esa-sceva/satcom-chunk-collection huggingface dataset:
# Space Object and Background Polarization Models and Detectability Analysis
## 5 Conclusions  
In this paper, based on the traditional BRDF model, considering that the normal of the rough surface and the normal of the microsurface element do not coincide, a microsurface element BRDF model considering the masking function is established, and a pBRDF model is further established. Aluminum and black paint are selected, and the physical parameters in the model are verified by genetic algorithm inversion, and it is found that the model in this paper is closer to the real value than the reference literature model. The astronomical coordinate system and the geodetic coordinate system are introduced to study the sky background polarization distribution characteristics, and the space object polarization angle information is derived according to the Rayleigh scattering law. A complex spherical projection is used to project the background polarization from 3-D to 2-D, which has less aberration. The target-background contrast values of the original intensity and the polarization intensity are obtained by selecting the zenith angles of 10\({}^{\circ}\) and 60\({}^{\circ}\) and their azimuth angles, respectively. The polarization method is found to be significantly higher than the original intensity method.  
Data curation, H.C.; Formal analysis, H.C. and Q.F.; Funding acquisition, Q.F. and Y.L.; Methodology, Q.F.; Project administration, Q.F.; Resources, Q.F. and F.Z.; Supervision, Q.F.; Writing--original draft, H.C.; Writing--review and editing, F.Z. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Changchun University of Science and Technology, China. [PERSON], National Natural Science Foundation of China, grant number 61890963.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Meso-Scale Simulation of Concrete Based on Fracture and Interaction Behavior
## 2 Finite Element Modeling Method
### Mortar Particle and Aggregate Material Model  
The linear elastic model is adopted for modeling the behavior of the mortar particles and aggregates. The mortar particles and aggregates are modeled by the six-node linear triangular prism (C3D6) elements. Although the mortar particles and aggregates are assumed to be isotropic in the two-dimensional plane, they need to be defined as anisotropic to eliminate the Poisson effect in the direction normal to the plane. The Poisson ratios normal to the plane are set to zero.  
Referring to previous material test results, different elastic moduli are set to the mortar particles and aggregates to simulate the crack propagation caused by local deformation difference. The macroscopic modulus (\(E_{m}\)) of specimens can be estimated by the following formula:  
\[E_{m}=\frac{(E_{a}/P_{a})\cdot(E_{m}/P_{m})}{E_{a}/P_{a}+E_{m}/P_{m}} \tag{1}\]  
where \(E_{a}\) and \(E_{m}\) are the moduli of aggregates and mortar, and \(P_{a}\) and \(P_{m}\) are the volume proportions of aggregates and mortar. The formula is obtained through a short derivation. In the derivation, it is assumed that mortar and aggregates are connected in series. Based on the volume proportions and moduli, Equation (1) was derived.  
Figure 1: Element mesh of model: (**a**) bulk elements of aggregate (grey colored) and mortar particle (white colored); (**b**) cohesive elements of ITZs (interfacial transitional zones, red colored) and mortar internal interface (blue coloured).
# Advances and Challenges in the Creation of Porous Metal Phosphonates
## 3 Applications
### Separation and Extraction
This study showed that the distribution of unreacted phosphonic acid moieties was one of the important criteria for improved cation binding to the hybrid framework [108]. In another study, porous adsorbents based on zirconium phosphonate have been prepared hydrothermally with ZrCl\({}_{4}\)/ZrOR\({}_{4}\) and ATMP. These hybrid sorbents were evaluated for competitive lanthanide separation from a mixture of cations. All zirconium phosphonate hybrids showed a selective uptake of lanthanides over Cs\({}^{+}\), Sr\({}^{2+}\) and Co\({}^{2+}\). Among the lanthanides, Eu\({}^{3+}\) was the most strongly sorbed lanthanide across all the hybrids. Interestingly, the amorphous hybrids in this study also displayed a higher uptake compared to the crystalline hybrid materials. The presence of fluoride ions  
Figure 8: Extraction of cations of different ionic radius by poorly crystalline ZrBTP-0.8, ZrBTP-0.7, ZrBTP-0.6, ZrBTP-0.5, prepared in the absence of HF from mixed cation solutions with each cation at nominal 20 mg/L in 0.10 M HNO\({}_{3}\). Reprinted with permission from [108]. Copyright (2016), American Chemical Society. had a detrimental effect on the uptake of lanthanide ions resulting in the lower capacity presented by the crystalline hybrids. Finally, the choice of metal precursor was evaluated, the zirconium chloride-based hybrids led to materials that displayed better lanthanides (>99% extraction of La\({}^{3+}\), Nd\({}^{3+}\), Eu\({}^{3+}\), Ho\({}^{3+}\) and Yb\({}^{3+}\)) uptake than the materials prepared from zirconium isopropoxide (\(\sim\)40-70% extraction of La\({}^{3+}\), Nd\({}^{3+}\), Eu\({}^{3+}\), Ho\({}^{3+}\) and Yb\({}^{3+}\)). However, the zirconium isopropoxide-based hybrids showed more selectivity among the lanthanides over Cs\({}^{+}\) and Sr\({}^{2+}\). The changes in sorption properties were attributed to the differences in the incorporation of the phosphonate moiety (ATMP) into the hybrid network [109].
## 4 Conclusions  
OMT and a binary flame-retardant system based on Exolit were used in this paper to flame- retard reABS, and a high flame-retardant level was reached.  
Figure 7: 3D analysis with regard to OMT and FR influence on the burning time.  
Fundamentally, nine ABS flame retardant specimens were developed and the experimental study clearly showed improvements in terms of flame retardancy, such as reduction in peak heat release rate and total heat released compared with the pure reABS. Nevertheless, all formulations showed slightly lower values of time to ignition.  
Based on flammability and fire tests of the ABS phosphorus flame retardant specimens, evidence of an important flame retardancy enhancement was noted when both flame retardants and montmorillonite were present in ABS. A combined gas and condensed-phase mode of action was observed during cone calorimeter tests, that led to the higher reduction of PHRR values, in good agreement with the obtained V0 classification under the UL-94 standard. In this context, a correlation was proposed to estimate the total burning time of a reABS-OMT-FR polymer nanocomposite. Finally, the cone calorimeter results revealed that both FR and OMT acted via flame inhibition and a protective flame-retardancy mechanism in the developed polymer nanocomposites.  
To conclude, it can be affirmed that the developed and tested materials may constitute a viable alternative to common ABS (i.e., an opaque thermoplastic and amorphous polymer with many real life applications) for the manufacture of components for the automobile industry, as well as construction materials or other parts of domestic equipment (i.e., electronics, cases, toys etc.). The use of improved fire-resistant materials is very important, especially when a serious fire scenario may be involved. Nevertheless, more research is needed to establish the best and cheapest FR-OMT-ABS ratio to attain both economic advantages and an increased security in a fire incident.  
[PERSON] and [PERSON] conceived and designed the study, [PERSON] conducted the experiments and [PERSON] and [PERSON] analyzed the results. [PERSON] participated in the design and helped to draft the manuscript. [PERSON] and [PERSON] wrote the paper. All authors read and approved the final manuscript.  
This research received no external funding.  
Authors declare no conflict of interest.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 4 Proposed Observer and Control Structure  
According to the control structure of the GFL-inverter, the grid-side currents, the grid voltages and the DC-link voltage are required for control of the inverter. By analyzing the system dynamic equation of (3), it can be seen that there is a coupling between \(W_{c}\), \(i_{d}\), and \(i_{q}\). Then, theoretically, it is possible to estimate the grid currents (\(i_{d}\), and \(i_{q}\)) by measuring the DC-link voltage. This is the key idea of this paper in developing an observer for the grid-side current estimation. In this respect, a well-adapted Luenberger observer is used in this paper, as the system is observable. In the following subsection, the fundamentals of the Luenberger observer are presented.
## 3 Proposed Solution to Improve Cooling Efficiency
### Cooling with Insufflation Downwards (DownFlow)  
The DownFlow insufflation is characterized by the process of collecting the hot air generated by the data center equipment through the air inlets located at the top of the CRAC. Then, after the cooling treatment of this collected hot air, the air is returned to the environment through insufflation to the confined space below the raised floor (plenum). Due to the natural pressure created in the environment, the cold air is pushed upwards through perforated floor plates or ventilation diffusers designed for this function. Therefore, in the environment above the raised floor, cold air is available for the servers' internal cooling fans and other equipment installed inside the racks. Once the equipment is cooled, the equipment's exhaust systems return a warm air current that rises to the environment and is again captured by the upper inlets of the CRAC, starting a new cooling cycle. The steps of the DownFlow type inflation are shown in Figure 1.  
We already used this cooling method in the old data center, entirely using 03 CRAC units (model Stultz ASD 1072 A, 30 TR), specifically prepared for this air conditioning method. However, due to the old building structural restrictions, it was only possible to install the raised floor with a height of 40 cm, which was enough to fit the passage of data and power cables. As a result, since the previous solution had three CRAC units, this allowed us to reduce the costs of adapting the new solution in the new data center and reuse the equipment, leading to savings in training costs and changes in the maintenance process.  
As a factor of improving and optimizing the system, we decided to adopt 70 cm of height, with an increase of 75% in the floor elevation, taking into account the statement by IBM [49] that \"the highest raised floor allows for a better balance of air conditioning in space\". Based on a study by Beitelmal [50], this height value brought us closer to the 76.2-91.4 cm height range, which is considered the best option for raised data center floors. Compared to the old setup, the main benefits are the more uniform airflow rates through the air diffuser grids, more uniform temperature at the top of the racks, and a higher uniformity of static pressure within the plenum.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.9 Comparison Analysis with Recent MAC Protocols
By ensuring efficient use of the communication channel, our protocol can significantly reduce the energy consumption required for retransmissions, thereby prolonging the operational life of the sensor nodes. In the case of UWSNs, energy consumption is a very important issue, because it is difficult to recharge or replace batteries underwater. Our proposed system demonstrates significant improvements in energy efficiency compared to the existing DR-ALOHA-Q and MACA-Q protocols, as evidenced by Figure 25. Our proposed system consistently exhibits the lowest energy consumption across all network sizes, ranging from 5 to 40 nodes. This trend highlights the protocol's ability to efficiently manage communication tasks, minimizing energy wastage. The green line in the graph clearly shows that our system maintains energy consumption well below 1 joule even as the number of nodes increases, whereas both the DR-ALOHA-Q and MACA-Q protocols consume significantly more energy. In particular, MACA-Q has the highest energy consumption among the three protocols, consistently above 1.3 joules, and it only decreases slightly with the increase in network nodes. This indicates inefficiencies in the MACA-Q protocol that lead to higher energy usage, likely due to increased collisions and retransmissions that are common in underwater environments. DR-ALOHA-Q performs better than MACA-Q but still lags behind our proposed system. As the number of nodes increases, DR-ALOHA-Q starts to consume less energy over time; however, it is always more than our proposed protocol. This suggests that while DR-ALOHA-Q attempts to manage energy usage, it cannot achieve the same level of efficiency as our protocol. Figure 24: Channel utilization vs. number of nodes. The superior performance of our proposed system can be attributed to several key factors. Firstly, the protocol effectively reduces collisions and retransmissions, which are major sources of energy drain in UWCNs. By optimizing the scheduling of transmissions and employing more efficient collision avoidance mechanisms, our system ensures that fewer packets need to be resent, conserving energy. Secondly, our protocol incorporates advanced energy management techniques that dynamically adjust transmission power and frequency based on the network conditions. This adaptability ensures that the system operates at optimal energy levels, extending the battery life of the sensor nodes. As a result, our proposed system not only outperforms DR-ALOHA-Q and MACA-Q in terms of channel utilization but also excels in energy efficiency. This makes it a highly suitable choice for UWCNs, where conserving energy is paramount to maintaining long-term network operations and reducing maintenance costs. The significant reduction in energy consumption ensures that our system can support more extensive and prolonged underwater monitoring and communication tasks.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 4 Knowledge Gaps and Challenges
### Troubles of Point Cloud  
The current 3D reconstruction process mainly relies on the point cloud, but there are some inherent shortcomings in point cloud-based 3D reconstruction. First, the point cloud-based method requires extensive computing resources to process huge amounts of data. It is time-consuming to remove all points of the backgrounds and the objects of no interest [50; 62; 70]. Second, there is no guarantee on the completeness of point clouds, and sufficient overlaps among images are required to cover all areas of interest [12; 71]. In large-scale projects, the area of point clouds collected at one time is limited. If the area of one-time acquisition is too large, the accuracy of the point clouds would be relatively low affecting the effect of 3D reconstruction, while reducing the acquisition area would lead to the soared acquisition cost. Third, the point clouds also have problems such as high noise, difficulty in segmentation and registration [46]. Therefore, are there other ways to reconstruct the as-built model without point clouds? Like image recognition. This is still an open challenge that needs further exploration.
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 3 FSO Link Performance
### Link's Reliability Performance Estimation  
The reliability of a link can be estimated by the BER, which expresses the percentage of the erroneous bit from a large bit sequence, received in a given time interval, or, equivalently,the probability of a bit to be erroneously received. The instantaneous BER for an OOK modulation is given as, [41; 42; 43]:  
\[P_{e}(I)=\frac{1}{2}erfc\Bigg{(}\frac{\sqrt{2}\eta I}{4\sqrt{N_{0}}}\Bigg{)} \Leftrightarrow P_{e}(\gamma)=\frac{1}{2}erfc\Bigg{(}\frac{\sqrt{2\gamma}}{4} \Bigg{)}, \tag{14}\]  
while the average BER (ABER) is given as [41; 42; 43]:  
\[\overline{P}_{e}(I)=\int\limits_{I_{\min}}^{BL}P_{e}(I)f_{I}(I)dI\Leftrightarrow \overline{P}_{e}\Big{(}\overline{\gamma}_{j}\Big{)}=\int\limits_{\gamma_{\min} }^{\gamma_{\max}}P_{e}(\gamma)f_{\gamma}(\gamma)d\gamma, \tag{15}\]  
where \(\gamma_{\max}=\overline{\gamma}(BL/E[I])^{2}\).  
Substituting (8) and (14) into (15), the ABER, as a function of average SNR, is given as:  
\[\overline{P}_{e}(\overline{\gamma})=\frac{g_{T}}{4}\int\limits_{\gamma_{\min} }^{\gamma_{\max}}erfc\Bigg{(}\frac{\sqrt{2\gamma}}{4}\Bigg{)}\frac{\exp\left[ -g_{T}\sqrt{\ln\Big{(}LB\sqrt{\overline{\gamma}/\gamma}/E[I]\Big{)}}\right] }{\gamma\sqrt{\ln\Big{(}LB\sqrt{\overline{\gamma}/\gamma}/E[I]\Big{)}}}d \gamma. \tag{16}\]  
Given that \(erfc(x)=2Q\Big{(}\sqrt{2}x\Big{)}\), \(Q(x)\approx\left[\exp\left(-x^{2}/2\right)+3\exp\left(-2x^{2}/3\right)\right]/12\), [44], and once again the \(y_{\gamma}\) RV transformation, (16) is rewritten in the following form:  
\[\overline{P}_{e}(y_{\gamma})=\frac{\delta_{T}}{12}\int\limits_{0}^{h_{T}}\exp (-g_{T}y_{\gamma})\Bigg{\{}\exp\left[-\frac{\overline{\gamma}(LB)^{2}}{8E^{2 }[I]}\exp\left(-2y_{\gamma}^{2}\right)\right]+3\exp\left[-\frac{\overline{ \gamma}(LB)^{2}}{6E^{2}[I]}\exp\left(-2y_{\gamma}^{2}\right)\right]\Bigg{\}}dy _{\gamma}. \tag{17}\]  
Replacing the external exponentials by applying \(\exp(x)=\sum\limits_{k=0}^{+\infty}x^{k}/kl\), the ABER expression is finally obtained:  
\[\overline{P}_{e}(\overline{\gamma})=\frac{\sqrt{\pi/2}g_{T}}{24}\sum\limits_{ k=0}^{+\infty}\frac{\left[-\overline{\gamma}(LB/E[I])^{2}\right]^{k}}{k!\sqrt{k}} \left(\frac{1}{8^{k}}+\frac{3}{6^{k}}\right)\exp\left(\frac{\delta_{T}^{2}}{8 k}\right)\left[erf\left(\frac{g_{T}+4 kl_{T}}{2\sqrt{2k}}\right)-erf\left(\frac{g_{T}}{2 \sqrt{2k}}\right)\right]. \tag{18}\]
# KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation
## 3 Methodology  
### Overview  
Distinct from the perturbation task in image classification, facial UAPs aim to optimize perturbations so that the similarity between adversarial samples and clean samples exceeds a predefined threshold. The corresponding formula is as follows:  
\[\text{Similarity}_{\text{x}\in\text{X}}\{\text{F}(\text{x}+\text{v}),\text{ F}(\text{x})\}<\text{t}, \tag{1}\]  
where X denotes the distribution of images, F\((\cdot)\) is defined as the target feature extractor, which outputs a feature vector F\((\text{x})\) for each input image x, and t denotes the designated threshold for discriminative similarity, while v represents the generated UAP. Our aim was to find a v for almost all data samples x sampled from the distribution X to deceive the neural network, which represents a fixed, image-independent perturbation that significantly alters the feature vectors extracted by the neural network from the original vectors, thus accomplishing the purpose of fooling the face recognition network.  
As an open-set classification task, the interpretability analysis of face recognition focuses on explaining the similarity between embedding vectors rather than predicting categories. Due to the high similarity in appearance among different individuals' faces, the key aspect of face recognition lies in distinguishing the subtle differences within these highly overlapping features. When performing interpretability analysis, researchers focus on the specific impact of different face regions on the embedding vector extraction process. Therefore, in the context of UAP research for face recognition, the key lies in identifying and leveraging common features among faces of different identities in the dataset. The architecture diagram of KRT-FUAP is shown in Figure 2.  
Existing methods for generating universal adversarial perturbations typically rely on directly training in the spatial domain globally. This approach depends entirely on the automatic learning process during training, leading to perturbations that may be somewhat lacking in both attack efficacy and stealth. In our approach, facial key regions are used as prior regions for training, allowing for a more precise identiflocations for embedding perturbations. This study focused on the key regions of the face, as the features of these regions play a crucial role in determining the similarity of the final embedding vectors. We utilized learnable flow field to fine-tune a mask of facial key regions. By finely adjusting the mask, we can generate perturbations with different weights in the spatial domain, ensuring that the perturbations are concentrated in the regions that have the greatest impact on face recognition. To balance the attack efficacy and stealth of the perturbations, we incorporate corresponding adversarial and stealth loss functions, jointly optimizing the final perturbations. This approach not only enhances the effectiveness of the attack but also increases the stealthiness of the perturbations, enabling attacks on face recognition systems without raising suspicion.
## 3 Experimental Results
### Initiation and Expansion Law of the Interlayer Gap  
During the repeated thermal loading, an interlayer gap is generated and expanded with a maximum width of approximately 0.5 mm between the track slab and the CA mortar near the fixed end section with the shear slots, as shown in Figure 9.  
The length of the interlayer gap extends in a \"stepped\" manner with the increase of the number of thermal cycles, as shown in Figure 10. The development process of the gap can be divided into three stages--initiation, expansion and stabilization--according to the expansion law of the gap length. The initiation stage of the gap occupies the first six thermal loading cycles. When the sixth thermal loading is completed, the interlayer gap is observed for the first time between the track slab and the CA mortar near the fixed end of the shear slot with a length of 340 mm, accounting for 21.3% of a single-track slab length. Starting from the 7 th to the 12 th thermal loading cycle, the interlayer gap gradually extended toward the mid-span. When the 12 th thermal loading cycle was completed, the entire track slab near  
Figure 8: Thermal load of the track structure.  
Figure 9: Track slab-CA mortar interlayer gap.  
the fixed end of the beam was completely separated from the CA mortar. The gap length was equal to the length of the single-track slab (1600 mm). From the 13 th to the 18 th thermal loading cycle, no more expansion of the generated gap was observed, and no new gaps were generated. Therefore, the period spanning from the 7 th to the 12 th thermal loading cycle is regarded as the expansion stage of the gap, and that spanning from the 13 th to 18 th thermal loading cycle is regarded as the stabilization stage of the gap.  
During the 18 th thermal loading cycle, the gap only appears in the track slab near the fixed end with the shear slot along the longitudinal direction of the bridge. There is no gap at the sliding end of the beam far away from the shear slots. This shows that the restraint of the shear slots on the free deformation of the track structure is one of the important causes of the interlayer gap initiation under the action of thermal loading. Due to the influence of construction technology, the bond strength of the interlayer interface between the CA mortar and the track bed is far greater than that of the interlayer interface between the CA mortar and the track slab. Therefore, the interface gap mainly occurs between the CA mortar and the track slab. The interface between the CA mortar and the track bed remains intact, and no gap was observed.