esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Shear Walls
#### 7.4.1 Steel Shear Walls  
The performance of steel shear walls under post-earthquake fire (PEF) conditions is notably influenced by fire-induced degradation. A significant finding from an ABAQUS model analysis of a steel plate shear wall (SPSW) was the rapid reduction in lateral stiffness as the fire duration increased. This degradation is primarily attributed to the weakening of steel at high temperatures, with the model indicating a 50% reduction in stiffness after 35 min of fire exposure and a near-total loss of rigidity after 40 min [94]. Additionally, the study highlighted the dynamics of out-of-plane displacement (OOPD) in the infill plate, a critical component of SPSWs. Initially, the OOPD increased due to the thermal expansion of the steel plate, but as the fire continued, the OOPD decreased, likely due to the degradation of steel's mechanical properties at elevated temperatures, rendering the plate more susceptible to deformation under its own weight and any applied loads. The study also underscored the importance of material selection in enhancing fire resistance. Specifically, the use of higher-grade steel, such as S275, in SPSWs significantly delayed the loss of rigidity compared to lower-grade steel. This finding highlights the potential benefits of selecting high-performance materials to improve the resilience of steel shear walls in PEF scenarios.
## 3 Frequency Domain Methods of Calculation
### Irregularities of the Track Support and Track Filtering  
#### 3.4.1 Geometric Irregularities of the Track Support  
At first, the track is excited by a harmonic irregularity of the soil surface:  
\[\mathbf{s_{S}}(\xi)=s_{S}e^{i\xi\chi}\mathbf{e_{n}}. \tag{9}\]  
The response \(\mathbf{u}\) to this excitation is also a harmonic function, which can be calculated in wavenumber domain according to [24]:  
\[\Big{(}\mathbf{EI}_{\xi}^{\star 4}+\mathbf{K}_{T0}(\xi)\Big{)}\mathbf{u}+\mathbf{K}_{S 0}(\mathbf{u}(\xi)-\mathbf{s_{S}}(\xi))=0 \tag{10}\]  
\(\mathbf{K_{T0}}\) is the static support stiffness inside the track beams and \(\mathbf{K_{S0}}\) is the static support stiffness under the track beams. No external force is present and only the difference \(\mathbf{u}\)-\(\mathbf{s_{S}}\) yields a force \(\mathbf{K_{S}}\) (\(\mathbf{u}\)-\(\mathbf{s_{S}}\)) between the track and the soil. The solution is:  
\[\begin{split}\mathbf{u}(\xi)&=\big{(}\mathbf{EI}_{ \xi}^{\star 4}+\mathbf{K}_{T0}(\xi)+\mathbf{K}_{S0}(\xi)\big{)}^{-1}\mathbf{K}_{S0}( \xi)\mathbf{s_{S}}(\xi)\\ \mathbf{u}(\xi)&=\big{(}\mathbf{EI}_{\xi}^{\star 4}+ \mathbf{K}_{T0}(\xi)\big{)}^{-1}\mathbf{e_{n}}k_{S0}(\xi)s_{S},\end{split} \tag{11}\]  
and the effective track irregularity \(s_{R}\) at the rail level is:  
\[s_{R}(\xi)=\mathbf{e_{1}}^{T}\Big{(}\mathbf{EI}_{\xi}^{\star 4}+\mathbf{K}_{T \mathbf{S}0}(\xi)\Big{)}^{-1}\mathbf{e_{n}}k_{S0}(\xi)s_{S}=H_{sT}(\omega= \xi v_{T})s_{S} \tag{12}\]  
This equation holds in the wavenumber domain and also in the frequency domain if the relation \(\omega=\xi v_{T}\) with train speed \(v_{T}\) is used.
# Comparative In Vitro Analysis of Composite Resins Used in Clear Aligner Attachments
###### Abstract  
**Background:** Attachments are essential components in clear aligner therapy, enhancing retention and improving the predictability of tooth movements. Mechanical and wear properties of the composite resins used for attachment reproduction are critical to maintaining their integrity and shape over time. This study aimed to evaluate and compare the mechanical properties, thermal behavior, and wear performance of the hybrid composite Aligner Connect (AC) and the flowable resin (Connect Flow, CF). **Methods:** Twenty samples (ten AC and ten CF) were reproduced. All specimens underwent differential scanning calorimetry (DSC), combustion analysis, flat instrumented indentation, compression stress relaxation tests, and tribological analysis. A 3D wear profile reconstruction was performed to assess wear surfaces. **Results:** DSC and combustion analyses revealed distinct thermal transitions, with CF showing significantly lower Tg values (103.8 \({}^{\circ}\)C/81.4 \({}^{\circ}\)C) than AC (110.8 \({}^{\circ}\)C/89.6 \({}^{\circ}\)C) and lower residual mass after combustion (23% vs. 61%), reflecting reduced filler content and greater polymer mobility. AC exhibited superior mechanical properties, with higher maximum load (585.9 \(\pm\) 22.36 N) and elastic modulus (231.5 \(\pm\) 9.1 MPa) than CF (290.2 \(\pm\) 5.52 N; 156 \(\pm\) 10.5 MPa). Stress relaxation decrease was less pronounced in AC (18 \(\pm\) 4%) than in CF (20 \(\pm\) 4%). AC also showed a significantly higher friction coefficient (0.62 \(\pm\) 0.060) than CF (0.55 \(\pm\) 0.095), along with greater wear volume (0.012 \(\pm\) 0.0055 mm\({}^{3}\) vs. 0.0070 \(\pm\) 0.0083 mm\({}^{3}\)) and maximum depth (36.88 \(\pm\) 3.642 \(\upmu\)m vs. 17.91 \(\pm\) 3.387 \(\upmu\)m). Surface roughness before wear was higher for AC (Ra, 0.577 \(\pm\) 0.035 \(\upmu\)m; Rt, 4.369 \(\pm\) 0.521 \(\upmu\)m) than for CF (Ra, 0.337 \(\pm\) 0.070 \(\upmu\)m; Rt, 2.862 \(\pm\) 0.549 \(\upmu\)m). After wear tests, roughness values converged (Ra, 0.247 \(\pm\) 0.036 \(\upmu\)m for AC; Ra, 0.236 \(\pm\) 0.019 \(\upmu\)m for CF) indicating smoothened and similar surfaces for both composites. **Conclusions:** The hybrid nanocomposite demonstrated greater properties in terms of stiffness, load-bearing capacity, and structural integrity when compared with flowable resin. Its use may ensure more durable attachment integrity and improved aligner-tooth interface performance over time.  
**Keywords:** nanocomposite resins; clear aligner treatment; attachments; tribological analysis; wear of polymers
## 3 Experimental Verification  
To verify the simulation results, radio link path loss (signal attenuation between the antennas) for the 2.2-2.8 GHz frequency range was measured for a scenario similar to that in simulations scenarios. The measurements were made in an empty room with geometry and dimensions similar to that used in computer simulations. The side walls were made of gypsum and the floor and ceiling of steel reinforced concrete. The measurement setup is presented in Figure 5.  
Figure 4: Simulated signal attenuation _vs._ frequency: A pair of antennas located on the chest and on an arm (1–3) simulated for different propagation environments comprising gypsum walls.  
Figure 5: Path loss experimental setup in an empty office room with a human subject: (**a**) human subject with antennas; (**b**) setup including ferrite beads on coaxial cables to reduce the influence of the cable outer conductor radiation.  
Two half-wave dipole antennas were placed on the body of the human subject in positions corresponding to the simulation scenarios. The antennas were dipoles shortened for resonance at 2.45 GHz. Rohde Schwarz ZVB-14 vector network analyzer was used to measure the signal attenuation between antennas _vs._ frequency. The ferrite beads were fitted on the measurement cables to reduce radiation from the cables that might decrease the isolation between the antennas and modify their radiation pattern. The distance between the ferrites was a quarter of the wavelength for the center of the measurement band, \(D=30\) mm (see Figure 5b).  
In Figure 6 the results obtained from measurements and simulations of signal attenuation between antennas located on chest and on the back (position 1-2 in Figure 1) are presented. In this case the  
Although the location of attenuation nulls differs between simulations and measurements, the median values exhibit good agreement. Differences between simulation and measurement attenuation curves shape can be attributed to an imprecise room geometry representation in computer models, only approximated values of wall material electromagnetic properties, as well as inhomogeneity of the real building walls and ceilings.
## 4 Applications
### Finger Bending Detection  
The fabric-based strain sensor was first applied to monitor finger flexion experiencing large deformations. After attaching the sensor to the proximal interphalangeal (PIP) joint of the index finger at its straight state, the relative changes in resistance for three consecutive finger bendings (0-45\({}^{\circ}\), 0-90\({}^{\circ}\), and 0-135\({}^{\circ}\)) were measured as shown in Figure 7a. It can be seen the relative change in resistance increases as the finger bending increases. The larger the finger bending, the greater the relative change in resistance. Furthermore, when the finger returns to be straight, the relative change in resistance recovers to be about zero.
## 5 Results and Analysis
### Discrete Wavelet Transform (DWT)-Based
It is evident from Table 6 that WDNN model with rank 48 in both training and testing (having MAE = 2.65, MAPE = 13.75, NSE = 0.98, R2 = 0.98, RMSE = 4.26, VAF = 98.36 in training and MAE = 3.96, MAPE = 17.21, NSE = 0.98, R2 = 0.97, RMSE = 6.65, VAF = 97.20 in testing) achieved better results as compared to WEO-ELM model with rank 40 in both training and testing (having MAE = 3.88, MAPE = 21.58, NSE = 0.96, R2 = 0.96, RMSE = 6.52, VAF = 95.99 in training and MAE = 5.12, MAPE = 25.49, NSE = 0.97, R2 = 0.96, RMSE = 8.44, VAF = 95.67 in testing). Figures 17 and 18 exhibit better prediction performance WDNN in train and test case respectively compared to other models with their linear equations for predicted and measured values. Interestingly, the EO algorithm adequately optimized ELM parameters than the PSO algorithm and enhanced the performance of ELM sufficiently compared to PSO. The Taylor plot (Figure 19a,b) confirms the better prediction performance of WDNN followed by WEO-ELM in terms of statistical comparison of all models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **MAE** & **MAPE** & **NSE** & **R2** & **RMSE** & **VAF** & **TOTAL** \\ \hline \multirow{4}{*}{WEO-ELM} & Train & 3.88 & 21.58 & 0.96 & 0.96 & 6.52 & 95.99 & \\ \cline{2-8}  & Rank & 6 & 6 & 7 & 7 & 7 & 7 & 40 \\ \cline{2-8}  & Test & 5.12 & 25.49 & 0.97 & 0.96 & 8.44 & 95.67 & \\ \cline{2-8}  & Rank & 6 & 6 & 7 & 7 & 7 & 7 & 40 \\ \hline \multirow{4}{*}{WDNN} & Train & 2.65 & 13.75 & 0.98 & 0.98 & 4.26 & 98.36 & \\ \cline{2-8}  & Rank & 8 & 8 & 8 & 8 & 8 & 8 & 48 \\ \cline{2-8}  & Test & 3.96 & 17.21 & 0.98 & 0.97 & 6.65 & 97.20 & \\ \cline{2-8}  & Rank & 8 & 8 & 8 & 8 & 8 & 8 & 48 \\ \hline \multirow{4}{*}{WSVR} & Train & 7.22 & 25.30 & 0.67 & 0.75 & 18.90 & 67.59 & \\ \cline{2-8}  & Rank & 3 & 5 & 1 & 3 & 1 & 1 & 14 \\ \cline{2-8}  & Test & 17.69 & 99.68 & 0.65 & 0.70 & 28.05 & 49.98 & \\ \cline{2-8}  & Rank & 1 & 1 & 1 & 3 & 1 & 1 & 8 \\ \hline \multirow{4}{*}{WANN} & Train & 6.21 & 38.07 & 0.93 & 0.93 & 8.60 & 93.13 & \\ \cline{2-8}  & Rank & 4 & 3 & 4 & 4 & 4 & 4 & 23 \\ \cline{2-8}  & Test & 6.94 & 41.07 & 0.95 & 0.94 & 10.39 & 93.14 & \\ \cline{2-8}  & Rank & 5 & 4 & 6 & 6 & 6 & 5 & 32 \\ \hline \multirow{4}{*}{WGBM} & Train & 12.84 & 99.42 & 0.71 & 0.72 & 17.66 & 71.94 & \\ \cline{2-8}  & Rank & 1 & 1 & 2 & 1 & 2 & 2 & 9 \\ \cline{2-8}  & Test & 15.78 & 83.81 & 0.76 & 0.72 & 23.24 & 65.86 & \\ \cline{2-8}  & Rank & 3 & 2 & 3 & 3 & 3 & 3 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 17: Prediction capability of the eight models in train case (**a**) WELM, (**b**) WKELM, (**c**) WPSO-ELM, (**d**) WEO-ELM, (**e**) WDNN, (**f**) WSVR, (**g**) WANN, and (**h**) WGBM for the catchment Teifi at Glanteifi.
## 4 Performance Evaluation
### Performance Analysis  
Using the analytical expressions given by Equations (21)-(23) and varying the number of buildings L, as well as the 3D spatial reuse factor \(\varepsilon_{\text{RF}}\) given by Equation (10), the impact of applying the proposed technique for 3D spatial reuse of 28 and 26 GHz mmWave spectra to in-building small cells for each building in terms of average SE and average EE is discussed in the following.
## 2 Carotenoids
### Carotenoid Synthesis Gene Cluster in Marine Microorganisms  
Numerous genes are involved in carotenoids synthesis and interact with one another, forming a gene cluster. The caroten and other isoprenoid precursors, such as isopentenyl pyrophosphate (IPP) and its isomer dimethylallyl pyrophosphate (DMAPP), are formed through the mevalonate and non-mevalonate pathways [34; 35]. The IPP isomerase, which is encoded by the _idi_ gene, assists in switching between IPP and DMAPP. Other genes that play a role are _crtE_, which encodes GGPP synthase, _crtB_, which encodes phytoene synthase, _crtI_, which encodes phytoene desaturase, _crtY_, which encodes lycopene \(\beta\)-monocyclase, _crtW_, which encodes \(\beta\)-carotene ketolase, _crtX_, which encodes for zeaxanthin glycolases, _crtZ_, which encodes \(\beta\)-caroenoid hydroxylase, _crtZ_, which encodes desaturase, _crtQ_, which encodes \(\zeta\)-carotene desaturase, _crtQ_, which encodes \(\beta\)-carotene ketolase, _crtG_, which encodes astaxanthin hydroxylase, and _crtC_, _crtD_, and _crtF_, which play a role in spirilloxanthin biosynthesis [50; 51; 36].  
Gene clusters that play a role in the synthesis of carotenoids vary in each bacterial species, resulting in different carotenoid products. Several gene clusters involved in the biosynthesis of carotenoids in marine bacteria are highlighted in this review article (Figure 4). In the bacteria _Brevundimonas_ sp. strain SD212, 2-hydroxyastaxanthin is synthesized with gene clusters, including _crtW_, _crtY_, _crtI_, _crtB_, _crtE_, _idi_, and _crtZ_[52]. [PERSON] et al. [37] demonstrated that a gene cluster composed of _crtE_, _crtB_, _crtI_, _crtY_, _crtW_ + _crtZ_, and _crtG_ from the bacterium _Brevundimonas scallop_ isolated from the noble scallop _Chlamys nobilis_ produces the final product, 2,2'-dihydroxy-astaxanthin. Meanwhile, in the marine bacterium _Agrobacteriumurium aurantiacum_ that produces astaxanthin, the gene clusters that play a role include _crtW_, _crtZ_, _crtY_, _crtI_, and _crtB_[53; 54]. There are some differences in the structure of the gene clusters of the three bacteria that produce the carotenoid astaxanthin. Gram-negative bacteria _Paracoccus zaxanthininificiens_ previously identified as _Flavobacterium_ sp. strain R1534 can synthesize zeaxanthin with carotenoid gene clusters found in its genome, including the _crtE_, _crtB_, _crtY_, _crtI_, and _crtZ_ genes [55]. Meanwhile, the carotenoid gene cluster found in _Brevibacterium linens_ consists of _crtB_, _crtI_, _crtK_, _crtU_, _crtY_, _crtY_, _crtE_,and _idi_ to produce the final product, \(\beta\)-carotene derivative in the form of isorenerietene [56]. Flexixanthin, synthesized by _Algoriphagus_ sp., cannot be separated from the role of the gene cluster consisting of _ctrl_, _crtB_, _crtY_, and _crtW_[57].
# Antioxidant Production in _Dunaliella_
## 4 Tuning Antioxidant Enzyme Activity in _Dunaliella_
### Metals  
Heavy metals can induce oxidative stress in microalgae, by generating ROS by auto-oxidation, blocking essential functional groups in biomolecules, or substituting essential metal ions [139]. When _D. tertiolecta_ was cultured with silver nanoparticle (AgNPs), CAT, SOD, and POD activity increased initially and then decreased over time [140], which indicate that antioxidant enzymes act as a first line of defence to protect the cells by mitigating metal-induced oxidative stress. Nonetheless, antioxidant enzyme activity could not eliminate the metal toxicity during prolonged exposure of cells [140].  
Short-term exposure of Hg\({}^{2+}\) increased APX activity in _D. tertiolecta_ compared to unstressed cells. Hg\({}^{2+}\) binds to a sulfhydryl group and disturbs the protein functions leading to stress conditions [141]. In contrast, long-term exposure to Hg\({}^{2+}\) increased \(\beta\)-carotenoid content in _Dunaliella_ cells. \(\beta\)-carotenoid acts as a supportive antioxidant. Pre-treatment of _Dunaliella_ with Zn followed by cultivation in the presence of ROS generating agents (e.g., H\({}_{2}\)O\({}_{2}\), paraquat) was found to decrease carotenoids level, CAT and APX activities [142], as cells were unable to enhance antioxidants levels. Moreover, the enhanced growth rate indicated that heavy metal toxicity in Zn-treated cells was detoxified by other antioxidants systems (either chelation reactions (metal to PC) or by displacements of Zn from a PC-Zn complex).  
Small quantities of Cu\({}^{2+}\) are essential for growing cells. During cultivation with Cu\({}^{2+}\) (\(>\)5 \(\upmu\)M CuCl\({}_{2}\)) oxidative stress was induced in _Dunaliella_ cells [143]. _D. salina_ and _D. tertiolecta_ exposed to Cu\({}^{2+}\) in the growth medium had enhanced APX activity. A higher enzymatic activity was measured in _D. tertiolecta_ compared to _D. salina_, which could be due to the generation of lower amounts of carotenoid in _D. tertiolecta_[143]. Metals such as cobalt (Co), copper (Cu), chromium (Cr), iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), selenium (Se), and zinc (Zn) are also essential micronutrients required for various biochemical and physiological functions during microalgae growth [144]. However, non-essential heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg) are systematic toxicants [145]. Epidemiological and experimental studies have shown that these metals have adverse effects on health in humans, including cardiovascular diseases, developmental abnormalities, neurologic and neurobehavioral disorders, diabetes, hearing loss, hematologic and immunologic disorders, and various types of cancer, an association between exposure and cancer incidence in humans and animals [146]. Using heavy metals enriched biomass in the food industry or as a feed in the aquaculture industry is not a viable strategy to induce stress in algae cultures.
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System
### Resource Sensing: Observe  
In order to enable effective JCMP, the prediction on the achievable communication quality is vital, which relies on the associated wireless channel measurement in the actual environment. We consider a realistic communication channel model, including both the path loss and the multipath fading.  
As the wireless channel measurement is heavily reliant on the actual environment, in order to emphasize the JCMP procedure, let us now consider a universally adopted narrow-band block-fading channel model, where the fading coefficients remain constant  
Figure 2: The OODA-based scheme for both conventional relay-assisted robot system (Section 2) the cognitive-relay-assisted robot system (Section 3).  
for the duration of a transmission packet and then they are faded independently from one packet to another over the time dimension. We assume the wireless fading channel is Rayleigh distributed.  
The channel bandwidth is \(B\), and \(N_{0}\) is the additive white Gaussian noise (AWGN) power spectral density. Given the transmit power \(P_{i}\), the distance \(d_{i,j}=\|x_{i}-x_{j}\|\) between the transmitter \(i\) and the receiver \(j\) and the pathloss exponent \(\beta\). The instantaneous received signal-to-noise power ratio (SNR) is expressed as \(\gamma_{ij}=\frac{p_{d_{ij}^{-\beta}}|h_{ij}|^{2}}{N_{0}B}\), where the instantaneous channel coefficient \(h_{ij}\) captures the small-scale fading effect of the channel spanning from the transmitter \(i\) to the receiver \(j\). In this paper, we assume that each robot is capable of measuring the instantaneous received SNR.
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 5 Discussion, Limitations, and Future Research Trends
### Limitations  
However, despite these advancements, the current state of research reveals several challenges and limitations that need addressing to push the field further.  
1. Dataset Diversity and Representation  
While datasets like Cityscapes and NuScenes provide extensive imagery and annotations, they predominantly focus on urban environments. This limits the ability of AI models trained on these datasets to generalize across varied geographical and environmental conditions, such as rural roads, adverse weather conditions, and nighttime driving. Datasets that encompass a broader spectrum of driving scenarios and conditions are needed to develop more versatile and robust AV systems.  
2. Multi-Sensor Fusion Limitations  
Many of the high-performing models rely heavily on multi-sensor data (e.g., cameras, LiDAR, and radar). Ensuring seamless and efficient sensor fusion remains challenging, particularly in terms of data synchronization, calibration, and dealing with inconsistencies across sensor modalities. Further, the cost and complexity of deploying multi-sensor systems can be prohibitive for their widespread adoption.  
3. Real-time processing  
Achieving high processing speeds without compromising detection and classification accuracy is a challenging trade-off. This becomes critical in real-time applications where latency must be minimized to ensure safety and reliability.  
4. Biases in AI models  
Models may inadvertently incorporate biases from training datasets, resulting in systemic prejudices that can affect decision making. Therefore, identifying and mitigating these biases remains a significant challenge.  
5. Adaptability to Environmental Variability  
Autonomous systems must improve their capacity to operate reliably under a wide range of environmental conditions, including adverse weather, poor lighting, and complex urban landscapes. Advanced models need to further refine their ability to dynamically adapt to such conditions.
## 4 Classification of Auto-Configuration Protocols
### Stateful Protocols
#### 4.1.5 Emap  
_Extensible Manet Auto-configuration Protocol_ (EMAP) [15] is an auto-configuration protocol based on the idea of a protocol of REQUEST/REPLAY messages. The main advantage of this protocol is the possibility of doing it extensibly, _i.e._, it can include new functionalities in the future that are analyzed in a theoretical way, such as _Domain Name Server_ (DNS).  
This protocol also considers the possibility of exterior communications to the mobile _ad hoc_ network via Internet. The route discovery mechanism among nodes is similar to the _Ad Hoc On-Demand Distance Vector_ (AODV) [16] protocol.  
The main idea of this auto-configuration protocol consists in having three different addresses for a non-configured node that wants to join the created network. These are three addresses for the interior communications: _temporary address_, _tentative address_ and _mobile_ ad hoc _network local address_.  
When a node wishes join the network, it randomly generates two valid IP addresses (with network known addresses), considering them as temporary and tentative addresses. These IP addresses are encapsulated into a _Detection Adress Detection RESponse_ (DAD_REP) message to know whether it is a valid address. The node keeps waiting for a _Detection Address Detection REQuest_ (DAD_REQ) message. If time runs out for this message, the node assumes that it can use its tentative address as unique, and assigns it to its network interface. If this node receives a DAD_REP message to its temporary address and this message contains the source with the tentative address that had been proposed, the node knows that this tentative address is being used and the previous process begins creating another pair of addresses again.