esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 5 Analysis of ANSYS Calculation Results
### The Results of Ultimate Bearing Capacity in ANSYS
#### 5.2.2 The Influence of Concrete Strength Grades  
It can be seen from Table 6 that the ultimate bearing capacity of C50 single-layer CFRP concrete-filled circular steel tubular columns is 11.5% higher than that of C40, that of C60 is 15.9% higher than that of C40, and that of C60 is 3.95% higher than that of C50. The results show that with the increase in concrete strength grades, the ultimate bearing capacity of the samples increases marginally. The improvement range is limited and smaller than that of the number of CFRP layers.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 1 Introduction
This study was motivated by the development of applications such as the one reflected in the scheme depicted in Figure 1. In this scheme, after the speaker has been automatically identified, a corresponding speaker-adapted VSR system is used to transcribe what the speaker is saying without the need for acoustic information. Experiments were conducted on the challenging Spanish LIP-RTVE database [49]. All the defined speaker-adapted VSR systems were based on a hybrid CTC/Attention architecture pretrained with hundreds of hours of data [22]. Thus, our key contributions are: (i) a comparative study on different speaker adaptation methods for end-to-end VSR systems; (ii) the proposal of three different adaptation strategies based on the fine-tuning technique; (iii) the use of the so-called _Adapters_[44] that, to the best of our knowledge, has not yet been explored in the field of VSR; (iv) an analysis of how these different adaptation approaches behave based on the amount of data available for estimation; and (v) a discussion of the deployment aspects, such as training time, storage cost, and real-time factors, of the best proposed methods.
## 4 Numerical Verification for Design
### Material Modeling  
Concrete is a composite material with unique properties, exhibiting anisotropic and nonlinear behavior. Therefore, material models that account for these characteristics should be applied. To depict the concrete damage behavior in numerical analysis, a model incorporating both compressive failure and tensile cracking (as shown in Figure 7) was employed. For this purpose, the Concrete Damaged Plasticity model provided in ABAQUS 6.20 (2020) was applied, following the approach of previous research related to concrete material modeling [40]. The concrete material properties were incorporated based on the Simplified Damage Plasticity Model (SDPM) to capture compression behavior [41]. Additionally, a linear model that can capture the tensile behavior of the anchor was considered. SDPM defines the compression behavior based on an elastic modulus of 24,830 MPa and a Poisson's ratio of 0.2, with a reference compressive strength of 30.0 Mpa for concrete specimens. Furthermore, the tensile strength was set to 15% of the compressive strength, and to enhance the convergence of the analysis results, a 1-linear strain corresponding to 1% of the yield strength was considered for tensile cracking deformation [42]. The mechanical properties of the M20 anchor were determined by the bolt grade, following the F8T standard [43]. The yield strength and tensile strength were 640 Mpa and 800 Mpa, respectively. The elastic modulus was 200,000 Mpa, and the elongation was 12%. Furthermore, the model was constructed based on a 2-linear model allowing for the yielding deformation of the anchor during load transfer [44].  
Figure 6: Friction coefficient of the contact for each material: (**a**) steel-to-steel and (**b**) steel-to-concrete.  
Figure 7: Response of concrete to a uniaxial loading condition: (**a**) compression behavior and (**b**) tension behavior.
## 6 Results: Computational Experiment
### Computational Results
#### 6.2.2 Effectiveness of the Two-Stage Decision Methodology  
We further validated the effectiveness of the two-stage ALNS method using the NSGA-II-based single-stage methodology. We examined the total inventory level and average cell worker's idle time variation of the two methods. Table 17 shows the results of the 40 test instances of all problem benchmarks. The results confirm the two-stage decision methodology to be more effective than the single-stage methodology, with the two-stage ALNS providing a feasible solution for all 40 test instances. However, a feasible solution could not be found using the NSGA-II method. All solutions obtained using the NSGA-II method were penalised by penalty functions. Another significant result from Figure 4 shows that the NSGA-II method consumed more CPU time than the two-stage ALNS method in all problem benchmarks--the two-stage ALNS found a feasible solution within 20 min for even the largest problem.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Performance on MARS  
On the MARS dataset, which presents more challenging real-world conditions, GAF-Net, utilizing MGH for appearance features, showed competitive performance, with a mean average precision (mAP) of 86.09% and a rank-1 accuracy of 89.78%. However, it fell slightly behind recent approaches such as DCCT (mAP: 87.5%; rank-1: 92.3%) and DenseIL (mAP: 87.0%; rank-1: 90.8%). This minor performance gap can be attributed to MARS's diverse conditions, including shorter and more variable sequence lengths, which can impede reliable gait feature extraction. Furthermore, the presence of distractors and false detections in the MARS dataset increases the complexity of the re-identification task, potentially affecting GAF-Net's performance.
## 3 Results
### Water Absorption of Concrete  
The test of concrete absorbability was made on the basis of the standard [50] on cubic samples with a side of 150 mm. The tests were carried out after 28 days of sample maturation on 5 samples on aluminum and Portland cement. The test cubes were placed in a tub in the way that distance between the samples was at least 15 mm and their base was not in contact with the bottom of the vessel. The samples were filled with water at a temperature of 18 \(\pm\) 2 \({}^{\circ}\)C up to half of their height. Then, after 24 h, the water was topped up so that its level was more than 20 mm above the upper surface of the sample. Then samples were removed from the tub every 24 h and then weighed with an accuracy of 0.2%. The study itself lasted until two further tests showed no differences. Saturated samples were placed in an oven at 105 \(\div\) 110 \({}^{\circ}\)C and dried until constant weight. The mass absorption of the samples was determined on the basis of the mass measurement.  
The obtained results are very similar in different groups (Figure 8). The average water absorption of concrete on aluminum cement is 3.15% and samples on Portland cement 5.25%. When comparing the obtained results, the mass absorption of SP concrete is greater of about 67% of SG concrete. However, it should be emphasized that for both concrete mixtures the values obtained are relatively small. For SG concrete, the value obtained meets the criterion described in the literature, not exceeding 4% of water absorption [52; 53].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Mass before** & **Mass after** & **Average Mass** & **Stress** & **Stress–Samples** & **Average Drop in** \\  & **Freezing (g)** & **Freezing (g)** & **Loss (\%)** & **(MPa)** & **from Water (MPa)** & **Strength (\%)** \\ \hline \multicolumn{8}{c}{Aluminous cement} \\ \hline
1 & 7645.0 & 7645.0 & & 47.56 & 51.60 & \\
2 & 7573.5 & 7503.0 & & 44.25 & 59.08 & \\
3 & 7519.0 & 7453.5 & 0.4 & 19.77 & 56.71 & 29.2 \\
4 & 7674.0 & 7673.0 & & 42.31 & 46.83 & \\
5 & 7613.5 & 7603.0 & & 36.26 & 54.33 & \\ \hline \multicolumn{8}{c}{Portland cement} \\ \hline
1 & 7220.5 & 7214.0 & & 65.44 & 62.89 & \\
2 & 7227.5 & 7225.5 & & 51.92 & 61.32 & \\
3 & 7386.5 & 7364.0 & 0.1 & 64.45 & 62.98 & 2.2 \\
4 & 7378.0 & 7380.5 & & 68.77 & 60.97 & \\
5 & 7269.0 & 7265.5 & & 49.15 & 58.40 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results for frost resistance of SP and SG concrete specified on cubic samples.  
Figure 8: Comparison of mass absorption of SP and SG concrete with standard deviations.
## 2 Distributed Optical Fibre Sensors  
The common assumption that enables the sensing feature in optical fibres is that the surrounding environment affects the local properties of the fibre itself. As mentioned above, at the basis of all the DOFSs, there are the following three scattering processes: Rayleigh, Raman, and Brillouin scattering [6]. Despite the different scattering processes, the sensing mechanism is the same for all of them: the back propagating light generated when an optical signal is fed into the fibre is used to probe the local properties of the fibre, and therefore to figure out the changes in the surrounding environment. Regarding Raman and Brillouin scattering, environmental conditions directly affect the corresponding backscattered signals used as probes. For example, the fibre's local temperature intrinsically affects the intensity of the anti-Stokes Raman scattered signal, and this dependence has been successfully exploited to implement distributed temperature sensors (DTSs). Similarly, local temperature and strain intrinsically influence frequency and intensity of Brillouin-scattered signal, and Brillouin scattering is used to implement distributed temperature and distributed strain sensors (DSSs).  
Conversely, Rayleigh-based distributed sensing is less straightforward: Rayleigh scattering is in fact intrinsically independent of almost any external physical fields that may affect the surrounding environment. Therefore, rather than the scattering process per se, Rayleigh scattering is used to measure environment-dependent propagation effects. Attenuation/gain, as well as phase interference and polarisation rotation, are among the propagation effects that are currently detected to implement Rayleigh-based DOFSs. Of course, Raman and Brillouin scattering can also be used in principle to measure these propagation effects, but direct sensing mechanisms are preferred due to their simplicity and effectiveness.  
In the following sections, the principle of distributed sensing for the three scattering processes are briefly overviewed: for an extensive description of working principles and recent research achievements (which are outside the scope of this paper), we direct the reader to some excellent reviews and resources [7, 8, 9, 10, 11].
## 4 Implementation
### Monitoring Model  
As mentioned above, each agent \(a_{i}\) is monitoring a number of IoT devices noted as \(D^{i}=\{d_{1}^{i},...d_{p}^{i}\}\) and calculates traffic metrics for the devices in the corresponding set. The metrics calculated are accumulated to a special data structure called path-message \(M\). This data structure is sent from agentto agent, in order to collect data from different installations. It is called path-message, because it accumulates data from agents, along a random path.  
A path-message is defined as a triple \(M=(P,Cr,T_{out})\). In this triple \(P=[a_{1},\ldots,a_{k}]\) is the list of agents the path-message has passed through, where \(\{a_{1},\ldots,a_{k}\}\subseteq A\). Variable \(Cr\) is an integer representing the creation time of the path-message and \(T_{out}\) is a mapping from the set of hosts \(H\) to the set of natural numbers \(\mathbb{N}\) that holds the current rate of packets being sent from the devices of the installation to the corresponding host \(h\in H\). Formally,  
\[T_{out}:H\rightarrow\mathbb{N}. \tag{6}\]  
Variable \(T_{out}\) represents the out-going traffic of the installation. Each device in the installation \(d_{i}\) has its own mapping that corresponds to the outbound packets rate sent from device \(d_{i}\) to hosts outside the installation. These packet rates are denoted as \(T_{out}^{d}\). Therefore, the agent \(a_{i}\) has to combine the information from its devices, that belongs to \(D^{i}=\{d_{1}^{i},\ldots d_{p}^{i}\}\) as follows:  
\[T_{out}^{i}(h)=\sum T_{out}^{d_{i}^{i}}(h),\ \forall h\in H,\ \forall d_{j}^{i} \in D^{i}. \tag{7}\]  
The path-message can actually be in two distinct states, the information gathering state, and the evaluation state. Every path-message starts in the information gathering state. While being in this state, the path-message is forwarded among multiple random agents, while traffic information is accumulated into it. When enough information has been accumulated, the path-message goes into the evaluation state and the data accumulated is evaluated, in order to check for ongoing DDoS attacks. Finally, after path-messages are evaluated, a consensus procedure takes place in order to detect attacks on a global scale.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 2 Background and Related Work
### Performance Improvement in Distributed Frameworks  
The fine-tuning of machine learning algorithms in big data platforms like Apache Spark has been another research subject in the recent past. The complexity of managing both computation and communication in distributed systems has resulted in the formulation of new frameworks and toolkits to overcome these performance issues [32; 33; 34; 35].  
CoCoNet is one of the closed frameworks that addresses the problem of the division of abstraction between computation and communication layers. CoCoNet allows for the coordination of these two dimensions such that the throughput optimizations are accrued as part of improving performance for distributed machine learning. Through the above optimization of data processing and communication, it has been proved that this framework can achieve dramatic speed-up in distributed settings [36].  
Another approach, the dPRO toolkit, has been particularly designed to diagnose and improve the performance problem in distributed deep neural network (DNN) training. The bottlenecks are detected by the fine-grained profiling of applications and offer solutions to enhance training speed in detail together with the DNN training time improvement across different ML frameworks through dPRO [37].
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 3 Decolorization Methods
### DL Method  
Convolutional neural networks are used in [30] because of the close alignment between their architecture and the way human visual system process images. The primary aim of the method is to minimize a perceptual loss function which captures low- and high-level perceptual differences between color and grayscale images. The loss function is computed using a pretrained VGG-19 network. By extracting features from various layers of VGG-19, the method ensures that both local features, such as edges, and global features, like saliency, are preserved during the color-to-grayscale conversion. Decolorization results of four challenging images from [6] dataset are compared with the state-of-the-art. Althoughshowing good results, the drawback of the method is the high demand for computational resources, long optimization time, and unsuitability for non-natural images.
# Quantification of Optical Chirality in Cellulose Nanocrystal Films Prepared by Shear-Coating
## 3 Results and Discussion
### Polarization Properties of CNC Chiral Films
This can be done by the polarization index \(P_{\Delta}\) of a Mueller matrix \(\mathbf{M}\) given by [25],  
\[P_{\Delta}=\sqrt{\frac{\text{tr}\mathbf{M}^{\text{T}}\mathbf{M}-1}{3}}, \tag{2}\]  
where tr stands for trace. A value of \(P_{\Delta}=0\) corresponds to an ideal depolarizer and \(P_{\Delta}=1\) to a non-depolarizing system. As can be seen in Figure 5, \(P_{\Delta}\) for \(\mathbf{M}\) measured in the transmission mode shows that the chiral CNC film practically does not depolarize incident polarized light, because the average value of \(P_{\Delta}\) is 0.998 \(\pm\) 0.004. On the other hand, \(P_{\Delta}\) for measurements in the reflection mode appreciably deviates from unity in the band of selective reflection (Figure A3 in Appendix A). Figure 4: Experimental Mueller matrix in transmission mode at normal incidence. The fit corresponds to Tellegen constitutive equations. Figure 5: Polarization index from Equation (2) of the experimental Mueller matrix in Figure 4.  
The difference in \(P_{\Delta}\) between normal incidence transmission and oblique incidence reflection measurements can be understood in terms of the model developed for the cuticle of scarab beetles with selective Bragg reflection [26]. In that model, non-uniformity in the thickness was assumed and ideal Mueller matrices were calculated for nine thicknesses (\(d\)) within the interval \(d-\Delta d\) and \(d+\Delta d\). The Gaussian weighted average of those ideal Mueller matrices represent lateral variations in the chiral structure, which gives rise to incoherent superposition of the light reflected from the non-uniform cuticle causing light depolarization. In the cuticle of beetles, it was found that a non-uniformity in thickness of about 2.4% produced deviations in \(P_{\Delta}\) of the same order as those observed for the CNC chiral films investigated here (Figure A3 in Appendix A). From a cross-sectional SEM image of the film (Figure A4 in Appendix A), a thickness \(d=18.1\pm 0.4\)\(\upmu\)m was determined. The standard deviation gave a 2.2% non-uniformity, supporting the origin of depolarization in the present case as being analogous to the case of beetles. On the other hand, for measurements in transmission at normal incidence, the emerging wave is built by a single trip across the film thickness and the left-handed polarization component is (partially) filtered out. As the variation in thickness is about 2%, only small differences are expected, and nearly coherent superposition is achieved. That is, the phase difference between the two orthogonal components of the incident polarized light changes continuously, but not randomly. A similar situation was reported for free-standing chiral CNC films, where \(P_{\Delta}\) was noticeably larger for oblique reflection than for normal incidence transmission [19].
## 3 Challenges for Designing MAC Protocols in UWSNs
### Data Aggregation  
Data aggregation in underwater communication is carried out by using three different mediums of communication (Acoustic, Optical and Magneto inductive) that varies with the necessity created with each specific application. Acoustics technology that is most feasible for long distance communication [69; 70], However, optical way of communication is used for medium range of communication where a high data rate is required to perform the task at hand [71; 72]. However, optical technology for data aggregation fails when there is abundance of organic material or any other kind of obstruction between anchor and sensor nodes. In such scenario where stopping communication is inevitable, MI communication is used for data aggregation [73; 74]. In UWSNs, data aggregation is a critical method since, when UWSNs are deployed in remote areas or hazardous environments, data aggregation reduces energy usage by removing redundancy. The most difficult challenge with UWSNs is to extend their lifetime, which can be achieved with the help of data aggregation [7; 14; 55]. Where as to achieve data aggregation a central network is require shown at Figure 6[75]. The main weakness of the central network is that it has one point of failure, means that one central anchor node is responsible for all the acknowledgments between the network of multiple nodes in water and base station on the ground. A central network will require a central channel too, which can be provided for a limited coverage area in UWSNs, but for large areas it is not the method of choice to be implemented [76].
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 1 Introduction
### Industrial Revolution and Logistics  
Industry 4.0, seen as the technological integration of the Internet of Things (IoT), big data, artificial intelligence (AI), and automation, is expected to overhaul many industries, including the logistics sector [3]. The use of these technologies will not only guarantee higher management levels and thus a better supply chain, but it will also decrease thenegative environmental impact of logistics. At the same time, the application of Industry 4.0 technologies involves high electricity consumption and resource use, mostly at the beginning. Supply chain emissions are one of the issues that has emerged in the logistics sector and questions the overall net effect of Industry 4.0. Will Industry 4.0 lead to a green economy, or will the first requirements in the setup phase results in the same effects of traditional economies?  
Recent studies have shown that the application of Industry 4.0 technologies can lead to significant improvements in supply chain efficiency, resilience, and environmental sustainability ([PERSON], et al., 2022; [PERSON], et al., 2023) [3,4]. However, these benefits are not uniformly distributed across all regions and sectors, with developing economies often experiencing slower adoption rates and less pronounced benefits. This study addresses this gap by focusing on China's logistics sector, which plays a critical role in the country's economic development but is also a major source of carbon emissions.  
The logistics industry is a significant contributor to global carbon emissions, accounting for nearly 14% of total emissions worldwide ([PERSON], et al., 2023) [4]. As China continues to expand its logistics capabilities to support economic growth, it faces the dual challenge of enhancing efficiency while minimizing environmental impact. Industry 4.0 technologies, such as IoT, AI, and automation, offer promising solutions for optimizing logistics operations. However, the adoption of these technologies is often accompanied by an initial increase in energy consumption and emissions, leading to a complex, non-linear relationship between technological advancement and environmental impact. This study aims to explore this dynamic relationship within the context of China's logistics sector, providing insights into how the strategic deployment of Industry 4.0 can support sustainable development goals ([PERSON], et al. 2023) [4].  
This introduction is expanded to include a comprehensive review of the existing literature on the environmental impact of Industry 4.0 and 5.0 in the logistics sector. This includes studies from North America, Europe, and Asia, highlighting both the potential benefits and challenges of adopting these technologies in various economic and regulatory contexts ([PERSON], et al., 2022; [PERSON], et al., 2023; [PERSON], et al., 2024; [PERSON], et al., 2024) [3, 4, 5]. This broader perspective provides a solid foundation for justifying the research gap addressed in this study.