esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 5 Discussion
### Advantages and Disadvantages of the Automatic Exhibition Guide System
#### 5.3.2 Disadvantages  
* System may become unstable during peak times or with multiple users: One of the main drawbacks is that the system can become unstable when there is a high volume of users, particularly during peak visiting times. This instability can lead to delays and a less seamless experience for visitors.
* Requires regular maintenance and updates, increasing operational costs: To ensure the system functions optimally, it requires regular maintenance and updates. These ongoing requirements can increase operational costs and necessitate a dedicated team to manage the system [22].
* Needs more data to improve personalized services: The effectiveness of the personalized experiences provided by the system depends on the availability of comprehensive data. Without sufficient data, the system's ability to tailor experiences to individual visitors may be limited, reducing its overall effectiveness.
* AI needs to relearn for different exhibition halls: Each exhibition hall may have unique characteristics and requirements, necessitating the AI to relearn and adapt to each new environment. This relearning process can be time-consuming and resource-intensive, posing a challenge for institutions with multiple exhibition spaces.
## 2 Material Properties
### Local Structural Properties  
Just as with pure semiconductors and semiconductor alloys, doping can be an effective approach to modify GST's electrical and optical properties for a variety of applications. As such, it is important to understand how atomic substitution and distortion of the crystal occur before designing an appropriate doping scheme. To better understand those effects, it is crucial to start by studying the pure GST alloy at its local structural level. Raman spectroscopy is a typical method to characterize the local structure of both pure and doped GST at different states.  
Figure 10a,b shows the Raman spectra for pure GST at both amorphous and hcp crystalline state, respectively. The local structure information was studied by decomposing the experimental Raman spectra into multiple Gaussian oscillators [51]. Different Gaussian peaks correspond to different vibration modes in the material. The peak positions are summarized in Table 2. By referring to [52; 53], the peak positions of the Gaussian oscillators can be assigned as follows. The peaks at amorphous state can be attributed to defective octahedra, GeTe\({}_{4-n}\)Ge\({}_{n}\) (n = 0, 1, 2) edge and/or corner-sharing tetrahedra and SbTe\({}_{3}\) pyramidal units. On the other hand, the peaks in Raman spectra for crystalline state can be assigned to defective octahedra, corner-sharing GeTe\({}_{4-n}\)Ge\({}_{n}\) (n = 0, 1, 2) and hexagonal Sb\({}_{2}\)Te\({}_{3}\). Raman technique is also useful to study the doping in the local structure of GST matrix. When an element is chosen with a similar atomic radius, it is believed that doping most likely occurs via substitution. For instance, the Raman shifts by tungsten and nickel doping have been studied to provide information on the atomic substitution in the local GST matrix [51; 52].  
Figure 9: XRD spectra of as-deposited (amorphous) and GST films annealed at 175 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 300 \({}^{\circ}\)C on fused silica. Adapted with permission from Reference [15]. ©2018 IEEE.
## 3 Distributed Estimation of Laplacian Spectrum
### Distributed Estimation of Non-Zero Laplacian Eigenvalues
#### 3.1.2 Implementation of the Coupling Quadratic Programming (QP)  
Now, we apply the Karush-Kuhn-Tucker (KKT) conditions for solving the quadratic programming (QP) (11). The Augmented Lagrange Function is described as follows:  
\[\mathbf{H}_{2} =\sum_{t=1}^{h}\left\{\frac{1}{2}\mathbf{\alpha}_{t}^{T}\mathbf{B}_{t }\Delta\mathbf{\alpha}_{t}+\mathbf{g}_{t}^{T}\Delta\mathbf{\alpha}_{t}+\mathbf{\lambda}_{ QP}^{T}\Delta\mathbf{\alpha}_{t}\right\}+(\mathbf{\lambda}^{T}-\mathbf{\lambda}_{ QP}^{T})\mathbf{s}+\frac{\mu}{2}\|\mathbf{s}\|^{2}\right.\] \[+\mathbf{\lambda}_{QP}^{T}\sum_{t=1}^{h}(\mathbf{\alpha}_{t}-\mathbf{y}_ {t})+\sum_{t=1}^{h}\eta_{t}^{T}\mathbf{C}_{t}\Delta\mathbf{\alpha}_{t} \tag{18}\]  
The KKT conditions, which are showed in the Appendix B yields a system of equations as follows:  
\[\left\{\begin{array}{rcl}\mathbf{B}_{t}\Delta\mathbf{\alpha}_{t}-\mathbf{\lambda}_ {QP}+\mathbf{C}_{t}^{T}\eta_{t}&=&-g_{t},t=1,2,\ldots,h\\ \sum_{t=1}^{h}\Delta\mathbf{\alpha}_{t}-\frac{1}{2}\mathbf{\lambda}_{QP}&=&-\frac{1}{ \mu}\mathbf{\lambda}-\sum_{t=1}^{h}(\mathbf{\alpha}_{t}-\mathbf{y}_{t})\\ \mathbf{C}_{t}\Delta\mathbf{\alpha}_{t}&=&0\end{array}\right.\]  
Solutions of this system of equations are \(\Delta\mathbf{\alpha}_{t}^{*},\mathbf{\lambda}_{QP}^{*},\eta_{t}^{*}\) respectively in the equivalent matrix form as follows:\[\begin{pmatrix}\mathbf{B}_{1}&\mathbf{0}&\dots&\mathbf{0}&\mathbf{I}&\mathbf{C}_{1}^ {T}&\mathbf{0}&\dots&\mathbf{0}\\ \mathbf{0}&\mathbf{B}_{2}&\dots&\mathbf{0}&\mathbf{I}&\mathbf{0}&\mathbf{C}_{2}^ {T}&\dots&\mathbf{0}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ \mathbf{0}&\mathbf{0}&\dots&\mathbf{B}_{h}&\mathbf{I}&\mathbf{0}&\mathbf{0} &\dots&\mathbf{C}_{h}^{T}\\ \mathbf{I}&\mathbf{I}&\dots&\mathbf{I}&-\frac{1}{\mu}\mathbf{I}&\mathbf{0}& \mathbf{0}&\dots&\mathbf{0}\\ \mathbf{C}_{1}&\mathbf{0}&\dots&\mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0} &\dots&\mathbf{0}\\ \mathbf{0}&\mathbf{C}_{2}&\dots&\mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0} &\dots&\mathbf{0}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ \mathbf{0}&\mathbf{0}&\dots&\mathbf{C}_{h}&\mathbf{0}&\mathbf{0}&\mathbf{0} &\dots&\mathbf{0}\end{pmatrix}\begin{pmatrix}\Delta a_{1}^{*}\\ \Delta a_{2}^{*}\\ \vdots\\ \Delta a_{h}^{*}\\ \mathbf{A}_{\mathbf{0}p}^{*}\end{pmatrix}=\begin{pmatrix}-\mathbf{g}_{1}\\ -\mathbf{g}_{2}\\ \Delta a_{0}^{*}\\ \mathbf{A}_{\mathbf{0}}^{*}\\ \eta_{1}^{*}\\ \eta_{2}^{*}\\ \vdots\\ \eta_{h}^{*}\end{pmatrix} \tag{19}\]  
This linear system can be solved by any linear solver. One thing to note here is the adaptive parameter \(\mu\). We can start with a quite small \(\mu\) and adapt it during the optimization progress to relax the coupling conditions.
## 4 Digital Domain Processing  
### Mcu  
The ARM Cortex-M3 processor is the industry-leading 32-bit processor for highly deterministic real-time application embedding microcontrollers such as home electronic appliances, industrial control systems and wireless sensor networking systems. The processor is built on a high performance 3-stage pipeline Harvard architecture core, making it possible to simultaneously occupy a significantly reduced physical area and possess exceptional power efficiency.  
For the implementation of the digital domain algorithms of our micro-modem, the MCU STM32F103 ZE containing Cortex-M3 processor was chosen. The main features of the MCU are as follows: When compared to the ATmega128 mounted on our previous modern [14], the STM32F103 ZE supports faster processing speed, larger memory and peripherals and smaller chip size which enable the modem to handle signal generation and detection algorithms in the digital domain.  
* Core: 32-bit ARM Cortex-M3 CPU
* Processing speed: 1.23 Dhrystone MIPS
* SRAM: 64 Kbytes
* Flash memory: 512 Kbytes
* Three 12-bit ADC, two DAC
* Four general-purpose 16-bit timers, two PWM timers
* Three SPI, five USART, two I\({}^{2}\)S
## 3 Results
### Comparative Experiments of the Performance of Different Network Models  
To validate the effectiveness of YOLOv11s-RCDWD, the proposed model was tested against several classic object detection models, including Faster R-CNN [38], SSD [39], YOLOv5s [40], YOLOv6s [41], YOLOv7 [42; 43], YOLOv9s [44], YOLOv9c [45], YOLOv10s [46], and YOLOv11s, under the same training environment. To ensure the models were optimized specifically for our target dataset, we trained them from scratch using the dataset specified in this study. The evaluation metrics were precision, recall, F1 score, [EMAIL_ADDRESS], [EMAIL_ADDRESS]-0.95, detection speed, parameter count, GFLOPs, and memory cost. The corresponding experimental results are reported in Table 5.  
The improved model, YOLOv11s-RCDWD, significantly outperformed the competitor models in all the accuracy metrics. Its precision reached 92.6%, recall was 85.4%, and the F1 score was 88.9%, notably higher than Faster R-CNN or SSD. Although the YOLO series models from YOLOv5s to YOLOv11s showed continuous improvements in accuracy, they all fell short of the improved model YOLOv11s-RCDWD. Particularly in the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 metrics, YOLOv11s-RCDWD achieved 90.2% and 72.5%, respectively, representing improvements of 4.9% and 9.0% over YOLOv11s, indicating its stronger capability for object detection in complex scenarios.  
Regarding parameter count and GFLOPs, YOLOv11s-RCDWD remained largely consistent with YOLOv11s, at 9.41 M and 10.52 M, respectively. These values are significantly lower than those achieved by more complex models like YOLOv9c (21.35 M parameters and 84.0 GFLOPs), whose memory cost was 16.4 MB, less than YOLOv11s. The improved YOLOv11s-RCDWD demonstrated superior efficiency in resource utilization.  
The YOLOv11s-RCDWD model demonstrates improved equilibrium among accuracy, speed, and complexity. Notably, its high accuracy and efficiency make it particularly suitable for practical agricultural scenarios such as maize disease detection tasks, and it offers significant advantages when deployed on resource-constrained mobile devices. The YOLOv11s-RCDWD model excels in maize disease detection tasks, with both accuracy and efficiency surpassing other comparative models. The improved model significantly enhances detection performance by introducing RepLKNet, CBAM, DynamicHead, WIoU loss function, and the DynamicATSS sample allocation strategy, providing an effective solution for intelligent detection of maize leaf diseases in complex agricultural environments.
# Roadmap Modeling and Assessment Approach for Defense Technology System of Systems
## 3 Roadmap Modeling and Assessment Approaches  
As mentioned in Section 1, the effects of requirement-pull and innovation-drive driving forces in the evolution of general technologies have been discussed. More specifically, the former factor emphasizes the economic activities that R&D projects require to input various resources while the outputs can be used to improve production efficiency and create social wealth. Besides, the latter concentrates on the discovery and diffusion of scientific and experiential knowledge, which is considered the basis of innovations. Technologies are created and ameliorated in the process of combining and applying the available knowledge to solve realistic problems.  
Figure 1 demonstrates that technologies, stemming from the evolution and organization of knowledges, have been evolving in different routes determined by their application objective. The development of civilian innovation is defined as the \"products-service-business/market\" path driven by economic interests. In detail, novel technologies are applied to improve the performance of products and to offer better services to clients; meanwhile, corporations can occupy more market  
shares and obtain business profits with such R&D achievements. On the other hand, the requirements of defense technologies concern with the strategic objectives of national security, which are defined by the \"strategies-capabilities-weapons\" structure. Similar to their application in a civilian area, defense technologies are expected to enhance/develop advanced weapons and strengthen/create military capabilities in order to reach the strategy objectives. Besides, it is also obvious that applied R&D projects depend on basic research. The knowledge acquired from logical reasoning and experimental observations which are discovered, recorded, and proliferated by scientists, and then selected and organized to cope with problems and obstacles by engineers. Additionally, knowledge can be accumulated, propagated, and reused for new innovations through theoretical discovery and engineering experience from past R&D projects. Consequently, the authors plan to design the model framework and assessment approaches for defense technologies with the viewpoints of requirement analysis and knowledge diffusion. The multi-view theory is utilized to guide the model framework by identifying core stakeholders and collecting related information to their viewpoints, with which one can extract the requirement elements and their interactions with defense technologies. Furthermore, the multi-layer network is used to depict the knowledge diffusion among different kinds of innovation units. With those mentioned models, the appropriate algorithms can be discussed and applied to assess the contribution rate of requirement elements and the importance of innovation units.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Hydrothermal Oxidation Technology  
#### 2.3.1 Comparison with Common Hydrothermal Techniques  
As shown in Table 2, the summary and discussion of traditional hydrothermal technologies indicate that they mainly focus on promoting the physical and chemical transformation of sludge components by adjusting reaction conditions. However, a significant amount of macromolecular organic substances may still remain in the treated sludge [24]. To address this issue, researchers have introduced hydrothermal oxidation, a process that incorporates oxygen or oxidants into the hydrothermal reaction to further oxidize organic matter. This technology breaks down macromolecular organic compounds into smaller molecules such as carbon dioxide and water, significantly improving the removal efficiency of organic pollutants [26; 28; 29]. Therefore, HTO demonstrates clear advantages over traditional HT methods, particularly in terms of sludge detoxification and reduction. For example, a study by [PERSON] et al. compared the effectiveness of hydrothermal and hydrothermal oxidation methods in removing heavy metals from sludge. The results showed that, during hydrothermal treatment, the presence of oxygen had a significant impact on most heavy metals (Hg, Cr, Ni, Cu, Zn, and Fe), and oxidation promoted greater dissolution of these metals. Notably, the most significant differences were observed in the behavior of Hg, Ni, and Cu. For Hg, in the presence of oxygen, the percentage of total Hg in the liquid phase decreased from 40% to 21% after 210 min at 200 \({}^{\circ}\)C. In contrast, under anoxic conditions, the concentration of Hg could only be reduced to 30%. The study also found that Hg was almost completely removed in less than 30 min, but after that, at the lowest temperature (160 \({}^{\circ}\)C), Hg began to re-dissolve. After further treatment, its concentration could only be reduced to a minimum of 30%. For Ni and Cu, under WO conditions, low temperatures were sufficient for their removal. At 160 - 170 \({}^{\circ}\)C, the content of Ni and Cu in the liquid phase accounted for only 1.5% (0.42 \(\pm\) 0.07 mg/kg DM) and 0.2% (0.34 \(\pm\) 0.01 mg/kg DM) of the total Ni and total Cu, respectively. In contrast, during the HT process, these two metals were almost entirely retained in the solid phase, which was the opposite trend to that observed during wet oxidation [30]. This advantage positions hydrothermal oxidation as a promising method for sludge treatment, offering efficient detoxification and effectively addressing the challenges of resource recovery. By comparing HTC, HTL, and HTG with hydrothermal oxidation, it becomes evident that HTO provides distinct benefits in sludge treatment. It not only achieves more thorough decomposition of organics but also efficiently removes heavy metals, reducing the environmental risks associated with sludge.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 3 Fabrication Techniques of Graphene-Based Membranes
### Drop Casting  
Drop casting is also a simple fabrication process for GO-based membranes. In this method, GO suspensions are drop-cast on smooth-surfaced substrates (such as silica or paper), and then dried at room temperature. A freestanding and uniform GO film can be separated from the substrate by direct lift-off. The drop casting technique has received extensive attention due to its simplicity, ease of operation, rapidity, and small substrate size. However, even under near-ideal conditions, differences in the evaporation rate or concentration gradient of the liquid phase across the substrate can lead to changes in the thickness or internal structure of the entire film [46]. [PERSON] et al. prepared polyvinyl alcohol (PVA) composite films filled with GO-Ag NPs with antibacterial activity using the solution casting method [55]. Characterization of its microstructural and morphological indicated that the fillers had good dispersion in the polymer matrix. In addition, the exfoliated structure of the nanocomposite results in enhancements to the thermal stability, mechanical properties, and water resistance of the film. The PVA membranes filled with 0.5-5 wt% GO-Ag NPs showed antibacterial effects against _Escherichia coli_ and _Staphylococcus aureus_, and the antibacterial effects were stronger for composites with a higher GO-Ag NPs content. Compared with _E. coli_, the PVA/GO-Ag NPs membrane showed a stronger inactivation of _Staphylococcus aureus_. The direct contact between bacteria and composite membranes offered a higher bactericidal effect than contact with the leachate of these membranes. [PERSON] et al. reported that high-concentration graphene oxide suspensions containing discotic nematic phases can be shear-aligned by blade casting to form ordered graphene oxide laminates [56].
### Addressing Challenges of Data Source Authenticity  
With the growing number of CAVs, tremendous driving-related IoV data, such as video streams from cameras, point cloud data from LiDAR (Light Detection and Ranging), and vehicle control messages are continuously produced. Due to the limited capability of CAVs, generated IoV data are transmitted to surrounding roadside units (RSUs), edge servers, or cloud servers, which process and store raw IoV data. Thus, ensuring the security of IoV data across their lifetime (data acquisition, data in transit, and data at rest) is a significant challenge, especially for the data source's authenticity and data storage integrity.  
By embedding imperceptible information within digital content, watermarking has been recognized as an effective technique for data security, such as copyright protection, content identification, and forensics [5]. Thanks to its key features like imperceptibility and robustness, digital watermarking (DW) is a prominent solution for safeguarding the security of IoV data throughout their life cycle, particularly in verifying the authenticity of the data and ensuring the integrity of stored data from multiple sources. DW is especially useful in forensic situations, such as investigating traffic accidents or analyzing vehicle malfunctions, wherein data are derived from video streams generated by vehicle vision technologies, including in-car cameras and video surveillance systems.  
Unlike traditional digital watermarking methods that are widely used for content authentication, copyright protection, and tamper detection [6], this paper explores deep learning-based models that can embed invisible and robust watermarks into specific video frames on the source side to ensure the authenticity of video data in IoV networks. By incorporating the attention mechanism in the Generative Adversarial Network (GAN) model, we proposed FIAE-GAN, a novel feature-integrated and attention-enhanced robust digital watermarking model based on GAN. Compared to deep learning-based digital watermarking solutions like ReDMark [7] and HiDDeN [8], our FIAE-GAN employs an  
Figure 1: The overview of an ITS consisting of multiple IoV networks.  
encoder-noise-decoder structure for end-to-end training. A Feature Integration Module (FIM) is incorporated into the encoder to address the issue of extracting only limited image features by capturing both shallow and deep features across multiple layers. Thus, using these learned features can enhance the encoder's ability to embed the watermark robustly. In addition, an Attention-Enhanced Module (AEM) in the encoder makes it possible to minimize image distortion during the watermark embedding process. A noise module can learn the distribution of differentiable noises through iterative training to increase robustness and tolerate image noise attacks. Moreover, the discriminator can improve the quality of the watermarked images through the adversarial training cycles.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 2 Related Work
### Knowledge Management for Smart Cities  
An important related research direction to be considered is knowledge management for smart cities and urban innovation [15]. The idea in this area is to collect and share knowledge regarding different projects, initiatives and concepts on organizational (e.g., with regard to human resources management [16]) and technical level (e.g., IoT activities 17), in order to arrive at a collaborative approach towards the development of future urban environments. Thus, cities are expected to become knowledge hubs for different participants in an urban innovation eco-system [17; 18]. The involved stakeholders who are considered for collaboration involve the public and the private sector. This includes city representatives and departments, citizens, (utility) companies, universities and research institutes as well as large scale industry such as software power houses or telecom and infrastructure providers.
## 2 Dispersive Fourier Transformation Technique  
DFT is originated from the analogy between spatial Fraunhofer diffraction and temporal chromatic dispersion. It is known that a duality exists between the far-field diffraction of a beam of light from a thin lens and the propagation of a temporal pulse in a dispersive device under the second-order dispersion approximation [52,53]. The time-space duality shows that a dispersive element which provides distortion to a temporal signal in the time domain is the analogue of a free-space propagation in the space domain. The time-space duality has led to an interesting conclusion that if a transform-limited ultrashort optical pulse passes through a dispersive element, which should have sufficient chromatic dispersion to suffice the temporal far-field condition, the stretched optical pulse will have an intensity envelope that mimics the optical power spectrum of the original optical pulse. The concept of DFT can be understood intuitively by considering the dispersion stretching effect. The chromatic dispersion introduced causes a frequency-dependent linear time delay to the input optical pulse. Sufficient dispersion fully separates the spectral components of the input broadband optical pulse in time and realize the unique mapping from the frequency domain to the time domain. A schematic diagram of DFT is illustrated in Figure 1.