esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Weld Inspection Application  
### Feasibility Analysis  
To facilitate the theoretical analysis of wave propagation in complex components, the finite-element model of the rib-to-deck component was established by the ABAQUS software explicit dynamics module. Based on the established model, the propagation of the ultrasonic wave in components was simulated and analyzed. According to the analysis results, the feasibility of the double-probe penetration method for detecting rib-to-deck structure detail was judged.  
As shown in Figure 10, the established model for the simulation of the crack-free specimen consists of two parts: the tested specimen and the probe. For the tested specimen, the thickness of the deck plate was 12 mm, and the thickness of the U-rib plate was 6 mm. The angle between the two plates was 77\({}^{\circ}\). In addition, the specimen and probe were simulated by a shell element. The material of the specimen was simulated by steel and the probe material was simulated with Plexiglass. The specimen and probe were divided into quadrilateral grids. Each specimen element was set as 0.06 mm and the probe elements were set as 0.02 mm. The bottom surface of the probe model was connected with the detected surface by a tie. Absorption layer was set at the model boundary. Each of the three plate boundaries of the steel member was set with 40 damping absorption layers, and the thickness of each single absorption layer was 0.06 mm. Some 55 damping absorption layers were set on each of the three surfaces of the probe model, and the thickness of the single-layer absorption layer was 0.02 mm.  
The propagation process of the wave in the rib-to-deck specimen was analyzed by waveform displacement nephogram. The simulated wave propagation routes are shown in Figure 11. Figure 11a  
Figure 10: Finite-element model of the rib-to-deck weld.  
Figure 9: Error analysis of crack width measurement. (**a**) Pulse reflection method and (**b**) penetration detection method.  
shows the schematic diagram of sound waves in the probe. The wafer normally emits longitudinal waves: when the P-wave meets the reflection from the bottom surface of the probe, waveform conversion occurs. When the wave meets the bottom surface of the specimen, the P-wave meets the bottom surface, reflects and converts, generating a reflected P-wave (L-L) and reflected S-wave (L-S; Figure 11b). When the wave propagates through the weld (Figure 11c), propagating to the U-rib and roof. The point of receipt of the first reflection of the U-rib external wall is where the receiving probe receives the maximum wave height, as shown in Figure 11d. According to the propagation process of the ultrasonic wave in the rib-to-deck in the above model, the simulation resulted in a model waveform along with the actual law of sound wave propagation. Thus, the double probe penetration method could be successfully used to detect the structural details of rib-to-deck.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 3 Purpose and Methodology  
### Purpose of Research  
Various factors influence the design of a building, such as \"Area\", \"Number of Floors\", and \"Use of Building\", which can be easily extracted from the \"Building General Properties\" during the pre-design stage. Once determined, these factors usually remain relatively stable, even if the shape of the building undergoes changes during the schematic design stage. However, frequent changes may occur in the placement of space and building design during the schematic design stage. Therefore, the designer must have the ability to promptly and accurately predict the total construction cost based on the design elements to ensure the feasibility and success of the project.  
This paper focuses on the use of Building Information Modeling (BIM) during the schematic design stage, which enables real-time extraction of data for each object of the building that may change with design modifications. The paper aims to identify the factors that can be extracted from BIM models and frequently undergo changes in response to design alterations. The objective of this study is to compare the accuracy of predicting total construction costs using the influencing factors extracted from BIM models with those obtained from the building general properties such as \"total area\" and \"building heights\".
## 5 Conclusions  
This study demonstrates a clear correlation between surface roughness and the electrical and vibrational properties of CdTe single crystals. Our findings reveal that, while the PL peak position remains unaffected by changes in surface roughness, increased roughness leads to a decrease in PL intensity, indicative of heightened light scattering. Additionally, a direct relationship was observed between increasing surface roughness and the FWHM of the PL peak. This effect is attributed to the formation of surface states induced by surface irregularities, which act as recombination and capture centers for charge carriers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phonon Mode**} & **Peak Position** & **FWHM** & **Intensity** \\  & **(cm\({}^{-1}\))** & **(cm\({}^{-1}\))** & **Intensity** \\ \hline TO & 141 & 14 & 0.05 \\ SO & 151 & 16 & 0.035 \\ 1 LO & 165.7 & 11.8 & 0.955 \\ 2 LO\(-\)SO & 180.5 & 20 & 0.095 \\ 1 LO+SO & 313 & 20 & 0.05 \\ 2 LO & 332.5 & 18 & 0.95 \\ 3 LO\(-\)SO & 348 & 20 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the parameters used to fit the RRS line shape shown in Figure 5.  
Consequently, the introduction of additional energy levels associated with these defects significantly affects the electronic properties of undoped CdTe crystals.  
Furthermore, our investigation reveals a notable variation in the 2 LO phonon mode related to surface roughness. Specifically, the intensity of the 2 LO phonon increases with greater surface roughness. This effect can be explained by the interaction of the 2 LO phonon with other phonons across the entire Brillouin zone under resonant excitation conditions. These phonon-phonon interactions, which involve energy exchange, establish phonon-phonon coupling. When the exchanged energy matches the energy of the 2 LO phonon mode, a resonance effect occurs, enhancing the participation of the 2 LO mode in Raman scattering and resulting in a stronger spectral peak. Surface roughness facilitates these phonon interactions, particularly for the 2 LO mode, creating a more favorable environment for resonance under resonant Raman excitation. Thus, the peak intensity increases with higher surface roughness.  
The findings of this study are significant as they provide valuable insights into how surface roughness affects both the optical and vibrational properties of CdTe single crystals. Understanding these relationships is crucial for optimizing the performance of CdTe-based devices and materials.  
Conceptualization, C.LM.-R. and R.C.; Formal Analysis, C.LM.-R., R.C., J.R.S.-E. and F.J.C.-R.; Investigation, C.I.M.-R., R.C., J.R.S.-E. and F.J.C.-R.; Methodology, C.I.M.-R.; Writing--Original Draft Preparation, C.I.M.-R. and R.C.; Writing--Review and Editing, C.I.M.-R., R.C., J.R.S.-E. and F.J.C.-R. All authors have read and agreed to the published version of the manuscript.  
The authors would also like to acknowledge the financial support from project 276959 of the 2024 announcement of \"Programa de Apoyo a la Mejora de las Condiciones de Produccion de los Miembros del SNI y SNCA (PROSNI 2024)\" of the Universidad de Guadalajara.
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
###### Abstract  
Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.  
principal component analysis; memory protection; error confinement Article
## 2 Methodology and Results
### Unconfined Uniaxial Compression  
For unconfined axial compression, s = 0 and Equation (10) takes the form  
\[\frac{t}{R}=\frac{1}{1+\frac{\sigma_{t,peak}}{n\,\sigma_{z}}}. \tag{11}\]  
Figure 7 illustrates the variation in the relative thickness of the cracked layer (Equation (11)) as a function of axial and circumferential stresses for three distinct values of Poisson's ratio: 0.05, 0.25, and 0.5.  
From Equation (11), it follows that an increase in the coefficient \(n\), which is related to the Poisson's ratio, as shown by Relation (6), leads to an increase in the thickness of the layer containing radial cracks induced by circumferential stresses. These findings are consistent with the study in [26], which, using the finite element method, determined that higher Poisson's ratios result in increased tensile stresses and a broader zone of their influence, given that all other factors remain unchanged. Figure 8 illustrates the influence of [PERSON]'s ratio on the thickness of the layer with radial cracks (calculated using Equation (11)) as a function of the ratio of peak stress to axial compressive stress.  
Figure 8: Dependence of the relative thickness of a layer with radial cracks on Poisson’s ratio and the ratio of circumferential to axial stresses.  
Figure 7: Effect of [PERSON]’s ratio and circumferential \(\sigma_{t,peak}\) and axial \(\sigma_{z}\) stresses on the relative thickness of a cracked layer under uniaxial compression without lateral pressure.  
As shown in Figure 8, small variations in Poisson's ratio within the range of 0 to 0.25, and in the ratio \(\frac{\sigma_{t,\,peak}}{\sigma_{z}}\) within the range of 0 to 0.2, lead to relatively significant changes in the thickness of the analyzed layer. Beyond the upper limits of these ranges, the response becomes more gradual and nearly linear. These trends highlight an increased sensitivity of relatively weak materials to minor alterations in their state, aligning with laboratory findings [28; 29; 30], which indicate that the external (bandage) reinforcement of columns in mining is more beneficial for weak rocks compared to strong ones. Given the practical relevance and importance of addressing these strength-related issues [31], a more detailed examination of the influence of lateral pressure on the thickness of the cracked surface layer under axial compression, as described by Equation (10), is warranted.
## 2 Materials and Methods
### Extraction of Image Features From the PET/CT Images  
The locations and diameters of the nodules to be analyzed using the acquired PET and conventional CT images were specified by a trained radiologist. Based on this information, the volumes of interest (VOI) around the pulmonary nodules in the analyzed CT and PET images were extracted. Using the VOI from the acquired PET and conventional CT images, 25 features, such as standardized uptake value (SUV), CT value, texture features, and the nodules' border contrast were calculated, as shown in Table 2. The number within the brackets ( ) in Table 2 refers to the number of features in that category. A details description of the calculation procedure is given in our previous paper [18]. The calculation of image features is summarized below.  
1. Texture patterns in the lungs are an important for assessing malignancy. In this study, a method that was proposed by [PERSON] et al. based on the grey level co-occurrence matrix (GLCM) was used. Five types of features (contrast, dissimilarity, correlation, homogeneity, and energy) were calculated using \(\theta\) of 0\({}^{\circ}\) (T\({}_{1}\)_0-T\({}_{5}\)_0) and 90\({}^{\circ}\) (T\({}_{1}\)_90-T\({}_{5}\)_90) in the trans-axial plane of the CT images.
2. Pixel intensity Malignant nodules generally have a high pixel intensity in PET and CT images. Therefore, max and peak values of standardized uptake values (SUV) of early and delayed PET images were measured. Then, the difference in SUV between the delayed and early phases was defined as \(\Delta\)SUV.
3. Spicula A spicula around the nodules is often presented in the malignant nodules. In this study, spicula in CT images was detected using Gabor filters.
4. Border condition The border of a malignant nodule is often unclear. Therefore, the contrast at the border was calculated using the difference between CT values of the outer and inner borderlines of the nodules.
5. Shape Nodule shapes differ between malignant and benign cases. Malignant nodules often have a ball-like shape, while benign ones have a line-like shape. We used the eigenvalues of the Hessian matrix to evaluate nodule shape.
# Energy Storage in Carbon Fiber-Based Batteries: Trends and Future Perspectives
###### Abstract
For this purpose, carbon fibers are activated as cathodes or coated as anodes/cathodes with LiFePO\({}_{4}\)[24; 25; 26], LiCoO\({}_{2}\)[27; 28], MoS\({}_{2}\)[29; 30], TiO\({}_{2}\)[31; 32], metal organic frameworks (MOFs) [33; 34], Mxenes [35; 36], graphene [8; 37] or carbon nanotubes (CNTs) [9; 38; 39]. However, despite these advantages, the integration of carbon fiber into battery systems presents several challenges. Key issues include the optimization of the fiber-matrix interface  
Figure 1: Schematic representation of the innovative carbon fibre-based battery architecture. The carbon fibres, integrated into a reinforced polymer composite, serve dual roles as both anode and cathode. to ensure efficient electron and ion transport, the prevention of degradation mechanisms that could compromise battery performance, and the scalability of manufacturing processes for large-scale production. Addressing these challenges requires a deep understanding of the interplay among the microstructure of carbon fibers, their electrochemical behavior, and the overall battery architecture. Various authors have made scientific reviews on structural batteries [40; 41; 42] or multi-functional composites for energy storage [43; 44; 45; 46]. However, there are no existing studies that provide a bibliometric analysis focusing on trends and future perspectives in carbon fiber-based batteries. Thus, this study aims to provide a detailed bibliometric analysis, tracing the evolution of this research field over the past decade while highlighting key trends, emerging technologies, and critical challenges. Additionally, it explores the potential of carbon fiber to revolutionize energy storage by discussing future directions and opportunities for innovation in this rapidly growing area of research. The insights gained from this study could pave the way for significant breakthroughs in the design and application of multifunctional materials in energy storage, ultimately supporting the global transition to a cleaner and more sustainable energy future.
## 6 Reprocessing and Enterprise Algorithms  
One of the major objectives of the JSTAR teams is to provide consistent long-term high-quality data products derived from four instruments on S-NPP. In the process of achieving Beta, Provisional, and Validated maturity stages, the science teams have implemented periodic updates of algorithms (e.g., fixing shortfalls through processing coefficient table (PCT) updates, improvements to quality flags, and performing mitigations to instrument sensor/channel mal-functions) for real-time product generation. Consequently, although the product accuracy and precision are improved in meeting the requirements for various maturity stages, the operational stream of data products may have varying characteristics, and the science teams recognized the need for science quality reprocessing to remove artifacts and to reduce biases. Based on the recommendations received from NOAA stakeholders and user communities through reprocessing, enterprise algorithm workshops, and STAR Annual Science Team Meetings [4], the JSTAR science teams have updated the SDR algorithms and calibration input parameters with the validated maturity algorithm codes and reprocessed the SDR products for the S-NPP mission. The reprocessed SDR products are currently hosted on the Cooperative Institute for Climate & Satellites (CICS) servers [111] and are going through final quality checks and verification. Meanwhile, the JSTAR science teams have implemented most of the EDR enterprise algorithms at NDE. Since the EDR products are impacted both by upstream SDR product changes as well as EDR algorithm changes (through various maturity stages), the teams are currently working on S-NPP EDR reprocessing using the reprocessed SDRs and NDE operational versions of the enterprise EDR algorithms to generate mission-long consistent EDR products. This will ensure long-term consistency of data product quality and enable users to utilize the high-quality products retroactively to evaluate impacts on user applications and prepare the necessary infrastructure to use the near-real time products.
## 3 Results and Discussion
### Blending Experiments
Figure 3 shows CFPP and density variation with the amount of D-FTS blended in the FD. To determine the optimal ratio for the D-FTS:FD mixture, the trend of density and CFPP was evaluated with the D-FTS addition. A simple linear correlation (equations shown in the Figure 3) indicates that the most optimal ratio for D-FTS: FD mixture was 23:77 wt.%, which fulfils these two critical EN590 standards for diesel fuels (i.e., 820-845 kg/m\({}^{3}\) and <0.0 \({}^{\circ}\)C, respectively).
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 2 System Architecture
### Principle of Operation
The parameter pair (\(P_{i}/M\), \(\mathbf{\tau}_{i,k}\)) can be used to draw the beam pattern and evaluate the quality of the received beams. The expression of the beam pattern is as follows:  
\[F_{k}(\theta)=\sum_{i=1}^{N}\sqrt{\frac{P_{i}}{4g}}\exp\left[i\omega_{RF} \left((i-1)\frac{d}{c}\sin\theta-\mathbf{\tau}_{i,k}\right)\right] \tag{2}\]  
The beam pattern of the multi-beam is the sum of the individual beam pattern, which can be described as:  
\[F_{sum}(\theta)=\sum_{k=1}^{K}F_{k}(\theta) \tag{3}\]
## 4 Discussion
### Secondary AaRS Proofreading Activities Permit Regulation of Translational Quality Control  
Half of the aaRSs in _E. coli_ encode for distinct secondary proofreading activities that either hydrolyze mis-activated aminoacyl-adenylates or mis-aminoacylated tRNAs. Perturbation to these activities has indicated a wide array of neutral to deleterious outcomes for cells in vivo [10; 13; 37]. The extensive evolutionary conservation of aaRS proofreading would suggest an important role for the discriminatory properties of these enzymes that would otherwise not be possible by active site discrimination alone.  
In this work, we have identified an AlaRS variant, AlaRS R561C which sufficiently prevents serine mistranslation in vivo but is unable to hydrolyze mis-aminoacylated in vitro and elicits no futile cycling of ATP, consistent with predictions that this enzyme has lost hydrolytic proofreading activity. This observation raises an interesting idea in that why would AlaRSs maintain an energetically-unfavorable proofreading activity when an AlaRS variant can exist that is sufficiently discriminatory and also does not require the consumption of ATP? Of the phenotypes explored in this report, the _E. coli_ AlaRS R561C variants displayed no differences compared to the wild-type counterpart _E. coli_. It is possible that there are other physiologically relevant environments that may elicit deviations from that trend however, there is another possibility why aaRS proofreading may be maintained that we would like to propose.  
Work from our group and others has continually shown the incredible cost associated with perturbation to AlaRS proofreading [13; 37; 38; 39]. However, thinking about aaRSs more globally, we know that some errors are better tolerated than others [13], and some aaRSs only need proofreading activity against non-proteinogenic substrates [40; 41]. There is a growing body of evidence that aaRS proofreading activity can also be modulated by environmental changes. Both ThrRS [42] and PheRS [21] alter proofreading activity upon oxidative stress, both by decreasing and increasing their discriminatory capacities respectively. Furthermore, ThrRS was recently shown to decrease proofreading activity upon post-translational acetylation [43]. These observations suggest a potential role for maintaining secondary proofreading activities as opposed to encoding for ubiquitously sufficient discriminatory activities in the primary active site. The maintenance of hydrolytic proofreading activities allows the opportunity to modulate enzyme discrimination during environmental or regulatory changes. All together suggesting that while it is possible for an aaRS to maintain active site discriminationwithout the necessity for distinct proofreading mechanisms, it abolishes the opportunity for modulation of proofreading activities and therefore has potentially not been selected for evolutionarily.
## 4 Our Proposed GCI-IGGM-PMBM Scheme
### Fusion  
Given the posterior density of multitarget states \(D_{k}(x)\) derived from a sensor with fusion weight \(w^{u}\), the fused result of their IGGM state density and cardinality density can be approximated as shown in Equations (79) and (80), respectively. \[ \begin{split}&\overline{D}_{f(x,k)}(X,\xi)=\left\{\sum\limits_{i=1}^{I _{B,k}+I_{x,k}}w_{k}\mathcal{IGG}(X,\xi_{i}\alpha_{k}^{i},\beta_{k}^{i},m_{k}^ {i},P_{k}^{i})\right\}^{W_{j}}\\ &\quad\quad\cong\sum\limits_{i=1}^{I_{B,k}+I_{x,k}}w_{k}^{W_{j}}[ \left(\mathcal{G}\mathcal{A}(X,\xi;\alpha_{k}^{i},\beta_{k}^{i})\right)^{W_{j} }[\mathcal{IG}(X,\xi_{i}\alpha_{k}^{i},\beta_{k}^{i})]^{W_{j}}[\mathcal{G}(X,\xi;m_{k}^{i},P_{k}^{i})]^{W_{j}}\\ &\quad\quad\quad\overline{D}_{f(x,k)}(x,\xi;m)=\overline{D}_{f(B,k )}(x,R)\otimes I_{B,k}+\overline{D}_{f(PMBM,k)}(x,R)\otimes I_{PMBM,k},\end{split}  \tag{79}\]  
where \(\otimes\) denotes the Kronecker product, and \(I_{B,k}\) and \(I_{PMBM,k}\) denote the identity matrix of dimensional \(D_{B,k}\) and \(D_{PMBM,k}\), respectively. The fusion cardinality can be expressed as shown in Equation (81). \[\overline{N}(n)=\frac{\prod_{k}\left[N_{k}(n)\right]^{w_{k}}\left\{\int\prod_{k }\left[D_{k}(x)\right]^{w_{k}}dx\right\}^{n}}{\sum\limits_{j=1}^{N_{k}}\prod_{ i}\left[N_{k}(j)\right]^{w_{k}}\left\{\int\prod_{k}\left[D_{k}(x)\right]^{w_{k}}dx \right\}^{j}}. \tag{81}\]  
The estimation sets of state density and cardinality can be computed according to true measurements and corresponding noise. The GCI-IGGM-PMBM scheme is shown in Algorithm 1. ```
Step 1: Initialization  For \(k=1\), \(N_{x}=0\),  Adopt HTC scheme and establish one-hop and two-hop neighbor lists;  then, obtain the location information at \(k=1\). At timestep \(k-1\);  Input: \(\underline{k}_{k},\underline{k}_{k},\underline{\epsilon}_{k,k-1},\underline{ \beta}_{k,k-1},\underline{\alpha}_{B,k-1},\underline{\beta}_{B,k-1},m_{B,k-1 },P_{B,k-1},m_{x,k-1},P_{x,k-1};M_{k}\);  Output: \(p_{k|k}(X,\underline{\epsilon}),p_{k}(X,\underline{\epsilon}),p_{k-1}^{j}(X, \underline{\epsilon}),\underline{\downarrow}_{k}(M|X,\underline{\epsilon}),D_{k -1}^{B}(X,\underline{\epsilon}),D_{k-1}^{u}(X,\underline{\epsilon})\).