribution of the QTL genotype of progeny \(i\), i.e.,  
\[\omega_{j|i}=\mathrm{Prob}(Q_{i}=j|\lambda,M_{ik},M_{i(k+1)},D_{k},D_{k+1}), \tag{3}\]  
which is the conditional probability of QTL genotype \(j\) given the marker genotype of progeny \(i\).  
The likelihood of parameters \(\lambda\), \(\mathbf{\Theta}\) and \(\mathbf{\Sigma}\) given observations can be written as:  
\[L(\lambda,\mathbf{\Theta},\mathbf{\Sigma}|\mathbf{y})=\prod_{i=1}^{n}\left[ \sum_{j=0}^{2}\omega_{j|i}\cdot\pi(\mathbf{y}_{i}|Q_{i}=j,\mathbf{\Theta}_{j},\mathbf{\Sigma})\right], \tag{4}\]  
where \(\mathbf{y}=\{\mathbf{y}_{i}\}_{i=1}^{n}\), \(\mathbf{\Theta}=\{\mathbf{\Theta}_{j}\}_{j=0}^{2}\) and \(\pi\) is assumed to follow a multivariate normal distribution with the genotype-specific mean vector expressed as:  
\[\mathbf{u}_{j} = g(\mathbf{t}|\mathbf{\Theta}_{j}) \tag{5}\] \[= \{u_{j}(t)\}_{t=1}^{T}\] \[= \left\{\frac{\alpha_{j}}{1+\beta_{j}e^{\gamma_{j}t}}\right\}_{t= 1}^{T}\]  
and covariance matrix \(\mathbf{\Sigma}\).
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 1 Introduction
(2019) proposed a computational fluid dynamic model to predict the pipeline displacement in liquefied soil, where the large deformation and reconsolidation of soil are considered. [PERSON] et al. [PERSON] et al. (2019) investigated the force applied by waves to a buried pipeline, in which the higher-order boundary element method was used to solve the related mathematical problem. The existing studies mainly focus on horizontal seabeds, and sloping seabeds are hardly involved although slopes are common in practical engineering. Inspired by previous investigations, the present research aims to investigate the uplift force applied by nonlinear waves to a pipeline buried in the sloping seabed. In the present study, the pressure applied by a wave to the mudline is obtained by a wave sub-model firstly, and then this pressure is applied into an ABAQUS model using subroutines. After model validation with the previous experiments, the effects of soil properties, wave parameters as well as slope on the nonlinear wave-induced uplift force onto the pipeline are analyzed in detail. Finally, a simple formula describing the relationship of the uplift force and the influencing factors is proposed.
## 4 CRISPR/Cas Mechanism
### Adaptation via Spacer Acquisition
#### 4.1.2 Protospacer Selection and Incorporation into CRISPR Arrays  
The minimal CRISPR requirements for spacer acquisition in the type I-E system are a single'repeat' and 60 bp of upstream sequence [43]. As the CRISPR promoter is not contained within this 60 bp, this suggests that CRISPR transcription is not required for incorporation, but indicates this region might be recognized by Cas1 and/or Cas2 to enable the directionality of integration at the leader end of the CRISPR array.  
In type I-E arrays with multiple repeats, the leader proximal repeat is the one that is duplicated (Figure 2A) [43]. In addition, the protospacer added to the CRISPR array contains the last nt of the PAM motif, which becomes the final nt of the 5' repeat (Figure 2A) [54,55,68]. Therefore, only the first 28 nt of the repeat are duplicated and thus, this unit has been called the 'duplicon' [68] and might provide fidelity to the direction of incorporation [69]. This duplicon mechanism cannot apply to all CRISPR/Cas types since the last nucleotide of some PAMs (e.g., CCN in _S. solfataricus_) is not conserved, so could not contribute to the conserved repeats [58]. Interestingly, internal spacer acquisition was recently observed for one CRISPR array in _S. solfataricus_[58], which challenges the dogma that spacer acquisition is always leader proximal [69].  
One critical question is how do the CRISPR/Cas systems acquire foreign DNA and not DNA from their own chromosome? In _S. thermophilus_, examination of the protospacers in the phage genomesenabled the identification of a short 3' protospacer adjacent motif (PAM) [37]. PAM sequences have since been identified via bioinformatics for other CRISPR/Cas systems [70]. In _E. coli,_ spacer selection was shown to require the presence of a PAM [43], but the short length of PAMs cannot provide the level of discrimination to avoid sampling chromosomal DNA. A self / non-self mechanism does occur, since the incorporation of spacers from the chromosome is rare [43]. It is possible that DNA modification, such as by a restriction-modification-like mechanism, might play a role.
## 3 Materials and Methods  
### Research Design  
#### 3.1.1 Spatial Autocorrelation Test  
Before conducting spatial econometric analysis, it is necessary to carry out the spatial autocorrelation test to judge whether there is a spatial correlation between different regions' S&T innovation ability. Only when there is spatial correlation is it necessary to further construct a spatial econometric model. This paper adopts Moran's I, which is the global spatial autocorrelation index, to judge whether there is spatial correlation between different regions in terms of S&T innovation ability. Moran's I is calculated as follows.  
\[Moran^{\prime}s\ I=\frac{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij}(X_{i }-\overline{X})(X_{j}-\overline{X})}{S^{2}\sum\limits_{i=1}^{n}\sum\limits_{j= 1}^{n}w_{ij}} \tag{1}\]  
where \(S^{2}=\frac{1}{n}\sum\limits_{i=1}^{n}(X_{i}-\overline{X})^{2};\overline{X}= \frac{1}{n}\sum\limits_{i=1}^{n}X_{i}\). \(X_{i}\), \(X_{j}\) represents the S&T innovation ability in the _i,j_th region, respectively. \(w_{ij}\) is the element of the _i_th row and the _j_th column in the spatial weight matrix. \(n\) represents the number of different regions. The value range of [PERSON]'s I is [\(-\)1,1]. When the value of [PERSON]'s I is greater than 0, it indicates that there is a positive spatial correlation between different regions in terms of S&T innovation ability. When the value is less than 0, it means there is negative spatial correlation. And if the value of [PERSON]'s I is equal to 0, it indicates that there is no spatial correlation between different regions in terms of S&T innovation ability.
# Seeking a Definition of Digital Twins for Construction and Infrastructure Management
## 5 Defining and Standarising the Concept of a Digital Twin for the Built Environment
### Data Management  
As is detailed in Section 5.3, \"Education and training\", many technologies and digital skills are needed to set up a complete DT environment. Figure 5 details the most common interoperability issues under a DT structure. Starting with 3D representation and 3D as a database, the infrastructure and environmental modelling is crucial, especially if data inside the model is needed for DT functionalities. In this sense, use of a common standard for defining elements and parameters is needed. Also, the output information format must be defined. In this regard, Industry Foundation Classes (IFC) is an extended format.  
Regarding the simulations, if data from IoT devices and 3D representation is required several issues might appear if the correct data transformations and formats have not been defined. These transformations should consider not only the programming languages used, APIs or communication protocols, but the characteristics of the simulation software. In this sense, a lack of standards is detected. Also, real-time data and simulation results should be provided not only for DT developers but for final infrastructure and environment users, aiming to achieve the maximum accessibility possible. This step demands interoperability between the front-end platform, back-end developments, IoT devices, and 3D representations. As it is common to find different standards for IoT devices, 3D modelling, data assignment, and AI simulations, it is of key importance to find formal guidelines to connect them. However, in the authors' knowledge, no general practice has been developed yet. Consequently, to achieve a total interoperable DT, it is recommended to use the most widespread formats in each technology. For instance, a LoRa network for IoT devices connecting IFC formats for 3D representation, API (Python or Java Script) for data management, and frameworks like Angular or React for front-end developments.
# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 6 Model Evaluation and Discussions
### Comparison with State-of-the-Art Methods  
To further analyze the effectiveness of the proposed model, we compared the proposed model with some of the best current methods and evaluated the performance differences between the proposed model and other methods. The result is shown in Figure 7. As can be seen from the figure, the proposed model has the best performance on the ToN_IoT dataset, outperforming existing models in terms of accuracy, precision, recall, and F1-score. Specifically, the proposed model achieved an increase in accuracy from 99.20% with DnRaNN to 99.55%, an increase of 0.35%. Compared to XSRU-IoMT, the accuracy increased from 99.38% to 99.55%, which is an increase of 0.17%. Additionally, the accuracy improved from 99.43% with Densely-ResNet to 99.55%, an increase of 0.12%. These results verify the validity of our proposed model.  
In addition, we compared the generalization ability of the model with several deep learning methods on the UNSW-NB15 dataset, as shown in Table 4. Our proposed LSTM-ResNet model achieved accuracy, precision, recall, and F1-score values of 89.23%, 88.83%, 87.77%, and 88.25%, respectively. This represents a significant improvement in performance metrics compared to other models. For instance, the accuracy of our model is 15.3% higher than that of the Densely-ResNet model, which is the second-best performing model in our comparison.  
The severe class imbalance problem in the UNSW-NB15 benchmark often leads to poor model generalization performance. However, our proposed model demonstrates robust performance despite this challenge. As shown in Table 4, the LSTM-ResNet model  
Figure 7: Performance comparison with other models.  
not only achieves the highest accuracy but also excels in precision, recall, and F1-score compared to LSTM, LuNet, Densely-ResNet, and CNN models. This indicates that our model has superior generalization capability and can effectively handle imbalanced data. These results highlight the robustness and reliability of our method in diverse scenarios, confirming its effectiveness in real-world applications.  
Moreover, the attention mechanism incorporated in our model helps it focus on important features, further enhancing its performance and robustness. This makes the LSTM-ResNet model particularly effective in identifying and classifying network intrusions, as evidenced by its leading performance across multiple evaluation metrics.
## 3 Testing Methodology
### Static Proof Test  
The load tests were planned to achieve the maximum design bending moments for the characteristic combination (SLS) in the most stressed cross-sections of the deck. In detail, together with the unloaded condition (U), two load configurations (C1 and C2) were adopted in order to get the maximum positive bending moments in the mid and lateral spans (Figure 3). Each configuration foresees the use of 12 fully loaded trucks that are distributed over three rows; each truck has a medium weight of about 42 t and 4 of the middle trucks of the first 2 rows (highlighted in blue in Figure 3), were also loaded with 2 additional concrete blocks per truck having dimensions of \(1\times 1\times 1\) m (corresponding to a load increment of about 5 t per truck). The static loads were applied through a stepped procedure in order to control the evolution of the bridge response, and to check any possible deviations from the linear behavior. The adopted loading phases (L#) are reported in Table 1.  
During the different phases of the load tests, the following quantities were measured (Figure 4):  
* Deflections of the main girders in 18 points (P1-P18 defined in Figure 4a) that were placed along the whole length of the girders (9 points per girder), through 2 Trimble Total Stations 5600 Dr 200+ series that were positioned on the upstream and downstream side, respectively, in proximity of the south abutment;
* The axial load of the stay-cable strands, through a mono-strand load cell (maximum measurable load equal to 150 kN, Figure 4d) that was permanently mounted on a strand of each stay, and a portable analog load reader. The stays were labelled with letter D for the downstream side and U for the upstream side, and they were numbered from south to north (as reported in Figure 4b);  
Figure 4: Static proof test instrumentation and sensor arrays: (**a**) layout of measurement points for girder deflections, support displacements and temperature, (**b**) nomenclature of the monitored stays, (**c**) bearing and isolator measurement layout for the horizontal displacements, (**d**) mono-strand load cell, (**e**) laser scanner, (**f**) digital gauges on Au, and (**g**) k-type thermocouples with NI 9219 acquisition module.  
* Spatial displacements of the pylons, through a Leica P40 Laser scanner that was placed in a fixed position in the right bank on the upstream side (Figure 4e);
* Horizontal displacements of the 2 north abutment bearings (Au for the upstream side and Ad for the downstream side girder) and of the isolator (I) that was placed in correspondence of the north pylons, through 6 Mitutoyo digital gauges model ID-C1050B (2 per support), measuring in the longitudinal (Y) and transverse (X) directions of the deck (Figure 4c,f);
* The air and road surface temperature, through k-type thermocouples that were connected by cables to a 4-channel NI 9219 acquisition module (Figure 4g).
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 3 Design of the Tag Antenna
### Structure of the Tag Antenna  
Using these considerations, the photograph and the final structure of the tag antenna are presented in Figure 2a,b, respectively. The tag consists of two symmetrical capacitive pads with two etched meandered substrate arms. Both pads are separated at the center by means of two different gaps \(s\) and \(g\), which are placed at the left and right sides, respectively. The IC chip (NXP UCODE G2 XL) is mounted across the gap \(s\) between the two pads, while the gap \(g\) is used to introduce a capacitive behavior to the tag antenna. The structure of the dipole antenna is meandered to achieve a compact design. Due to the good flexibility of the Kapton polyimide, the tag antenna can be easily conformed to the curved shape of the blood tube as shown in Figure 2c.